System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting visit data for a web site; means for analyzing the collected data and identifying improvements to the site; means for generating a proposed modification based on the identified improvements; means for automatically generating a virtual web site reflecting the generated proposed modification; means for providing a preview of the virtual web site; and means for applying the proposed modification to the actual web site after receiving user approval.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] The problems of personnel shortages and operational costs remain serious in modern website management. In particular, it is difficult to update websites in a timely manner and make effective modifications, which often leads to reduced usability and outdated information. Furthermore, manually analyzing vast amounts of data to identify optimal modifications requires a great deal of time and effort. The purpose of this invention is to solve these problems and enable website operators to update their websites efficiently and with high quality. [Means for solving the problem]

[0005] The present invention first provides a means for collecting website visit data. Next, a means is provided for analyzing the collected data and identifying areas for improvement on the website. A means is provided for generating proposed modifications based on the analyzed data and the areas for improvement. The invention further includes a means for automatically generating a virtual website that reflects the proposed modifications. The invention also includes a means for providing a preview of the virtual website to the user and applying the proposed modifications to the actual website after obtaining the user's approval. This allows website operators to easily and efficiently update their websites, thereby improving usability and website quality.

[0006] "Means of collecting website visitation data" refers to technologies and systems that automatically collect data such as the number of visitors, page views, duration of visits, click patterns, scrolling behavior, and user feedback on a website, using Google Analytics, heat map tools, inquiry forms, etc.

[0007] "Means of analyzing collected data and identifying areas for improvement on the site" refers to data analysis techniques and generative AI that use collected visit data to evaluate usability and content performance on the site and identify problems such as bounce rates and pages with short dwell times.

[0008] "Means for generating improvement proposals based on identified areas for improvement" refers to generative AI or content generation tools that automatically generate specific improvement proposals, such as correcting text on a site, changing its layout, or inserting images, based on the analysis results.

[0009] The "means for automatically generating a virtual website that reflects the generated modification proposals" is a system that builds a virtual modified website based on the modification proposals proposed by the generation AI and provides a preview of it.

[0010] "Means for providing a virtual website preview" refers to a technology that presents a virtually generated preview of a renewed website to website operators or administrators, and provides interfaces and links for checking the appearance and functionality.

[0011] "Means for applying proposed modifications to the actual website after receiving user approval" refers to a system or code application technology that allows a website operator or administrator to preview a virtual website, approve the proposed modifications, and then automatically reflect the modifications on the actual website. [Brief explanation of the drawings]

[0012] [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

[0013] 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.

[0014] First, the terms used in the following description will be explained.

[0015] 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).

[0016] 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.

[0017] 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.

[0018] 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.

[0019] 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."

[0020] [First embodiment]

[0021] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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."

[0033] This paper describes a system that collects website visitation data, generates optimal improvement plans, and reflects them in a virtual website. This system enables website operators to efficiently update their websites with high quality. Below, we will create a program for this system and explain its processing in natural language.

[0034] 1. Collection of visit data

[0035] The server uses the Google Analytics API to collect data such as website page views, number of visitors, and duration of visits. It also uses heat map tools to obtain data on user click patterns and scrolling behavior. It also uses contact forms and feedback functions to collect user comments and questions.

[0036] 2. Data Analysis

[0037] Based on the collected data, the server uses generative AI to perform a detailed analysis of the website's usability and content performance, identifying pages with high bounce rates and short viewing times and analyzing the causes.

[0038] 3. Content Generation

[0039] Based on the analysis results, the server identifies areas for improvement and automatically generates proposed modifications (text improvements, layout changes, image insertion, etc.) These modifications are aimed at improving usability and conversion rates.

[0040] 4. Automatic generation of virtual websites

[0041] The server creates a virtual renewal website based on the automatically generated revision plan, generates a preview of the virtual website, and provides its URL to the operator.

[0042] 5. User Acknowledgment and Implementation

[0043] The user sees a preview of the virtual website and approves the proposed changes.

[0044] After receiving user approval, the device applies the proposed modifications to the actual website using automatically generated code, so the new content and layout are instantly reflected on the real website.

[0045] Examples:

[0046] 1. Collection of visit data

[0047] The server uses the Google Analytics API to collect data on page views, number of visitors and time spent on the site over a period of time, as well as a heatmap tool to visualize click patterns on specific pages and collect comments from feedback forms.

[0048] 2. Data Analysis

[0049] The server analyzes data on pages with particularly high bounce rates, and then analyzes feedback comments and heat map data to identify the cause. For example, if the analysis shows that the bounce rate is high on the inquiry page, feedback such as "the form is too long" or "it's difficult to understand how to make an inquiry" can be extracted as the cause.

[0050] 3. Content Generation

[0051] Based on the analysis results, the server automatically generates improvement proposals such as "simplifying the form" and "adding an explanation of how to make an inquiry." It also generates appropriate images and guidance text.

[0052] 4. Automatic generation of virtual websites

[0053] The server creates a virtual renewal website incorporating the proposed modifications and sends a preview link to the operator.

[0054] 5. User Acknowledgment and Implementation

[0055] Users can open the preview link, view the virtual redesigned website, and approve the proposed changes.

[0056] After receiving user approval, the device applies the automatically generated code to the actual website and updates it, publishing a new page with a simplified form and additional instructions on how to contact the website.

[0057] As described above, the present invention provides a specific method for website operators to update their websites efficiently and with high quality, thereby improving usability and website quality.

[0058] The processing flow will be explained below.

[0059] Step 1:

[0060] The server uses the Google Analytics API to collect data such as website page views, number of visitors, and duration of visits. This allows basic website visit data to be obtained. This data is formatted by period and page and stored in a database.

[0061] Step 2:

[0062] The server uses the Heatmap Tool API to collect data on users' click patterns and scrolling behavior. Specifically, it visualizes user behavior on a web page and obtains that data. This makes it clear which areas of the page users are interested in. This data is also stored in a database.

[0063] Step 3:

[0064] The server collects comments and questions from users using inquiry forms and feedback functions. This text data is analyzed using natural language processing technology to extract frequently occurring problems and requests for improvement. The extracted results are stored in a database.

[0065] Step 4:

[0066] The server integrates the collected Google Analytics data and heat map data and performs a comprehensive analysis using generative AI. This analysis reveals the causes of high bounce rates on specific pages and usability issues.

[0067] Step 5:

[0068] Based on the analysis results, the server generates proposals for improvements, such as the text content of the pages that need to be improved, areas for layout improvement, and the addition or deletion of images. This generation process utilizes generative AI, which automatically generates specific proposals for improvements based on the areas that need improvement.

[0069] Step 6:

[0070] The server creates a virtual website based on the automatically generated revision proposals, which reflects the proposed changes, such as new text, layout, and images, and generates a preview link for the virtual website and sends it to the website operator.

[0071] Step 7:

[0072] The user can check the proposed changes by clicking the preview link of the virtual website provided. This preview allows the user to visually confirm how the proposed changes will actually be reflected on the website.

[0073] Step 8:

[0074] The user approves or requests modifications to the virtual website. The approved modification is confirmed as the final modification.

[0075] Step 9:

[0076] After receiving user approval, the device applies the proposed modifications to the actual website using automatically generated code, which reflects the new text, layout, and image content on the actual website.

[0077] Through the above steps, the present invention automates website modifications, allowing website operators to update the content of their websites efficiently and effectively.

[0078] Example 1

[0079] 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."

[0080] In the traditional website improvement process, collecting and analyzing visitor data and generating improvement proposals requires a significant amount of time and effort. Furthermore, applying the improvement proposals requires manual operations, which is inefficient and often results in inconsistent update quality. Therefore, a method for website operators to update their sites efficiently and with high quality is needed.

[0081] 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.

[0082] In this invention, the server includes means for collecting website visit data, means for analyzing the collected data and identifying improvements to the website, means for generating modification proposals using a generative AI model based on the identified improvements, means for automatically generating a virtual website that reflects the generated modification proposals, means for providing a preview of the virtual website, and means for applying the modification proposals to the actual website after receiving user approval, thereby enabling website operators to update their websites efficiently and with high quality.

[0083] A "website" is an information providing system that is published on the Internet and consists of multiple web pages that users can access via a browser.

[0084] "Visit Data" refers to records of access to a website, including various statistical information such as page views, number of visitors, duration of visit, click patterns, and scrolling behavior.

[0085] "Analysis" is the process of processing and analyzing collected visit data to identify specific performance indicators and issues.

[0086] "Areas for Improvement" means areas that need to be improved to improve the efficiency and usability of the website as identified through the analysis.

[0087] A "generative AI model" is an algorithm that uses machine learning and generative AI technology to automatically generate suggested text, image, and layout modifications based on collected data and analysis results.

[0088] "Proposed Fixes" refers to specific changes or correction plans proposed to address improvements identified using generative AI models.

[0089] A "virtual website" is a test site that is temporarily constructed to reflect proposed modifications to an actual website and allow users to check them in advance.

[0090] A "preview" is a display screen of a virtual website that allows users to check in advance the appearance and functionality of the website after the proposed modifications are applied.

[0091] "User" means the person or organization that manages and operates the website and is the entity that gives final approval to the proposed modifications.

[0092] A "means" refers to a method, technique, device, or part of a system used to achieve a particular purpose.

[0093] This invention relates to a system that collects website visitation data, analyzes that data, and generates optimal improvement plans. This system enables website operators to update their websites efficiently and with high quality.

[0094] System Overview

[0095] The basic configuration of this system is as follows: The system is composed of a server, terminals, and users, and each component operates in cooperation with each other.

