System

Abstract

A system is provided.SOLUTION: The system includes a means for scanning all pages of a Web site, a means for analyzing the HTML contents and page structure of all pages, and a means for automatically generating the correction plan of the internal link structure, meta tag, and heading tag of a page which can be efficiently crawled by a search engine based on the analysis result.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] Search engine optimization (SEO) is extremely important for modern website management. However, because the correct answer for SEO is not clearly defined, it tends to depend on the skill and experience of the person in charge. This creates the problem that effective measures are not implemented consistently, leading to inconsistent results. Furthermore, many SEO tasks require time and effort when performed manually, which creates the problem of inefficiency. Furthermore, the difficulty of responding immediately to changes in search engine algorithms makes it difficult to continuously maintain an effective SEO strategy. [Means for solving the problem]

[0005] The present invention provides a system that automatically executes and optimizes SEO measures using machine learning and generative AI. The system includes a means for automatically generating suggested modifications to the internal link structure, meta tags, and heading tags of a page to enable efficient crawling by search engines through scanning all website pages and analyzing HTML content. To increase the number of pages indexed, the system also provides a means for detecting unindexed pages, automatically generating content that is likely to be indexed using generative AI, and automatically submitting indexing requests. To improve page rankings, the system also provides a function for periodically monitoring the keyword rankings of target pages, researching related keywords using generative AI, automatically generating new content and suggested modifications to existing content, and incorporating them into the site. Finally, the system includes a means for analyzing traffic data in real time, identifying pages with low conversion rates, automatically generating suggested improvements using generative AI, and prompting users to review and correct the content, thereby improving conversion rates. In this way, the system automates a series of SEO tasks and provides efficient and consistent SEO measures, reducing the burden on website operators and enabling them to maintain long-term SEO effectiveness.

[0006] A "website" is a collection of information or content made publicly available on the Internet.

[0007] "Scanning" is the process of reading all pages within a website for analysis.

[0008] "HTML content" is a collection of text and tags written in the HTML language that make up a web page.

[0009] "Page structure" is a design that shows the arrangement and relationships of elements (headings, paragraphs, links, etc.) within a web page.

[0010] A "search engine" is a system for searching, organizing, and providing information on the Internet.

[0011] "Crawling" is the process by which a search engine visits the pages of a website to gather information about it.

[0012] An "internal linking structure" is the arrangement of links that connect different pages of the same website to each other.

[0013] A "meta tag" is an HTML tag included in the header of a web page that provides information such as a description of the page and keywords.

[0014] A "heading tag" is a tag of heading level (H1, H2, H3, etc.) defined in HTML, and is used to hierarchically indicate the content of a page.

[0015] "Generative AI" is a system that uses artificial intelligence technology to automatically generate new information and content.

[0016] "Indexing" is the process by which search engines add web pages to their databases so that they can be displayed in search results.

[0017] An "indexing request" is the process of requesting that a web page be added to a search engine's index.

[0018] "Keyword ranking" refers to the position of a web page in search results for a particular keyword.

[0019] "Traffic Data" refers to data related to access, such as the number of visitors to a website and the number of pages viewed.

[0020] "Conversion rate" is the percentage of website visitors who perform a desired action (such as purchasing a product or making an inquiry).

[0021] "Improvement proposals" are proposals for solving specific issues and improving performance. [Brief explanation of the drawings]

[0022] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0023] 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.

[0024] First, the terms used in the following description will be explained.

[0025] 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, a 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), and an APU (Accelerated Processing Unit).

[0026] 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.

[0027] 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.

[0028] 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), Bluetooth (registered trademark), etc.

[0029] 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."

[0030] [First embodiment]

[0031] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0032] 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.

[0033] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0034] 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.

[0035] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0036] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0037] 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.

[0038] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0039] 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.

[0040] 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.

[0041] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0042] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0043] This invention is a system that utilizes machine learning and generative AI to automatically execute and optimize SEO measures. This system efficiently processes various SEO tasks by combining elements of the server, terminal, and user.

[0044] Crawlability optimization

[0045] server

[0046] The server scans every page of a website, analyzing its HTML content and page structure, and then uses generative AI to automatically generate suggested modifications to help search engines crawl it more efficiently, such as optimizing internal link structures, meta tags, and heading tags.

[0047] Specific examples

[0048] If the server determines that the "About Us" page has not been crawled, the generation AI will automatically generate the appropriate internal links and meta tags and add them to the "About Us" page.

[0049] Increased indexing

[0050] server

[0051] The server detects and identifies unindexed pages using the Google Search Console API, etc. For the detected pages, it automatically generates content that is likely to be indexed using generative AI and adds it to the defective pages. It also automatically sends indexing requests using the Google Search Console API.

[0052] Specific examples

[0053] If a particular product page is not indexed, the server will identify it and use generative AI to optimize the product description and keyword density to promote indexing.

[0054] Improved rankings

[0055] server

[0056] The server regularly monitors the keyword rankings of the target page and uses generative AI to research related keywords, automatically generating new content that includes them or suggestions for modifying existing content to improve the page's clicks and ranking.

[0057] Specific examples

[0058] If the ranking for the keyword "diet supplement" is dropping, the server will use generative AI to generate content including effective usage instructions, benefits, ingredient information, etc. and add it to the page.

[0059] Increased site traffic and conversions

[0060] Terminal

[0061] The device analyzes traffic data in real time to identify pages with low conversion rates and uses generative AI to automatically generate recommendations for improving conversion rates, including redesigning, optimizing calls to action, and adding user reviews.

[0062] User

[0063] Users can review the AI's suggestions through an admin panel, manually correct them if necessary, and then approve the proposed changes to be applied to the site.

[0064] Specific examples

[0065] If the homepage's conversion rate is low, the device analyzes traffic data, and the AI ​​generator proposes new layouts and appealing points to the user. Once the user confirms and approves the suggestions, the homepage is automatically updated.

[0066] effect

[0067] This system ensures accuracy and consistency in SEO strategies, eliminating the problems of relying on individual staff. Automatically generated content and algorithmic optimization improve search engine rankings, leading to increased traffic and conversions.

[0068] The processing flow will be explained below.

[0069] Crawlability optimization

[0070] Step 1:

[0071] The server scans all pages of the website, analysing the sitemap file (sitemap.xml) and robots.txt file to list the URLs to crawl.

[0072] Step 2:

[0073] The server retrieves the HTML content of each listed page, which includes extracting each page's meta tags, heading tags (H1, H2, etc.), and internal link components.

[0074] Step 3:

[0075] The server analyzes the extracted page structure and uses generative AI to create suggestions for internal link structures, meta tags, and heading tags that are optimal for search engines.

[0076] Step 4:

[0077] The server applies the generated suggested modifications to the website and verifies the changes.

[0078] Increased indexing

[0079] Step 1:

[0080] The server checks the indexing status of the website using the Google Search Console API, and detects and lists any pages that are not indexed.

[0081] Step 2:

[0082] For unindexed pages, the server uses generative AI to automatically generate indexable content (text containing keywords, alt tags for images, etc.) and add it to the page.

[0083] Step 3:

[0084] The server automatically sends an indexing request using the Google Search Console API.

[0085] Improved rankings

[0086] Step 1:

[0087] The server periodically monitors the keyword rankings of target pages, collecting and analyzing the current ranking data for each page.

[0088] Step 2:

[0089] The server uses generative AI to review related keywords, taking into account data such as search volume and competition.

[0090] Step 3:

[0091] The server uses generative AI to automatically generate new content or revisions to existing content that includes related keywords, including new articles, product reviews, user testimonials, and more.

[0092] Step 4:

[0093] The server then posts the generated content to the website, which increases the page's clicks and ranking.

[0094] Increased site traffic and conversions

[0095] Step 1:

[0096] The terminal analyzes real-time traffic data, including the number of visitors, dwell time, and bounce rate.

[0097] Step 2:

[0098] The device identifies pages with low conversion rates by analyzing which pages are prone to bounce and which pages do not drive the desired action.

[0099] Step 3:

[0100] The device uses generative AI to automatically generate improvement suggestions (e.g., changing the design, changing the text of the CTA button, adding new appealing content) to improve conversion rates.

[0101] Step 4:

[0102] Users can review the AI's suggestions through an administration panel and manually apply corrections as needed.

[0103] Step 5:

[0104] The user approves the proposed improvements and they are reflected on the site, which aims to improve the conversion rate.

[0105] The above are the specific processing steps in the program of this system. By going through these steps, SEO measures can be implemented automatically and effectively.

[0106] Example 1

[0107] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0108] SEO is an important element in website management, but performing it manually requires a lot of time and expertise. Furthermore, there are various challenges in applying SEO, such as low crawlability, incomplete indexing, fluctuations in keyword rankings, and low site traffic and conversion rates. Therefore, there is a need for an efficient, automated method for optimizing the SEO of an entire website.

[0109] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0110] In this invention, the server includes means for scanning all pages of the website, means for analyzing the markup language content and page structure of all pages, and means for automatically generating suggestions for modifying the internal link structure, meta tags, and heading tags of the pages based on the analysis results so that search engines can crawl them efficiently, thereby optimizing the crawlability and SEO effect of the entire website.

[0111] A "website" is a collection of web pages that store information or data and that can be viewed and navigated over the Internet.

[0112] "All Pages" means all web pages belonging to a particular website.

[0113] "Markup language content" refers to the content of a web page that is structured using a markup language such as HTML or XML.

[0114] "Page structure" refers to the layout, navigation, and other design aspects of how the content of a web page is arranged and related.

[0115] A "search engine" is a system that collects information on the Internet and displays web pages related to a user's query as search results.

[0116] "Crawlability" refers to the ability of search engine crawlers to effectively navigate a web page and add its content to their index.

[0117] "Internal link structure" refers to the placement of links between different pages on a website and the path of the links.

[0118] "Meta tags" refer to tags included in the head of a web page that provide search engines with the content of the page and other relevant information.

[0119] "Heading tags" are tags from h1 to h6 that indicate the heading hierarchy in an HTML document, and are used to indicate the overview and structure of the page content.

[0120] "Indexing" refers to the state in which a web page is registered in a database of web pages collected by a search engine and is ready to be displayed in search results.

[0121] A "generative AI model" refers to an artificial intelligence model that generates new information or content from data based on machine learning algorithms.

[0122] An "indexing request" is a request to a search engine to add a particular web page to its index.

[0123] "Keyword ranking" refers to the position where a web page appears in search results for a particular search keyword.

[0124] "Traffic Data" refers to data regarding the access and behavior of visitors to a website.

[0125] "Conversion rate" is a metric that indicates the percentage of website visitors who achieve a specific goal.

[0126] This invention is a system that utilizes machine learning and generative AI to automatically execute and optimize SEO measures. This system works by combining elements of the server, terminal, and user to improve the SEO performance of the entire website.

[0127] Crawlability optimization

[0128] The server scans all pages of the target website, lists the URLs to be scanned, and retrieves the HTML content of each page. The server then analyzes the retrieved HTML content and page structure, including detecting internal links and analyzing the structure of heading tags (h1, h2, h3, etc.).

[0129] The server inputs prompts into the generative AI model based on the analysis results, and automatically generates suggested modifications to optimize crawlability. For example, a prompt might be, "What internal links should be added to the webpage?" The server then applies the automatically generated suggestions to the specific webpage. For example, adding new internal links, meta tags, or heading tags to the HTML.

[0130] Increased indexing

[0131] The server uses the Google Search Console API to detect unindexed pages. For the detected pages, the server uses a generative AI model to generate content that is likely to be indexed. The prompt text is something like, "What content does this page need to be indexed?" The generated content is added to specific pages, and an indexing request is automatically sent using the Google Search Console API.

[0132] Improved rankings

[0133] The server periodically monitors the keyword rankings of the target page. To monitor, it obtains data using Google Analytics and other SEO tools. Then, it uses a generative AI model to research keywords related to the target page. The prompt is, "What are the most relevant keywords?"

[0134] The server uses the generative AI model to generate new content, including specific use cases, benefits, and recommended usage, and applies the automatically generated content to the target page to improve keyword density and relevance.

[0135] Increased site traffic and conversions

[0136] The device analyzes traffic data in real time, using data from sources such as Google Analytics to analyze page views and user behavior. The device identifies pages with low conversion rates based on the traffic data.

[0137] The device then uses the generative AI model to generate improvement suggestions to improve the conversion rate. The prompt is, "What can we do to improve the conversion rate of this page?" The user can review the generated improvement suggestions, manually correct them if necessary, and then approve the proposed changes to be applied to the site.

[0138] The above is an embodiment of the present invention, which can improve the SEO performance of the entire website and improve its ranking in search engines, thereby increasing traffic and conversions.

[0139] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0140] Crawlability optimization

[0141] Step 1:

[0142] server

[0143] Input: List of website URLs

[0144] The server scans all pages of the website, lists the URLs to scan, and retrieves the HTML content of each page.

[0145] Output: HTML content of each page

[0146] Step 2:

[0147] server

[0148] Input: HTML content for each page

[0149] The server analyzes the retrieved HTML content and page structure, including detecting internal links and heading tags (h1, h2, h3, etc.).

[0150] Output: Analysis results (internal link structure, heading tag placement)

[0151] Step 3:

[0152] server

[0153] Input: Analysis results

[0154] The server then inputs prompts into the generative AI model based on the analysis results to automatically generate suggested modifications to optimize crawlability, such as "What internal links should be added to the webpage?"

[0155] Output: Suggested fixes (new internal links, meta tags, heading tags)

[0156] Step 4:

[0157] server

[0158] Input: Correction Suggestion

[0159] The server applies the automatically generated fixes to a specific web page, adding new internal links, meta tags, and heading tags to the HTML.

[0160] Output: Optimized HTML content

[0161] Increased indexing

[0162] Step 1:

[0163] server

[0164] Input: None

[0165] The server uses the Google Search Console API to find unindexed pages.

[0166] Output: List of unindexed pages

[0167] Step 2:

[0168] server

[0169] Input: List of unindexed pages

[0170] The server uses a generative AI model to automatically generate indexable content, using the prompt "What content does this page need to be indexed?"

[0171] Output: Indexable content

[0172] Step 3:

[0173] server

[0174] Input: Indexable content

[0175] The server adds the auto-generated content to a specific page.

[0176] Output: Optimized page content

[0177] Step 4:

[0178] server

[0179] Input: Optimized page content

[0180] The server automatically sends indexing requests using the Google Search Console API.

[0181] Output: Indexing request sent successfully

[0182] Improved rankings

[0183] Step 1:

[0184] server

[0185] Input: None

[0186] The server periodically monitors the keyword rankings of the target pages, using Google Analytics and other SEO tools to obtain the data.

[0187] Output: Keyword ranking data

[0188] Step 2:

[0189] server

[0190] Input: Keyword ranking data

[0191] The server uses a generative AI model to research related keywords, using the prompt "What are the most relevant keywords?"

[0192] Output: Related keyword list

[0193] Step 3:

[0194] server

[0195] Input: Related keyword list

[0196] The server uses the generative AI model to generate new content, including specific use cases, effects, and recommended usage.

[0197] Output: New content

[0198] Step 4:

[0199] server

[0200] Input: New content

[0201] The server applies automatically generated content to the target page, improving keyword density and relevance.

[0202] Output: Improved page content

[0203] Increased site traffic and conversions

[0204] Step 1:

[0205] Terminal

[0206] Input: Traffic data

[0207] The device analyzes traffic data in real time, using data from Google Analytics and other sources to analyze page views and user behavior.

[0208] Output: Traffic analysis results

[0209] Step 2:

[0210] Terminal

[0211] Input: Traffic analysis results

[0212] The device identifies pages with low conversion rates based on traffic data.

[0213] Output: List of pages with low conversion rates

[0214] Step 3:

[0215] Terminal

[0216] Input: List of low-converting pages

[0217] The device uses a generative AI model to generate improvement suggestions to improve the conversion rate, using the prompt "What can we do to improve the conversion rate of this page?"

[0218] Output: Improvement plan

[0219] Step 4:

[0220] User

[0221] Input: Improvement idea

[0222] Users can review the AI's suggestions through an admin panel, manually correct them if necessary, and then approve the proposed changes to be applied to the site.

[0223] Output: Optimized web page

[0224] (Application example 1)

[0225] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0226] SEO measures for online shopping sites are extremely important, as they directly affect product rankings in search results and conversion rates. However, currently, these SEO tasks are often performed manually, requiring specialized knowledge and effort, making them inefficient. In particular, optimizing internal link structures and meta tags, indexing, monitoring keyword rankings, and creating and modifying content are complex and often require real-time response, making them impractical. There is a need for systems that enable efficient, automated execution and optimization of these SEO tasks, as well as the ability to improve conversion rates based on traffic data.

[0227] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0228] In this invention, the server includes means for scanning all pages of a website, means for analyzing the HTML content and page structure of all pages, means for automatically generating suggested modifications to the internal link structure, meta tags, and heading tags of the pages that can be efficiently crawled by search engines based on the analysis results, means for analyzing detected traffic data, means for proposing optimal design changes and calls to action using generative AI, and means for automatically generating or modifying content based on the suggested improvements, thereby efficiently and automatically executing SEO tasks, improving rankings in search engines, and increasing traffic and conversions.

[0229] A "means of scanning all pages of a website" is a means of collecting data relating to the HTML content of all pages of a website.

[0230] "Means for analyzing the HTML content and page structure of all pages" refers to means for analyzing the structure and content of all collected HTML data of pages and extracting information for optimization.

[0231] "Means for automatically generating suggested modifications to the internal link structure, meta tags, and heading tags of a page that can be crawled efficiently by search engines" refers to a means for automatically generating suggested modifications to the link structure, meta tags, heading tags, etc., based on the analysis results, that will enable search engines to crawl web pages more efficiently.

[0232] "Means for analyzing detected traffic data" means means for analyzing traffic data collected from website visitors to identify patterns or issues based on that data.

[0233] "Method of using generative AI to suggest optimal design changes and calls to action" refers to a method of using generative AI to automatically suggest changes to optimize website design and elements that prompt users to take action (CTA).

[0234] "Means for automatically generating or modifying content based on improvement suggestions" refers to means for generating new content or automatically modifying existing content based on improvement suggestions proposed by the generative AI.

[0235] MODE FOR CARRYING OUT THE INVENTION

[0236] This invention is a system that efficiently and automatically implements SEO measures for online shopping sites. This system optimizes SEO tasks by combining server, terminal, and user elements, improving conversion rates and search engine rankings.

[0237] The server scans all pages of a website and analyzes their HTML content and page structure. Based on the results of this analysis, a generative AI model (such as GPT-4) is used to automatically generate suggestions for modifying the internal link structure, meta tags, and heading tags of the page so that search engines can crawl it more efficiently. This improves crawlability.

[0238] In addition, the server uses the Google Search Console API and Google Analytics API to detect unindexed pages. It uses generative AI models to automatically generate content that is likely to be indexed and adds this content to the defective pages. Indexing requests are also automatically sent, which can increase the number of indexes.

[0239] The server regularly monitors the keyword rankings of the target page and uses a generative AI model to research related keywords. It then automatically generates new content that includes those keywords or proposes modifications to existing content to improve the page's ranking. Furthermore, the generated content and modifications to the internal link structure are automatically reflected on the site.

[0240] The device analyzes traffic data in real time to identify pages with low conversion rates. Using a generative AI model, it automatically generates recommendations for improving conversion rates, including design changes, optimizing calls to action (CTA), and adding user reviews. Users can review the recommendations made by the generative AI through an admin panel, manually correct them as needed, and then approve the proposed changes to apply them to their site.

[0241] Specific use cases

[0242] The hardware and software used includes the following:

[0243] Server: AWS EC2

[0244] Database: Amazon RDS

[0245] Machine learning model: Transformers library (Hugging Face)

[0246] API: Google Search Console API, Google Analytics API

[0247] Interface: React Native (for smartphone apps)

[0248] For example, if the server identifies that the "About Us" page has not been crawled, the generative AI will automatically generate the appropriate internal links and meta tags to add to the "About Us" page. An example of a prompt for the generative AI model is:

[0249] Optimize the internal link structure and meta tags of your "About Us" page.

[0250] Additionally, if a particular product page is not indexed, the server will identify it and use generative AI to generate content that optimizes product descriptions and keyword density. An example prompt is below:

[0251] To optimize your unindexed product pages for indexing, generate content with high description and keyword density.

[0252] So, for example, if your rankings for the keyword "diet supplements" are dropping, you can use generative AI to generate new content to add to the page, including effective instructions, benefits, and ingredient information. Here's an example prompt:

[0253] Generate new content with effective usage, benefits, and ingredient information to improve your ranking for the keyword "diet supplements."

[0254] This system allows online shopping sites to efficiently and automatically implement SEO measures, improving their rankings in search engines and increasing traffic and conversions.

[0255] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0256] Step 1:

[0257] The server scans all pages of a website and collects their HTML content and page structure. The input of this process is the website URL, and the output is the retrieved HTML data and page structure. The server uses a crawler to collect data for each web page.

[0258] Step 2:

[0259] The server analyzes the collected HTML data and page structure. In this analysis process, HTML tags, meta tags, internal link structures, etc. are extracted and the analysis results are obtained. The input is the HTML data obtained in step 1, and the output is the analyzed page structure and meta information. The server performs data analysis using a specific parser tool.

[0260] Step 3:

[0261] Based on the analysis results, the server uses generative AI to automatically generate suggested modifications to internal link structures, meta tags, and heading tags that search engines can crawl efficiently. The input here is the analysis results, and the output is suggested modifications. The following prompt is input to the generative AI model:

[0262] Optimize the internal link structure and meta tags of your "About Us" page.

[0263] Step 4:

[0264] The server uses the Google Search Console API to identify pages that are not indexed. The input is a list of indexed pages, and the output is a list of pages that are not indexed. The server makes an API call to check the index status of the pages.

[0265] Step 5:

[0266] The server uses generative AI to automatically generate content that is likely to be indexed for unindexed pages. The input to this process is the HTML data of the unindexed page, and the output is the generated new content. The generative AI model is given the following prompt:

[0267] To optimize your unindexed product pages for indexing, generate content with high description and keyword density.

[0268] Step 6:

[0269] The server automatically sends an indexing request using the Google Search Console API. The input is the new content generated, and the output is the result of the indexing request submission. Provide the API with the new content and submit the request.

[0270] Step 7:

[0271] The server periodically monitors the keyword rankings of the target page and uses the generative AI to search for related keywords. The input to this process is the current keyword ranking data of the target page, and the output is a list of related keywords. The generative AI model is given the following prompt:

[0272] Generate new content with effective usage, benefits, and ingredient information to improve your ranking for the keyword "diet supplements."

[0273] Step 8:

[0274] The terminal analyzes traffic data in real time and identifies pages with low conversion rates. The input of this process is traffic data, and the output is a list of pages with low conversion rates. The terminal obtains data from the Google Analytics API and performs analysis.

[0275] Step 9:

[0276] The device uses generative AI to automatically generate design and call-to-action improvements to improve conversion rates. The input is a list of pages with low conversion rates, and the output is improvement recommendations. The following prompt is input to the generative AI model:

[0277] Generate suggestions to optimize this page's design and call-to-action to improve conversion rates.

[0278] Step 10:

[0279] Users can review the suggestions made by the generative AI through an administration panel, manually correct them as necessary, and then approve the proposed changes and have them reflected on the site. The input is the generative AI's suggestions, and the output is final approval and reflection on the site. Users operate through a web interface.

[0280] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0281] This invention is a system that automatically executes and optimizes SEO measures using machine learning and generative AI, and further improves the user experience by combining it with an emotion engine that recognizes user emotions. The specific processing flow is explained below.

[0282] Crawlability optimization

[0283] server

[0284] The server scans every page of a website, analyzing its HTML content and page structure, and then uses generative AI to automatically generate suggested modifications to help search engines crawl it more efficiently, such as optimizing internal link structures, meta tags, and heading tags.

[0285] Specific examples

[0286] If the server determines that the "About Us" page has not been crawled, the generation AI will automatically generate the appropriate internal links and meta tags and add them to the "About Us" page.

[0287] Increased indexing

[0288] server

[0289] The server uses the Google Search Console API to check the indexing status of the website, detects and identifies pages that are not indexed, automatically generates content that is likely to be indexed using generative AI for the detected pages and adds it to the defective pages, and automatically sends indexing requests using the Google Search Console API.

[0290] Specific examples

[0291] If a particular product page is not indexed, the server will identify it and use generative AI to optimize the product description and keyword density to promote indexing.

[0292] Improved rankings

[0293] server

[0294] The server periodically monitors the keyword rankings of the target page and uses generative AI to research related keywords, automatically generating new content that includes them or suggestions for modifying existing content to improve the page's clicks and ranking.

[0295] Specific examples

[0296] If the ranking for the keyword "diet supplement" is dropping, the server will use generative AI to generate content including effective usage instructions, benefits, ingredient information, etc. and add it to the page.

[0297] Increased site traffic and conversions

[0298] Terminal

[0299] The device analyzes real-time traffic data to identify pages with low conversion rates and uses generative AI to automatically generate recommendations for improving conversion rates, including redesigning, optimizing calls to action, and adding user reviews.

[0300] User

[0301] Users can review the AI's suggestions through an admin panel, manually correct them if necessary, and then approve the proposed changes to be applied to the site.

[0302] Specific examples

[0303] If the homepage's conversion rate is low, the device analyzes traffic data, and the AI ​​generator proposes new layouts and appealing points to the user. Once the user confirms and approves the suggestions, the homepage is automatically updated.

[0304] Utilizing the Emotion Engine

[0305] server

[0306] The server collects user emotion data using an emotion engine, which analyzes the user's facial expressions and behavior while visiting the website to grasp the user's emotional state in real time.

[0307] Specific examples

[0308] When a user is browsing a particular product page, if the emotion engine detects interest or enjoyment in the user's facial expression, the server can use that emotion data to display additional product information or special offers.

[0309] Terminal

[0310] The device analyzes the collected emotional data and adjusts website content according to the user's emotional tendencies, including rearranging content and optimizing design.

[0311] Specific examples

[0312] If a user shows signs of frustration while browsing multiple pages, the device will detect this emotion and offer more concise navigation and relevant product recommendations.

[0313] User

[0314] Users can review the changes and adjustments suggested by the emotion engine in the admin panel, make any necessary corrections, and apply them to their site.

[0315] Specific examples

[0316] This will be done automatically once the user confirms the results of the emotion engine analysis in the admin panel and approves the change in the position of the review section on the product page.

[0317] effect

[0318] This system ensures accuracy and consistency in SEO measures and eliminates problems caused by personalization. Furthermore, by combining it with an emotion engine, it is possible to provide a personalized experience based on the user's emotions, which is expected to increase traffic and conversions.

[0319] The processing flow will be explained below.

[0320] Crawlability optimization

[0321] Step 1:

[0322] The server scans all pages of the website and analyzes the sitemap file (sitemap.xml) and robots.txt file to list the URLs to crawl.

[0323] Step 2:

[0324] The server accesses each URL and retrieves the HTML content, extracting tags and text data, including meta tags, heading tags (H1, H2, H3, etc.), and internal links.

[0325] Step 3:

[0326] Based on the collected data, the server uses generative AI to automatically generate suggestions for the optimal internal link structure, effective meta tags, and appropriate heading tag revisions.

[0327] Step 4:

[0328] The server applies the generated suggested modifications to the website and verifies the changes.

[0329] Increased indexing

[0330] Step 1:

[0331] The server checks the indexing status of the website using the Google Search Console API, and detects and lists any pages that are not indexed.

[0332] Step 2:

[0333] For unindexed pages, the server uses generative AI to automatically generate index-friendly content, including adding text containing keywords and adding appropriate alt tags to images.

[0334] Step 3:

[0335] The server adds auto-generated content to the page and automatically sends an indexing request using the Google Search Console API.

[0336] Improved rankings

[0337] Step 1:

[0338] The server periodically monitors the keyword rankings of target pages, collects the current ranking data of each page, and performs statistical analysis.

[0339] Step 2:

[0340] The server uses generative AI to re-examine related keywords, re-evaluate appropriate keywords, and categorize them based on search volume and competition.

[0341] Step 3:

[0342] The server uses generative AI to automatically generate new content or revisions to existing content that includes relevant keywords, including new articles, product reviews, and adding user testimonials.

[0343] Step 4:

[0344] The server then posts the generated content to websites, aiming to improve their visibility, clicks, and ranking in search results.

[0345] Traffic data and increased conversions

[0346] Step 1:

[0347] The terminal analyzes real-time traffic data, including visitor behavior, length of stay, and bounce rate.

[0348] Step 2:

[0349] The device identifies pages with low conversion rates, evaluates the performance of each page, and identifies problems.

[0350] Step 3:

[0351] The device uses generative AI to automatically generate recommendations for improving conversion rates, including redesigning, changing the text of the call-to-action (CTA) button, and adding new appealing content.

[0352] Step 4:

[0353] Users can review the AI's suggestions through an admin panel, manually correct them if necessary, and then approve the proposed changes to be applied to the site.

[0354] Step 5:

[0355] The user approves the proposed improvements and they are reflected on the site, which aims to improve the conversion rate.

[0356] Utilizing the Emotion Engine

[0357] Step 1:

[0358] The server uses an emotion engine to collect user emotional data, analyzing the user's facial expressions, tone of voice, behavioral patterns, etc. in real time.

[0359] Step 2:

[0360] The device analyzes the collected emotional data and adjusts the content on the website according to the user's emotional tendencies, changing the content layout and optimizing the design.

[0361] Step 3:

[0362] The server uses generative AI to automatically generate content to personalize the user experience based on emotional data.

[0363] Step 4:

[0364] The user can review the generated changes through the administration panel and manually correct them if necessary.

[0365] Step 5:

[0366] The user approves the suggestions and modifications made by the emotion engine and reflects them on the site.

[0367] These are the specific processing steps in the program of this system. By going through these steps, it is possible to automatically and effectively implement SEO measures and also to provide a personalized experience by utilizing user emotional data.

[0368] Example 2

[0369] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0370] Traditional website optimization methods involve individual SEO measures such as crawling and indexing pages and monitoring keyword rankings, making it difficult to achieve comprehensive optimization. Furthermore, dynamic content adjustments based on user sentiment are not performed, resulting in insufficient improvements to the user experience.

[0371] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for scanning all pages of the website, means for analyzing the HTML content and page structure of all pages, means for automatically generating, using a generation AI, revision suggestions for the internal link structure, meta tags, and heading tags of the pages that can be efficiently crawled by search engines based on the analysis results, means for using an emotion engine for collecting user emotion data, and means for dynamically adjusting site content based on the emotion data. This enables comprehensive and automatic SEO measures and further personalization based on user emotion.

[0372] A "website" is a collection of multiple web pages published on the Internet, and is constructed using technologies such as HTML and CSS.

[0373] "Scanning" refers to the process of crawling all pages of a website and retrieving and analyzing data.

[0374] "HTML content" refers to data in HTML (HyperText Markup Language) format that describes the content of a web page.

[0375] "Page structure" refers to the overall structure of a web page, including the layout and hierarchy of heading tags, meta tags, link structure, etc.

[0376] "Analysis" refers to the process of examining data and extracting useful information for a specific purpose.

[0377] A "search engine" is a system that searches for information on the Internet and provides the results to users. Examples include Google and Bing.

[0378] "Internal linking structure" refers to the link relationships between pages on a website, designed to help users and search engines navigate the site effectively.

[0379] A "meta tag" is a tag written in the head section of a web page that provides search engines and browsers with metadata such as a description of the page, keywords, and author.

[0380] "Generative AI" refers to systems or models that use artificial intelligence to automatically perform specific tasks, and in this case refers to technologies that generate content and make optimization suggestions.

[0381] An "emotion engine" is a system that collects and analyzes users' emotional data, grasps their state in real time, and provides appropriate feedback and adjustments.

[0382] "Dynamic adjustment" refers to changing the content or design of a website in response to data or circumstances in real time.

[0383] MODE FOR CARRYING OUT THE INVENTION

[0384] This invention is a system that improves user experience by automatically implementing SEO measures using machine learning and generative AI, and by combining it with an emotion engine that recognizes user emotions. This system integrates website crawling, indexing, keyword ranking monitoring, and real-time emotion data collection and analysis.

[0385] Hardware and software used

[0386] Server: The server scans all pages of the website and analyzes the HTML code of each page using an HTML parser (e.g., BeautifulSoup). Generative AI can be provided by OpenAI's GPT series or similar.

[0387] Device: The device analyzes real-time traffic data using the Google Analytics API to identify pages with low conversion rates.

[0388] Emotion engine: The emotion engine uses Affectiva's SDK and other tools to analyze the user's facial expressions and behavior in real time to collect emotional data.

[0389] Specific processing flow

[0390] Crawlability optimization

[0391] server

[0392] The server scans every page of a website, analyzes its HTML content and page structure, and then gives the AI ​​prompts such as "Please suggest optimizations for internal link structure and meta tags," which then automatically generates suggested modifications.

[0393] Specific examples

[0394] For example, if the "About Us" page isn't crawled, the server will use generative AI to auto-generate the appropriate internal links and meta tags and add them to the page.

[0395] Prompt Sentence Examples

[0396] "If a particular web page is not being crawled, generate optimization recommendations for the HTML content and metadata of that page."

[0397] Increased indexing

[0398] server

[0399] The server checks the indexing status using the Google Search Console API, detects unindexed pages, and sends a prompt to the generation AI, such as "Optimize the product description on this page and generate content that is more likely to be indexed."

[0400] Specific examples

[0401] If a particular product page isn't indexed, the generative AI generates content that optimizes the product description and keyword density, and the server adds that content to the page.

[0402] Prompt Sentence Examples

[0403] "Optimize the product description on this page to generate indexable content."

[0404] Improved rankings

[0405] server

[0406] The server periodically monitors the keyword rankings of target pages through the Google Analytics API, and provides prompts to the AI ​​generator, such as "Please generate content that includes new and effective usage methods, benefits, and ingredient information," to generate content that contributes to improving rankings.

[0407] Specific examples

[0408] If the ranking for the keyword "diet supplement" is dropping, the server will use generative AI to automatically generate content including effective usage instructions, benefits, and ingredient information and add it to the page.

[0409] Prompt Sentence Examples

[0410] "Generate new content related to the keyword 'diet supplements' with effective usage, benefits, and ingredient information."

[0411] Increased site traffic and conversions

[0412] Terminal

[0413] The device analyzes real-time traffic data to identify pages with low conversion rates, and sends a prompt to the AI ​​generator to "generate new layout proposals and selling points to improve the conversion rate of this page," automatically generating improvement proposals.

[0414] User

[0415] Users can review the suggestions made by the AI ​​through an administration panel, manually correct them if necessary, and then approve the changes to reflect them on the site.

[0416] Specific examples

[0417] If the homepage conversion rate is low, the terminal will display suggestions from the generation AI on the management panel, which the user can confirm and then implement.

[0418] Prompt Sentence Examples

[0419] "Generate new layout ideas and selling points to improve the conversion rate of this page."

[0420] Utilizing the Emotion Engine

[0421] server

[0422] The server uses an emotion engine to collect user emotion data, analyze the user's facial expressions and behavior in real time, and understand their emotional state.

[0423] Specific examples

[0424] If the emotion engine detects interest or enjoyment while a user is viewing a particular product page, the server can use that emotion data to display additional product information or special offers.

[0425] Terminal

[0426] The terminal analyzes the collected emotional data and adjusts the content on the website according to the user's emotional tendencies.

[0427] Specific examples

[0428] If a user shows signs of frustration while browsing multiple pages, the device will detect this emotion and offer more concise navigation and relevant product recommendations.

[0429] User

[0430] Users can review the changes and adjustments suggested by the emotion engine in the administration panel, make corrections as necessary, and reflect them on the site.

[0431] Specific examples

[0432] This will be done automatically once the user confirms the results of the emotion engine analysis in the admin panel and approves the repositioning of the review section on the product page.

[0433] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0434] Step 1: Scan the website

[0435] server

[0436] The server scans all pages of a website. It receives a list of URLs as input and parses the HTML code of each page using an HTML parser (e.g. BeautifulSoup). It gets the parsed data for each page as output.

[0437] Specific actions

[0438] For example, the server retrieves all page URLs for "example.com" in sequence and parses each page using an HTML parser.

[0439] Step 2: Analyzing the page structure

[0440] server

[0441] The server analyzes the HTML content and page structure of the scanned page, using the analysis data from step 1 as input to identify internal links, meta tags, heading tags, etc. As output, it gets data containing details of the page structure.

[0442] Specific actions

[0443] For example, the server checks the presence of internal links and meta tags on the "About Us" page to assess whether it is optimized.

[0444] Step 3: Generative AI generates revision suggestions

[0445] server

[0446] Based on the analysis results, the server sends prompts to the generation AI to generate suggested modifications to the internal link structure, meta tags, and heading tags. The server provides details of the page structure and a prompt as input, and receives suggested modifications from the generation AI as output.

[0447] Specific actions

[0448] For example, you can send a prompt like "Add an internal link to this page" to the generative AI and get suggested fixes.

[0449] Step 4: Applying the proposed amendment

[0450] server

[0451] The server applies the suggested revisions from the generative AI to the web page, using the suggested revision data as input and generating an output with updated HTML code.

[0452] Specific actions

[0453] For example, add the internal link code obtained from the generation AI to the "About Us" page and save the updated HTML.

[0454] Step 5: Check the index status

[0455] server

[0456] The server checks the index status of the website using the Google Search Console API. It uses the website URL as input and gets the index information from the API. It gets the list of pages that are not indexed as output.

[0457] Specific actions

[0458] For example, the server checks the indexing status of each page on "example.com" using the Google Search Console API and lists the 404 error pages.

[0459] Step 6: Content generation with generative AI

[0460] server

[0461] The server automatically generates content for unindexed pages. It uses the list of unindexed pages and prompts as input and gets optimized content from the generation AI. It gets optimized content data as output.

[0462] Specific actions

[0463] For example, for an unindexed product page, a prompt such as "Please optimize the product description" is sent to the generation AI, and the generated content is retrieved.

[0464] Step 7: Submitting an indexing request

[0465] server

[0466] The server adds the auto-generated content to the page and sends an indexing request through the Google Search Console API, using the updated HTML code as input and sending an indexing request to the API to get the output.

[0467] Specific actions

[0468] For example, add the generated product description to the relevant page and send an indexing request using the Google Search Console API.

[0469] Step 8: Monitor your keyword rankings

[0470] server

[0471] The server periodically monitors keyword rankings using the Google Analytics API. It uses the target keywords as input and retrieves ranking data from the API. It gets a list of ranking data as output.

[0472] Specific actions

[0473] For example, monitor the keyword "diet supplements" and obtain its rankings periodically.

[0474] Step 9: Generative AI creates new content

[0475] server

[0476] If the server detects a drop in keyword rankings, it uses a generative AI to generate new content and suggested revisions. It uses ranking data and prompts as inputs to obtain new content from the generative AI, and obtains new content data as output.

[0477] Specific actions

[0478] For example, send a prompt like "Suggest a new use for 'Diet Supplement'" and get the generated content.

[0479] Step 10: Analyzing real-time traffic data

[0480] Terminal

[0481] The device analyzes real-time traffic data using the Google Analytics API. It uses website URLs as input to identify pages with low conversion rates, and gets a list of pages with low conversion rates as output.

[0482] Specific actions

[0483] For example, the terminal retrieves traffic data for each page of "example.com" and lists pages with low conversion rates.

[0484] Step 11: Generative AI generates improvement proposals

[0485] Terminal

[0486] The device uses a generative AI to automatically generate improvement suggestions for pages with low conversion rates. It uses the conversion rate data and prompt text as input and obtains improvement suggestion data from the generative AI. It then obtains the improvement suggestion data as output.

[0487] Specific actions

[0488] For example, you can send a prompt like, "Suggest a new layout to increase the conversion rate of this homepage," and get the generated layout suggestions.

[0489] Step 12: User Acceptance and Application

[0490] User

[0491] The user can review the suggestions made by the generative AI through an admin panel, manually correct them if necessary, and then approve the changes to be reflected on the site. The user uses the suggested data from the generative AI as input and applies the final updates to the site, obtaining the updated webpage as output.

[0492] Specific actions

[0493] For example, users can check new homepage layout suggestions made by the generative AI in the administration panel and implement them after approval.

[0494] Step 13: Collect emotion data

[0495] server

[0496] The server uses an emotion engine to collect user emotion data. It uses the user's real-time behavior data as input and obtains analysis results from the emotion engine. It obtains real-time emotion data as output.

[0497] Specific actions

[0498] For example, the emotion engine detects interest or enjoyment when a user is viewing a particular product page.

[0499] Step 14: Analyze and apply emotion data

[0500] Terminal

[0501] The device analyzes the collected emotional data and adjusts the content on the website according to the user's emotional tendencies. It uses the emotional data as input to dynamically change the layout and display of the site content, and obtains updated web page content as output.

[0502] Specific actions

[0503] For example, if the user shows signs of frustration, the navigation can be simplified based on data from the emotion engine.

[0504] Step 15: User Review and Approval

[0505] User

[0506] The user reviews the changes and adjustments suggested by the emotion engine in the admin panel, manually corrects them if necessary, and approves the changes. The system uses the suggested data from the emotion engine as input and finally updates the content on the site, obtaining an updated web page as output.

[0507] Specific actions

[0508] For example, if a user checks the analysis results of the sentiment engine in the admin panel and approves changing the position of the review section on the product page, this will be implemented automatically.

[0509] (Application example 2)

[0510] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0511] Existing SEO systems are inefficient because they require specialized technical expertise and require advanced skills and time. Furthermore, when it comes to improving user experience, there is a lack of methods for grasping user sentiment in real time and providing content that responds to that sentiment. Therefore, there is a need for a system that can simultaneously improve both SEO and user experience.

[0512] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for scanning all pages of a website, means for analyzing the HTML content and page structure of all pages, means for automatically generating, based on the analysis results, revision suggestions for the internal link structure, meta tags, and heading tags of the pages that can be efficiently crawled by search engines, means for collecting and analyzing user emotion data, and means for automatically generating, based on the analysis results, revision suggestions for content that correspond to the user's emotions. This makes it possible to automate SEO measures and personalize the user experience.

[0513] "Means for scanning all pages of a website" means a function for automatically scanning all pages of a website and acquiring their contents.

[0514] "Means for analyzing HTML content and page structure" refers to the ability to analyze the HTML code of a web page and its design, such as headings, meta tags, and link structure.

[0515] "Means for automatically generating suggested modifications to internal link structures, meta tags, and heading tags" is a function that automatically generates and suggests links and meta information to encourage optimal crawling and indexing of websites.

[0516] The "means for collecting and analyzing user emotional data" is a function that captures visitors' facial expressions and behavioral data in real time and analyzes their emotional state.

[0517] "Means for automatically generating suggested revisions to content according to the user's emotions" is a function that automatically generates and suggests content that is appropriate for the user's interests and emotional state based on collected emotional data.

[0518] "Means for detecting unindexed pages" refers to a function that identifies web pages that are not registered in a search engine's index.

[0519] "Means for automatically generating content that is likely to be indexed using a generative AI model" is a function that uses artificial intelligence to automatically generate content that is likely to be indexed by search engines.

[0520] "Means for automatically sending indexing requests" means a function that automatically sends indexing requests to search engines for content created by a generative AI model.

[0521] "Means of sending the received prompt text to a generation AI and generating content or promotional text that suits the user's emotions" refers to a function that sends a prompt text that takes the user's emotions into consideration to a generation AI, and as a result, automatically generates content or promotional text that suits the user's emotions.

[0522] This invention relates to a system that aims to improve user experience by combining automated SEO measures using machine learning and generative AI models with an emotion engine. The system includes a means for scanning and analyzing all pages of a website and automatically generating optimal revision suggestions based on the results. It also includes a means for collecting user emotions in real time and providing content that corresponds to those emotions.

[0523] System configuration

[0524] server

[0525] The server is responsible for scanning all pages of a website and analyzing their HTML content and page structure. Through this analysis, generative AI models are used to automatically generate suggested modifications to internal link structures, meta tags, and heading tags to help search engines crawl them more efficiently.

[0526] Specifically, the server uses BeautifulSoup to parse the HTML of web pages to detect unindexed pages, then uses generative AI models to generate appropriate content for those pages and automatically submits indexing requests using the Google Search Console API.

[0527] Terminal

[0528] The device collects user emotional data and adjusts content based on the analysis results. This process involves capturing the user's facial expressions using a camera and using OpenCV and an emotion recognition model. Based on this emotional data, the device sends prompts to a generative AI model, which then generates content and promotional text appropriate to the user's emotions.

[0529] User

[0530] Users can review the suggestions made by the generated AI through an admin panel, make manual corrections if necessary, and then approve the proposed changes to reflect them on the site.

[0531] Hardware and Software

[0532] Hardware: Servers, devices (smartphones, PCs)

[0533] Software: BeautifulSoup, OpenCV, Google Search Console API, generative AI models

[0534] Specific examples

[0535] When a user is browsing a product page on an online shopping site, the camera recognizes the user's expression of surprise. Based on this information, the AI ​​generates prompts such as the following, which then display an appropriate promotional message:

[0536] The user's emotion is "surprise." Generate optimal product recommendations and promotional messages based on this emotion.

[0537] It also discovers that certain pages are not indexed, uses generative AI to create optimal keywords and meta tags, and sends a request to Google Search Console to include them, as shown below.

[0538] Generate the best keywords and meta tags to help index this page.

[0539] This allows for automated SEO and personalized user experience.

[0540] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0541] Step 1:

[0542] The server scans all pages of the website to obtain the HTML content and page structure. Through this scan, the server collects the URL and HTML code of each page of the website as input data and stores the results in an internal database, which allows it to understand the structure of the entire website.