[0096] 1. Collection of visit data

[0097] The server uses the Google Analytics API to collect data such as website page views, number of visitors, and duration of visits. It also uses heat map tools to obtain data on user click patterns and scrolling behavior. It also uses contact forms and feedback functions to collect user comments and questions.

[0098] 2. Data Analysis

[0099] Based on the collected data, the server uses generative AI to perform a detailed analysis of the website's usability and content performance, identifying pages with high bounce rates and short viewing times and analyzing the causes.

[0100] 3. Content Generation

[0101] Based on the analysis results, the server identifies areas for improvement and automatically generates proposed modifications (text improvements, layout changes, image insertion, etc.) using a generative AI model.These proposed modifications aim to improve usability and conversion rates.

[0102] 4. Automatic generation of virtual websites

[0103] The server builds a virtual renewal website based on the automatically generated revision plan, generates a preview of the virtual website, and provides its URL to the operator (user).

[0104] 5. User Acknowledgment and Implementation

[0105] The user sees a preview of the virtual website and approves the proposed changes.

[0106] After receiving user approval, the device applies the proposed modifications to the actual website using automatically generated code, so the new content and layout are instantly reflected on the real website.

[0107] Specific examples

[0108] 1. Collection of visit data

[0109] The server uses the Google Analytics API to collect data on page views, number of visitors and time spent on the site over a period of time, as well as a heatmap tool to visualize click patterns on specific pages and collect comments from feedback forms.

[0110] 2. Data Analysis

[0111] The server analyzes data on pages with particularly high bounce rates, and then analyzes feedback comments and heat map data to identify the cause. For example, if the analysis shows that the bounce rate is high on the inquiry page, feedback such as "the form is too long" or "it's difficult to understand how to make an inquiry" can be extracted as the cause.

[0112] 3. Content Generation

[0113] Based on the analysis results, the server automatically generates improvement proposals such as "simplifying the form" and "adding an explanation of how to make an inquiry." It also generates appropriate images and guidance text.

[0114] 4. Automatic generation of virtual websites

[0115] The server creates a virtual renewal website incorporating the proposed modifications and sends a preview link to the operator.

[0116] 5. User Acknowledgment and Implementation

[0117] Users can open the preview link, view the virtual redesigned website, and approve the proposed changes.

[0118] After receiving user approval, the device applies the automatically generated code to the actual website and updates it, publishing a new page with a simplified form and additional instructions on how to contact the website.

[0119] An example of an input prompt for a generative AI model is:

[0120] Input Prompt: "Analyze the usability and content performance of our website using data collected from the Google Analytics API and heatmap tools. Generate recommendations for improving pages with high bounce rates and poor performance."

[0121] Example response: "We're seeing high bounce rates on your contact page, so we suggest simplifying the form and adding detailed instructions on how to contact us. We'll also add appropriate images to make it easier to understand visually."

[0122] By the above means, the present invention provides a specific method for website operators to update their websites efficiently and with high quality, thereby improving usability and website quality.

[0123] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0124] Step 1: Collect visit data

[0125] The server sends a "GET" request to the Google Analytics API to retrieve data such as website page views, number of visitors, and time spent. The request includes any necessary authentication information and the period of time to retrieve. The input data is the Google Analytics API endpoint, and the output data is the collected statistics on page views, number of visitors, and time spent.

[0126] The server uses the heatmap tool's API to collect users' click patterns and scrolling behavior on a particular web page. The input data is the heatmap tool's endpoint, and the output data is a record of the click patterns and scrolling behavior.

[0127] The server collects user comments and questions using the inquiry form and feedback function installed on the website. The input data is text information from the inquiry form and feedback tool, and the output data is a list of comments and questions.

[0128] Step 2: Analyze the data

[0129] The server cleans the collected data and converts it into the appropriate format. Specifically, it executes data cleansing scripts, imputes missing values, and unifies data types. The input data is the collected raw data, and the output data is the cleaned data.

[0130] The server analyzes the formatted data using a generative AI model to identify pages with high bounce rates and pages with short viewing times. The input data is the formatted clean data, and the output data is a list of identified problem pages.

[0131] The server inputs the feedback comments into a text mining tool to extract key issues. The input data are the user feedback comments, and the output data is a list of extracted key issues.

[0132] Step 3: Generate content

[0133] The server identifies appropriate improvements based on the analysis results. Using a generative AI model, it sends prompts such as "List specific improvements." The input data is the analysis results, and the output data is a list of specific improvements.

[0134] The server automatically generates improvement proposals based on the identified improvements. Specifically, improvement proposals such as "simplify the form" and "add an explanation of how to contact us" are created using a generative AI model. The input data is a list of improvements, and the output data is a list of improvement proposals.

[0135] The server generates new content (text, images) as needed. It sends a prompt to the generative AI model saying, "Generate explanatory text for the inquiry page," and uses the resulting text. The input data is the prompt, and the output data is the new content (text and images).

[0136] Step 4: Automatically generate a virtual website

[0137] The server incorporates the automatically generated modification suggestions into the existing website code. The input data is a list of modification suggestions, and the output data is the website code with the modification suggestions incorporated.

[0138] The server builds a virtual website that reflects the proposed modifications and hosts it on a temporary preview server so that users can check it. The input data is the code incorporating the proposed modifications, and the output data is the preview virtual website.

[0139] The server generates a preview link of the virtual website and provides it to the user via email or dashboard. The input data is the URL of the virtual website, and the output data is the preview link.

[0140] Step 5: User Acceptance and Implementation

[0141] The user opens the preview link sent from the server and checks the virtual website. The input data is the preview link, and the output data is the user's evaluation and feedback.

[0142] If the user is satisfied with the preview, he or she clicks a button to approve the proposed modifications. The input data is the preview screen, and the output data is the approval information.

[0143] The terminal applies the automatically generated code to the actual website after receiving the user's approval. The input data is the approved code, and the output data is the website with the proposed modifications applied.

[0144] (Application example 1)

[0145] 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."

[0146] Improving user experience and optimizing conversion rates are extremely important issues in modern online shopping site operations. Improving usability and placing appropriate content requires effective collection and analysis of visit data, as well as rapid site modifications. However, these processes are time-consuming and require specialized knowledge, placing a heavy burden on many site operators. Therefore, there is a need for automation technology to achieve efficient, high-quality site modifications.

[0147] 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.

[0148] In this invention, the server includes means for collecting website visit data, means for analyzing the collected data and identifying improvements to the website, means for generating a modification plan based on the identified improvements, means for automatically generating a virtual website that reflects the generated modification plan, means for providing a preview of the virtual website, means for applying the modification plan to the actual website after receiving user approval, and means for notifying the user of the modification plan to a user terminal and providing a preview link until the operator approves it. This enables online shopping website operators to perform site modifications efficiently and with high quality, improving the user experience and optimizing the conversion rate.

[0149] "Visitation Data" means data about website visitors, such as page views, number of visitors, duration of visits, click patterns, and scrolling behavior.

[0150] "Analysis" is the process of analyzing collected data in detail to identify specific trends or problems.

[0151] "Areas for improvement" are areas that need to be fixed to improve the website's usability and conversion rate.

[0152] The "improvement proposal" is a proposal generated based on the improvement points, proposing specific improvement methods such as correcting text, changing the layout, and adding images.

[0153] A "virtual website" is a website in a virtual environment that reflects the generated modification proposal.

[0154] "Preview" means a display provided to allow the operator to check the generated virtual website in advance.

[0155] "User approval" refers to the act of the operator reviewing the generated modification plan and approving its application to the actual website.

[0156] "Automatic generation" refers to the process in which a system automatically generates data without manual intervention, using generative AI or other technologies.

[0157] A "heat map" is a tool that visually displays users' click patterns and scrolling depth.

[0158] "Natural language processing technology" is a computer science technology for analyzing text data and understanding its meaning and intent.

[0159] This invention is a system that collects website visitation data, generates optimal modification plans, and reflects them in a virtual website. This system can be used particularly by operators of online shopping sites to perform efficient, high-quality site modifications. A specific embodiment of this system is described below.

[0160] First, the server collects website visit data. Specifically, it uses the Google Analytics API to obtain data such as page views, number of visitors, and duration of visit. It also uses a heat map tool to collect data on user click patterns and scrolling behavior. Finally, it uses a feedback collection API to collect user comments and questions.

[0161] The server then analyzes the collected data, using generative AI models to perform detailed analysis of the website's usability and content performance. Pages with high drop-off rates and short viewing times are identified, and feedback comments and heat map data are also analyzed to identify the causes.

[0162] The server then automatically generates a proposal for modification based on the analysis results. By inputting the following prompts to the generative AI model, specific proposals (e.g., correcting text, changing layout, adding images, etc.) are generated:

[0163] Analytics data: {'pagePath': ' / contact', 'sessions': 120}

[0164] Heatmap data: {'clicks': {'submit_button': 30, 'scroll_depth': '50%'}}

[0165] Feedback data: {'comments': ['The form is too long', 'It's hard to understand how to contact us']}

[0166] Once the proposed modifications are generated, the server automatically generates a virtual website that reflects the modifications. This virtual website is provided as a link that allows the administrator to preview the modifications. By clicking the link, the administrator can check the preview of the virtual website and approve the modifications.

[0167] Once the user approves the proposed changes, the device applies them to the actual website using automatically generated code, so the new content and layout are instantly reflected on the live website.

[0168] As a concrete example, consider a case where the bounce rate on a contact page is high. In this case, feedback includes comments such as "the form is too long" and "it's hard to understand how to make an inquiry." Heat map data also confirms that the number of clicks on the form submit button is low. By analyzing this data, the server generates improvement proposals such as "simplify the form" and "add explanations on how to make an inquiry." These are reflected on the virtual website, and after the operator checks the preview link and approves it, they are finally applied to the actual website.

[0169] In this way, this system provides a concrete method for operators of online shopping sites to update their sites efficiently and with high quality, thereby improving usability and site quality.

[0170] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0171] Step 1:

[0172] The server collects website visit data. Specifically, it uses the Google Analytics API to obtain data such as page views, number of visitors, and duration of visit. The input is a request to the Google Analytics API, and the output is the collected visit data. Data processing involves converting the obtained data into a unified format and saving it.

[0173] Step 2:

[0174] The server uses a heat map tool to collect data on users' click patterns and scrolling behavior. This allows it to understand which areas are frequently clicked and how far the user has scrolled. The input is a request to the heat map tool, and the output is heat map data. The data is then analyzed for calculation purposes, including information such as the number of clicks and scroll depth.

[0175] Step 3:

[0176] The server uses a feedback collection API to collect user comments and questions. This information is used to identify areas for usability improvement. The input is a request to the feedback collection API, and the output is the collected feedback data. Data processing involves organizing the text data and formatting it into a form suitable for analysis.

[0177] Step 4:

[0178] The server analyzes the collected data. It uses a generative AI model to perform a detailed analysis of the website's usability and content performance. It identifies pages with high bounce rates and short viewing times, and analyzes feedback comments and heat map data to find the causes. The inputs are visit data, heat map data, and feedback data, and the output is the analysis results. Data calculations include multivariate analysis and clustering.

[0179] Step 5:

[0180] The server automatically generates a modification plan based on the analysis results. A specific modification plan is generated by inputting a prompt statement to the generation AI model. The input is the analysis results and the prompt statement, and the output is the generated modification plan. Specifically, a prompt statement of the following format is input to the generation AI:

[0181] Analytics data: {'pagePath': ' / contact', 'sessions': 120}

[0182] Heatmap data: {'clicks': {'submit_button': 30, 'scroll_depth': '50%'}}

[0183] Feedback data: {'comments': ['The form is too long', 'It's hard to understand how to contact us']}

[0184] Step 6:

[0185] The server automatically generates a virtual website that reflects the generated modification proposal. This virtual website is used by the administrator to preview the modification proposal. The input is the generated modification proposal, and the output is the preview URL of the virtual website. Data processing involves generating HTML and CSS code based on the modification proposal.

[0186] Step 7:

[0187] The user checks the preview link of the virtual website provided by the server and approves the proposed modifications. The input is the preview URL of the virtual website, and the output is the user's approval. Specifically, the user checks the preview using a browser and clicks the approve button.

[0188] Step 8:

[0189] The device receives user approval and applies the generated code to the actual website. This allows the new content and layout to be instantly reflected on the actual website. The input is the user approval and the generated code, and the output is the updated website. The code application process is performed on the server side as a data calculation.

[0190] 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.

[0191] This paper describes a system that collects website visit data and user emotion data, generates optimal modification proposals, and reflects them in a virtual website. This system enables website operators to efficiently and efficiently update their websites with high quality, while taking user emotions into consideration. Below, we will create a program for this system and explain its processing in natural language.

[0192] 1. Collection of visit and sentiment data

[0193] The server uses the Google Analytics API to collect data such as website page views, number of visitors, and duration of visits. It also uses a heat map tool to obtain data on users' click patterns and scrolling behavior. It also uses an emotion engine that recognizes users' emotions to collect emotional data when users use the website.

[0194] 2. Data Analysis

[0195] The server analyzes the collected visit data and sentiment data. Generative AI analyzes this data comprehensively and performs a detailed evaluation of the website's usability and content performance. This analysis identifies pages with high drop-off rates and pages where users express negative sentiment.

[0196] 3. Content Generation

[0197] Based on the analysis results, the server identifies areas for improvement and automatically generates proposals (such as text improvements, layout changes, and image insertion). This generation process utilizes generative AI, which creates specific proposals based on the identified areas for improvement. Furthermore, it also takes into account the user's emotional data and generates customized proposals based on their emotions.

[0198] 4. Automatic generation of virtual websites

[0199] The server builds a virtual website based on the automatically generated revision proposals. This virtual website reflects the proposed changes, such as new text, layout, and images. It also provides a customized preview based on the user's emotions. A preview link for the virtual website is generated and sent to the website operator.

[0200] 5. User Acknowledgment and Implementation

[0201] The user can check the proposed changes by clicking the preview link of the virtual website provided. This preview allows the user to visually confirm how the proposed changes will actually be reflected on the website, and confirm that the suggestions based on user sentiment are reflected.

[0202] The user approves or requests modifications to the virtual website. The approved modification is confirmed as the final modification.

[0203] 6. Reflection on the actual website

[0204] After receiving user approval, the device applies the proposed modifications to the actual website using automatically generated code. This application process reflects the new text, layout, images, etc. on the actual website. Furthermore, customization based on the user's emotions is also taken into consideration.

[0205] Examples:

[0206] 1. Collection of visit and sentiment data

[0207] The server collects Google Analytics data over a period of time, visualizes click patterns on specific pages as heat maps, and uses an emotion engine to collect user emotions (e.g., satisfaction, dissatisfaction, joy, anger, etc.) in real time.

[0208] 2. Data Analysis

[0209] The server focuses on pages with high drop-off rates and analyzes the visit and sentiment data for those pages. For example, the analysis may reveal that "the drop-off rate is high on product detail pages, and users are dissatisfied."

[0210] 3. Content Generation

[0211] The server identifies specific areas for improvement, such as "the description text on the product detail page is insufficient" or "users are looking closely at the images but are not satisfied," and generates suggestions for improvements based on these. Specifically, it generates suggestions for adding more detailed description text or inserting high-resolution images.

[0212] 4. Automatic generation of virtual websites

[0213] The server then creates a virtual website that reflects the proposed modifications and sends a preview link to the operator, allowing the operator to see the customizations based on the user's emotions (for example, special offers to improve satisfaction).

[0214] 5. User Acknowledgment and Implementation

[0215] The user opens the preview link, checks the virtual website, and approves or requests modifications. Approved modifications are confirmed as the final modifications.

[0216] 6. Reflection on the actual website

[0217] The device then applies the proposed changes to the live website using automatically generated code, after which the product detail page will reflect the new text, images, and customizations, improving the user experience.

[0218] As described above, the present invention, which combines user emotion data, allows website operators to make more effective website improvements that take user emotions into consideration.

[0219] The processing flow will be explained below.

[0220] Step 1:

[0221] The server uses the Google Analytics API to collect data such as website page views, number of visitors, and length of stay, and then formats this data by period and page and stores it in a database.

[0222] Step 2:

[0223] The server uses the Heatmap Tool API to collect data on users' click patterns and scrolling behavior, which is then visualized and stored in a database.

[0224] Step 3:

[0225] The server collects comments and questions from users using inquiry forms and feedback functions. The collected text data is analyzed using natural language processing technology to extract frequently occurring problems and requests for improvement.

[0226] Step 4:

[0227] The server uses an emotion engine to collect user emotion data. It recognizes the emotions (e.g., satisfaction, dissatisfaction, joy, anger, etc.) of users when they use the website in real time and stores them in a database.

[0228] Step 5:

[0229] The server integrates the collected visit data and sentiment data and uses generative AI to perform detailed analysis of this data, specifically identifying pages with high dropout rates and pages where users express negative sentiment.

[0230] Step 6:

[0231] The server identifies areas for improvement based on the analysis results and automatically generates proposals (such as text improvements, layout changes, and image insertion) that also take into account user emotional data.

[0232] Step 7:

[0233] The server builds a virtual website based on the automatically generated modification proposals, generates a preview link for the virtual website, and sends it to the website operator. This preview also includes customizations based on the user's emotions.

[0234] Step 8:

[0235] The user checks the preview link of the virtual website provided and approves or requests corrections. The approved proposal is confirmed as the final modification content.

[0236] Step 9:

[0237] After receiving user approval, the device applies the proposed changes to the live website using automatically generated code, resulting in the live website being updated with the new text, layout, images, and customizations.

[0238] Through the above steps, the present invention automates website modifications, allowing website operators to update their website content efficiently and effectively. By combining user emotion data, the modifications will be more satisfying to users.

[0239] Example 2

[0240] 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."

[0241] Conventional website modification methods do not fully utilize user behavioral and emotional data, limiting the accuracy and effectiveness of modification proposals. It is particularly difficult to identify pages with high bounce rates or pages where users express negative emotions, and provide appropriate modification proposals. Furthermore, the process of reviewing modification proposals on a virtual website and obtaining user approval is time-consuming, requiring efficient operation.

[0242] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0243] In this invention, the server includes means for collecting website visit data and emotion data, means for analyzing the collected data and identifying improvements to the website, means for generating modification proposals based on the identified improvements, means for automatically generating a virtual website that reflects the generated modification proposals, means for providing a preview of the virtual website, and means for applying the modification proposals to the actual website after receiving user approval. This enables the generation of highly accurate modification proposals based on user behavior and emotion data, allowing operators to modify their websites efficiently and effectively.

[0244] A "website" is a collection of web pages that provide information or functionality available on the Internet.

[0245] "Visit data" refers to data about the behavior of users when they browse a website, such as page views, number of visitors, and length of stay.

[0246] "Emotional data" is data that indicates the emotional state (e.g., satisfaction, dissatisfaction, joy, anger) of a user when using a website.

[0247] A "heat map" is a data representation that visually shows users' click patterns and scrolling behavior.

[0248] A "virtual website" is a virtual website that reflects proposed modifications and is provided as a preview before the actual modifications are made.

[0249] "Preview" is a feature that allows you to visually check how the proposed changes will be reflected on the website before actually making the changes.