[0543] Step 2:

[0544] The server analyzes the retrieved HTML content and page structure. Specifically, it uses BeautifulSoup to parse the HTML code and extract elements such as meta tags, heading tags, and internal links. The HTML code is provided as input data, and the analysis results are obtained as output. Based on these analysis results, improvements to the website can be identified.

[0545] Step 3:

[0546] Based on the analysis results, the server uses a generative AI model to automatically generate suggested modifications to internal link structures, meta tags, and heading tags to enable search engines to crawl efficiently. The generative AI is prompted with the message "Please generate suggested modifications for index optimization," and the optimized HTML elements are returned as the output.

[0547] Step 4:

[0548] The server uses the Google Search Console API to find non-indexed pages. The input is the website URL, and the output is the indexing status. Non-indexed pages are identified.

[0549] Step 5:

[0550] The server uses a generative AI model to automatically generate content that is likely to be indexed for the detected pages, prompting them to "generate content that will promote indexing," and the appropriate content is generated as a result.

[0551] Step 6:

[0552] The server automatically sends an indexing request containing the generated content using the Google Search Console API, providing the generated content and the target page URL as input, and returning the indexing completion status as output.

[0553] Step 7:

[0554] To collect user emotion data, the device uses a camera to capture the user's facial expressions. Real-time camera footage is used as input data, and facial expression images are obtained as output. These facial expression images are used for subsequent emotion analysis.

[0555] Step 8:

[0556] The device inputs the collected facial expression images into an emotion recognition model to analyze the user's emotions. Using facial expression images as input data, emotion classification results are obtained as output. For example, emotions such as "surprise," "joy," and "sadness" are identified.

[0557] Step 9:

[0558] Based on the user's emotional data, the device sends a prompt to the generative AI model, which then generates content and promotional text appropriate to the user's emotions.The prompt might say, "The user's emotion is 'surprise.' Please generate optimal product recommendations and promotional messages," and the output is appropriate text and content.

[0559] Step 10:

[0560] The device displays the generated content to the user, providing a personalized experience based on the user's emotions. The generated content is used as input data, and the final display content is obtained as output. For example, specific product recommendations or special offers are displayed.

[0561] This allows for efficient automation of SEO measures and personalization of the user experience.

[0562] 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.

[0563] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. 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.

[0564] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0565] [Second embodiment]

[0566] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0567] 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.

[0568] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0569] 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.

[0570] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0571] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0572] 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.

[0573] 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.

[0574] 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 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.

[0575] 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.

[0576] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0577] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0578] This invention is a system that utilizes machine learning and generative AI to automatically execute and optimize SEO measures. This system efficiently processes various SEO tasks by combining elements of the server, terminal, and user.

[0579] Crawlability optimization

[0580] server

[0581] The server scans every page of a website, analyzing its HTML content and page structure, and then uses generative AI to automatically generate suggested modifications to help search engines crawl it more efficiently, such as optimizing internal link structures, meta tags, and heading tags.

[0582] Specific examples

[0583] If the server determines that the "About Us" page has not been crawled, the generation AI will automatically generate the appropriate internal links and meta tags and add them to the "About Us" page.

[0584] Increased indexing

[0585] server

[0586] The server detects and identifies unindexed pages using the Google Search Console API, etc. For the detected pages, it automatically generates content that is likely to be indexed using generative AI and adds it to the defective pages. It also automatically sends indexing requests using the Google Search Console API.

[0587] Specific examples

[0588] If a particular product page is not indexed, the server will identify it and use generative AI to optimize the product description and keyword density to promote indexing.

[0589] Improved rankings

[0590] server

[0591] The server regularly monitors the keyword rankings of the target page and uses generative AI to research related keywords, automatically generating new content that includes them or suggestions for modifying existing content to improve the page's clicks and ranking.

[0592] Specific examples

[0593] If the ranking for the keyword "diet supplement" is dropping, the server will use generative AI to generate content including effective usage instructions, benefits, ingredient information, etc. and add it to the page.

[0594] Increased site traffic and conversions

[0595] Terminal

[0596] The device analyzes traffic data in real time to identify pages with low conversion rates and uses generative AI to automatically generate recommendations for improving conversion rates, including redesigning, optimizing calls to action, and adding user reviews.

[0597] User

[0598] Users can review the AI's suggestions through an admin panel, manually correct them if necessary, and then approve the proposed changes to be applied to the site.

[0599] Specific examples

[0600] If the homepage's conversion rate is low, the device analyzes traffic data, and the AI ​​generator proposes new layouts and appealing points to the user. Once the user confirms and approves the suggestions, the homepage is automatically updated.

[0601] effect

[0602] This system ensures accuracy and consistency in SEO strategies, eliminating the problems of relying on individual staff. Automatically generated content and algorithmic optimization improve search engine rankings, leading to increased traffic and conversions.

[0603] The processing flow will be explained below.

[0604] Crawlability optimization

[0605] Step 1:

[0606] The server scans all pages of the website, analysing the sitemap file (sitemap.xml) and robots.txt file to list the URLs to crawl.

[0607] Step 2:

[0608] The server retrieves the HTML content of each listed page, which includes extracting each page's meta tags, heading tags (H1, H2, etc.), and internal link components.

[0609] Step 3:

[0610] The server analyzes the extracted page structure and uses generative AI to create suggestions for internal link structures, meta tags, and heading tags that are optimal for search engines.

[0611] Step 4:

[0612] The server applies the generated suggested modifications to the website and verifies the changes.

[0613] Increased indexing

[0614] Step 1:

[0615] The server checks the indexing status of the website using the Google Search Console API, and detects and lists any pages that are not indexed.

[0616] Step 2:

[0617] For unindexed pages, the server uses generative AI to automatically generate indexable content (text containing keywords, alt tags for images, etc.) and add it to the page.

[0618] Step 3:

[0619] The server automatically sends an indexing request using the Google Search Console API.

[0620] Improved rankings

[0621] Step 1:

[0622] The server periodically monitors the keyword rankings of target pages, collecting and analyzing the current ranking data for each page.

[0623] Step 2:

[0624] The server uses generative AI to review related keywords, taking into account data such as search volume and competition.

[0625] Step 3:

[0626] The server uses generative AI to automatically generate new content or revisions to existing content that includes related keywords, including new articles, product reviews, user testimonials, and more.

[0627] Step 4:

[0628] The server then posts the generated content to the website, which increases the page's clicks and ranking.

[0629] Increased site traffic and conversions

[0630] Step 1:

[0631] The terminal analyzes real-time traffic data, including the number of visitors, dwell time, and bounce rate.

[0632] Step 2:

[0633] The device identifies pages with low conversion rates by analyzing which pages are prone to bounce and which pages do not drive the desired action.

[0634] Step 3:

[0635] The device uses generative AI to automatically generate improvement suggestions (e.g., changing the design, changing the text of the CTA button, adding new appealing content) to improve conversion rates.

[0636] Step 4:

[0637] Users can review the AI's suggestions through an administration panel and manually apply corrections as needed.

[0638] Step 5:

[0639] The user approves the proposed improvements and they are reflected on the site, which aims to improve the conversion rate.

[0640] The above are the specific processing steps in the program of this system. By going through these steps, SEO measures can be implemented automatically and effectively.

[0641] Example 1

[0642] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0643] SEO is an important element in website management, but performing it manually requires a lot of time and expertise. Furthermore, there are various challenges in applying SEO, such as low crawlability, incomplete indexing, fluctuations in keyword rankings, and low site traffic and conversion rates. Therefore, there is a need for an efficient, automated method for optimizing the SEO of an entire website.

[0644] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0645] In this invention, the server includes means for scanning all pages of the website, means for analyzing the markup language content and page structure of all pages, and means for automatically generating suggestions for modifying the internal link structure, meta tags, and heading tags of the pages based on the analysis results so that search engines can crawl them efficiently, thereby optimizing the crawlability and SEO effect of the entire website.

[0646] A "website" is a collection of web pages that store information or data and that can be viewed and navigated over the Internet.

[0647] "All Pages" means all web pages belonging to a particular website.

[0648] "Markup language content" refers to the content of a web page that is structured using a markup language such as HTML or XML.

[0649] "Page structure" refers to the layout, navigation, and other design aspects of how the content of a web page is arranged and related.

[0650] A "search engine" is a system that collects information on the Internet and displays web pages related to a user's query as search results.

[0651] "Crawlability" refers to the ability of search engine crawlers to effectively navigate a web page and add its content to their index.

[0652] "Internal link structure" refers to the placement of links between different pages on a website and the path of the links.

[0653] "Meta tags" refer to tags included in the head of a web page that provide search engines with the content of the page and other relevant information.

[0654] "Heading tags" are tags from h1 to h6 that indicate the heading hierarchy in an HTML document, and are used to indicate the overview and structure of the page content.

[0655] "Indexing" refers to the state in which a web page is registered in a database of web pages collected by a search engine and is ready to be displayed in search results.

[0656] A "generative AI model" refers to an artificial intelligence model that generates new information or content from data based on machine learning algorithms.

[0657] An "indexing request" is a request to a search engine to add a particular web page to its index.

[0658] "Keyword ranking" refers to the position where a web page appears in search results for a particular search keyword.

[0659] "Traffic Data" refers to data regarding the access and behavior of visitors to a website.

[0660] "Conversion rate" is a metric that indicates the percentage of website visitors who achieve a specific goal.

[0661] This invention is a system that utilizes machine learning and generative AI to automatically execute and optimize SEO measures. This system works by combining elements of the server, terminal, and user to improve the SEO performance of the entire website.

[0662] Crawlability optimization

[0663] The server scans all pages of the target website, lists the URLs to be scanned, and retrieves the HTML content of each page. The server then analyzes the retrieved HTML content and page structure, including detecting internal links and analyzing the structure of heading tags (h1, h2, h3, etc.).

[0664] The server inputs prompts into the generative AI model based on the analysis results, and automatically generates suggested modifications to optimize crawlability. For example, a prompt might be, "What internal links should be added to the webpage?" The server then applies the automatically generated suggestions to the specific webpage. For example, adding new internal links, meta tags, or heading tags to the HTML.

[0665] Increased indexing

[0666] The server uses the Google Search Console API to detect unindexed pages. For the detected pages, the server uses a generative AI model to generate content that is likely to be indexed. The prompt text is something like, "What content does this page need to be indexed?" The generated content is added to specific pages, and an indexing request is automatically sent using the Google Search Console API.

[0667] Improved rankings

[0668] The server periodically monitors the keyword rankings of the target page. To monitor, it obtains data using Google Analytics and other SEO tools. Then, it uses a generative AI model to research keywords related to the target page. The prompt is, "What are the most relevant keywords?"

[0669] The server uses the generative AI model to generate new content, including specific use cases, benefits, and recommended usage, and applies the automatically generated content to the target page to improve keyword density and relevance.

[0670] Increased site traffic and conversions

[0671] The device analyzes traffic data in real time, using data from sources such as Google Analytics to analyze page views and user behavior. The device identifies pages with low conversion rates based on the traffic data.

[0672] The device then uses the generative AI model to generate improvement suggestions to improve the conversion rate. The prompt is, "What can we do to improve the conversion rate of this page?" The user can review the generated improvement suggestions, manually correct them if necessary, and then approve the proposed changes to be applied to the site.

[0673] The above is an embodiment of the present invention, which can improve the SEO performance of the entire website and improve its ranking in search engines, thereby increasing traffic and conversions.

[0674] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0675] Crawlability optimization

[0676] Step 1:

[0677] server

[0678] Input: List of website URLs

[0679] The server scans all pages of the website, lists the URLs to scan, and retrieves the HTML content of each page.

[0680] Output: HTML content of each page

[0681] Step 2:

[0682] server

[0683] Input: HTML content for each page

[0684] The server analyzes the retrieved HTML content and page structure, including detecting internal links and heading tags (h1, h2, h3, etc.).

[0685] Output: Analysis results (internal link structure, heading tag placement)

[0686] Step 3:

[0687] server

[0688] Input: Analysis results

[0689] The server then inputs prompts into the generative AI model based on the analysis results to automatically generate suggested modifications to optimize crawlability, such as "What internal links should be added to the webpage?"

[0690] Output: Suggested fixes (new internal links, meta tags, heading tags)

[0691] Step 4:

[0692] server

[0693] Input: Correction Suggestion

[0694] The server applies the automatically generated fixes to a specific web page, adding new internal links, meta tags, and heading tags to the HTML.

[0695] Output: Optimized HTML content

[0696] Increased indexing

[0697] Step 1:

[0698] server

[0699] Input: None

[0700] The server uses the Google Search Console API to find unindexed pages.

[0701] Output: List of unindexed pages

[0702] Step 2:

[0703] server

[0704] Input: List of unindexed pages

[0705] The server uses a generative AI model to automatically generate indexable content, using the prompt "What content does this page need to be indexed?"

[0706] Output: Indexable content

[0707] Step 3:

[0708] server

[0709] Input: Indexable content

[0710] The server adds the auto-generated content to a specific page.

[0711] Output: Optimized page content

[0712] Step 4:

[0713] server

[0714] Input: Optimized page content

[0715] The server automatically sends indexing requests using the Google Search Console API.

[0716] Output: Indexing request sent successfully

[0717] Improved rankings

[0718] Step 1:

[0719] server

[0720] Input: None

[0721] The server periodically monitors the keyword rankings of the target pages, using Google Analytics and other SEO tools to obtain the data.

[0722] Output: Keyword ranking data

[0723] Step 2:

[0724] server

[0725] Input: Keyword ranking data

[0726] The server uses a generative AI model to research related keywords, using the prompt "What are the most relevant keywords?"

[0727] Output: Related keyword list

[0728] Step 3:

[0729] server

[0730] Input: Related keyword list

[0731] The server uses the generative AI model to generate new content, including specific use cases, effects, and recommended usage.

[0732] Output: New content

[0733] Step 4:

[0734] server

[0735] Input: New content

[0736] The server applies automatically generated content to the target page, improving keyword density and relevance.

[0737] Output: Improved page content

[0738] Increased site traffic and conversions

[0739] Step 1:

[0740] Terminal

[0741] Input: Traffic data

[0742] The device analyzes traffic data in real time, using data from Google Analytics and other sources to analyze page views and user behavior.

[0743] Output: Traffic analysis results

[0744] Step 2:

[0745] Terminal

[0746] Input: Traffic analysis results

[0747] The device identifies pages with low conversion rates based on traffic data.

[0748] Output: List of pages with low conversion rates

[0749] Step 3:

[0750] Terminal

[0751] Input: List of low-converting pages

[0752] The device uses a generative AI model to generate improvement suggestions to improve the conversion rate, using the prompt "What can we do to improve the conversion rate of this page?"

[0753] Output: Improvement plan

[0754] Step 4:

[0755] User

[0756] Input: Improvement idea

[0757] Users can review the AI's suggestions through an admin panel, manually correct them if necessary, and then approve the proposed changes to be applied to the site.

[0758] Output: Optimized web page

[0759] (Application example 1)

[0760] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0761] SEO measures for online shopping sites are extremely important, as they directly affect product rankings in search results and conversion rates. However, currently, these SEO tasks are often performed manually, requiring specialized knowledge and effort, making them inefficient. In particular, optimizing internal link structures and meta tags, indexing, monitoring keyword rankings, and creating and modifying content are complex and often require real-time response, making them impractical. There is a need for systems that enable efficient, automated execution and optimization of these SEO tasks, as well as the ability to improve conversion rates based on traffic data.

[0762] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0763] In this invention, the server includes means for scanning all pages of a website, means for analyzing the HTML content and page structure of all pages, means for automatically generating suggested modifications to the internal link structure, meta tags, and heading tags of the pages that can be efficiently crawled by search engines based on the analysis results, means for analyzing detected traffic data, means for proposing optimal design changes and calls to action using generative AI, and means for automatically generating or modifying content based on the suggested improvements, thereby efficiently and automatically executing SEO tasks, improving rankings in search engines, and increasing traffic and conversions.

[0764] A "means of scanning all pages of a website" is a means of collecting data relating to the HTML content of all pages of a website.

[0765] "Means for analyzing the HTML content and page structure of all pages" refers to means for analyzing the structure and content of all collected HTML data of pages and extracting information for optimization.

[0766] "Means for automatically generating suggested modifications to the internal link structure, meta tags, and heading tags of a page that can be crawled efficiently by search engines" refers to a means for automatically generating suggested modifications to the link structure, meta tags, heading tags, etc., based on the analysis results, that will enable search engines to crawl web pages more efficiently.

[0767] "Means for analyzing detected traffic data" means means for analyzing traffic data collected from website visitors to identify patterns or issues based on that data.

[0768] "Method of using generative AI to suggest optimal design changes and calls to action" refers to a method of using generative AI to automatically suggest changes to optimize website design and elements that prompt users to take action (CTA).

[0769] "Means for automatically generating or modifying content based on improvement suggestions" refers to means for generating new content or automatically modifying existing content based on improvement suggestions proposed by the generative AI.

[0770] MODE FOR CARRYING OUT THE INVENTION

[0771] This invention is a system that efficiently and automatically implements SEO measures for online shopping sites. This system optimizes SEO tasks by combining server, terminal, and user elements, improving conversion rates and search engine rankings.

[0772] The server scans all pages of a website and analyzes their HTML content and page structure. Based on the results of this analysis, a generative AI model (such as GPT-4) is used to automatically generate suggestions for modifying the internal link structure, meta tags, and heading tags of the page so that search engines can crawl it more efficiently. This improves crawlability.

[0773] In addition, the server uses the Google Search Console API and Google Analytics API to detect unindexed pages. It uses generative AI models to automatically generate content that is likely to be indexed and adds this content to the defective pages. Indexing requests are also automatically sent, which can increase the number of indexes.

[0774] The server regularly monitors the keyword rankings of the target page and uses a generative AI model to research related keywords. It then automatically generates new content that includes those keywords or proposes modifications to existing content to improve the page's ranking. Furthermore, the generated content and modifications to the internal link structure are automatically reflected on the site.

[0775] The device analyzes traffic data in real time to identify pages with low conversion rates. Using a generative AI model, it automatically generates recommendations for improving conversion rates, including design changes, optimizing calls to action (CTA), and adding user reviews. Users can review the recommendations made by the generative AI through an admin panel, manually correct them as needed, and then approve the proposed changes to apply them to their site.

[0776] Specific use cases

[0777] The hardware and software used includes the following:

[0778] Server: AWS EC2

[0779] Database: Amazon RDS

[0780] Machine learning model: Transformers library (Hugging Face)

[0781] API: Google Search Console API, Google Analytics API

[0782] Interface: React Native (for smartphone apps)

[0783] For example, if the server identifies that the "About Us" page has not been crawled, the generative AI will automatically generate the appropriate internal links and meta tags to add to the "About Us" page. An example of a prompt for the generative AI model is:

[0784] Optimize the internal link structure and meta tags of your "About Us" page.

[0785] Additionally, if a particular product page is not indexed, the server will identify it and use generative AI to generate content that optimizes product descriptions and keyword density. An example prompt is below:

[0786] To optimize your unindexed product pages for indexing, generate content with high description and keyword density.

[0787] So, for example, if your rankings for the keyword "diet supplements" are dropping, you can use generative AI to generate new content to add to the page, including effective instructions, benefits, and ingredient information. Here's an example prompt:

[0788] Generate new content with effective usage, benefits, and ingredient information to improve your ranking for the keyword "diet supplements."

[0789] This system allows online shopping sites to efficiently and automatically implement SEO measures, improving their rankings in search engines and increasing traffic and conversions.

[0790] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0791] Step 1:

[0792] The server scans all pages of a website and collects their HTML content and page structure. The input of this process is the website URL, and the output is the retrieved HTML data and page structure. The server uses a crawler to collect data for each web page.

[0793] Step 2:

[0794] The server analyzes the collected HTML data and page structure. In this analysis process, HTML tags, meta tags, internal link structures, etc. are extracted and the analysis results are obtained. The input is the HTML data obtained in step 1, and the output is the analyzed page structure and meta information. The server performs data analysis using a specific parser tool.

[0795] Step 3:

[0796] Based on the analysis results, the server uses generative AI to automatically generate suggested modifications to internal link structures, meta tags, and heading tags that search engines can crawl efficiently. The input here is the analysis results, and the output is suggested modifications. The following prompt is input to the generative AI model:

[0797] Optimize the internal link structure and meta tags of your "About Us" page.

[0798] Step 4:

[0799] The server uses the Google Search Console API to identify pages that are not indexed. The input is a list of indexed pages, and the output is a list of pages that are not indexed. The server makes an API call to check the index status of the pages.

[0800] Step 5:

[0801] The server uses generative AI to automatically generate content that is likely to be indexed for unindexed pages. The input to this process is the HTML data of the unindexed page, and the output is the generated new content. The generative AI model is given the following prompt:

[0802] To optimize your unindexed product pages for indexing, generate content with high description and keyword density.

[0803] Step 6:

[0804] The server automatically sends an indexing request using the Google Search Console API. The input is the new content generated, and the output is the result of the indexing request submission. Provide the API with the new content and submit the request.

[0805] Step 7:

[0806] The server periodically monitors the keyword rankings of the target page and uses the generative AI to search for related keywords. The input to this process is the current keyword ranking data of the target page, and the output is a list of related keywords. The generative AI model is given the following prompt:

[0807] Generate new content with effective usage, benefits, and ingredient information to improve your ranking for the keyword "diet supplements."

[0808] Step 8:

[0809] The terminal analyzes traffic data in real time and identifies pages with low conversion rates. The input of this process is traffic data, and the output is a list of pages with low conversion rates. The terminal obtains data from the Google Analytics API and performs analysis.

[0810] Step 9:

[0811] The device uses generative AI to automatically generate design and call-to-action improvements to improve conversion rates. The input is a list of pages with low conversion rates, and the output is improvement recommendations. The following prompt is input to the generative AI model:

[0812] Generate suggestions to optimize this page's design and call-to-action to improve conversion rates.

[0813] Step 10:

[0814] Users can review the suggestions made by the generative AI through an administration panel, manually correct them as necessary, and then approve the proposed changes and have them reflected on the site. The input is the generative AI's suggestions, and the output is final approval and reflection on the site. Users operate through a web interface.

[0815] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0816] This invention is a system that automatically executes and optimizes SEO measures using machine learning and generative AI, and further improves the user experience by combining it with an emotion engine that recognizes user emotions. The specific processing flow is explained below.

[0817] Crawlability optimization

[0818] server

[0819] The server scans every page of a website, analyzing its HTML content and page structure, and then uses generative AI to automatically generate suggested modifications to help search engines crawl it more efficiently, such as optimizing internal link structures, meta tags, and heading tags.

[0820] Specific examples

[0821] If the server determines that the "About Us" page has not been crawled, the generation AI will automatically generate the appropriate internal links and meta tags and add them to the "About Us" page.

[0822] Increased indexing

[0823] server

[0824] The server uses the Google Search Console API to check the indexing status of the website, detects and identifies pages that are not indexed, automatically generates content that is likely to be indexed using generative AI for the detected pages and adds it to the defective pages, and automatically sends indexing requests using the Google Search Console API.

[0825] Specific examples

[0826] If a particular product page is not indexed, the server will identify it and use generative AI to optimize the product description and keyword density to promote indexing.

[0827] Improved rankings

[0828] server

[0829] The server periodically monitors the keyword rankings of the target page and uses generative AI to research related keywords, automatically generating new content that includes them or suggestions for modifying existing content to improve the page's clicks and ranking.

[0830] Specific examples

[0831] If the ranking for the keyword "diet supplement" is dropping, the server will use generative AI to generate content including effective usage instructions, benefits, ingredient information, etc. and add it to the page.

[0832] Increased site traffic and conversions

[0833] Terminal

[0834] The device analyzes real-time traffic data to identify pages with low conversion rates and uses generative AI to automatically generate recommendations for improving conversion rates, including redesigning, optimizing calls to action, and adding user reviews.

[0835] User

[0836] Users can review the AI's suggestions through an admin panel, manually correct them if necessary, and then approve the proposed changes to be applied to the site.

[0837] Specific examples

[0838] If the homepage's conversion rate is low, the device analyzes traffic data, and the AI ​​generator proposes new layouts and appealing points to the user. Once the user confirms and approves the suggestions, the homepage is automatically updated.

[0839] Utilizing the Emotion Engine

[0840] server

[0841] The server collects user emotion data using an emotion engine, which analyzes the user's facial expressions and behavior while visiting the website to grasp the user's emotional state in real time.

[0842] Specific examples

[0843] When a user is browsing a particular product page, if the emotion engine detects interest or enjoyment in the user's facial expression, the server can use that emotion data to display additional product information or special offers.

[0844] Terminal

[0845] The device analyzes the collected emotional data and adjusts website content according to the user's emotional tendencies, including rearranging content and optimizing design.

[0846] Specific examples

[0847] If a user shows signs of frustration while browsing multiple pages, the device will detect this emotion and offer more concise navigation and relevant product recommendations.

[0848] User

[0849] Users can review the changes and adjustments suggested by the emotion engine in the admin panel, make any necessary corrections, and apply them to their site.

[0850] Specific examples

[0851] This will be done automatically once the user confirms the results of the emotion engine analysis in the admin panel and approves the change in the position of the review section on the product page.

[0852] effect

[0853] This system ensures accuracy and consistency in SEO measures and eliminates problems caused by personalization. Furthermore, by combining it with an emotion engine, it is possible to provide a personalized experience based on the user's emotions, which is expected to increase traffic and conversions.

[0854] The processing flow will be explained below.

[0855] Crawlability optimization

[0856] Step 1:

[0857] The server scans all pages of the website and analyzes the sitemap file (sitemap.xml) and robots.txt file to list the URLs to crawl.

[0858] Step 2:

[0859] The server accesses each URL and retrieves the HTML content, extracting tags and text data, including meta tags, heading tags (H1, H2, H3, etc.), and internal links.

[0860] Step 3:

[0861] Based on the collected data, the server uses generative AI to automatically generate suggestions for the optimal internal link structure, effective meta tags, and appropriate heading tag revisions.

[0862] Step 4:

[0863] The server applies the generated suggested modifications to the website and verifies the changes.

[0864] Increased indexing

[0865] Step 1:

[0866] The server checks the indexing status of the website using the Google Search Console API, and detects and lists any pages that are not indexed.

[0867] Step 2:

[0868] For unindexed pages, the server uses generative AI to automatically generate index-friendly content, including adding text containing keywords and adding appropriate alt tags to images.

[0869] Step 3:

[0870] The server adds auto-generated content to the page and automatically sends an indexing request using the Google Search Console API.

[0871] Improved rankings

[0872] Step 1:

[0873] The server periodically monitors the keyword rankings of target pages, collects the current ranking data of each page, and performs statistical analysis.

[0874] Step 2:

[0875] The server uses generative AI to re-examine related keywords, re-evaluate appropriate keywords, and categorize them based on search volume and competition.

[0876] Step 3:

[0877] The server uses generative AI to automatically generate new content or revisions to existing content that includes relevant keywords, including new articles, product reviews, and adding user testimonials.

[0878] Step 4:

[0879] The server then posts the generated content to websites, aiming to improve their visibility, clicks, and ranking in search results.

[0880] Traffic data and increased conversions

[0881] Step 1:

[0882] The terminal analyzes real-time traffic data, including visitor behavior, length of stay, and bounce rate.

[0883] Step 2:

[0884] The device identifies pages with low conversion rates, evaluates the performance of each page, and identifies problems.

[0885] Step 3:

[0886] The device uses generative AI to automatically generate recommendations for improving conversion rates, including redesigning, changing the text of the call-to-action (CTA) button, and adding new appealing content.

[0887] Step 4:

[0888] Users can review the AI's suggestions through an admin panel, manually correct them if necessary, and then approve the proposed changes to be applied to the site.

[0889] Step 5:

[0890] The user approves the proposed improvements and they are reflected on the site, which aims to improve the conversion rate.

[0891] Utilizing the Emotion Engine

[0892] Step 1:

[0893] The server uses an emotion engine to collect user emotional data, analyzing the user's facial expressions, tone of voice, behavioral patterns, etc. in real time.

[0894] Step 2:

[0895] The device analyzes the collected emotional data and adjusts the content on the website according to the user's emotional tendencies, changing the content layout and optimizing the design.

[0896] Step 3:

[0897] The server uses generative AI to automatically generate content to personalize the user experience based on emotional data.

[0898] Step 4:

[0899] The user can review the generated changes through the administration panel and manually correct them if necessary.

[0900] Step 5:

[0901] The user approves the suggestions and modifications made by the emotion engine and reflects them on the site.

[0902] These are the specific processing steps in the program of this system. By going through these steps, it is possible to automatically and effectively implement SEO measures and also to provide a personalized experience by utilizing user emotional data.

[0903] Example 2

[0904] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0905] Traditional website optimization methods involve individual SEO measures such as crawling and indexing pages and monitoring keyword rankings, making it difficult to achieve comprehensive optimization. Furthermore, dynamic content adjustments based on user sentiment are not performed, resulting in insufficient improvements to the user experience.

[0906] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for scanning all pages of the website, means for analyzing the HTML content and page structure of all pages, means for automatically generating, using a generation AI, revision suggestions for the internal link structure, meta tags, and heading tags of the pages that can be efficiently crawled by search engines based on the analysis results, means for using an emotion engine for collecting user emotion data, and means for dynamically adjusting site content based on the emotion data. This enables comprehensive and automatic SEO measures and further personalization based on user emotion.

[0907] A "website" is a collection of multiple web pages published on the Internet, and is constructed using technologies such as HTML and CSS.

[0908] "Scanning" refers to the process of crawling all pages of a website and retrieving and analyzing data.

[0909] "HTML content" refers to data in HTML (HyperText Markup Language) format that describes the content of a web page.

[0910] "Page structure" refers to the overall structure of a web page, including the layout and hierarchy of heading tags, meta tags, link structure, etc.

[0911] "Analysis" refers to the process of examining data and extracting useful information for a specific purpose.

[0912] A "search engine" is a system that searches for information on the Internet and provides the results to users. Examples include Google and Bing.

[0913] "Internal linking structure" refers to the link relationships between pages on a website, designed to help users and search engines navigate the site effectively.

[0914] A "meta tag" is a tag written in the head section of a web page that provides search engines and browsers with metadata such as a description of the page, keywords, and author.

[0915] "Generative AI" refers to systems or models that use artificial intelligence to automatically perform specific tasks, and in this case refers to technologies that generate content and make optimization suggestions.

[0916] An "emotion engine" is a system that collects and analyzes users' emotional data, grasps their state in real time, and provides appropriate feedback and adjustments.

[0917] "Dynamic adjustment" refers to changing the content or design of a website in response to data or circumstances in real time.

[0918] MODE FOR CARRYING OUT THE INVENTION

[0919] This invention is a system that improves user experience by automatically implementing SEO measures using machine learning and generative AI, and by combining it with an emotion engine that recognizes user emotions. This system integrates website crawling, indexing, keyword ranking monitoring, and real-time emotion data collection and analysis.

[0920] Hardware and software used

[0921] Server: The server scans all pages of the website and analyzes the HTML code of each page using an HTML parser (e.g., BeautifulSoup). Generative AI can be provided by OpenAI's GPT series or similar.

[0922] Device: The device analyzes real-time traffic data using the Google Analytics API to identify pages with low conversion rates.

[0923] Emotion engine: The emotion engine uses Affectiva's SDK and other tools to analyze the user's facial expressions and behavior in real time to collect emotional data.

[0924] Specific processing flow

[0925] Crawlability optimization

[0926] server

[0927] The server scans every page of a website, analyzes its HTML content and page structure, and then gives the AI ​​prompts such as "Please suggest optimizations for internal link structure and meta tags," which then automatically generates suggested modifications.

[0928] Specific examples

[0929] For example, if the "About Us" page isn't crawled, the server will use generative AI to auto-generate the appropriate internal links and meta tags and add them to the page.

[0930] Prompt Sentence Examples

[0931] "If a particular web page is not being crawled, generate optimization recommendations for the HTML content and metadata of that page."

[0932] Increased indexing

[0933] server

[0934] The server checks the indexing status using the Google Search Console API, detects unindexed pages, and sends a prompt to the generation AI, such as "Optimize the product description on this page and generate content that is more likely to be indexed."

[0935] Specific examples

[0936] If a particular product page isn't indexed, the generative AI generates content that optimizes the product description and keyword density, and the server adds that content to the page.

[0937] Prompt Sentence Examples

[0938] "Optimize the product description on this page to generate indexable content."

[0939] Improved rankings

[0940] server

[0941] The server periodically monitors the keyword rankings of target pages through the Google Analytics API, and provides prompts to the AI ​​generator, such as "Please generate content that includes new and effective usage methods, benefits, and ingredient information," to generate content that contributes to improving rankings.

[0942] Specific examples

[0943] If the ranking for the keyword "diet supplement" is dropping, the server will use generative AI to automatically generate content including effective usage instructions, benefits, and ingredient information and add it to the page.

[0944] Prompt Sentence Examples

[0945] "Generate new content related to the keyword 'diet supplements' with effective usage, benefits, and ingredient information."

[0946] Increased site traffic and conversions

[0947] Terminal

[0948] The device analyzes real-time traffic data to identify pages with low conversion rates, and sends a prompt to the AI ​​generator to "generate new layout proposals and selling points to improve the conversion rate of this page," automatically generating improvement proposals.

[0949] User

[0950] Users can review the suggestions made by the AI ​​through an administration panel, manually correct them if necessary, and then approve the changes to reflect them on the site.

[0951] Specific examples

[0952] If the homepage conversion rate is low, the terminal will display suggestions from the generation AI on the management panel, which the user can confirm and then implement.

[0953] Prompt Sentence Examples

[0954] "Generate new layout ideas and selling points to improve the conversion rate of this page."

[0955] Utilizing the Emotion Engine

[0956] server

[0957] The server uses an emotion engine to collect user emotion data, analyze the user's facial expressions and behavior in real time, and understand their emotional state.

[0958] Specific examples

[0959] If the emotion engine detects interest or enjoyment while a user is viewing a particular product page, the server can use that emotion data to display additional product information or special offers.

[0960] Terminal

[0961] The terminal analyzes the collected emotional data and adjusts the content on the website according to the user's emotional tendencies.

[0962] Specific examples

[0963] If a user shows signs of frustration while browsing multiple pages, the device will detect this emotion and offer more concise navigation and relevant product recommendations.

[0964] User

[0965] Users can review the changes and adjustments suggested by the emotion engine in the administration panel, make corrections as necessary, and reflect them on the site.

[0966] Specific examples

[0967] This will be done automatically once the user confirms the results of the emotion engine analysis in the admin panel and approves the repositioning of the review section on the product page.

[0968] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0969] Step 1: Scan the website

[0970] server

[0971] The server scans all pages of a website. It receives a list of URLs as input and parses the HTML code of each page using an HTML parser (e.g. BeautifulSoup). It gets the parsed data for each page as output.

[0972] Specific actions

[0973] For example, the server retrieves all page URLs for "example.com" in sequence and parses each page using an HTML parser.

[0974] Step 2: Analyzing the page structure

[0975] server

[0976] The server analyzes the HTML content and page structure of the scanned page, using the analysis data from step 1 as input to identify internal links, meta tags, heading tags, etc. As output, it gets data containing details of the page structure.

[0977] Specific actions

[0978] For example, the server checks the presence of internal links and meta tags on the "About Us" page to assess whether it is optimized.

[0979] Step 3: Generative AI generates revision suggestions

[0980] server

[0981] Based on the analysis results, the server sends prompts to the generation AI to generate suggested modifications to the internal link structure, meta tags, and heading tags. The server provides details of the page structure and a prompt as input, and receives suggested modifications from the generation AI as output.

[0982] Specific actions

[0983] For example, you can send a prompt like "Add an internal link to this page" to the generative AI and get suggested fixes.

[0984] Step 4: Applying the proposed amendment

[0985] server

[0986] The server applies the suggested revisions from the generative AI to the web page, using the suggested revision data as input and generating an output with updated HTML code.

[0987] Specific actions

[0988] For example, add the internal link code obtained from the generation AI to the "About Us" page and save the updated HTML.

[0989] Step 5: Check the index status

[0990] server

[0991] The server checks the index status of the website using the Google Search Console API. It uses the website URL as input and gets the index information from the API. It gets the list of pages that are not indexed as output.

[0992] Specific actions

[0993] For example, the server checks the indexing status of each page on "example.com" using the Google Search Console API and lists the 404 error pages.

[0994] Step 6: Content generation with generative AI

[0995] server

[0996] The server automatically generates content for unindexed pages. It uses the list of unindexed pages and prompts as input and gets optimized content from the generation AI. It gets optimized content data as output.

[0997] Specific actions

[0998] For example, for an unindexed product page, a prompt such as "Please optimize the product description" is sent to the generation AI, and the generated content is retrieved.

[0999] Step 7: Submitting an indexing request

[1000] server

[1001] The server adds the auto-generated content to the page and sends an indexing request through the Google Search Console API, using the updated HTML code as input and sending an indexing request to the API to get the output.

[1002] Specific actions

[1003] For example, add the generated product description to the relevant page and send an indexing request using the Google Search Console API.

[1004] Step 8: Monitor your keyword rankings

[1005] server

[1006] The server periodically monitors keyword rankings using the Google Analytics API. It uses the target keywords as input and retrieves ranking data from the API. It gets a list of ranking data as output.

[1007] Specific actions

[1008] For example, monitor the keyword "diet supplements" and obtain its rankings periodically.

[1009] Step 9: Generative AI creates new content

[1010] server

[1011] If the server detects a drop in keyword rankings, it uses a generative AI to generate new content and suggested revisions. It uses ranking data and prompts as inputs to obtain new content from the generative AI, and obtains new content data as output.

[1012] Specific actions

[1013] For example, send a prompt like "Suggest a new use for 'Diet Supplement'" and get the generated content.

[1014] Step 10: Analyzing real-time traffic data

[1015] Terminal

[1016] The device analyzes real-time traffic data using the Google Analytics API. It uses website URLs as input to identify pages with low conversion rates, and gets a list of pages with low conversion rates as output.

[1017] Specific actions

[1018] For example, the terminal retrieves traffic data for each page of "example.com" and lists pages with low conversion rates.

[1019] Step 11: Generative AI generates improvement proposals

[1020] Terminal

[1021] The device uses a generative AI to automatically generate improvement suggestions for pages with low conversion rates. It uses the conversion rate data and prompt text as input and obtains improvement suggestion data from the generative AI. It then obtains the improvement suggestion data as output.

[1022] Specific actions

[1023] For example, you can send a prompt like, "Suggest a new layout to increase the conversion rate of this homepage," and get the generated layout suggestions.

[1024] Step 12: User Acceptance and Application

[1025] User

[1026] The user can review the suggestions made by the generative AI through an admin panel, manually correct them if necessary, and then approve the changes to be reflected on the site. The user uses the suggested data from the generative AI as input and applies the final updates to the site, obtaining the updated webpage as output.

[1027] Specific actions

[1028] For example, users can check new homepage layout suggestions made by the generative AI in the administration panel and implement them after approval.

[1029] Step 13: Collect emotion data

[1030] server

[1031] The server uses an emotion engine to collect user emotion data. It uses the user's real-time behavior data as input and obtains analysis results from the emotion engine. It obtains real-time emotion data as output.

[1032] Specific actions

[1033] For example, the emotion engine detects interest or enjoyment when a user is viewing a particular product page.

[1034] Step 14: Analyze and apply emotion data

[1035] Terminal

[1036] The device analyzes the collected emotional data and adjusts the content on the website according to the user's emotional tendencies. It uses the emotional data as input to dynamically change the layout and display of the site content, and obtains updated web page content as output.

[1037] Specific actions

[1038] For example, if the user shows signs of frustration, the navigation can be simplified based on data from the emotion engine.

[1039] Step 15: User Review and Approval

[1040] User

[1041] The user reviews the changes and adjustments suggested by the emotion engine in the admin panel, manually corrects them if necessary, and approves the changes. The system uses the suggested data from the emotion engine as input and finally updates the content on the site, obtaining an updated web page as output.

[1042] Specific actions

[1043] For example, if a user checks the analysis results of the sentiment engine in the admin panel and approves changing the position of the review section on the product page, this will be implemented automatically.

[1044] (Application example 2)

[1045] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1046] Existing SEO systems are inefficient because they require specialized technical expertise and require advanced skills and time. Furthermore, when it comes to improving user experience, there is a lack of methods for grasping user sentiment in real time and providing content that responds to that sentiment. Therefore, there is a need for a system that can simultaneously improve both SEO and user experience.

[1047] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for scanning all pages of a website, means for analyzing the HTML content and page structure of all pages, means for automatically generating, based on the analysis results, revision suggestions for the internal link structure, meta tags, and heading tags of the pages that can be efficiently crawled by search engines, means for collecting and analyzing user emotion data, and means for automatically generating, based on the analysis results, revision suggestions for content that correspond to the user's emotions. This makes it possible to automate SEO measures and personalize the user experience.

[1048] "Means for scanning all pages of a website" means a function for automatically scanning all pages of a website and acquiring their contents.

[1049] "Means for analyzing HTML content and page structure" refers to the ability to analyze the HTML code of a web page and its design, such as headings, meta tags, and link structure.

[1050] "Means for automatically generating suggested modifications to internal link structures, meta tags, and heading tags" is a function that automatically generates and suggests links and meta information to encourage optimal crawling and indexing of websites.

[1051] The "means for collecting and analyzing user emotional data" is a function that captures visitors' facial expressions and behavioral data in real time and analyzes their emotional state.

[1052] "Means for automatically generating suggested revisions to content according to the user's emotions" is a function that automatically generates and suggests content that is appropriate for the user's interests and emotional state based on collected emotional data.

[1053] "Means for detecting unindexed pages" refers to a function that identifies web pages that are not registered in a search engine's index.

[1054] "Means for automatically generating content that is likely to be indexed using a generative AI model" is a function that uses artificial intelligence to automatically generate content that is likely to be indexed by search engines.

[1055] "Means for automatically sending indexing requests" means a function that automatically sends indexing requests to search engines for content created by a generative AI model.

[1056] "Means of sending the received prompt text to a generation AI and generating content or promotional text that suits the user's emotions" refers to a function that sends a prompt text that takes the user's emotions into consideration to a generation AI, and as a result, automatically generates content or promotional text that suits the user's emotions.

[1057] This invention relates to a system that aims to improve user experience by combining automated SEO measures using machine learning and generative AI models with an emotion engine. The system includes a means for scanning and analyzing all pages of a website and automatically generating optimal revision suggestions based on the results. It also includes a means for collecting user emotions in real time and providing content that corresponds to those emotions.

[1058] System configuration

[1059] server

[1060] The server is responsible for scanning all pages of a website and analyzing their HTML content and page structure. Through this analysis, generative AI models are used to automatically generate suggested modifications to internal link structures, meta tags, and heading tags to help search engines crawl them more efficiently.

[1061] Specifically, the server uses BeautifulSoup to parse the HTML of web pages to detect unindexed pages, then uses generative AI models to generate appropriate content for those pages and automatically submits indexing requests using the Google Search Console API.

[1062] Terminal

[1063] The device collects user emotional data and adjusts content based on the analysis results. This process involves capturing the user's facial expressions using a camera and using OpenCV and an emotion recognition model. Based on this emotional data, the device sends prompts to a generative AI model, which then generates content and promotional text appropriate to the user's emotions.

[1064] User

[1065] Users can review the suggestions made by the generated AI through an admin panel, make manual corrections if necessary, and then approve the proposed changes to reflect them on the site.

[1066] Hardware and Software

[1067] Hardware: Servers, devices (smartphones, PCs)

[1068] Software: BeautifulSoup, OpenCV, Google Search Console API, generative AI models

[1069] Specific examples

[1070] When a user is browsing a product page on an online shopping site, the camera recognizes the user's expression of surprise. Based on this information, the AI ​​generates prompts such as the following, which then display an appropriate promotional message:

[1071] The user's emotion is "surprise." Generate optimal product recommendations and promotional messages based on this emotion.

[1072] It also discovers that certain pages are not indexed, uses generative AI to create optimal keywords and meta tags, and sends a request to Google Search Console to include them, as shown below.

[1073] Generate the best keywords and meta tags to help index this page.

[1074] This allows for automated SEO and personalized user experience.

[1075] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1076] Step 1:

[1077] The server scans all pages of the website to obtain the HTML content and page structure. Through this scan, the server collects the URL and HTML code of each page of the website as input data and stores the results in an internal database, which allows it to understand the structure of the entire website.

[1078] Step 2:

[1079] The server analyzes the retrieved HTML content and page structure. Specifically, it uses BeautifulSoup to parse the HTML code and extract elements such as meta tags, heading tags, and internal links. The HTML code is provided as input data, and the analysis results are obtained as output. Based on these analysis results, improvements to the website can be identified.

[1080] Step 3:

[1081] Based on the analysis results, the server uses a generative AI model to automatically generate suggested modifications to internal link structures, meta tags, and heading tags to enable search engines to crawl efficiently. The generative AI is prompted with the message "Please generate suggested modifications for index optimization," and the optimized HTML elements are returned as the output.

[1082] Step 4:

[1083] The server uses the Google Search Console API to find non-indexed pages. The input is the website URL, and the output is the indexing status. Non-indexed pages are identified.

[1084] Step 5:

[1085] The server uses a generative AI model to automatically generate content that is likely to be indexed for the detected pages, prompting them to "generate content that will promote indexing," and the appropriate content is generated as a result.

[1086] Step 6:

[1087] The server automatically sends an indexing request containing the generated content using the Google Search Console API, providing the generated content and the target page URL as input, and returning the indexing completion status as output.