[0250] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and automatically generate specific renovation proposals.

[0251] "User" means a website operator or administrator who is responsible for reviewing the preview of the virtual website and approving or modifying the proposed changes.

[0252] A "server" is a computer system that collects and analyzes website data, generates modification proposals, and automatically generates virtual websites.

[0253] This invention relates to a system that collects website visit data and user emotion data, generates optimal modification proposals, and reflects them in a virtual website. This system enables website operators to efficiently and efficiently update their websites with high quality, while taking user emotions into consideration.

[0254] The server performs the process in the following procedure.

[0255] Collection of visit and sentiment data

[0256] The server automatically collects data such as website page views, number of visitors, and visit duration using the Google Analytics API. It also uses tools like Hotjar to obtain data on user click patterns and scrolling behavior using heat map tools. It also uses an emotion engine to collect emotional data (e.g., satisfaction, dissatisfaction, joy, anger) as users use the website. It uses the Emotion API to obtain this emotional data in real time and store it in a database.

[0257] Data analysis

[0258] The server integrates the collected visit data and sentiment data and performs a comprehensive analysis using a generative AI model. This involves using methods such as correlation analysis, regression analysis, and clustering to identify pages with particularly high drop-off rates and pages where users express negative sentiment. This analysis allows for a detailed evaluation of the site's usability and content performance.

[0259] Content generation

[0260] The server identifies areas for improvement based on the analysis results and automatically generates specific improvement proposals using a generative AI model. For example, it suggests text improvements, layout changes, image insertion, etc. for identified issues. It also considers user emotional data and creates customized improvement proposals based on emotions.

[0261] Automatic generation of virtual websites

[0262] The server then creates a virtual website based on the proposed changes, incorporating the new text, layout, and images, and generates a preview link that can be sent to the website owner, allowing them to visually inspect the proposed changes.

[0263] User approval and implementation

[0264] The user opens the provided preview link, checks the proposed modifications to the virtual website, and then approves the proposed modifications or requests modifications as necessary. The approved modifications are confirmed as the final modifications.

[0265] Reflection on the actual website

[0266] The device then applies the user-approved modifications to the actual website, using automatically generated code to update the website with new text, layout, and images, as well as customizations based on the user's emotions.

[0267] Examples:

[0268] Collection of visit and sentiment data

[0269] The server collects Google Analytics data for the past month and visualizes click patterns on product detail pages as heat maps. It also uses an emotion engine to collect user emotion data (e.g., satisfaction, dissatisfaction) in real time.

[0270] Data analysis

[0271] The server analyzes the visit data and emotion data on the product detail page and finds that "the dropout rate is high and users are dissatisfied."

[0272] Content generation

[0273] The server generates specific suggestions for modifications to the product detail page, such as adding detailed description text or inserting high-resolution images.

[0274] Automatic generation of virtual websites

[0275] The server then creates a virtual website that reflects the proposed changes and sends the operator a preview link, which includes customizations based on the user's emotions.

[0276] User approval and implementation

[0277] The user opens the preview link, checks the virtual website, and approves or requests modifications. Approved modifications are confirmed as the final modifications.

[0278] Reflection on the actual website

[0279] The device then uses the auto-generated code to apply the proposed changes to the live website, where the product detail page will then reflect the new text, images, and customizations, improving the user experience.

[0280] Example prompt sentence:

[0281] "Analyze 30 days of visit data and user sentiment data to generate specific improvement recommendations for product detail pages."

[0282] "Generate specific suggestions to improve the text and images on pages with high bounce rates and dissatisfied comments."

[0283] As described above, the present invention is a system that enables website operators to efficiently and effectively improve their websites by utilizing user behavior and emotion data.

[0284] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0285] Step 1: Collect visit and sentiment data

[0286] The server uses the Google Analytics API to obtain data such as website page views, number of visitors, and duration of visits. Google Analytics account information and API authentication key are required as input. The obtained data is saved in a database (output).

[0287] The server uses a heatmap tool (e.g., Hotjar) to collect data on users' click patterns and scrolling behavior. The inputs require Hotjar account information and the URLs of the pages to be collected. The collected data is visualized and stored in a database (output).

[0288] The server uses the Emotion API to collect user emotion data in real time. The input requires the Emotion API authentication key and the user's image or text data. The acquired emotion data is stored in a database (output).

[0289] Step 2: Integrate the data

[0290] The server aggregates visit data, heatmap data, and sentiment data collected from databases, and requires multiple datasets from each data source as input.

[0291] The integration process merges data based on the same timestamp or user ID to create a single dataset (output), allowing information from different data sources to be analyzed in a unified manner.

[0292] Step 3: Analyze the data

[0293] The server uses the integrated dataset and performs analysis using a generative AI model, with the integrated dataset and the analytical model as inputs.

[0294] The generative AI model performs correlation analysis, regression analysis, and clustering to evaluate the usability and content performance of each page, and outputs an analysis report that identifies pages with high dropoff rates and pages that evoke negative sentiment.

[0295] Step 4: Identify areas for improvement

[0296] The server identifies specific improvements based on the analysis result report, and the analysis result report is used as input.

[0297] For example, based on information such as "the bounce rate on a particular product detail page is high, causing users to feel dissatisfied," areas for improvement are listed (output).

[0298] Step 5: Generate renovation proposals

[0299] The server generates a proposed fix based on the identified improvements, taking the list of improvements as input.

[0300] Using a generative AI model, specific improvement proposals (e.g., text improvement, layout changes, image addition) are automatically generated. The output is a specific improvement proposal.

[0301] Step 6: Automatically Generate Virtual Websites

[0302] The server constructs a virtual website based on the generated modification proposals. The input is a set of modification proposals.

[0303] The system builds a virtual website based on the automatically generated revision proposals, and then generates a preview link and sends it to the website operator (output).

[0304] Step 7: User Acceptance and Implementation

[0305] The user opens the provided preview link and checks the proposed modifications to the virtual website. The input is the preview link.

[0306] The user either approves the proposed revision or requests a revision. The approved revision is saved in the database as the final version (output).

[0307] Step 8: Applying to the actual website

[0308] The terminal applies the proposed modifications approved by the user to the actual website, and the final approved modification is used as input.

[0309] The new text, layout, and images are applied to the website using the automatically generated code, and finally, the changes are verified (output).

[0310] Through these steps, the present invention utilizes user behavioral and emotional data to enable the generation and implementation of highly accurate modification proposals.

[0311] (Application example 2)

[0312] 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."

[0313] Conventional website modification methods do not take user emotional data into account, making it difficult to improve usability and modify websites effectively. Furthermore, the lack of a system that can integrate and analyze emotional data and visitor data makes it difficult to understand users' true needs and dissatisfaction. Furthermore, the lack of virtual website generation and preview functionality means that site managers are unable to visually confirm the effects of proposed modifications.

[0314] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0315] In this invention, the server includes means for collecting website visit data and user emotion data, means for analyzing the collected visit data and emotion data to identify site improvements, means for generating modification proposals based on the identified improvements, means for automatically generating a virtual website that reflects the generated modification proposals, means for providing a preview of the virtual website, means for applying the modification proposals to the actual website after receiving user approval, means for collecting user emotion data using a smartphone or a head-mounted display, and means for generating modification proposals using a generative AI model based on the collected data, thereby enabling effective site modifications that take user emotions into consideration.

[0316] "Website visit data" refers to behavioral data such as page views, time spent, and click patterns when a user accesses a website.

[0317] "User emotional data" refers to data on the emotional state of a user, derived from facial expressions, tone of voice, etc., collected using emotion recognition technology.

[0318] "Means of collection" refers to the technical means for acquiring data using servers, sensors, etc.

[0319] "Analytical means" means the technical means used to analyze collected data using statistical or machine learning techniques to derive patterns and trends.

[0320] "Means for identifying areas for improvement" refers to technical means for identifying problems with the usability and performance of a website from the analysis results and identifying areas that need improvement.

[0321] "Means for generating improvement proposals" refers to technical means for creating specific improvement measures (such as changes to text, images, or layout) based on the identified improvement points.

[0322] "Means for automatically generating a virtual website" refers to a technical means for constructing a virtual website based on the generated modification plan.

[0323] "Means for providing a preview" refers to a technical means for presenting proposed modifications to a virtual website so that the operator can visually check them.

[0324] "Means for applying proposed modifications to the actual website after receiving user approval" refers to the technical means for reflecting proposed modifications that have been approved by the operator on the actual website.

[0325] A "smartphone" is a portable information terminal that has Internet access, various sensor functions, and can run a variety of applications.

[0326] A "head-mounted display" is a display device that a user wears on their head to display visual information.

[0327] A "generative AI model" is an artificial intelligence technology that uses machine learning algorithms learned from large amounts of data to generate new data and content.

[0328] This invention is a system that generates optimal modification plans using website visit data and user emotion data, and reflects the modification plans in a virtual website. This system includes the following means.

[0329] 1. Collection of visit and sentiment data

[0330] The server collects user emotional data using smartphones and head-mounted displays. Specifically, it collects the user's facial expressions and tone of voice through the smartphone's camera and microphone, and analyzes them using emotion recognition technology. It also uses the Google Analytics API to collect website visit data (page views, number of visitors, duration of visits, click patterns, etc.).

[0331] 2. Data Analysis

[0332] The server integrates and analyzes the collected visit data and sentiment data, using TensorFlow and other machine learning algorithms to evaluate the usability and content performance of websites and identify problems on specific pages, particularly those with high bounce rates or negative user sentiment.

[0333] 3. Generation of renovation proposals

[0334] The server uses a generative AI model to automatically generate suggested improvements based on the analysis results, such as revising text, replacing images, or presenting special offers. For example, if a page's explanatory text is insufficient, a suggestion to add more detailed information will be generated.