[1088] Step 7:

[1089] To collect user emotion data, the device uses a camera to capture the user's facial expressions. Real-time camera footage is used as input data, and facial expression images are obtained as output. These facial expression images are used for subsequent emotion analysis.

[1090] Step 8:

[1091] The device inputs the collected facial expression images into an emotion recognition model to analyze the user's emotions. Using facial expression images as input data, emotion classification results are obtained as output. For example, emotions such as "surprise," "joy," and "sadness" are identified.

[1092] Step 9:

[1093] Based on the user's emotional data, the device sends a prompt to the generative AI model, which then generates content and promotional text appropriate to the user's emotions.The prompt might say, "The user's emotion is 'surprise.' Please generate optimal product recommendations and promotional messages," and the output is appropriate text and content.

[1094] Step 10:

[1095] The device displays the generated content to the user, providing a personalized experience based on the user's emotions. The generated content is used as input data, and the final display content is obtained as output. For example, specific product recommendations or special offers are displayed.

[1096] This allows for efficient automation of SEO measures and personalization of the user experience.

[1097] 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.

[1098] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. 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.

[1099] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1100] [Third embodiment]

[1101] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1102] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1104] 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.

[1105] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1106] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1107] 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.

[1108] 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.

[1109] 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 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.

[1110] 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.

[1111] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1112] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1113] This invention is a system that utilizes machine learning and generative AI to automatically execute and optimize SEO measures. This system efficiently processes various SEO tasks by combining elements of the server, terminal, and user.

[1114] Crawlability optimization

[1115] server

[1116] The server scans every page of a website, analyzing its HTML content and page structure, and then uses generative AI to automatically generate suggested modifications to help search engines crawl it more efficiently, such as optimizing internal link structures, meta tags, and heading tags.

[1117] Specific examples

[1118] If the server determines that the "About Us" page has not been crawled, the generation AI will automatically generate the appropriate internal links and meta tags and add them to the "About Us" page.

[1119] Increased indexing

[1120] server

[1121] The server detects and identifies unindexed pages using the Google Search Console API, etc. For the detected pages, it automatically generates content that is likely to be indexed using generative AI and adds it to the defective pages. It also automatically sends indexing requests using the Google Search Console API.

[1122] Specific examples

[1123] If a particular product page is not indexed, the server will identify it and use generative AI to optimize the product description and keyword density to promote indexing.

[1124] Improved rankings

[1125] server

[1126] The server regularly monitors the keyword rankings of the target page and uses generative AI to research related keywords, automatically generating new content that includes them or suggestions for modifying existing content to improve the page's clicks and ranking.

[1127] Specific examples

[1128] If the ranking for the keyword "diet supplement" is dropping, the server will use generative AI to generate content including effective usage instructions, benefits, ingredient information, etc. and add it to the page.

[1129] Increased site traffic and conversions

[1130] Terminal

[1131] The device analyzes traffic data in real time to identify pages with low conversion rates and uses generative AI to automatically generate recommendations for improving conversion rates, including redesigning, optimizing calls to action, and adding user reviews.

[1132] User

[1133] Users can review the AI's suggestions through an admin panel, manually correct them if necessary, and then approve the proposed changes to be applied to the site.

[1134] Specific examples

[1135] If the homepage's conversion rate is low, the device analyzes traffic data, and the AI ​​generator proposes new layouts and appealing points to the user. Once the user confirms and approves the suggestions, the homepage is automatically updated.

[1136] effect

[1137] This system ensures accuracy and consistency in SEO strategies, eliminating the problems of relying on individual staff. Automatically generated content and algorithmic optimization improve search engine rankings, leading to increased traffic and conversions.

[1138] The processing flow will be explained below.

[1139] Crawlability optimization

[1140] Step 1:

[1141] The server scans all pages of the website, analysing the sitemap file (sitemap.xml) and robots.txt file to list the URLs to crawl.

[1142] Step 2:

[1143] The server retrieves the HTML content of each listed page, which includes extracting each page's meta tags, heading tags (H1, H2, etc.), and internal link components.

[1144] Step 3:

[1145] The server analyzes the extracted page structure and uses generative AI to create suggestions for internal link structures, meta tags, and heading tags that are optimal for search engines.

[1146] Step 4:

[1147] The server applies the generated suggested modifications to the website and verifies the changes.

[1148] Increased indexing

[1149] Step 1:

[1150] The server checks the indexing status of the website using the Google Search Console API, and detects and lists any pages that are not indexed.

[1151] Step 2:

[1152] For unindexed pages, the server uses generative AI to automatically generate indexable content (text containing keywords, alt tags for images, etc.) and add it to the page.

[1153] Step 3:

[1154] The server automatically sends an indexing request using the Google Search Console API.

[1155] Improved rankings

[1156] Step 1:

[1157] The server periodically monitors the keyword rankings of target pages, collecting and analyzing the current ranking data for each page.

[1158] Step 2:

[1159] The server uses generative AI to review related keywords, taking into account data such as search volume and competition.

[1160] Step 3:

[1161] The server uses generative AI to automatically generate new content or revisions to existing content that includes related keywords, including new articles, product reviews, user testimonials, and more.

[1162] Step 4:

[1163] The server then posts the generated content to the website, which increases the page's clicks and ranking.

[1164] Increased site traffic and conversions

[1165] Step 1:

[1166] The terminal analyzes real-time traffic data, including the number of visitors, dwell time, and bounce rate.

[1167] Step 2:

[1168] The device identifies pages with low conversion rates by analyzing which pages are prone to bounce and which pages do not drive the desired action.

[1169] Step 3:

[1170] The device uses generative AI to automatically generate improvement suggestions (e.g., changing the design, changing the text of the CTA button, adding new appealing content) to improve conversion rates.

[1171] Step 4:

[1172] Users can review the AI's suggestions through an administration panel and manually apply corrections as needed.

[1173] Step 5:

[1174] The user approves the proposed improvements and they are reflected on the site, which aims to improve the conversion rate.

[1175] The above are the specific processing steps in the program of this system. By going through these steps, SEO measures can be implemented automatically and effectively.

[1176] Example 1

[1177] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1178] SEO is an important element in website management, but performing it manually requires a lot of time and expertise. Furthermore, there are various challenges in applying SEO, such as low crawlability, incomplete indexing, fluctuations in keyword rankings, and low site traffic and conversion rates. Therefore, there is a need for an efficient, automated method for optimizing the SEO of an entire website.

[1179] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1180] In this invention, the server includes means for scanning all pages of the website, means for analyzing the markup language content and page structure of all pages, and means for automatically generating suggestions for modifying the internal link structure, meta tags, and heading tags of the pages based on the analysis results so that search engines can crawl them efficiently, thereby optimizing the crawlability and SEO effect of the entire website.

[1181] A "website" is a collection of web pages that store information or data and that can be viewed and navigated over the Internet.

[1182] "All Pages" means all web pages belonging to a particular website.

[1183] "Markup language content" refers to the content of a web page that is structured using a markup language such as HTML or XML.

[1184] "Page structure" refers to the layout, navigation, and other design aspects of how the content of a web page is arranged and related.

[1185] A "search engine" is a system that collects information on the Internet and displays web pages related to a user's query as search results.

[1186] "Crawlability" refers to the ability of search engine crawlers to effectively navigate a web page and add its content to their index.

[1187] "Internal link structure" refers to the placement of links between different pages on a website and the path of the links.

[1188] "Meta tags" refer to tags included in the head of a web page that provide search engines with the content of the page and other relevant information.

[1189] "Heading tags" are tags from h1 to h6 that indicate the heading hierarchy in an HTML document, and are used to indicate the overview and structure of the page content.

[1190] "Indexing" refers to the state in which a web page is registered in a database of web pages collected by a search engine and is ready to be displayed in search results.

[1191] A "generative AI model" refers to an artificial intelligence model that generates new information or content from data based on machine learning algorithms.

[1192] An "indexing request" is a request to a search engine to add a particular web page to its index.

[1193] "Keyword ranking" refers to the position where a web page appears in search results for a particular search keyword.

[1194] "Traffic Data" refers to data regarding the access and behavior of visitors to a website.

[1195] "Conversion rate" is a metric that indicates the percentage of website visitors who achieve a specific goal.

[1196] This invention is a system that utilizes machine learning and generative AI to automatically execute and optimize SEO measures. This system works by combining elements of the server, terminal, and user to improve the SEO performance of the entire website.

[1197] Crawlability optimization

[1198] The server scans all pages of the target website, lists the URLs to be scanned, and retrieves the HTML content of each page. The server then analyzes the retrieved HTML content and page structure, including detecting internal links and analyzing the structure of heading tags (h1, h2, h3, etc.).

[1199] The server inputs prompts into the generative AI model based on the analysis results, and automatically generates suggested modifications to optimize crawlability. For example, a prompt might be, "What internal links should be added to the webpage?" The server then applies the automatically generated suggestions to the specific webpage. For example, adding new internal links, meta tags, or heading tags to the HTML.

[1200] Increased indexing

[1201] The server uses the Google Search Console API to detect unindexed pages. For the detected pages, the server uses a generative AI model to generate content that is likely to be indexed. The prompt text is something like, "What content does this page need to be indexed?" The generated content is added to specific pages, and an indexing request is automatically sent using the Google Search Console API.

[1202] Improved rankings

[1203] The server periodically monitors the keyword rankings of the target page. To monitor, it obtains data using Google Analytics and other SEO tools. Then, it uses a generative AI model to research keywords related to the target page. The prompt is, "What are the most relevant keywords?"

[1204] The server uses the generative AI model to generate new content, including specific use cases, benefits, and recommended usage, and applies the automatically generated content to the target page to improve keyword density and relevance.

[1205] Increased site traffic and conversions

[1206] The device analyzes traffic data in real time, using data from sources such as Google Analytics to analyze page views and user behavior. The device identifies pages with low conversion rates based on the traffic data.

[1207] The device then uses the generative AI model to generate improvement suggestions to improve the conversion rate. The prompt is, "What can we do to improve the conversion rate of this page?" The user can review the generated improvement suggestions, manually correct them if necessary, and then approve the proposed changes to be applied to the site.

[1208] The above is an embodiment of the present invention, which can improve the SEO performance of the entire website and improve its ranking in search engines, thereby increasing traffic and conversions.

[1209] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1210] Crawlability optimization

[1211] Step 1:

[1212] server

[1213] Input: List of website URLs

[1214] The server scans all pages of the website, lists the URLs to scan, and retrieves the HTML content of each page.

[1215] Output: HTML content of each page

[1216] Step 2:

[1217] server

[1218] Input: HTML content for each page

[1219] The server analyzes the retrieved HTML content and page structure, including detecting internal links and heading tags (h1, h2, h3, etc.).

[1220] Output: Analysis results (internal link structure, heading tag placement)

[1221] Step 3:

[1222] server

[1223] Input: Analysis results

[1224] The server then inputs prompts into the generative AI model based on the analysis results to automatically generate suggested modifications to optimize crawlability, such as "What internal links should be added to the webpage?"

[1225] Output: Suggested fixes (new internal links, meta tags, heading tags)

[1226] Step 4:

[1227] server

[1228] Input: Correction Suggestion

[1229] The server applies the automatically generated fixes to a specific web page, adding new internal links, meta tags, and heading tags to the HTML.

[1230] Output: Optimized HTML content

[1231] Increased indexing

[1232] Step 1:

[1233] server

[1234] Input: None

[1235] The server uses the Google Search Console API to find unindexed pages.

[1236] Output: List of unindexed pages

[1237] Step 2:

[1238] server

[1239] Input: List of unindexed pages

[1240] The server uses a generative AI model to automatically generate indexable content, using the prompt "What content does this page need to be indexed?"

[1241] Output: Indexable content

[1242] Step 3:

[1243] server

[1244] Input: Indexable content

[1245] The server adds the auto-generated content to a specific page.

[1246] Output: Optimized page content

[1247] Step 4:

[1248] server

[1249] Input: Optimized page content

[1250] The server automatically sends indexing requests using the Google Search Console API.

[1251] Output: Indexing request sent successfully

[1252] Improved rankings

[1253] Step 1:

[1254] server

[1255] Input: None

[1256] The server periodically monitors the keyword rankings of the target pages, using Google Analytics and other SEO tools to obtain the data.

[1257] Output: Keyword ranking data

[1258] Step 2:

[1259] server

[1260] Input: Keyword ranking data

[1261] The server uses a generative AI model to research related keywords, using the prompt "What are the most relevant keywords?"

[1262] Output: Related keyword list

[1263] Step 3:

[1264] server

[1265] Input: Related keyword list

[1266] The server uses the generative AI model to generate new content, including specific use cases, effects, and recommended usage.

[1267] Output: New content

[1268] Step 4:

[1269] server

[1270] Input: New content

[1271] The server applies automatically generated content to the target page, improving keyword density and relevance.

[1272] Output: Improved page content

[1273] Increased site traffic and conversions

[1274] Step 1:

[1275] Terminal

[1276] Input: Traffic data

[1277] The device analyzes traffic data in real time, using data from Google Analytics and other sources to analyze page views and user behavior.

[1278] Output: Traffic analysis results

[1279] Step 2:

[1280] Terminal

[1281] Input: Traffic analysis results

[1282] The device identifies pages with low conversion rates based on traffic data.

[1283] Output: List of pages with low conversion rates

[1284] Step 3:

[1285] Terminal

[1286] Input: List of low-converting pages

[1287] The device uses a generative AI model to generate improvement suggestions to improve the conversion rate, using the prompt "What can we do to improve the conversion rate of this page?"

[1288] Output: Improvement plan

[1289] Step 4:

[1290] User

[1291] Input: Improvement idea

[1292] Users can review the AI's suggestions through an admin panel, manually correct them if necessary, and then approve the proposed changes to be applied to the site.

[1293] Output: Optimized web page

[1294] (Application example 1)

[1295] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1296] SEO measures for online shopping sites are extremely important, as they directly affect product rankings in search results and conversion rates. However, currently, these SEO tasks are often performed manually, requiring specialized knowledge and effort, making them inefficient. In particular, optimizing internal link structures and meta tags, indexing, monitoring keyword rankings, and creating and modifying content are complex and often require real-time response, making them impractical. There is a need for systems that enable efficient, automated execution and optimization of these SEO tasks, as well as the ability to improve conversion rates based on traffic data.

[1297] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1298] In this invention, the server includes means for scanning all pages of a website, means for analyzing the HTML content and page structure of all pages, means for automatically generating suggested modifications to the internal link structure, meta tags, and heading tags of the pages that can be efficiently crawled by search engines based on the analysis results, means for analyzing detected traffic data, means for proposing optimal design changes and calls to action using generative AI, and means for automatically generating or modifying content based on the suggested improvements, thereby efficiently and automatically executing SEO tasks, improving rankings in search engines, and increasing traffic and conversions.

[1299] A "means of scanning all pages of a website" is a means of collecting data relating to the HTML content of all pages of a website.

[1300] "Means for analyzing the HTML content and page structure of all pages" refers to means for analyzing the structure and content of all collected HTML data of pages and extracting information for optimization.

[1301] "Means for automatically generating suggested modifications to the internal link structure, meta tags, and heading tags of a page that can be crawled efficiently by search engines" refers to a means for automatically generating suggested modifications to the link structure, meta tags, heading tags, etc., based on the analysis results, that will enable search engines to crawl web pages more efficiently.

[1302] "Means for analyzing detected traffic data" means means for analyzing traffic data collected from website visitors to identify patterns or issues based on that data.

[1303] "Method of using generative AI to suggest optimal design changes and calls to action" refers to a method of using generative AI to automatically suggest changes to optimize website design and elements that prompt users to take action (CTA).

[1304] "Means for automatically generating or modifying content based on improvement suggestions" refers to means for generating new content or automatically modifying existing content based on improvement suggestions proposed by the generative AI.

[1305] MODE FOR CARRYING OUT THE INVENTION

[1306] This invention is a system that efficiently and automatically implements SEO measures for online shopping sites. This system optimizes SEO tasks by combining server, terminal, and user elements, improving conversion rates and search engine rankings.

[1307] The server scans all pages of a website and analyzes their HTML content and page structure. Based on the results of this analysis, a generative AI model (such as GPT-4) is used to automatically generate suggestions for modifying the internal link structure, meta tags, and heading tags of the page so that search engines can crawl it more efficiently. This improves crawlability.

[1308] In addition, the server uses the Google Search Console API and Google Analytics API to detect unindexed pages. It uses generative AI models to automatically generate content that is likely to be indexed and adds this content to the defective pages. Indexing requests are also automatically sent, which can increase the number of indexes.

[1309] The server regularly monitors the keyword rankings of the target page and uses a generative AI model to research related keywords. It then automatically generates new content that includes those keywords or proposes modifications to existing content to improve the page's ranking. Furthermore, the generated content and modifications to the internal link structure are automatically reflected on the site.

[1310] The device analyzes traffic data in real time to identify pages with low conversion rates. Using a generative AI model, it automatically generates recommendations for improving conversion rates, including design changes, optimizing calls to action (CTA), and adding user reviews. Users can review the recommendations made by the generative AI through an admin panel, manually correct them as needed, and then approve the proposed changes to apply them to their site.

[1311] Specific use cases

[1312] The hardware and software used includes the following:

[1313] Server: AWS EC2

[1314] Database: Amazon RDS

[1315] Machine learning model: Transformers library (Hugging Face)

[1316] API: Google Search Console API, Google Analytics API

[1317] Interface: React Native (for smartphone apps)

[1318] For example, if the server identifies that the "About Us" page has not been crawled, the generative AI will automatically generate the appropriate internal links and meta tags to add to the "About Us" page. An example of a prompt for the generative AI model is:

[1319] Optimize the internal link structure and meta tags of your "About Us" page.

[1320] Additionally, if a particular product page is not indexed, the server will identify it and use generative AI to generate content that optimizes product descriptions and keyword density. An example prompt is below:

[1321] To optimize your unindexed product pages for indexing, generate content with high description and keyword density.

[1322] So, for example, if your rankings for the keyword "diet supplements" are dropping, you can use generative AI to generate new content to add to the page, including effective instructions, benefits, and ingredient information. Here's an example prompt:

[1323] Generate new content with effective usage, benefits, and ingredient information to improve your ranking for the keyword "diet supplements."

[1324] This system allows online shopping sites to efficiently and automatically implement SEO measures, improving their rankings in search engines and increasing traffic and conversions.

[1325] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1326] Step 1:

[1327] The server scans all pages of a website and collects their HTML content and page structure. The input of this process is the website URL, and the output is the retrieved HTML data and page structure. The server uses a crawler to collect data for each web page.

[1328] Step 2:

[1329] The server analyzes the collected HTML data and page structure. In this analysis process, HTML tags, meta tags, internal link structures, etc. are extracted and the analysis results are obtained. The input is the HTML data obtained in step 1, and the output is the analyzed page structure and meta information. The server performs data analysis using a specific parser tool.

[1330] Step 3:

[1331] Based on the analysis results, the server uses generative AI to automatically generate suggested modifications to internal link structures, meta tags, and heading tags that search engines can crawl efficiently. The input here is the analysis results, and the output is suggested modifications. The following prompt is input to the generative AI model:

[1332] Optimize the internal link structure and meta tags of your "About Us" page.

[1333] Step 4:

[1334] The server uses the Google Search Console API to identify pages that are not indexed. The input is a list of indexed pages, and the output is a list of pages that are not indexed. The server makes an API call to check the index status of the pages.

[1335] Step 5:

[1336] The server uses generative AI to automatically generate content that is likely to be indexed for unindexed pages. The input to this process is the HTML data of the unindexed page, and the output is the generated new content. The generative AI model is given the following prompt:

[1337] To optimize your unindexed product pages for indexing, generate content with high description and keyword density.

[1338] Step 6:

[1339] The server automatically sends an indexing request using the Google Search Console API. The input is the new content generated, and the output is the result of the indexing request submission. Provide the API with the new content and submit the request.

[1340] Step 7:

[1341] The server periodically monitors the keyword rankings of the target page and uses the generative AI to search for related keywords. The input to this process is the current keyword ranking data of the target page, and the output is a list of related keywords. The generative AI model is given the following prompt:

[1342] Generate new content with effective usage, benefits, and ingredient information to improve your ranking for the keyword "diet supplements."

[1343] Step 8:

[1344] The terminal analyzes traffic data in real time and identifies pages with low conversion rates. The input of this process is traffic data, and the output is a list of pages with low conversion rates. The terminal obtains data from the Google Analytics API and performs analysis.

[1345] Step 9:

[1346] The device uses generative AI to automatically generate design and call-to-action improvements to improve conversion rates. The input is a list of pages with low conversion rates, and the output is improvement recommendations. The following prompt is input to the generative AI model:

[1347] Generate suggestions to optimize this page's design and call-to-action to improve conversion rates.

[1348] Step 10:

[1349] Users can review the suggestions made by the generative AI through an administration panel, manually correct them as necessary, and then approve the proposed changes and have them reflected on the site. The input is the generative AI's suggestions, and the output is final approval and reflection on the site. Users operate through a web interface.

[1350] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1351] This invention is a system that automatically executes and optimizes SEO measures using machine learning and generative AI, and further improves the user experience by combining it with an emotion engine that recognizes user emotions. The specific processing flow is explained below.

[1352] Crawlability optimization

[1353] server

[1354] The server scans every page of a website, analyzing its HTML content and page structure, and then uses generative AI to automatically generate suggested modifications to help search engines crawl it more efficiently, such as optimizing internal link structures, meta tags, and heading tags.

[1355] Specific examples

[1356] If the server determines that the "About Us" page has not been crawled, the generation AI will automatically generate the appropriate internal links and meta tags and add them to the "About Us" page.

[1357] Increased indexing

[1358] server

[1359] The server uses the Google Search Console API to check the indexing status of the website, detects and identifies pages that are not indexed, automatically generates content that is likely to be indexed using generative AI for the detected pages and adds it to the defective pages, and automatically sends indexing requests using the Google Search Console API.

[1360] Specific examples

[1361] If a particular product page is not indexed, the server will identify it and use generative AI to optimize the product description and keyword density to promote indexing.

[1362] Improved rankings

[1363] server

[1364] The server periodically monitors the keyword rankings of the target page and uses generative AI to research related keywords, automatically generating new content that includes them or suggestions for modifying existing content to improve the page's clicks and ranking.

[1365] Specific examples

[1366] If the ranking for the keyword "diet supplement" is dropping, the server will use generative AI to generate content including effective usage instructions, benefits, ingredient information, etc. and add it to the page.

[1367] Increased site traffic and conversions

[1368] Terminal

[1369] The device analyzes real-time traffic data to identify pages with low conversion rates and uses generative AI to automatically generate recommendations for improving conversion rates, including redesigning, optimizing calls to action, and adding user reviews.

[1370] User

[1371] Users can review the AI's suggestions through an admin panel, manually correct them if necessary, and then approve the proposed changes to be applied to the site.