[0335] 4. Automatic generation of virtual websites

[0336] The server creates a virtual website based on the proposed changes, including new text, images, and layouts that reflect the proposed changes, and generates a preview link for the virtual website and sends it to the operator.

[0337] 5. User Acknowledgment and Implementation

[0338] The user checks the preview link of the provided virtual website and visually confirms the contents of the proposed modifications. They then approve or request modifications, and the approved modification is confirmed as the final modification.

[0339] 6. Reflection on the actual website

[0340] After receiving user approval, the device uses automatically generated code to apply the proposed changes to the actual website, improving the user experience by incorporating new text, layout, images, and more.

[0341] Specific examples

[0342] When a customer visited a certain product page, emotions such as "dissatisfaction" and "anger" were detected via camera. Furthermore, Google Analytics data revealed that the page had a high bounce rate. Based on this data, the invented system automatically generated revisions to the product description text, added high-resolution images, and presented special offers, which were then reflected on the virtual website. The operator approved these revisions and applied them to the actual online shopping site. As a result, the bounce rate on the page decreased and user satisfaction improved.

[0343] Prompt Sentence Examples

[0344] "Collect visit data and user sentiment data for 7 days. Obtain click patterns and time spent on specific pages from Google Analytics, and collect sentiment data in real time using users' facial expressions and tone of voice. Analyze the collected data and automatically generate improvement proposals using a generative AI model. Generate a preview link of the virtual website and send it to the operator."

[0345] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0346] Step 1:

[0347] The server collects user emotional data using a smartphone or head-mounted display. The input includes the user's facial expressions and tone of voice captured by the smartphone's camera and microphone, which are then analyzed using emotion recognition technology to output data representing the user's emotional state (e.g., joy, dissatisfaction, anger, etc.).

[0348] Step 2:

[0349] The server uses the Google Analytics API to collect website visit data (page views, number of visitors, duration, click patterns, etc.) The server requires a Google Analytics API key and website URL as input, and using these, the server sends a request to the API, which outputs behavioral data such as page views and number of visitors.

[0350] Step 3:

[0351] The server integrates and analyzes the emotion data and visit data collected in steps 1 and 2. Emotion data and visit data are given as input, and by analyzing them using a machine learning algorithm (e.g., TensorFlow), an evaluation result of the website's usability and content performance is output. This evaluation result includes pages with high dropout rates and pages where users express negative emotions.

[0352] Step 4:

[0353] The server uses the generative AI model to automatically generate improvement proposals based on the analysis results from step 3. The analysis results are input, and the generative AI model generates improvement proposals (e.g., revising text, replacing images, presenting special offers, etc.) based on the analysis results, and specific improvement proposals are output.

[0354] Step 5:

[0355] The server automatically generates a virtual website that reflects the proposed modifications. The server takes the proposed modifications as input, automatically generates a page that reflects the proposed modifications in the virtual website template, and outputs a preview link for the virtual website.

[0356] Step 6:

[0357] The user checks the preview link of the virtual website provided. The preview link is given as input, and the user visually checks the pages of the virtual website, checks the contents of the proposed modifications, and approves or requests modifications. At this stage, the approved modifications are finalized.

[0358] Step 7:

[0359] The device applies the user-approved modifications to the actual website. It takes the approved modifications as input and processes them on the website using automatically generated code. As a result, new text, layout, images, etc. are reflected on the actual website, improving the user experience.

[0360] 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.

[0361] 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.

[0362] 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.

[0363] [Second embodiment]

[0364] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0365] 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.

[0366] 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).

[0367] 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.

[0368] 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.

[0369] 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).

[0370] 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.

[0371] 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.

[0372] 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.

[0373] 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.

[0374] 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.

[0375] 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."

[0376] This paper describes a system that collects website visitation data, generates optimal improvement plans, and reflects them in a virtual website. This system enables website operators to efficiently update their websites with high quality. Below, we will create a program for this system and explain its processing in natural language.

[0377] 1. Collection of visit data

[0378] The server uses the Google Analytics API to collect data such as website page views, number of visitors, and duration of visits. It also uses heat map tools to obtain data on user click patterns and scrolling behavior. It also uses contact forms and feedback functions to collect user comments and questions.

[0379] 2. Data Analysis

[0380] Based on the collected data, the server uses generative AI to perform a detailed analysis of the website's usability and content performance, identifying pages with high bounce rates and short viewing times and analyzing the causes.

[0381] 3. Content Generation

[0382] Based on the analysis results, the server identifies areas for improvement and automatically generates proposed modifications (text improvements, layout changes, image insertion, etc.) These modifications are aimed at improving usability and conversion rates.

[0383] 4. Automatic generation of virtual websites

[0384] The server creates a virtual renewal website based on the automatically generated revision plan, generates a preview of the virtual website, and provides its URL to the operator.

[0385] 5. User Acknowledgment and Implementation

[0386] The user sees a preview of the virtual website and approves the proposed changes.

[0387] After receiving user approval, the device applies the proposed modifications to the actual website using automatically generated code, so the new content and layout are instantly reflected on the real website.

[0388] Examples:

[0389] 1. Collection of visit data

[0390] The server uses the Google Analytics API to collect data on page views, number of visitors and time spent on the site over a period of time, as well as a heatmap tool to visualize click patterns on specific pages and collect comments from feedback forms.

[0391] 2. Data Analysis

[0392] The server analyzes data on pages with particularly high bounce rates, and then analyzes feedback comments and heat map data to identify the cause. For example, if the analysis shows that the bounce rate is high on the inquiry page, feedback such as "the form is too long" or "it's difficult to understand how to make an inquiry" can be extracted as the cause.

[0393] 3. Content Generation

[0394] Based on the analysis results, the server automatically generates improvement proposals such as "simplifying the form" and "adding an explanation of how to make an inquiry." It also generates appropriate images and guidance text.

[0395] 4. Automatic generation of virtual websites

[0396] The server creates a virtual renewal website incorporating the proposed modifications and sends a preview link to the operator.

[0397] 5. User Acknowledgment and Implementation

[0398] Users can open the preview link, view the virtual redesigned website, and approve the proposed changes.

[0399] After receiving user approval, the device applies the automatically generated code to the actual website and updates it, publishing a new page with a simplified form and additional instructions on how to contact the website.

[0400] As described above, the present invention provides a specific method for website operators to update their websites efficiently and with high quality, thereby improving usability and website quality.

[0401] The processing flow will be explained below.

[0402] Step 1:

[0403] The server uses the Google Analytics API to collect data such as website page views, number of visitors, and duration of visits. This allows basic website visit data to be obtained. This data is formatted by period and page and stored in a database.

[0404] Step 2:

[0405] The server uses the Heatmap Tool API to collect data on users' click patterns and scrolling behavior. Specifically, it visualizes user behavior on a web page and obtains that data. This makes it clear which areas of the page users are interested in. This data is also stored in a database.

[0406] Step 3:

[0407] The server collects comments and questions from users using inquiry forms and feedback functions. This text data is analyzed using natural language processing technology to extract frequently occurring problems and requests for improvement. The extracted results are stored in a database.

[0408] Step 4:

[0409] The server integrates the collected Google Analytics data and heat map data and performs a comprehensive analysis using generative AI. This analysis reveals the causes of high bounce rates on specific pages and usability issues.

[0410] Step 5:

[0411] Based on the analysis results, the server generates proposals for improvements, such as the text content of the pages that need to be improved, areas for layout improvement, and the addition or deletion of images. This generation process utilizes generative AI, which automatically generates specific proposals for improvements based on the areas that need improvement.

[0412] Step 6:

[0413] The server creates a virtual website based on the automatically generated revision proposals, which reflects the proposed changes, such as new text, layout, and images, and generates a preview link for the virtual website and sends it to the website operator.

[0414] Step 7:

[0415] The user can check the proposed changes by clicking the preview link of the virtual website provided. This preview allows the user to visually confirm how the proposed changes will actually be reflected on the website.

[0416] Step 8:

[0417] The user approves or requests modifications to the virtual website. The approved modification is confirmed as the final modification.

[0418] Step 9:

[0419] After receiving user approval, the device applies the proposed modifications to the actual website using automatically generated code, which reflects the new text, layout, and image content on the actual website.

[0420] Through the above steps, the present invention automates website modifications, allowing website operators to update the content of their websites efficiently and effectively.

[0421] Example 1

[0422] 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."

[0423] In the traditional website improvement process, collecting and analyzing visitor data and generating improvement proposals requires a significant amount of time and effort. Furthermore, applying the improvement proposals requires manual operations, which is inefficient and often results in inconsistent update quality. Therefore, a method for website operators to update their sites efficiently and with high quality is needed.

[0424] 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.

[0425] In this invention, the server includes means for collecting website visit data, means for analyzing the collected data and identifying improvements to the website, means for generating modification proposals using a generative AI model based on the identified improvements, means for automatically generating a virtual website that reflects the generated modification proposals, means for providing a preview of the virtual website, and means for applying the modification proposals to the actual website after receiving user approval, thereby enabling website operators to update their websites efficiently and with high quality.

[0426] A "website" is an information providing system that is published on the Internet and consists of multiple web pages that users can access via a browser.

[0427] "Visit Data" refers to records of access to a website, including various statistical information such as page views, number of visitors, duration of visit, click patterns, and scrolling behavior.

[0428] "Analysis" is the process of processing and analyzing collected visit data to identify specific performance indicators and issues.

[0429] "Areas for Improvement" means areas that need to be improved to improve the efficiency and usability of the website as identified through the analysis.