[1372] Specific examples

[1373] If the homepage's conversion rate is low, the device analyzes traffic data, and the AI ​​generator proposes new layouts and appealing points to the user. Once the user confirms and approves the suggestions, the homepage is automatically updated.

[1374] Utilizing the Emotion Engine

[1375] server

[1376] The server collects user emotion data using an emotion engine, which analyzes the user's facial expressions and behavior while visiting the website to grasp the user's emotional state in real time.

[1377] Specific examples

[1378] When a user is browsing a particular product page, if the emotion engine detects interest or enjoyment in the user's facial expression, the server can use that emotion data to display additional product information or special offers.

[1379] Terminal

[1380] The device analyzes the collected emotional data and adjusts website content according to the user's emotional tendencies, including rearranging content and optimizing design.

[1381] Specific examples

[1382] If a user shows signs of frustration while browsing multiple pages, the device will detect this emotion and offer more concise navigation and relevant product recommendations.

[1383] User

[1384] Users can review the changes and adjustments suggested by the emotion engine in the admin panel, make any necessary corrections, and apply them to their site.

[1385] Specific examples

[1386] This will be done automatically once the user confirms the results of the emotion engine analysis in the admin panel and approves the change in the position of the review section on the product page.

[1387] effect

[1388] This system ensures accuracy and consistency in SEO measures and eliminates problems caused by personalization. Furthermore, by combining it with an emotion engine, it is possible to provide a personalized experience based on the user's emotions, which is expected to increase traffic and conversions.

[1389] The processing flow will be explained below.

[1390] Crawlability optimization

[1391] Step 1:

[1392] The server scans all pages of the website and analyzes the sitemap file (sitemap.xml) and robots.txt file to list the URLs to crawl.

[1393] Step 2:

[1394] The server accesses each URL and retrieves the HTML content, extracting tags and text data, including meta tags, heading tags (H1, H2, H3, etc.), and internal links.

[1395] Step 3:

[1396] Based on the collected data, the server uses generative AI to automatically generate suggestions for the optimal internal link structure, effective meta tags, and appropriate heading tag revisions.

[1397] Step 4:

[1398] The server applies the generated suggested modifications to the website and verifies the changes.

[1399] Increased indexing

[1400] Step 1:

[1401] The server checks the indexing status of the website using the Google Search Console API, and detects and lists any pages that are not indexed.

[1402] Step 2:

[1403] For unindexed pages, the server uses generative AI to automatically generate index-friendly content, including adding text containing keywords and adding appropriate alt tags to images.

[1404] Step 3:

[1405] The server adds auto-generated content to the page and automatically sends an indexing request using the Google Search Console API.

[1406] Improved rankings

[1407] Step 1:

[1408] The server periodically monitors the keyword rankings of target pages, collects the current ranking data of each page, and performs statistical analysis.

[1409] Step 2:

[1410] The server uses generative AI to re-examine related keywords, re-evaluate appropriate keywords, and categorize them based on search volume and competition.

[1411] Step 3:

[1412] The server uses generative AI to automatically generate new content or revisions to existing content that includes relevant keywords, including new articles, product reviews, and adding user testimonials.

[1413] Step 4:

[1414] The server then posts the generated content to websites, aiming to improve their visibility, clicks, and ranking in search results.

[1415] Traffic data and increased conversions

[1416] Step 1:

[1417] The terminal analyzes real-time traffic data, including visitor behavior, length of stay, and bounce rate.

[1418] Step 2:

[1419] The device identifies pages with low conversion rates, evaluates the performance of each page, and identifies problems.

[1420] Step 3:

[1421] The device uses generative AI to automatically generate recommendations for improving conversion rates, including redesigning, changing the text of the call-to-action (CTA) button, and adding new appealing content.

[1422] Step 4:

[1423] Users can review the AI's suggestions through an admin panel, manually correct them if necessary, and then approve the proposed changes to be applied to the site.

[1424] Step 5:

[1425] The user approves the proposed improvements and they are reflected on the site, which aims to improve the conversion rate.

[1426] Utilizing the Emotion Engine

[1427] Step 1:

[1428] The server uses an emotion engine to collect user emotional data, analyzing the user's facial expressions, tone of voice, behavioral patterns, etc. in real time.

[1429] Step 2:

[1430] The device analyzes the collected emotional data and adjusts the content on the website according to the user's emotional tendencies, changing the content layout and optimizing the design.

[1431] Step 3:

[1432] The server uses generative AI to automatically generate content to personalize the user experience based on emotional data.

[1433] Step 4:

[1434] The user can review the generated changes through the administration panel and manually correct them if necessary.

[1435] Step 5:

[1436] The user approves the suggestions and modifications made by the emotion engine and reflects them on the site.

[1437] These are the specific processing steps in the program of this system. By going through these steps, it is possible to automatically and effectively implement SEO measures and also to provide a personalized experience by utilizing user emotional data.

[1438] Example 2

[1439] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1440] Traditional website optimization methods involve individual SEO measures such as crawling and indexing pages and monitoring keyword rankings, making it difficult to achieve comprehensive optimization. Furthermore, dynamic content adjustments based on user sentiment are not performed, resulting in insufficient improvements to the user experience.

[1441] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for scanning all pages of the website, means for analyzing the HTML content and page structure of all pages, means for automatically generating, using a generation AI, revision suggestions for the internal link structure, meta tags, and heading tags of the pages that can be efficiently crawled by search engines based on the analysis results, means for using an emotion engine for collecting user emotion data, and means for dynamically adjusting site content based on the emotion data. This enables comprehensive and automatic SEO measures and further personalization based on user emotion.

[1442] A "website" is a collection of multiple web pages published on the Internet, and is constructed using technologies such as HTML and CSS.

[1443] "Scanning" refers to the process of crawling all pages of a website and retrieving and analyzing data.

[1444] "HTML content" refers to data in HTML (HyperText Markup Language) format that describes the content of a web page.

[1445] "Page structure" refers to the overall structure of a web page, including the layout and hierarchy of heading tags, meta tags, link structure, etc.

[1446] "Analysis" refers to the process of examining data and extracting useful information for a specific purpose.

[1447] A "search engine" is a system that searches for information on the Internet and provides the results to users. Examples include Google and Bing.

[1448] "Internal linking structure" refers to the link relationships between pages on a website, designed to help users and search engines navigate the site effectively.

[1449] A "meta tag" is a tag written in the head section of a web page that provides search engines and browsers with metadata such as a description of the page, keywords, and author.

[1450] "Generative AI" refers to systems or models that use artificial intelligence to automatically perform specific tasks, and in this case refers to technologies that generate content and make optimization suggestions.

[1451] An "emotion engine" is a system that collects and analyzes users' emotional data, grasps their state in real time, and provides appropriate feedback and adjustments.

[1452] "Dynamic adjustment" refers to changing the content or design of a website in response to data or circumstances in real time.

[1453] MODE FOR CARRYING OUT THE INVENTION

[1454] This invention is a system that improves user experience by automatically implementing SEO measures using machine learning and generative AI, and by combining it with an emotion engine that recognizes user emotions. This system integrates website crawling, indexing, keyword ranking monitoring, and real-time emotion data collection and analysis.

[1455] Hardware and software used

[1456] Server: The server scans all pages of the website and analyzes the HTML code of each page using an HTML parser (e.g., BeautifulSoup). Generative AI can be provided by OpenAI's GPT series or similar.

[1457] Device: The device analyzes real-time traffic data using the Google Analytics API to identify pages with low conversion rates.

[1458] Emotion engine: The emotion engine uses Affectiva's SDK and other tools to analyze the user's facial expressions and behavior in real time to collect emotional data.

[1459] Specific processing flow

[1460] Crawlability optimization

[1461] server

[1462] The server scans every page of a website, analyzes its HTML content and page structure, and then gives the AI ​​prompts such as "Please suggest optimizations for internal link structure and meta tags," which then automatically generates suggested modifications.

[1463] Specific examples

[1464] For example, if the "About Us" page isn't crawled, the server will use generative AI to auto-generate the appropriate internal links and meta tags and add them to the page.

[1465] Prompt Sentence Examples

[1466] "If a particular web page is not being crawled, generate optimization recommendations for the HTML content and metadata of that page."

[1467] Increased indexing

[1468] server

[1469] The server checks the indexing status using the Google Search Console API, detects unindexed pages, and sends a prompt to the generation AI, such as "Optimize the product description on this page and generate content that is more likely to be indexed."

[1470] Specific examples

[1471] If a particular product page isn't indexed, the generative AI generates content that optimizes the product description and keyword density, and the server adds that content to the page.

[1472] Prompt Sentence Examples

[1473] "Optimize the product description on this page to generate indexable content."

[1474] Improved rankings

[1475] server

[1476] The server periodically monitors the keyword rankings of target pages through the Google Analytics API, and provides prompts to the AI ​​generator, such as "Please generate content that includes new and effective usage methods, benefits, and ingredient information," to generate content that contributes to improving rankings.

[1477] Specific examples

[1478] If the ranking for the keyword "diet supplement" is dropping, the server will use generative AI to automatically generate content including effective usage instructions, benefits, and ingredient information and add it to the page.

[1479] Prompt Sentence Examples

[1480] "Generate new content related to the keyword 'diet supplements' with effective usage, benefits, and ingredient information."

[1481] Increased site traffic and conversions

[1482] Terminal

[1483] The device analyzes real-time traffic data to identify pages with low conversion rates, and sends a prompt to the AI ​​generator to "generate new layout proposals and selling points to improve the conversion rate of this page," automatically generating improvement proposals.

[1484] User

[1485] Users can review the suggestions made by the AI ​​through an administration panel, manually correct them if necessary, and then approve the changes to reflect them on the site.

[1486] Specific examples

[1487] If the homepage conversion rate is low, the terminal will display suggestions from the generation AI on the management panel, which the user can confirm and then implement.

[1488] Prompt Sentence Examples

[1489] "Generate new layout ideas and selling points to improve the conversion rate of this page."

[1490] Utilizing the Emotion Engine

[1491] server

[1492] The server uses an emotion engine to collect user emotion data, analyze the user's facial expressions and behavior in real time, and understand their emotional state.

[1493] Specific examples

[1494] If the emotion engine detects interest or enjoyment while a user is viewing a particular product page, the server can use that emotion data to display additional product information or special offers.

[1495] Terminal

[1496] The terminal analyzes the collected emotional data and adjusts the content on the website according to the user's emotional tendencies.

[1497] Specific examples

[1498] If a user shows signs of frustration while browsing multiple pages, the device will detect this emotion and offer more concise navigation and relevant product recommendations.

[1499] User

[1500] Users can review the changes and adjustments suggested by the emotion engine in the administration panel, make corrections as necessary, and reflect them on the site.

[1501] Specific examples

[1502] This will be done automatically once the user confirms the results of the emotion engine analysis in the admin panel and approves the repositioning of the review section on the product page.

[1503] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1504] Step 1: Scan the website

[1505] server

[1506] The server scans all pages of a website. It receives a list of URLs as input and parses the HTML code of each page using an HTML parser (e.g. BeautifulSoup). It gets the parsed data for each page as output.

[1507] Specific actions

[1508] For example, the server retrieves all page URLs for "example.com" in sequence and parses each page using an HTML parser.

[1509] Step 2: Analyzing the page structure

[1510] server

[1511] The server analyzes the HTML content and page structure of the scanned page, using the analysis data from step 1 as input to identify internal links, meta tags, heading tags, etc. As output, it gets data containing details of the page structure.

[1512] Specific actions

[1513] For example, the server checks the presence of internal links and meta tags on the "About Us" page to assess whether it is optimized.

[1514] Step 3: Generative AI generates revision suggestions

[1515] server

[1516] Based on the analysis results, the server sends prompts to the generation AI to generate suggested modifications to the internal link structure, meta tags, and heading tags. The server provides details of the page structure and a prompt as input, and receives suggested modifications from the generation AI as output.

[1517] Specific actions

[1518] For example, you can send a prompt like "Add an internal link to this page" to the generative AI and get suggested fixes.

[1519] Step 4: Applying the proposed amendment

[1520] server

[1521] The server applies the suggested revisions from the generative AI to the web page, using the suggested revision data as input and generating an output with updated HTML code.

[1522] Specific actions

[1523] For example, add the internal link code obtained from the generation AI to the "About Us" page and save the updated HTML.

[1524] Step 5: Check the index status

[1525] server

[1526] The server checks the index status of the website using the Google Search Console API. It uses the website URL as input and gets the index information from the API. It gets the list of pages that are not indexed as output.

[1527] Specific actions

[1528] For example, the server checks the indexing status of each page on "example.com" using the Google Search Console API and lists the 404 error pages.

[1529] Step 6: Content generation with generative AI

[1530] server

[1531] The server automatically generates content for unindexed pages. It uses the list of unindexed pages and prompts as input and gets optimized content from the generation AI. It gets optimized content data as output.

[1532] Specific actions

[1533] For example, for an unindexed product page, a prompt such as "Please optimize the product description" is sent to the generation AI, and the generated content is retrieved.

[1534] Step 7: Submitting an indexing request

[1535] server

[1536] The server adds the auto-generated content to the page and sends an indexing request through the Google Search Console API, using the updated HTML code as input and sending an indexing request to the API to get the output.

[1537] Specific actions

[1538] For example, add the generated product description to the relevant page and send an indexing request using the Google Search Console API.

[1539] Step 8: Monitor your keyword rankings

[1540] server

[1541] The server periodically monitors keyword rankings using the Google Analytics API. It uses the target keywords as input and retrieves ranking data from the API. It gets a list of ranking data as output.

[1542] Specific actions

[1543] For example, monitor the keyword "diet supplements" and obtain its rankings periodically.

[1544] Step 9: Generative AI creates new content

[1545] server

[1546] If the server detects a drop in keyword rankings, it uses a generative AI to generate new content and suggested revisions. It uses ranking data and prompts as inputs to obtain new content from the generative AI, and obtains new content data as output.

[1547] Specific actions

[1548] For example, send a prompt like "Suggest a new use for 'Diet Supplement'" and get the generated content.

[1549] Step 10: Analyzing real-time traffic data

[1550] Terminal

[1551] The device analyzes real-time traffic data using the Google Analytics API. It uses website URLs as input to identify pages with low conversion rates, and gets a list of pages with low conversion rates as output.

[1552] Specific actions

[1553] For example, the terminal retrieves traffic data for each page of "example.com" and lists pages with low conversion rates.

[1554] Step 11: Generative AI generates improvement proposals

[1555] Terminal

[1556] The device uses a generative AI to automatically generate improvement suggestions for pages with low conversion rates. It uses the conversion rate data and prompt text as input and obtains improvement suggestion data from the generative AI. It then obtains the improvement suggestion data as output.

[1557] Specific actions

[1558] For example, you can send a prompt like, "Suggest a new layout to increase the conversion rate of this homepage," and get the generated layout suggestions.

[1559] Step 12: User Acceptance and Application

[1560] User

[1561] The user can review the suggestions made by the generative AI through an admin panel, manually correct them if necessary, and then approve the changes to be reflected on the site. The user uses the suggested data from the generative AI as input and applies the final updates to the site, obtaining the updated webpage as output.

[1562] Specific actions

[1563] For example, users can check new homepage layout suggestions made by the generative AI in the administration panel and implement them after approval.

[1564] Step 13: Collect emotion data

[1565] server

[1566] The server uses an emotion engine to collect user emotion data. It uses the user's real-time behavior data as input and obtains analysis results from the emotion engine. It obtains real-time emotion data as output.

[1567] Specific actions

[1568] For example, the emotion engine detects interest or enjoyment when a user is viewing a particular product page.

[1569] Step 14: Analyze and apply emotion data

[1570] Terminal

[1571] The device analyzes the collected emotional data and adjusts the content on the website according to the user's emotional tendencies. It uses the emotional data as input to dynamically change the layout and display of the site content, and obtains updated web page content as output.

[1572] Specific actions

[1573] For example, if the user shows signs of frustration, the navigation can be simplified based on data from the emotion engine.

[1574] Step 15: User Review and Approval

[1575] User

[1576] The user reviews the changes and adjustments suggested by the emotion engine in the admin panel, manually corrects them if necessary, and approves the changes. The system uses the suggested data from the emotion engine as input and finally updates the content on the site, obtaining an updated web page as output.

[1577] Specific actions

[1578] For example, if a user checks the analysis results of the sentiment engine in the admin panel and approves changing the position of the review section on the product page, this will be implemented automatically.

[1579] (Application example 2)

[1580] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1581] Existing SEO systems are inefficient because they require specialized technical expertise and require advanced skills and time. Furthermore, when it comes to improving user experience, there is a lack of methods for grasping user sentiment in real time and providing content that responds to that sentiment. Therefore, there is a need for a system that can simultaneously improve both SEO and user experience.

[1582] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for scanning all pages of a website, means for analyzing the HTML content and page structure of all pages, means for automatically generating, based on the analysis results, revision suggestions for the internal link structure, meta tags, and heading tags of the pages that can be efficiently crawled by search engines, means for collecting and analyzing user emotion data, and means for automatically generating, based on the analysis results, revision suggestions for content that correspond to the user's emotions. This makes it possible to automate SEO measures and personalize the user experience.

[1583] "Means for scanning all pages of a website" means a function for automatically scanning all pages of a website and acquiring their contents.

[1584] "Means for analyzing HTML content and page structure" refers to the ability to analyze the HTML code of a web page and its design, such as headings, meta tags, and link structure.

[1585] "Means for automatically generating suggested modifications to internal link structures, meta tags, and heading tags" is a function that automatically generates and suggests links and meta information to encourage optimal crawling and indexing of websites.

[1586] The "means for collecting and analyzing user emotional data" is a function that captures visitors' facial expressions and behavioral data in real time and analyzes their emotional state.

[1587] "Means for automatically generating suggested revisions to content according to the user's emotions" is a function that automatically generates and suggests content that is appropriate for the user's interests and emotional state based on collected emotional data.

[1588] "Means for detecting unindexed pages" refers to a function that identifies web pages that are not registered in a search engine's index.

[1589] "Means for automatically generating content that is likely to be indexed using a generative AI model" is a function that uses artificial intelligence to automatically generate content that is likely to be indexed by search engines.

[1590] "Means for automatically sending indexing requests" means a function that automatically sends indexing requests to search engines for content created by a generative AI model.

[1591] "Means of sending the received prompt text to a generation AI and generating content or promotional text that suits the user's emotions" refers to a function that sends a prompt text that takes the user's emotions into consideration to a generation AI, and as a result, automatically generates content or promotional text that suits the user's emotions.

[1592] This invention relates to a system that aims to improve user experience by combining automated SEO measures using machine learning and generative AI models with an emotion engine. The system includes a means for scanning and analyzing all pages of a website and automatically generating optimal revision suggestions based on the results. It also includes a means for collecting user emotions in real time and providing content that corresponds to those emotions.

[1593] System configuration

[1594] server

[1595] The server is responsible for scanning all pages of a website and analyzing their HTML content and page structure. Through this analysis, generative AI models are used to automatically generate suggested modifications to internal link structures, meta tags, and heading tags to help search engines crawl them more efficiently.

[1596] Specifically, the server uses BeautifulSoup to parse the HTML of web pages to detect unindexed pages, then uses generative AI models to generate appropriate content for those pages and automatically submits indexing requests using the Google Search Console API.

[1597] Terminal

[1598] The device collects user emotional data and adjusts content based on the analysis results. This process involves capturing the user's facial expressions using a camera and using OpenCV and an emotion recognition model. Based on this emotional data, the device sends prompts to a generative AI model, which then generates content and promotional text appropriate to the user's emotions.

[1599] User

[1600] Users can review the suggestions made by the generated AI through an admin panel, make manual corrections if necessary, and then approve the proposed changes to reflect them on the site.

[1601] Hardware and Software

[1602] Hardware: Servers, devices (smartphones, PCs)

[1603] Software: BeautifulSoup, OpenCV, Google Search Console API, generative AI models

[1604] Specific examples

[1605] When a user is browsing a product page on an online shopping site, the camera recognizes the user's expression of surprise. Based on this information, the AI ​​generates prompts such as the following, which then display an appropriate promotional message:

[1606] The user's emotion is "surprise." Generate optimal product recommendations and promotional messages based on this emotion.

[1607] It also discovers that certain pages are not indexed, uses generative AI to create optimal keywords and meta tags, and sends a request to Google Search Console to include them, as shown below.

[1608] Generate the best keywords and meta tags to help index this page.

[1609] This allows for automated SEO and personalized user experience.

[1610] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1611] Step 1:

[1612] The server scans all pages of the website to obtain the HTML content and page structure. Through this scan, the server collects the URL and HTML code of each page of the website as input data and stores the results in an internal database, which allows it to understand the structure of the entire website.

[1613] Step 2:

[1614] The server analyzes the retrieved HTML content and page structure. Specifically, it uses BeautifulSoup to parse the HTML code and extract elements such as meta tags, heading tags, and internal links. The HTML code is provided as input data, and the analysis results are obtained as output. Based on these analysis results, improvements to the website can be identified.

[1615] Step 3:

[1616] Based on the analysis results, the server uses a generative AI model to automatically generate suggested modifications to internal link structures, meta tags, and heading tags to enable search engines to crawl efficiently. The generative AI is prompted with the message "Please generate suggested modifications for index optimization," and the optimized HTML elements are returned as the output.

[1617] Step 4:

[1618] The server uses the Google Search Console API to find non-indexed pages. The input is the website URL, and the output is the indexing status. Non-indexed pages are identified.

[1619] Step 5:

[1620] The server uses a generative AI model to automatically generate content that is likely to be indexed for the detected pages, prompting them to "generate content that will promote indexing," and the appropriate content is generated as a result.

[1621] Step 6:

[1622] The server automatically sends an indexing request containing the generated content using the Google Search Console API, providing the generated content and the target page URL as input, and returning the indexing completion status as output.

[1623] Step 7:

[1624] To collect user emotion data, the device uses a camera to capture the user's facial expressions. Real-time camera footage is used as input data, and facial expression images are obtained as output. These facial expression images are used for subsequent emotion analysis.

[1625] Step 8:

[1626] The device inputs the collected facial expression images into an emotion recognition model to analyze the user's emotions. Using facial expression images as input data, emotion classification results are obtained as output. For example, emotions such as "surprise," "joy," and "sadness" are identified.

[1627] Step 9:

[1628] Based on the user's emotional data, the device sends a prompt to the generative AI model, which then generates content and promotional text appropriate to the user's emotions.The prompt might say, "The user's emotion is 'surprise.' Please generate optimal product recommendations and promotional messages," and the output is appropriate text and content.

[1629] Step 10:

[1630] The device displays the generated content to the user, providing a personalized experience based on the user's emotions. The generated content is used as input data, and the final display content is obtained as output. For example, specific product recommendations or special offers are displayed.

[1631] This allows for efficient automation of SEO measures and personalization of the user experience.

[1632] 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.

[1633] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. 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.