[0430] A "generative AI model" is an algorithm that uses machine learning and generative AI technology to automatically generate suggested text, image, and layout modifications based on collected data and analysis results.

[0431] "Proposed Fixes" refers to specific changes or correction plans proposed to address improvements identified using generative AI models.

[0432] A "virtual website" is a test site that is temporarily constructed to reflect proposed modifications to an actual website and allow users to check them in advance.

[0433] A "preview" is a display screen of a virtual website that allows users to check in advance the appearance and functionality of the website after the proposed modifications are applied.

[0434] "User" means the person or organization that manages and operates the website and is the entity that gives final approval to the proposed modifications.

[0435] A "means" refers to a method, technique, device, or part of a system used to achieve a particular purpose.

[0436] This invention relates to a system that collects website visitation data, analyzes that data, and generates optimal improvement plans. This system enables website operators to update their websites efficiently and with high quality.

[0437] System Overview

[0438] The basic configuration of this system is as follows: The system is composed of a server, terminals, and users, and each component operates in cooperation with each other.

[0439] 1. Collection of visit data

[0440] The server uses the Google Analytics API to collect data such as website page views, number of visitors, and duration of visits. It also uses heat map tools to obtain data on user click patterns and scrolling behavior. It also uses contact forms and feedback functions to collect user comments and questions.

[0441] 2. Data Analysis

[0442] Based on the collected data, the server uses generative AI to perform a detailed analysis of the website's usability and content performance, identifying pages with high bounce rates and short viewing times and analyzing the causes.

[0443] 3. Content Generation

[0444] Based on the analysis results, the server identifies areas for improvement and automatically generates proposed modifications (text improvements, layout changes, image insertion, etc.) using a generative AI model.These proposed modifications aim to improve usability and conversion rates.

[0445] 4. Automatic generation of virtual websites

[0446] The server builds a virtual renewal website based on the automatically generated revision plan, generates a preview of the virtual website, and provides its URL to the operator (user).

[0447] 5. User Acknowledgment and Implementation

[0448] The user sees a preview of the virtual website and approves the proposed changes.

[0449] After receiving user approval, the device applies the proposed modifications to the actual website using automatically generated code, so the new content and layout are instantly reflected on the real website.

[0450] Specific examples

[0451] 1. Collection of visit data

[0452] The server uses the Google Analytics API to collect data on page views, number of visitors and time spent on the site over a period of time, as well as a heatmap tool to visualize click patterns on specific pages and collect comments from feedback forms.

[0453] 2. Data Analysis

[0454] The server analyzes data on pages with particularly high bounce rates, and then analyzes feedback comments and heat map data to identify the cause. For example, if the analysis shows that the bounce rate is high on the inquiry page, feedback such as "the form is too long" or "it's difficult to understand how to make an inquiry" can be extracted as the cause.

[0455] 3. Content Generation

[0456] Based on the analysis results, the server automatically generates improvement proposals such as "simplifying the form" and "adding an explanation of how to make an inquiry." It also generates appropriate images and guidance text.

[0457] 4. Automatic generation of virtual websites

[0458] The server creates a virtual renewal website incorporating the proposed modifications and sends a preview link to the operator.

[0459] 5. User Acknowledgment and Implementation

[0460] Users can open the preview link, view the virtual redesigned website, and approve the proposed changes.

[0461] After receiving user approval, the device applies the automatically generated code to the actual website and updates it, publishing a new page with a simplified form and additional instructions on how to contact the website.

[0462] An example of an input prompt for a generative AI model is:

[0463] Input Prompt: "Analyze the usability and content performance of our website using data collected from the Google Analytics API and heatmap tools. Generate recommendations for improving pages with high bounce rates and poor performance."

[0464] Example response: "We're seeing high bounce rates on your contact page, so we suggest simplifying the form and adding detailed instructions on how to contact us. We'll also add appropriate images to make it easier to understand visually."

[0465] By the above means, the present invention provides a specific method for website operators to update their websites efficiently and with high quality, thereby improving usability and website quality.

[0466] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0467] Step 1: Collect visit data

[0468] The server sends a "GET" request to the Google Analytics API to retrieve data such as website page views, number of visitors, and time spent. The request includes any necessary authentication information and the period of time to retrieve. The input data is the Google Analytics API endpoint, and the output data is the collected statistics on page views, number of visitors, and time spent.

[0469] The server uses the heatmap tool's API to collect users' click patterns and scrolling behavior on a particular web page. The input data is the heatmap tool's endpoint, and the output data is a record of the click patterns and scrolling behavior.

[0470] The server collects user comments and questions using the inquiry form and feedback function installed on the website. The input data is text information from the inquiry form and feedback tool, and the output data is a list of comments and questions.

[0471] Step 2: Analyze the data

[0472] The server cleans the collected data and converts it into the appropriate format. Specifically, it executes data cleansing scripts, imputes missing values, and unifies data types. The input data is the collected raw data, and the output data is the cleaned data.

[0473] The server analyzes the formatted data using a generative AI model to identify pages with high bounce rates and pages with short viewing times. The input data is the formatted clean data, and the output data is a list of identified problem pages.

[0474] The server inputs the feedback comments into a text mining tool to extract key issues. The input data are the user feedback comments, and the output data is a list of extracted key issues.

[0475] Step 3: Generate content

[0476] The server identifies appropriate improvements based on the analysis results. Using a generative AI model, it sends prompts such as "List specific improvements." The input data is the analysis results, and the output data is a list of specific improvements.

[0477] The server automatically generates improvement proposals based on the identified improvements. Specifically, improvement proposals such as "simplify the form" and "add an explanation of how to contact us" are created using a generative AI model. The input data is a list of improvements, and the output data is a list of improvement proposals.

[0478] The server generates new content (text, images) as needed. It sends a prompt to the generative AI model saying, "Generate explanatory text for the inquiry page," and uses the resulting text. The input data is the prompt, and the output data is the new content (text and images).

[0479] Step 4: Automatically generate a virtual website

[0480] The server incorporates the automatically generated modification suggestions into the existing website code. The input data is a list of modification suggestions, and the output data is the website code with the modification suggestions incorporated.

[0481] The server builds a virtual website that reflects the proposed modifications and hosts it on a temporary preview server so that users can check it. The input data is the code incorporating the proposed modifications, and the output data is the preview virtual website.

[0482] The server generates a preview link of the virtual website and provides it to the user via email or dashboard. The input data is the URL of the virtual website, and the output data is the preview link.

[0483] Step 5: User Acceptance and Implementation

[0484] The user opens the preview link sent from the server and checks the virtual website. The input data is the preview link, and the output data is the user's evaluation and feedback.

[0485] If the user is satisfied with the preview, he or she clicks a button to approve the proposed modifications. The input data is the preview screen, and the output data is the approval information.

[0486] The terminal applies the automatically generated code to the actual website after receiving the user's approval. The input data is the approved code, and the output data is the website with the proposed modifications applied.

[0487] (Application example 1)

[0488] 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."

[0489] Improving user experience and optimizing conversion rates are extremely important issues in modern online shopping site operations. Improving usability and placing appropriate content requires effective collection and analysis of visit data, as well as rapid site modifications. However, these processes are time-consuming and require specialized knowledge, placing a heavy burden on many site operators. Therefore, there is a need for automation technology to achieve efficient, high-quality site modifications.

[0490] 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.

[0491] In this invention, the server includes means for collecting website visit data, means for analyzing the collected data and identifying improvements to the website, means for generating a modification plan based on the identified improvements, means for automatically generating a virtual website that reflects the generated modification plan, means for providing a preview of the virtual website, means for applying the modification plan to the actual website after receiving user approval, and means for notifying the user of the modification plan to a user terminal and providing a preview link until the operator approves it. This enables online shopping website operators to perform site modifications efficiently and with high quality, improving the user experience and optimizing the conversion rate.

[0492] "Visitation Data" means data about website visitors, such as page views, number of visitors, duration of visits, click patterns, and scrolling behavior.

[0493] "Analysis" is the process of analyzing collected data in detail to identify specific trends or problems.

[0494] "Areas for improvement" are areas that need to be fixed to improve the website's usability and conversion rate.

[0495] The "improvement proposal" is a proposal generated based on the improvement points, proposing specific improvement methods such as correcting text, changing the layout, and adding images.

[0496] A "virtual website" is a website in a virtual environment that reflects the generated modification proposal.

[0497] "Preview" means a display provided to allow the operator to check the generated virtual website in advance.

[0498] "User approval" refers to the act of the operator reviewing the generated modification plan and approving its application to the actual website.

[0499] "Automatic generation" refers to the process in which a system automatically generates data without manual intervention, using generative AI or other technologies.

[0500] A "heat map" is a tool that visually displays users' click patterns and scrolling depth.

[0501] "Natural language processing technology" is a computer science technology for analyzing text data and understanding its meaning and intent.

[0502] This invention is a system that collects website visitation data, generates optimal modification plans, and reflects them in a virtual website. This system can be used particularly by operators of online shopping sites to perform efficient, high-quality site modifications. A specific embodiment of this system is described below.

[0503] First, the server collects website visit data. Specifically, it uses the Google Analytics API to obtain data such as page views, number of visitors, and duration of visit. It also uses a heat map tool to collect data on user click patterns and scrolling behavior. Finally, it uses a feedback collection API to collect user comments and questions.

[0504] The server then analyzes the collected data, using generative AI models to perform detailed analysis of the website's usability and content performance. Pages with high drop-off rates and short viewing times are identified, and feedback comments and heat map data are also analyzed to identify the causes.

[0505] The server then automatically generates a proposal for modification based on the analysis results. By inputting the following prompts to the generative AI model, specific proposals (e.g., correcting text, changing layout, adding images, etc.) are generated:

[0506] Analytics data: {'pagePath': ' / contact', 'sessions': 120}

[0507] Heatmap data: {'clicks': {'submit_button': 30, 'scroll_depth': '50%'}}

[0508] Feedback data: {'comments': ['The form is too long', 'It's hard to understand how to contact us']}

[0509] Once the proposed modifications are generated, the server automatically generates a virtual website that reflects the modifications. This virtual website is provided as a link that allows the administrator to preview the modifications. By clicking the link, the administrator can check the preview of the virtual website and approve the modifications.

[0510] Once the user approves the proposed changes, the device applies them to the actual website using automatically generated code, so the new content and layout are instantly reflected on the live website.

[0511] As a concrete example, consider a case where the bounce rate on a contact page is high. In this case, feedback includes comments such as "the form is too long" and "it's hard to understand how to make an inquiry." Heat map data also confirms that the number of clicks on the form submit button is low. By analyzing this data, the server generates improvement proposals such as "simplify the form" and "add explanations on how to make an inquiry." These are reflected on the virtual website, and after the operator checks the preview link and approves it, they are finally applied to the actual website.

[0512] In this way, this system provides a concrete method for operators of online shopping sites to update their sites efficiently and with high quality, thereby improving usability and site quality.

[0513] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0514] Step 1:

[0515] The server collects website visit data. Specifically, it uses the Google Analytics API to obtain data such as page views, number of visitors, and duration of visit. The input is a request to the Google Analytics API, and the output is the collected visit data. Data processing involves converting the obtained data into a unified format and saving it.

[0516] Step 2:

[0517] The server uses a heat map tool to collect data on users' click patterns and scrolling behavior. This allows it to understand which areas are frequently clicked and how far the user has scrolled. The input is a request to the heat map tool, and the output is heat map data. The data is then analyzed for calculation purposes, including information such as the number of clicks and scroll depth.

[0518] Step 3:

[0519] The server uses a feedback collection API to collect user comments and questions. This information is used to identify areas for usability improvement. The input is a request to the feedback collection API, and the output is the collected feedback data. Data processing involves organizing the text data and formatting it into a form suitable for analysis.

[0520] Step 4:

[0521] The server analyzes the collected data. It uses a generative AI model to perform a detailed analysis of the website's usability and content performance. It identifies pages with high bounce rates and short viewing times, and analyzes feedback comments and heat map data to find the causes. The inputs are visit data, heat map data, and feedback data, and the output is the analysis results. Data calculations include multivariate analysis and clustering.

[0522] Step 5:

[0523] The server automatically generates a modification plan based on the analysis results. A specific modification plan is generated by inputting a prompt statement to the generation AI model. The input is the analysis results and the prompt statement, and the output is the generated modification plan. Specifically, a prompt statement of the following format is input to the generation AI:

[0524] Analytics data: {'pagePath': ' / contact', 'sessions': 120}

[0525] Heatmap data: {'clicks': {'submit_button': 30, 'scroll_depth': '50%'}}

[0526] Feedback data: {'comments': ['The form is too long', 'It's hard to understand how to contact us']}

[0527] Step 6:

[0528] The server automatically generates a virtual website that reflects the generated modification proposal. This virtual website is used by the administrator to preview the modification proposal. The input is the generated modification proposal, and the output is the preview URL of the virtual website. Data processing involves generating HTML and CSS code based on the modification proposal.

[0529] Step 7:

[0530] The user checks the preview link of the virtual website provided by the server and approves the proposed modifications. The input is the preview URL of the virtual website, and the output is the user's approval. Specifically, the user checks the preview using a browser and clicks the approve button.

[0531] Step 8:

[0532] The device receives user approval and applies the generated code to the actual website. This allows the new content and layout to be instantly reflected on the actual website. The input is the user approval and the generated code, and the output is the updated website. The code application process is performed on the server side as a data calculation.

[0533] 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.

[0534] This paper describes a system that collects website visit data and user emotion data, generates optimal modification proposals, and reflects them in a virtual website. This system enables website operators to efficiently and efficiently update their websites with high quality, while taking user emotions into consideration. Below, we will create a program for this system and explain its processing in natural language.

[0535] 1. Collection of visit and sentiment data

[0536] The server uses the Google Analytics API to collect data such as website page views, number of visitors, and duration of visits. It also uses a heat map tool to obtain data on users' click patterns and scrolling behavior. It also uses an emotion engine that recognizes users' emotions to collect emotional data when users use the website.

[0537] 2. Data Analysis

[0538] The server analyzes the collected visit data and sentiment data. Generative AI analyzes this data comprehensively and performs a detailed evaluation of the website's usability and content performance. This analysis identifies pages with high drop-off rates and pages where users express negative sentiment.

[0539] 3. Content Generation

[0540] Based on the analysis results, the server identifies areas for improvement and automatically generates proposals (such as text improvements, layout changes, and image insertion). This generation process utilizes generative AI, which creates specific proposals based on the identified areas for improvement. Furthermore, it also takes into account the user's emotional data and generates customized proposals based on their emotions.

[0541] 4. Automatic generation of virtual websites

[0542] The server builds a virtual website based on the automatically generated revision proposals. This virtual website reflects the proposed changes, such as new text, layout, and images. It also provides a customized preview based on the user's emotions. A preview link for the virtual website is generated and sent to the website operator.

[0543] 5. User Acknowledgment and Implementation

[0544] The user can check the proposed changes by clicking the preview link of the virtual website provided. This preview allows the user to visually confirm how the proposed changes will actually be reflected on the website, and confirm that the suggestions based on user sentiment are reflected.

[0545] The user approves or requests modifications to the virtual website. The approved modification is confirmed as the final modification.

[0546] 6. Reflection on the actual website

[0547] After receiving user approval, the device applies the proposed modifications to the actual website using automatically generated code. This application process reflects the new text, layout, images, etc. on the actual website. Furthermore, customization based on the user's emotions is also taken into consideration.

[0548] Examples:

[0549] 1. Collection of visit and sentiment data

[0550] The server collects Google Analytics data over a period of time, visualizes click patterns on specific pages as heat maps, and uses an emotion engine to collect user emotions (e.g., satisfaction, dissatisfaction, joy, anger, etc.) in real time.

[0551] 2. Data Analysis

[0552] The server focuses on pages with high drop-off rates and analyzes the visit and sentiment data for those pages. For example, the analysis may reveal that "the drop-off rate is high on product detail pages, and users are dissatisfied."

[0553] 3. Content Generation

[0554] The server identifies specific areas for improvement, such as "the description text on the product detail page is insufficient" or "users are looking closely at the images but are not satisfied," and generates suggestions for improvements based on these. Specifically, it generates suggestions for adding more detailed description text or inserting high-resolution images.

[0555] 4. Automatic generation of virtual websites

[0556] The server then creates a virtual website that reflects the proposed modifications and sends a preview link to the operator, allowing the operator to see the customizations based on the user's emotions (for example, special offers to improve satisfaction).

[0557] 5. User Acknowledgment and Implementation

[0558] The user opens the preview link, checks the virtual website, and approves or requests modifications. Approved modifications are confirmed as the final modifications.

[0559] 6. Reflection on the actual website

[0560] The device then applies the proposed changes to the live website using automatically generated code, after which the product detail page will reflect the new text, images, and customizations, improving the user experience.

[0561] As described above, the present invention, which combines user emotion data, allows website operators to make more effective website improvements that take user emotions into consideration.

[0562] The processing flow will be explained below.

[0563] Step 1:

[0564] The server uses the Google Analytics API to collect data such as website page views, number of visitors, and length of stay, and then formats this data by period and page and stores it in a database.

[0565] Step 2:

[0566] The server uses the Heatmap Tool API to collect data on users' click patterns and scrolling behavior, which is then visualized and stored in a database.

[0567] Step 3:

[0568] The server collects comments and questions from users using inquiry forms and feedback functions. The collected text data is analyzed using natural language processing technology to extract frequently occurring problems and requests for improvement.

[0569] Step 4:

[0570] The server uses an emotion engine to collect user emotion data. It recognizes the emotions (e.g., satisfaction, dissatisfaction, joy, anger, etc.) of users when they use the website in real time and stores them in a database.

[0571] Step 5:

[0572] The server integrates the collected visit data and sentiment data and uses generative AI to perform detailed analysis of this data, specifically identifying pages with high dropout rates and pages where users express negative sentiment.

[0573] Step 6:

[0574] The server identifies areas for improvement based on the analysis results and automatically generates proposals (such as text improvements, layout changes, and image insertion) that also take into account user emotional data.

[0575] Step 7:

[0576] The server builds a virtual website based on the automatically generated modification proposals, generates a preview link for the virtual website, and sends it to the website operator. This preview also includes customizations based on the user's emotions.

[0577] Step 8:

[0578] The user checks the preview link of the virtual website provided and approves or requests corrections. The approved proposal is confirmed as the final modification content.

[0579] Step 9:

[0580] After receiving user approval, the device applies the proposed changes to the live website using automatically generated code, resulting in the live website being updated with the new text, layout, images, and customizations.

[0581] Through the above steps, the present invention automates website modifications, allowing website operators to update their website content efficiently and effectively. By combining user emotion data, the modifications will be more satisfying to users.

[0582] Example 2

[0583] 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."

[0584] Conventional website modification methods do not fully utilize user behavioral and emotional data, limiting the accuracy and effectiveness of modification proposals. It is particularly difficult to identify pages with high bounce rates or pages where users express negative emotions, and provide appropriate modification proposals. Furthermore, the process of reviewing modification proposals on a virtual website and obtaining user approval is time-consuming, requiring efficient operation.