[1634] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1635] [Fourth embodiment]

[1636] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1637] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1638] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1639] 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.

[1640] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1641] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1642] 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.

[1643] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1644] 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.

[1645] 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 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.

[1646] 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.

[1647] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1648] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1649] This invention is a system that utilizes machine learning and generative AI to automatically execute and optimize SEO measures. This system efficiently processes various SEO tasks by combining elements of the server, terminal, and user.

[1650] Crawlability optimization

[1651] server

[1652] The server scans every page of a website, analyzing its HTML content and page structure, and then uses generative AI to automatically generate suggested modifications to help search engines crawl it more efficiently, such as optimizing internal link structures, meta tags, and heading tags.

[1653] Specific examples

[1654] If the server determines that the "About Us" page has not been crawled, the generation AI will automatically generate the appropriate internal links and meta tags and add them to the "About Us" page.

[1655] Increased indexing

[1656] server

[1657] The server detects and identifies unindexed pages using the Google Search Console API, etc. For the detected pages, it automatically generates content that is likely to be indexed using generative AI and adds it to the defective pages. It also automatically sends indexing requests using the Google Search Console API.

[1658] Specific examples

[1659] If a particular product page is not indexed, the server will identify it and use generative AI to optimize the product description and keyword density to promote indexing.

[1660] Improved rankings

[1661] server

[1662] The server regularly monitors the keyword rankings of the target page and uses generative AI to research related keywords, automatically generating new content that includes them or suggestions for modifying existing content to improve the page's clicks and ranking.

[1663] Specific examples

[1664] If the ranking for the keyword "diet supplement" is dropping, the server will use generative AI to generate content including effective usage instructions, benefits, ingredient information, etc. and add it to the page.

[1665] Increased site traffic and conversions

[1666] Terminal

[1667] The device analyzes traffic data in real time to identify pages with low conversion rates and uses generative AI to automatically generate recommendations for improving conversion rates, including redesigning, optimizing calls to action, and adding user reviews.

[1668] User

[1669] Users can review the AI's suggestions through an admin panel, manually correct them if necessary, and then approve the proposed changes to be applied to the site.

[1670] Specific examples

[1671] If the homepage's conversion rate is low, the device analyzes traffic data, and the AI ​​generator proposes new layouts and appealing points to the user. Once the user confirms and approves the suggestions, the homepage is automatically updated.

[1672] effect

[1673] This system ensures accuracy and consistency in SEO strategies, eliminating the problems of relying on individual staff. Automatically generated content and algorithmic optimization improve search engine rankings, leading to increased traffic and conversions.

[1674] The processing flow will be explained below.

[1675] Crawlability optimization

[1676] Step 1:

[1677] The server scans all pages of the website, analysing the sitemap file (sitemap.xml) and robots.txt file to list the URLs to crawl.

[1678] Step 2:

[1679] The server retrieves the HTML content of each listed page, which includes extracting each page's meta tags, heading tags (H1, H2, etc.), and internal link components.

[1680] Step 3:

[1681] The server analyzes the extracted page structure and uses generative AI to create suggestions for internal link structures, meta tags, and heading tags that are optimal for search engines.

[1682] Step 4:

[1683] The server applies the generated suggested modifications to the website and verifies the changes.

[1684] Increased indexing

[1685] Step 1:

[1686] The server checks the indexing status of the website using the Google Search Console API, and detects and lists any pages that are not indexed.

[1687] Step 2:

[1688] For unindexed pages, the server uses generative AI to automatically generate indexable content (text containing keywords, alt tags for images, etc.) and add it to the page.

[1689] Step 3:

[1690] The server automatically sends an indexing request using the Google Search Console API.

[1691] Improved rankings

[1692] Step 1:

[1693] The server periodically monitors the keyword rankings of target pages, collecting and analyzing the current ranking data for each page.

[1694] Step 2:

[1695] The server uses generative AI to review related keywords, taking into account data such as search volume and competition.

[1696] Step 3:

[1697] The server uses generative AI to automatically generate new content or revisions to existing content that includes related keywords, including new articles, product reviews, user testimonials, and more.

[1698] Step 4:

[1699] The server then posts the generated content to the website, which increases the page's clicks and ranking.

[1700] Increased site traffic and conversions

[1701] Step 1:

[1702] The terminal analyzes real-time traffic data, including the number of visitors, dwell time, and bounce rate.

[1703] Step 2:

[1704] The device identifies pages with low conversion rates by analyzing which pages are prone to bounce and which pages do not drive the desired action.

[1705] Step 3:

[1706] The device uses generative AI to automatically generate improvement suggestions (e.g., changing the design, changing the text of the CTA button, adding new appealing content) to improve conversion rates.

[1707] Step 4:

[1708] Users can review the AI's suggestions through an administration panel and manually apply corrections as needed.

[1709] Step 5:

[1710] The user approves the proposed improvements and they are reflected on the site, which aims to improve the conversion rate.

[1711] The above are the specific processing steps in the program of this system. By going through these steps, SEO measures can be implemented automatically and effectively.

[1712] Example 1

[1713] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1714] SEO is an important element in website management, but performing it manually requires a lot of time and expertise. Furthermore, there are various challenges in applying SEO, such as low crawlability, incomplete indexing, fluctuations in keyword rankings, and low site traffic and conversion rates. Therefore, there is a need for an efficient, automated method for optimizing the SEO of an entire website.

[1715] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1716] In this invention, the server includes means for scanning all pages of the website, means for analyzing the markup language content and page structure of all pages, and means for automatically generating suggestions for modifying the internal link structure, meta tags, and heading tags of the pages based on the analysis results so that search engines can crawl them efficiently, thereby optimizing the crawlability and SEO effect of the entire website.

[1717] A "website" is a collection of web pages that store information or data and that can be viewed and navigated over the Internet.

[1718] "All Pages" means all web pages belonging to a particular website.

[1719] "Markup language content" refers to the content of a web page that is structured using a markup language such as HTML or XML.

[1720] "Page structure" refers to the layout, navigation, and other design aspects of how the content of a web page is arranged and related.

[1721] A "search engine" is a system that collects information on the Internet and displays web pages related to a user's query as search results.

[1722] "Crawlability" refers to the ability of search engine crawlers to effectively navigate a web page and add its content to their index.

[1723] "Internal link structure" refers to the placement of links between different pages on a website and the path of the links.

[1724] "Meta tags" refer to tags included in the head of a web page that provide search engines with the content of the page and other relevant information.

[1725] "Heading tags" are tags from h1 to h6 that indicate the heading hierarchy in an HTML document, and are used to indicate the overview and structure of the page content.

[1726] "Indexing" refers to the state in which a web page is registered in a database of web pages collected by a search engine and is ready to be displayed in search results.

[1727] A "generative AI model" refers to an artificial intelligence model that generates new information or content from data based on machine learning algorithms.

[1728] An "indexing request" is a request to a search engine to add a particular web page to its index.

[1729] "Keyword ranking" refers to the position where a web page appears in search results for a particular search keyword.

[1730] "Traffic Data" refers to data regarding the access and behavior of visitors to a website.

[1731] "Conversion rate" is a metric that indicates the percentage of website visitors who achieve a specific goal.

[1732] This invention is a system that utilizes machine learning and generative AI to automatically execute and optimize SEO measures. This system works by combining elements of the server, terminal, and user to improve the SEO performance of the entire website.

[1733] Crawlability optimization

[1734] The server scans all pages of the target website, lists the URLs to be scanned, and retrieves the HTML content of each page. The server then analyzes the retrieved HTML content and page structure, including detecting internal links and analyzing the structure of heading tags (h1, h2, h3, etc.).

[1735] The server inputs prompts into the generative AI model based on the analysis results, and automatically generates suggested modifications to optimize crawlability. For example, a prompt might be, "What internal links should be added to the webpage?" The server then applies the automatically generated suggestions to the specific webpage. For example, adding new internal links, meta tags, or heading tags to the HTML.

[1736] Increased indexing

[1737] The server uses the Google Search Console API to detect unindexed pages. For the detected pages, the server uses a generative AI model to generate content that is likely to be indexed. The prompt text is something like, "What content does this page need to be indexed?" The generated content is added to specific pages, and an indexing request is automatically sent using the Google Search Console API.

[1738] Improved rankings

[1739] The server periodically monitors the keyword rankings of the target page. To monitor, it obtains data using Google Analytics and other SEO tools. Then, it uses a generative AI model to research keywords related to the target page. The prompt is, "What are the most relevant keywords?"

[1740] The server uses the generative AI model to generate new content, including specific use cases, benefits, and recommended usage, and applies the automatically generated content to the target page to improve keyword density and relevance.

[1741] Increased site traffic and conversions

[1742] The device analyzes traffic data in real time, using data from sources such as Google Analytics to analyze page views and user behavior. The device identifies pages with low conversion rates based on the traffic data.

[1743] The device then uses the generative AI model to generate improvement suggestions to improve the conversion rate. The prompt is, "What can we do to improve the conversion rate of this page?" The user can review the generated improvement suggestions, manually correct them if necessary, and then approve the proposed changes to be applied to the site.

[1744] The above is an embodiment of the present invention, which can improve the SEO performance of the entire website and improve its ranking in search engines, thereby increasing traffic and conversions.

[1745] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1746] Crawlability optimization

[1747] Step 1:

[1748] server

[1749] Input: List of website URLs

[1750] The server scans all pages of the website, lists the URLs to scan, and retrieves the HTML content of each page.

[1751] Output: HTML content of each page

[1752] Step 2:

[1753] server

[1754] Input: HTML content for each page

[1755] The server analyzes the retrieved HTML content and page structure, including detecting internal links and heading tags (h1, h2, h3, etc.).

[1756] Output: Analysis results (internal link structure, heading tag placement)

[1757] Step 3:

[1758] server

[1759] Input: Analysis results

[1760] The server then inputs prompts into the generative AI model based on the analysis results to automatically generate suggested modifications to optimize crawlability, such as "What internal links should be added to the webpage?"

[1761] Output: Suggested fixes (new internal links, meta tags, heading tags)

[1762] Step 4:

[1763] server

[1764] Input: Correction Suggestion

[1765] The server applies the automatically generated fixes to a specific web page, adding new internal links, meta tags, and heading tags to the HTML.

[1766] Output: Optimized HTML content

[1767] Increased indexing

[1768] Step 1:

[1769] server

[1770] Input: None

[1771] The server uses the Google Search Console API to find unindexed pages.

[1772] Output: List of unindexed pages

[1773] Step 2:

[1774] server

[1775] Input: List of unindexed pages

[1776] The server uses a generative AI model to automatically generate indexable content, using the prompt "What content does this page need to be indexed?"

[1777] Output: Indexable content

[1778] Step 3:

[1779] server

[1780] Input: Indexable content

[1781] The server adds the auto-generated content to a specific page.

[1782] Output: Optimized page content

[1783] Step 4:

[1784] server

[1785] Input: Optimized page content

[1786] The server automatically sends indexing requests using the Google Search Console API.

[1787] Output: Indexing request sent successfully

[1788] Improved rankings

[1789] Step 1:

[1790] server

[1791] Input: None

[1792] The server periodically monitors the keyword rankings of the target pages, using Google Analytics and other SEO tools to obtain the data.

[1793] Output: Keyword ranking data

[1794] Step 2:

[1795] server

[1796] Input: Keyword ranking data

[1797] The server uses a generative AI model to research related keywords, using the prompt "What are the most relevant keywords?"

[1798] Output: Related keyword list

[1799] Step 3:

[1800] server

[1801] Input: Related keyword list

[1802] The server uses the generative AI model to generate new content, including specific use cases, effects, and recommended usage.

[1803] Output: New content

[1804] Step 4:

[1805] server

[1806] Input: New content

[1807] The server applies automatically generated content to the target page, improving keyword density and relevance.

[1808] Output: Improved page content

[1809] Increased site traffic and conversions

[1810] Step 1:

[1811] Terminal

[1812] Input: Traffic data

[1813] The device analyzes traffic data in real time, using data from Google Analytics and other sources to analyze page views and user behavior.

[1814] Output: Traffic analysis results

[1815] Step 2:

[1816] Terminal

[1817] Input: Traffic analysis results

[1818] The device identifies pages with low conversion rates based on traffic data.

[1819] Output: List of pages with low conversion rates

[1820] Step 3:

[1821] Terminal

[1822] Input: List of low-converting pages

[1823] The device uses a generative AI model to generate improvement suggestions to improve the conversion rate, using the prompt "What can we do to improve the conversion rate of this page?"

[1824] Output: Improvement plan

[1825] Step 4:

[1826] User

[1827] Input: Improvement idea

[1828] Users can review the AI's suggestions through an admin panel, manually correct them if necessary, and then approve the proposed changes to be applied to the site.

[1829] Output: Optimized web page

[1830] (Application example 1)

[1831] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1832] SEO measures for online shopping sites are extremely important, as they directly affect product rankings in search results and conversion rates. However, currently, these SEO tasks are often performed manually, requiring specialized knowledge and effort, making them inefficient. In particular, optimizing internal link structures and meta tags, indexing, monitoring keyword rankings, and creating and modifying content are complex and often require real-time response, making them impractical. There is a need for systems that enable efficient, automated execution and optimization of these SEO tasks, as well as the ability to improve conversion rates based on traffic data.

[1833] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1834] In this invention, the server includes means for scanning all pages of a website, means for analyzing the HTML content and page structure of all pages, means for automatically generating suggested modifications to the internal link structure, meta tags, and heading tags of the pages that can be efficiently crawled by search engines based on the analysis results, means for analyzing detected traffic data, means for proposing optimal design changes and calls to action using generative AI, and means for automatically generating or modifying content based on the suggested improvements, thereby efficiently and automatically executing SEO tasks, improving rankings in search engines, and increasing traffic and conversions.

[1835] A "means of scanning all pages of a website" is a means of collecting data relating to the HTML content of all pages of a website.

[1836] "Means for analyzing the HTML content and page structure of all pages" refers to means for analyzing the structure and content of all collected HTML data of pages and extracting information for optimization.

[1837] "Means for automatically generating suggested modifications to the internal link structure, meta tags, and heading tags of a page that can be crawled efficiently by search engines" refers to a means for automatically generating suggested modifications to the link structure, meta tags, heading tags, etc., based on the analysis results, that will enable search engines to crawl web pages more efficiently.

[1838] "Means for analyzing detected traffic data" means means for analyzing traffic data collected from website visitors to identify patterns or issues based on that data.

[1839] "Method of using generative AI to suggest optimal design changes and calls to action" refers to a method of using generative AI to automatically suggest changes to optimize website design and elements that prompt users to take action (CTA).

[1840] "Means for automatically generating or modifying content based on improvement suggestions" refers to means for generating new content or automatically modifying existing content based on improvement suggestions proposed by the generative AI.

[1841] MODE FOR CARRYING OUT THE INVENTION

[1842] This invention is a system that efficiently and automatically implements SEO measures for online shopping sites. This system optimizes SEO tasks by combining server, terminal, and user elements, improving conversion rates and search engine rankings.

[1843] The server scans all pages of a website and analyzes their HTML content and page structure. Based on the results of this analysis, a generative AI model (such as GPT-4) is used to automatically generate suggestions for modifying the internal link structure, meta tags, and heading tags of the page so that search engines can crawl it more efficiently. This improves crawlability.

[1844] In addition, the server uses the Google Search Console API and Google Analytics API to detect unindexed pages. It uses generative AI models to automatically generate content that is likely to be indexed and adds this content to the defective pages. Indexing requests are also automatically sent, which can increase the number of indexes.

[1845] The server regularly monitors the keyword rankings of the target page and uses a generative AI model to research related keywords. It then automatically generates new content that includes those keywords or proposes modifications to existing content to improve the page's ranking. Furthermore, the generated content and modifications to the internal link structure are automatically reflected on the site.

[1846] The device analyzes traffic data in real time to identify pages with low conversion rates. Using a generative AI model, it automatically generates recommendations for improving conversion rates, including design changes, optimizing calls to action (CTA), and adding user reviews. Users can review the recommendations made by the generative AI through an admin panel, manually correct them as needed, and then approve the proposed changes to apply them to their site.

[1847] Specific use cases

[1848] The hardware and software used includes the following:

[1849] Server: AWS EC2

[1850] Database: Amazon RDS

[1851] Machine learning model: Transformers library (Hugging Face)

[1852] API: Google Search Console API, Google Analytics API

[1853] Interface: React Native (for smartphone apps)

[1854] For example, if the server identifies that the "About Us" page has not been crawled, the generative AI will automatically generate the appropriate internal links and meta tags to add to the "About Us" page. An example of a prompt for the generative AI model is:

[1855] Optimize the internal link structure and meta tags of your "About Us" page.

[1856] Additionally, if a particular product page is not indexed, the server will identify it and use generative AI to generate content that optimizes product descriptions and keyword density. An example prompt is below:

[1857] To optimize your unindexed product pages for indexing, generate content with high description and keyword density.

[1858] So, for example, if your rankings for the keyword "diet supplements" are dropping, you can use generative AI to generate new content to add to the page, including effective instructions, benefits, and ingredient information. Here's an example prompt:

[1859] Generate new content with effective usage, benefits, and ingredient information to improve your ranking for the keyword "diet supplements."

[1860] This system allows online shopping sites to efficiently and automatically implement SEO measures, improving their rankings in search engines and increasing traffic and conversions.

[1861] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1862] Step 1:

[1863] The server scans all pages of a website and collects their HTML content and page structure. The input of this process is the website URL, and the output is the retrieved HTML data and page structure. The server uses a crawler to collect data for each web page.

[1864] Step 2:

[1865] The server analyzes the collected HTML data and page structure. In this analysis process, HTML tags, meta tags, internal link structures, etc. are extracted and the analysis results are obtained. The input is the HTML data obtained in step 1, and the output is the analyzed page structure and meta information. The server performs data analysis using a specific parser tool.

[1866] Step 3:

[1867] Based on the analysis results, the server uses generative AI to automatically generate suggested modifications to internal link structures, meta tags, and heading tags that search engines can crawl efficiently. The input here is the analysis results, and the output is suggested modifications. The following prompt is input to the generative AI model:

[1868] Optimize the internal link structure and meta tags of your "About Us" page.

[1869] Step 4:

[1870] The server uses the Google Search Console API to identify pages that are not indexed. The input is a list of indexed pages, and the output is a list of pages that are not indexed. The server makes an API call to check the index status of the pages.

[1871] Step 5:

[1872] The server uses generative AI to automatically generate content that is likely to be indexed for unindexed pages. The input to this process is the HTML data of the unindexed page, and the output is the generated new content. The generative AI model is given the following prompt:

[1873] To optimize your unindexed product pages for indexing, generate content with high description and keyword density.

[1874] Step 6:

[1875] The server automatically sends an indexing request using the Google Search Console API. The input is the new content generated, and the output is the result of the indexing request submission. Provide the API with the new content and submit the request.

[1876] Step 7:

[1877] The server periodically monitors the keyword rankings of the target page and uses the generative AI to search for related keywords. The input to this process is the current keyword ranking data of the target page, and the output is a list of related keywords. The generative AI model is given the following prompt:

[1878] Generate new content with effective usage, benefits, and ingredient information to improve your ranking for the keyword "diet supplements."

[1879] Step 8:

[1880] The terminal analyzes traffic data in real time and identifies pages with low conversion rates. The input of this process is traffic data, and the output is a list of pages with low conversion rates. The terminal obtains data from the Google Analytics API and performs analysis.

[1881] Step 9:

[1882] The device uses generative AI to automatically generate design and call-to-action improvements to improve conversion rates. The input is a list of pages with low conversion rates, and the output is improvement recommendations. The following prompt is input to the generative AI model:

[1883] Generate suggestions to optimize this page's design and call-to-action to improve conversion rates.

[1884] Step 10:

[1885] Users can review the suggestions made by the generative AI through an administration panel, manually correct them as necessary, and then approve the proposed changes and have them reflected on the site. The input is the generative AI's suggestions, and the output is final approval and reflection on the site. Users operate through a web interface.

[1886] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1887] This invention is a system that automatically executes and optimizes SEO measures using machine learning and generative AI, and further improves the user experience by combining it with an emotion engine that recognizes user emotions. The specific processing flow is explained below.

[1888] Crawlability optimization

[1889] server

[1890] The server scans every page of a website, analyzing its HTML content and page structure, and then uses generative AI to automatically generate suggested modifications to help search engines crawl it more efficiently, such as optimizing internal link structures, meta tags, and heading tags.

[1891] Specific examples

[1892] If the server determines that the "About Us" page has not been crawled, the generation AI will automatically generate the appropriate internal links and meta tags and add them to the "About Us" page.

[1893] Increased indexing

[1894] server

[1895] The server uses the Google Search Console API to check the indexing status of the website, detects and identifies pages that are not indexed, automatically generates content that is likely to be indexed using generative AI for the detected pages and adds it to the defective pages, and automatically sends indexing requests using the Google Search Console API.

[1896] Specific examples

[1897] If a particular product page is not indexed, the server will identify it and use generative AI to optimize the product description and keyword density to promote indexing.

[1898] Improved rankings

[1899] server

[1900] The server periodically monitors the keyword rankings of the target page and uses generative AI to research related keywords, automatically generating new content that includes them or suggestions for modifying existing content to improve the page's clicks and ranking.

[1901] Specific examples

[1902] If the ranking for the keyword "diet supplement" is dropping, the server will use generative AI to generate content including effective usage instructions, benefits, ingredient information, etc. and add it to the page.

[1903] Increased site traffic and conversions

[1904] Terminal

[1905] The device analyzes real-time traffic data to identify pages with low conversion rates and uses generative AI to automatically generate recommendations for improving conversion rates, including redesigning, optimizing calls to action, and adding user reviews.

[1906] User

[1907] Users can review the AI's suggestions through an admin panel, manually correct them if necessary, and then approve the proposed changes to be applied to the site.

[1908] Specific examples

[1909] If the homepage's conversion rate is low, the device analyzes traffic data, and the AI ​​generator proposes new layouts and appealing points to the user. Once the user confirms and approves the suggestions, the homepage is automatically updated.

[1910] Utilizing the Emotion Engine

[1911] server

[1912] The server collects user emotion data using an emotion engine, which analyzes the user's facial expressions and behavior while visiting the website to grasp the user's emotional state in real time.

[1913] Specific examples

[1914] When a user is browsing a particular product page, if the emotion engine detects interest or enjoyment in the user's facial expression, the server can use that emotion data to display additional product information or special offers.

[1915] Terminal

[1916] The device analyzes the collected emotional data and adjusts website content according to the user's emotional tendencies, including rearranging content and optimizing design.

[1917] Specific examples

[1918] If a user shows signs of frustration while browsing multiple pages, the device will detect this emotion and offer more concise navigation and relevant product recommendations.

[1919] User

[1920] Users can review the changes and adjustments suggested by the emotion engine in the admin panel, make any necessary corrections, and apply them to their site.

[1921] Specific examples

[1922] This will be done automatically once the user confirms the results of the emotion engine analysis in the admin panel and approves the change in the position of the review section on the product page.

[1923] effect

[1924] This system ensures accuracy and consistency in SEO measures and eliminates problems caused by personalization. Furthermore, by combining it with an emotion engine, it is possible to provide a personalized experience based on the user's emotions, which is expected to increase traffic and conversions.

[1925] The processing flow will be explained below.

[1926] Crawlability optimization

[1927] Step 1:

[1928] The server scans all pages of the website and analyzes the sitemap file (sitemap.xml) and robots.txt file to list the URLs to crawl.

[1929] Step 2:

[1930] The server accesses each URL and retrieves the HTML content, extracting tags and text data, including meta tags, heading tags (H1, H2, H3, etc.), and internal links.

[1931] Step 3:

[1932] Based on the collected data, the server uses generative AI to automatically generate suggestions for the optimal internal link structure, effective meta tags, and appropriate heading tag revisions.

[1933] Step 4:

[1934] The server applies the generated suggested modifications to the website and verifies the changes.

[1935] Increased indexing

[1936] Step 1:

[1937] The server checks the indexing status of the website using the Google Search Console API, and detects and lists any pages that are not indexed.

[1938] Step 2:

[1939] For unindexed pages, the server uses generative AI to automatically generate index-friendly content, including adding text containing keywords and adding appropriate alt tags to images.

[1940] Step 3:

[1941] The server adds auto-generated content to the page and automatically sends an indexing request using the Google Search Console API.

[1942] Improved rankings

[1943] Step 1:

[1944] The server periodically monitors the keyword rankings of target pages, collects the current ranking data of each page, and performs statistical analysis.

[1945] Step 2:

[1946] The server uses generative AI to re-examine related keywords, re-evaluate appropriate keywords, and categorize them based on search volume and competition.

[1947] Step 3:

[1948] The server uses generative AI to automatically generate new content or revisions to existing content that includes relevant keywords, including new articles, product reviews, and adding user testimonials.

[1949] Step 4:

[1950] The server then posts the generated content to websites, aiming to improve their visibility, clicks, and ranking in search results.

[1951] Traffic data and increased conversions

[1952] Step 1:

[1953] The terminal analyzes real-time traffic data, including visitor behavior, length of stay, and bounce rate.

[1954] Step 2:

[1955] The device identifies pages with low conversion rates, evaluates the performance of each page, and identifies problems.

[1956] Step 3:

[1957] The device uses generative AI to automatically generate recommendations for improving conversion rates, including redesigning, changing the text of the call-to-action (CTA) button, and adding new appealing content.

[1958] Step 4:

[1959] Users can review the AI's suggestions through an admin panel, manually correct them if necessary, and then approve the proposed changes to be applied to the site.

[1960] Step 5:

[1961] The user approves the proposed improvements and they are reflected on the site, which aims to improve the conversion rate.

[1962] Utilizing the Emotion Engine

[1963] Step 1:

[1964] The server uses an emotion engine to collect user emotional data, analyzing the user's facial expressions, tone of voice, behavioral patterns, etc. in real time.

[1965] Step 2:

[1966] The device analyzes the collected emotional data and adjusts the content on the website according to the user's emotional tendencies, changing the content layout and optimizing the design.

[1967] Step 3:

[1968] The server uses generative AI to automatically generate content to personalize the user experience based on emotional data.

[1969] Step 4:

[1970] The user can review the generated changes through the administration panel and manually correct them if necessary.

[1971] Step 5:

[1972] The user approves the suggestions and modifications made by the emotion engine and reflects them on the site.

[1973] These are the specific processing steps in the program of this system. By going through these steps, it is possible to automatically and effectively implement SEO measures and also to provide a personalized experience by utilizing user emotional data.

[1974] Example 2

[1975] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1976] Traditional website optimization methods involve individual SEO measures such as crawling and indexing pages and monitoring keyword rankings, making it difficult to achieve comprehensive optimization. Furthermore, dynamic content adjustments based on user sentiment are not performed, resulting in insufficient improvements to the user experience.

[1977] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for scanning all pages of the website, means for analyzing the HTML content and page structure of all pages, means for automatically generating, using a generation AI, revision suggestions for the internal link structure, meta tags, and heading tags of the pages that can be efficiently crawled by search engines based on the analysis results, means for using an emotion engine for collecting user emotion data, and means for dynamically adjusting site content based on the emotion data. This enables comprehensive and automatic SEO measures and further personalization based on user emotion.

[1978] A "website" is a collection of multiple web pages published on the Internet, and is constructed using technologies such as HTML and CSS.

[1979] "Scanning" refers to the process of crawling all pages of a website and retrieving and analyzing data.

[1980] "HTML content" refers to data in HTML (HyperText Markup Language) format that describes the content of a web page.

[1981] "Page structure" refers to the overall structure of a web page, including the layout and hierarchy of heading tags, meta tags, link structure, etc.

[1982] "Analysis" refers to the process of examining data and extracting useful information for a specific purpose.

[1983] A "search engine" is a system that searches for information on the Internet and provides the results to users. Examples include Google and Bing.

[1984] "Internal linking structure" refers to the link relationships between pages on a website, designed to help users and search engines navigate the site effectively.

[1985] A "meta tag" is a tag written in the head section of a web page that provides search engines and browsers with metadata such as a description of the page, keywords, and author.

[1986] "Generative AI" refers to systems or models that use artificial intelligence to automatically perform specific tasks, and in this case refers to technologies that generate content and make optimization suggestions.

[1987] An "emotion engine" is a system that collects and analyzes users' emotional data, grasps their state in real time, and provides appropriate feedback and adjustments.

[1988] "Dynamic adjustment" refers to changing the content or design of a website in response to data or circumstances in real time.

[1989] MODE FOR CARRYING OUT THE INVENTION

[1990] This invention is a system that improves user experience by automatically implementing SEO measures using machine learning and generative AI, and by combining it with an emotion engine that recognizes user emotions. This system integrates website crawling, indexing, keyword ranking monitoring, and real-time emotion data collection and analysis.

[1991] Hardware and software used

[1992] Server: The server scans all pages of the website and analyzes the HTML code of each page using an HTML parser (e.g., BeautifulSoup). Generative AI can be provided by OpenAI's GPT series or similar.

[1993] Device: The device analyzes real-time traffic data using the Google Analytics API to identify pages with low conversion rates.

[1994] Emotion engine: The emotion engine uses Affectiva's SDK and other tools to analyze the user's facial expressions and behavior in real time to collect emotional data.

[1995] Specific processing flow

[1996] Crawlability optimization

[1997] server

[1998] The server scans every page of a website, analyzes its HTML content and page structure, and then gives the AI ​​prompts such as "Please suggest optimizations for internal link structure and meta tags," which then automatically generates suggested modifications.

[1999] Specific examples

[2000] For example, if the "About Us" page isn't crawled, the server will use generative AI to auto-generate the appropriate internal links and meta tags and add them to the page.

[2001] Prompt Sentence Examples

[2002] "If a particular web page is not being crawled, generate optimization recommendations for the HTML content and metadata of that page."

[2003] Increased indexing

[2004] server

[2005] The server checks the indexing status using the Google Search Console API, detects unindexed pages, and sends a prompt to the generation AI, such as "Optimize the product description on this page and generate content that is more likely to be indexed."

[2006] Specific examples

[2007] If a particular product page isn't indexed, the generative AI generates content that optimizes the product description and keyword density, and the server adds that content to the page.

[2008] Prompt Sentence Examples

[2009] "Optimize the product description on this page to generate indexable content."

[2010] Improved rankings

[2011] server

[2012] The server periodically monitors the keyword rankings of target pages through the Google Analytics API, and provides prompts to the AI ​​generator, such as "Please generate content that includes new and effective usage methods, benefits, and ingredient information," to generate content that contributes to improving rankings.

[2013] Specific examples

[2014] If the ranking for the keyword "diet supplement" is dropping, the server will use generative AI to automatically generate content including effective usage instructions, benefits, and ingredient information and add it to the page.

[2015] Prompt Sentence Examples

[2016] "Generate new content related to the keyword 'diet supplements' with effective usage, benefits, and ingredient information."

[2017] Increased site traffic and conversions

[2018] Terminal

[2019] The device analyzes real-time traffic data to identify pages with low conversion rates, and sends a prompt to the AI ​​generator to "generate new layout proposals and selling points to improve the conversion rate of this page," automatically generating improvement proposals.

[2020] User

[2021] Users can review the suggestions made by the AI ​​through an administration panel, manually correct them if necessary, and then approve the changes to reflect them on the site.

[2022] Specific examples

[2023] If the homepage conversion rate is low, the terminal will display suggestions from the generation AI on the management panel, which the user can confirm and then implement.

[2024] Prompt Sentence Examples

[2025] "Generate new layout ideas and selling points to improve the conversion rate of this page."

[2026] Utilizing the Emotion Engine

[2027] server

[2028] The server uses an emotion engine to collect user emotion data, analyze the user's facial expressions and behavior in real time, and understand their emotional state.

[2029] Specific examples

[2030] If the emotion engine detects interest or enjoyment while a user is viewing a particular product page, the server can use that emotion data to display additional product information or special offers.

[2031] Terminal

[2032] The terminal analyzes the collected emotional data and adjusts the content on the website according to the user's emotional tendencies.

[2033] Specific examples

[2034] If a user shows signs of frustration while browsing multiple pages, the device will detect this emotion and offer more concise navigation and relevant product recommendations.

[2035] User

[2036] Users can review the changes and adjustments suggested by the emotion engine in the administration panel, make corrections as necessary, and reflect them on the site.

[2037] Specific examples

[2038] This will be done automatically once the user confirms the results of the emotion engine analysis in the admin panel and approves the repositioning of the review section on the product page.

[2039] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2040] Step 1: Scan the website

[2041] server

[2042] The server scans all pages of a website. It receives a list of URLs as input and parses the HTML code of each page using an HTML parser (e.g. BeautifulSoup). It gets the parsed data for each page as output.

[2043] Specific actions

[2044] For example, the server retrieves all page URLs for "example.com" in sequence and parses each page using an HTML parser.

[2045] Step 2: Analyzing the page structure

[2046] server

[2047] The server analyzes the HTML content and page structure of the scanned page, using the analysis data from step 1 as input to identify internal links, meta tags, heading tags, etc. As output, it gets data containing details of the page structure.

[2048] Specific actions

[2049] For example, the server checks the presence of internal links and meta tags on the "About Us" page to assess whether it is optimized.

[2050] Step 3: Generative AI generates revision suggestions

[2051] server

[2052] Based on the analysis results, the server sends prompts to the generation AI to generate suggested modifications to the internal link structure, meta tags, and heading tags. The server provides details of the page structure and a prompt as input, and receives suggested modifications from the generation AI as output.

[2053] Specific actions

[2054] For example, you can send a prompt like "Add an internal link to this page" to the generative AI and get suggested fixes.

[2055] Step 4: Applying the proposed amendment

[2056] server

[2057] The server applies the suggested revisions from the generative AI to the web page, using the suggested revision data as input and generating an output with updated HTML code.

[2058] Specific actions

[2059] For example, add the internal link code obtained from the generation AI to the "About Us" page and save the updated HTML.

[2060] Step 5: Check the index status

[2061] server

[2062] The server checks the index status of the website using the Google Search Console API. It uses the website URL as input and gets the index information from the API. It gets the list of pages that are not indexed as output.

[2063] Specific actions

[2064] For example, the server checks the indexing status of each page on "example.com" using the Google Search Console API and lists the 404 error pages.

[2065] Step 6: Content generation with generative AI

[2066] server

[2067] The server automatically generates content for unindexed pages. It uses the list of unindexed pages and prompts as input and gets optimized content from the generation AI. It gets optimized content data as output.

[2068] Specific actions

[2069] For example, for an unindexed product page, a prompt such as "Please optimize the product description" is sent to the generation AI, and the generated content is retrieved.

[2070] Step 7: Submitting an indexing request

[2071] server

[2072] The server adds the auto-generated content to the page and sends an indexing request through the Google Search Console API, using the updated HTML code as input and sending an indexing request to the API to get the output.

[2073] Specific actions

[2074] For example, add the generated product description to the relevant page and send an indexing request using the Google Search Console API.

[2075] Step 8: Monitor your keyword rankings

[2076] server

[2077] The server periodically monitors keyword rankings using the Google Analytics API. It uses the target keywords as input and retrieves ranking data from the API. It gets a list of ranking data as output.

[2078] Specific actions

[2079] For example, monitor the keyword "diet supplements" and obtain its rankings periodically.

[2080] Step 9: Generative AI creates new content

[2081] server

[2082] If the server detects a drop in keyword rankings, it uses a generative AI to generate new content and suggested revisions. It uses ranking data and prompts as inputs to obtain new content from the generative AI, and obtains new content data as output.

[2083] Specific actions

[2084] For example, send a prompt like "Suggest a new use for 'Diet Supplement'" and get the generated content.

[2085] Step 10: Analyzing real-time traffic data

[2086] Terminal

[2087] The device analyzes real-time traffic data using the Google Analytics API. It uses website URLs as input to identify pages with low conversion rates, and gets a list of pages with low conversion rates as output.

[2088] Specific actions

[2089] For example, the terminal retrieves traffic data for each page of "example.com" and lists pages with low conversion rates.

[2090] Step 11: Generative AI generates improvement proposals

[2091] Terminal

[2092] The device uses a generative AI to automatically generate improvement suggestions for pages with low conversion rates. It uses the conversion rate data and prompt text as input and obtains improvement suggestion data from the generative AI. It then obtains the improvement suggestion data as output.

[2093] Specific actions

[2094] For example, you can send a prompt like, "Suggest a new layout to increase the conversion rate of this homepage," and get the generated layout suggestions.

[2095] Step 12: User Acceptance and Application

[2096] User

[2097] The user can review the suggestions made by the generative AI through an admin panel, manually correct them if necessary, and then approve the changes to be reflected on the site. The user uses the suggested data from the generative AI as input and applies the final updates to the site, obtaining the updated webpage as output.

[2098] Specific actions

[2099] For example, users can check new homepage layout suggestions made by the generative AI in the administration panel and implement them after approval.

[2100] Step 13: Collect emotion data

[2101] server

[2102] The server uses an emotion engine to collect user emotion data. It uses the user's real-time behavior data as input and obtains analysis results from the emotion engine. It obtains real-time emotion data as output.

[2103] Specific actions

[2104] For example, the emotion engine detects interest or enjoyment when a user is viewing a particular product page.

[2105] Step 14: Analyze and apply emotion data

[2106] Terminal

[2107] The device analyzes the collected emotional data and adjusts the content on the website according to the user's emotional tendencies. It uses the emotional data as input to dynamically change the layout and display of the site content, and obtains updated web page content as output.

[2108] Specific actions

[2109] For example, if the user shows signs of frustration, the navigation can be simplified based on data from the emotion engine.

[2110] Step 15: User Review and Approval

[2111] User

[2112] The user reviews the changes and adjustments suggested by the emotion engine in the admin panel, manually corrects them if necessary, and approves the changes. The system uses the suggested data from the emotion engine as input and finally updates the content on the site, obtaining an updated web page as output.

[2113] Specific actions

[2114] For example, if a user checks the analysis results of the sentiment engine in the admin panel and approves changing the position of the review section on the product page, this will be implemented automatically.

[2115] (Application example 2)

[2116] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2117] Existing SEO systems are inefficient because they require specialized technical expertise and require advanced skills and time. Furthermore, when it comes to improving user experience, there is a lack of methods for grasping user sentiment in real time and providing content that responds to that sentiment. Therefore, there is a need for a system that can simultaneously improve both SEO and user experience.

[2118] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for scanning all pages of a website, means for analyzing the HTML content and page structure of all pages, means for automatically generating, based on the analysis results, revision suggestions for the internal link structure, meta tags, and heading tags of the pages that can be efficiently crawled by search engines, means for collecting and analyzing user emotion data, and means for automatically generating, based on the analysis results, revision suggestions for content that correspond to the user's emotions. This makes it possible to automate SEO measures and personalize the user experience.

[2119] "Means for scanning all pages of a website" means a function for automatically scanning all pages of a website and acquiring their contents.

[2120] "Means for analyzing HTML content and page structure" refers to the ability to analyze the HTML code of a web page and its design, such as headings, meta tags, and link structure.

[2121] "Means for automatically generating suggested modifications to internal link structures, meta tags, and heading tags" is a function that automatically generates and suggests links and meta information to encourage optimal crawling and indexing of websites.

[2122] The "means for collecting and analyzing user emotional data" is a function that captures visitors' facial expressions and behavioral data in real time and analyzes their emotional state.

[2123] "Means for automatically generating suggested revisions to content according to the user's emotions" is a function that automatically generates and suggests content that is appropriate for the user's interests and emotional state based on collected emotional data.

[2124] "Means for detecting unindexed pages" refers to a function that identifies web pages that are not registered in a search engine's index.

[2125] "Means for automatically generating content that is likely to be indexed using a generative AI model" is a function that uses artificial intelligence to automatically generate content that is likely to be indexed by search engines.

[2126] "Means for automatically sending indexing requests" means a function that automatically sends indexing requests to search engines for content created by a generative AI model.

[2127] "Means of sending the received prompt text to a generation AI and generating content or promotional text that suits the user's emotions" refers to a function that sends a prompt text that takes the user's emotions into consideration to a generation AI, and as a result, automatically generates content or promotional text that suits the user's emotions.

[2128] This invention relates to a system that aims to improve user experience by combining automated SEO measures using machine learning and generative AI models with an emotion engine. The system includes a means for scanning and analyzing all pages of a website and automatically generating optimal revision suggestions based on the results. It also includes a means for collecting user emotions in real time and providing content that corresponds to those emotions.

[2129] System configuration

[2130] server

[2131] The server is responsible for scanning all pages of a website and analyzing their HTML content and page structure. Through this analysis, generative AI models are used to automatically generate suggested modifications to internal link structures, meta tags, and heading tags to help search engines crawl them more efficiently.

[2132] Specifically, the server uses BeautifulSoup to parse the HTML of web pages to detect unindexed pages, then uses generative AI models to generate appropriate content for those pages and automatically submits indexing requests using the Google Search Console API.

[2133] Terminal

[2134] The device collects user emotional data and adjusts content based on the analysis results. This process involves capturing the user's facial expressions using a camera and using OpenCV and an emotion recognition model. Based on this emotional data, the device sends prompts to a generative AI model, which then generates content and promotional text appropriate to the user's emotions.

[2135] User

[2136] Users can review the suggestions made by the generated AI through an admin panel, make manual corrections if necessary, and then approve the proposed changes to reflect them on the site.

[2137] Hardware and Software

[2138] Hardware: Servers, devices (smartphones, PCs)

[2139] Software: BeautifulSoup, OpenCV, Google Search Console API, generative AI models

[2140] Specific examples

[2141] When a user is browsing a product page on an online shopping site, the camera recognizes the user's expression of surprise. Based on this information, the AI ​​generates prompts such as the following, which then display an appropriate promotional message:

[2142] The user's emotion is "surprise." Generate optimal product recommendations and promotional messages based on this emotion.

[2143] It also discovers that certain pages are not indexed, uses generative AI to create optimal keywords and meta tags, and sends a request to Google Search Console to include them, as shown below.

[2144] Generate the best keywords and meta tags to help index this page.

[2145] This allows for automated SEO and personalized user experience.

[2146] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2147] Step 1:

[2148] The server scans all pages of the website to obtain the HTML content and page structure. Through this scan, the server collects the URL and HTML code of each page of the website as input data and stores the results in an internal database, which allows it to understand the structure of the entire website.

[2149] Step 2:

[2150] The server analyzes the retrieved HTML content and page structure. Specifically, it uses BeautifulSoup to parse the HTML code and extract elements such as meta tags, heading tags, and internal links. The HTML code is provided as input data, and the analysis results are obtained as output. Based on these analysis results, improvements to the website can be identified.

[2151] Step 3:

[2152] Based on the analysis results, the server uses a generative AI model to automatically generate suggested modifications to internal link structures, meta tags, and heading tags to enable search engines to crawl efficiently. The generative AI is prompted with the message "Please generate suggested modifications for index optimization," and the optimized HTML elements are returned as the output.

[2153] Step 4:

[2154] The server uses the Google Search Console API to find non-indexed pages. The input is the website URL, and the output is the indexing status. Non-indexed pages are identified.

[2155] Step 5:

[2156] The server uses a generative AI model to automatically generate content that is likely to be indexed for the detected pages, prompting them to "generate content that will promote indexing," and the appropriate content is generated as a result.

[2157] Step 6:

[2158] The server automatically sends an indexing request containing the generated content using the Google Search Console API, providing the generated content and the target page URL as input, and returning the indexing completion status as output.

[2159] Step 7:

[2160] To collect user emotion data, the device uses a camera to capture the user's facial expressions. Real-time camera footage is used as input data, and facial expression images are obtained as output. These facial expression images are used for subsequent emotion analysis.

[2161] Step 8:

[2162] The device inputs the collected facial expression images into an emotion recognition model to analyze the user's emotions. Using facial expression images as input data, emotion classification results are obtained as output. For example, emotions such as "surprise," "joy," and "sadness" are identified.

[2163] Step 9:

[2164] Based on the user's emotional data, the device sends a prompt to the generative AI model, which then generates content and promotional text appropriate to the user's emotions.The prompt might say, "The user's emotion is 'surprise.' Please generate optimal product recommendations and promotional messages," and the output is appropriate text and content.

[2165] Step 10:

[2166] The device displays the generated content to the user, providing a personalized experience based on the user's emotions. The generated content is used as input data, and the final display content is obtained as output. For example, specific product recommendations or special offers are displayed.

[2167] This allows for efficient automation of SEO measures and personalization of the user experience.

[2168] 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.

[2169] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. 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.

[2170] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2171] 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.

[2172] 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 includes both affect 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.

[2173] 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.

[2174] 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).

[2175] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2176] 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."

[2177] 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.

[2178] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2179] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2180] 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.

[2181] 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.

[2182] 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.

[2183] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[2184] The ...

Claims

1. A means of scanning all pages of a website; A means of analyzing the HTML content and page structure of a full page; Based on the analysis results, we will automatically generate suggestions for modifying the internal link structure, meta tags, and heading tags of pages so that search engines can crawl them more efficiently. A system including:

2. A means for detecting unindexed pages based on the analysis results; A method to automatically generate content that is likely to be indexed on the detected pages using generation AI, A way to automatically send indexing requests, and The system of claim 1 , comprising:

3. A means of regularly monitoring the keyword rankings of target pages, A method to use generative AI to research related keywords and automatically generate new content containing them or revisions to existing content. A way to automatically update your site with auto-generated content; The system of claim 1 , comprising:

4. a means for analyzing traffic data in real time; A way to identify pages with low conversion rates, A means to automatically generate improvement proposals to improve conversion rates using generative AI, A way for users to review the generated improvement proposals, manually make corrections, and reflect them on the site. The system of claim 1 , comprising:

Images