[0585] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0586] In this invention, the server includes means for collecting website visit data and emotion data, means for analyzing the collected data and identifying improvements to the website, means for generating modification proposals based on the identified improvements, means for automatically generating a virtual website that reflects the generated modification proposals, means for providing a preview of the virtual website, and means for applying the modification proposals to the actual website after receiving user approval. This enables the generation of highly accurate modification proposals based on user behavior and emotion data, allowing operators to modify their websites efficiently and effectively.

[0587] A "website" is a collection of web pages that provide information or functionality available on the Internet.

[0588] "Visit data" refers to data about the behavior of users when they browse a website, such as page views, number of visitors, and length of stay.

[0589] "Emotional data" is data that indicates the emotional state (e.g., satisfaction, dissatisfaction, joy, anger) of a user when using a website.

[0590] A "heat map" is a data representation that visually shows users' click patterns and scrolling behavior.

[0591] A "virtual website" is a virtual website that reflects proposed modifications and is provided as a preview before the actual modifications are made.

[0592] "Preview" is a feature that allows you to visually check how the proposed changes will be reflected on the website before actually making the changes.

[0593] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and automatically generate specific renovation proposals.

[0594] "User" means a website operator or administrator who is responsible for reviewing the preview of the virtual website and approving or modifying the proposed changes.

[0595] A "server" is a computer system that collects and analyzes website data, generates modification proposals, and automatically generates virtual websites.

[0596] This invention relates to a system that collects website visit data and user emotion data, generates optimal modification proposals, and reflects them in a virtual website. This system enables website operators to efficiently and efficiently update their websites with high quality, while taking user emotions into consideration.

[0597] The server performs the process in the following procedure.

[0598] Collection of visit and sentiment data

[0599] The server automatically collects data such as website page views, number of visitors, and visit duration using the Google Analytics API. It also uses tools like Hotjar to obtain data on user click patterns and scrolling behavior using heat map tools. It also uses an emotion engine to collect emotional data (e.g., satisfaction, dissatisfaction, joy, anger) as users use the website. It uses the Emotion API to obtain this emotional data in real time and store it in a database.

[0600] Data analysis

[0601] The server integrates the collected visit data and sentiment data and performs a comprehensive analysis using a generative AI model. This involves using methods such as correlation analysis, regression analysis, and clustering to identify pages with particularly high drop-off rates and pages where users express negative sentiment. This analysis allows for a detailed evaluation of the site's usability and content performance.

[0602] Content generation

[0603] The server identifies areas for improvement based on the analysis results and automatically generates specific improvement proposals using a generative AI model. For example, it suggests text improvements, layout changes, image insertion, etc. for identified issues. It also considers user emotional data and creates customized improvement proposals based on emotions.

[0604] Automatic generation of virtual websites

[0605] The server then creates a virtual website based on the proposed changes, incorporating the new text, layout, and images, and generates a preview link that can be sent to the website owner, allowing them to visually inspect the proposed changes.

[0606] User approval and implementation

[0607] The user opens the provided preview link, checks the proposed modifications to the virtual website, and then approves the proposed modifications or requests modifications as necessary. The approved modifications are confirmed as the final modifications.

[0608] Reflection on the actual website

[0609] The device then applies the user-approved modifications to the actual website, using automatically generated code to update the website with new text, layout, and images, as well as customizations based on the user's emotions.

[0610] Examples:

[0611] Collection of visit and sentiment data

[0612] The server collects Google Analytics data for the past month and visualizes click patterns on product detail pages as heat maps. It also uses an emotion engine to collect user emotion data (e.g., satisfaction, dissatisfaction) in real time.

[0613] Data analysis

[0614] The server analyzes the visit data and emotion data on the product detail page and finds that "the dropout rate is high and users are dissatisfied."

[0615] Content generation

[0616] The server generates specific suggestions for modifications to the product detail page, such as adding detailed description text or inserting high-resolution images.

[0617] Automatic generation of virtual websites

[0618] The server then creates a virtual website that reflects the proposed changes and sends the operator a preview link, which includes customizations based on the user's emotions.

[0619] User approval and implementation

[0620] The user opens the preview link, checks the virtual website, and approves or requests modifications. Approved modifications are confirmed as the final modifications.

[0621] Reflection on the actual website

[0622] The device then uses the auto-generated code to apply the proposed changes to the live website, where the product detail page will then reflect the new text, images, and customizations, improving the user experience.

[0623] Example prompt sentence:

[0624] "Analyze 30 days of visit data and user sentiment data to generate specific improvement recommendations for product detail pages."

[0625] "Generate specific suggestions to improve the text and images on pages with high bounce rates and dissatisfied comments."

[0626] As described above, the present invention is a system that enables website operators to efficiently and effectively improve their websites by utilizing user behavior and emotion data.

[0627] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0628] Step 1: Collect visit and sentiment data

[0629] The server uses the Google Analytics API to obtain data such as website page views, number of visitors, and duration of visits. Google Analytics account information and API authentication key are required as input. The obtained data is saved in a database (output).

[0630] The server uses a heatmap tool (e.g., Hotjar) to collect data on users' click patterns and scrolling behavior. The inputs require Hotjar account information and the URLs of the pages to be collected. The collected data is visualized and stored in a database (output).

[0631] The server uses the Emotion API to collect user emotion data in real time. The input requires the Emotion API authentication key and the user's image or text data. The acquired emotion data is stored in a database (output).

[0632] Step 2: Integrate the data

[0633] The server aggregates visit data, heatmap data, and sentiment data collected from databases, and requires multiple datasets from each data source as input.

[0634] The integration process merges data based on the same timestamp or user ID to create a single dataset (output), allowing information from different data sources to be analyzed in a unified manner.

[0635] Step 3: Analyze the data

[0636] The server uses the integrated dataset and performs analysis using a generative AI model, with the integrated dataset and the analytical model as inputs.

[0637] The generative AI model performs correlation analysis, regression analysis, and clustering to evaluate the usability and content performance of each page, and outputs an analysis report that identifies pages with high dropoff rates and pages that evoke negative sentiment.

[0638] Step 4: Identify areas for improvement

[0639] The server identifies specific improvements based on the analysis result report, and the analysis result report is used as input.

[0640] For example, based on information such as "the bounce rate on a particular product detail page is high, causing users to feel dissatisfied," areas for improvement are listed (output).

[0641] Step 5: Generate renovation proposals

[0642] The server generates a proposed fix based on the identified improvements, taking the list of improvements as input.

[0643] Using a generative AI model, specific improvement proposals (e.g., text improvement, layout changes, image addition) are automatically generated. The output is a specific improvement proposal.

[0644] Step 6: Automatically Generate Virtual Websites

[0645] The server constructs a virtual website based on the generated modification proposals. The input is a set of modification proposals.

[0646] The system builds a virtual website based on the automatically generated revision proposals, and then generates a preview link and sends it to the website operator (output).

[0647] Step 7: User Acceptance and Implementation

[0648] The user opens the provided preview link and checks the proposed modifications to the virtual website. The input is the preview link.

[0649] The user either approves the proposed revision or requests a revision. The approved revision is saved in the database as the final version (output).

[0650] Step 8: Applying to the actual website

[0651] The terminal applies the proposed modifications approved by the user to the actual website, and the final approved modification is used as input.

[0652] The new text, layout, and images are applied to the website using the automatically generated code, and finally, the changes are verified (output).

[0653] Through these steps, the present invention utilizes user behavioral and emotional data to enable the generation and implementation of highly accurate modification proposals.

[0654] (Application example 2)

[0655] 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."

[0656] Conventional website modification methods do not take user emotional data into account, making it difficult to improve usability and modify websites effectively. Furthermore, the lack of a system that can integrate and analyze emotional data and visitor data makes it difficult to understand users' true needs and dissatisfaction. Furthermore, the lack of virtual website generation and preview functionality means that site managers are unable to visually confirm the effects of proposed modifications.

[0657] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0658] In this invention, the server includes means for collecting website visit data and user emotion data, means for analyzing the collected visit data and emotion data to identify site improvements, means for generating modification proposals based on the identified improvements, means for automatically generating a virtual website that reflects the generated modification proposals, means for providing a preview of the virtual website, means for applying the modification proposals to the actual website after receiving user approval, means for collecting user emotion data using a smartphone or a head-mounted display, and means for generating modification proposals using a generative AI model based on the collected data, thereby enabling effective site modifications that take user emotions into consideration.

[0659] "Website visit data" refers to behavioral data such as page views, time spent, and click patterns when a user accesses a website.

[0660] "User emotional data" refers to data on the emotional state of a user, derived from facial expressions, tone of voice, etc., collected using emotion recognition technology.

[0661] "Means of collection" refers to the technical means for acquiring data using servers, sensors, etc.

[0662] "Analytical means" means the technical means used to analyze collected data using statistical or machine learning techniques to derive patterns and trends.

[0663] "Means for identifying areas for improvement" refers to technical means for identifying problems with the usability and performance of a website from the analysis results and identifying areas that need improvement.

[0664] "Means for generating improvement proposals" refers to technical means for creating specific improvement measures (such as changes to text, images, or layout) based on the identified improvement points.

[0665] "Means for automatically generating a virtual website" refers to a technical means for constructing a virtual website based on the generated modification plan.

[0666] "Means for providing a preview" refers to a technical means for presenting proposed modifications to a virtual website so that the operator can visually check them.

[0667] "Means for applying proposed modifications to the actual website after receiving user approval" refers to the technical means for reflecting proposed modifications that have been approved by the operator on the actual website.

[0668] A "smartphone" is a portable information terminal that has Internet access, various sensor functions, and can run a variety of applications.

[0669] A "head-mounted display" is a display device that a user wears on their head to display visual information.

[0670] A "generative AI model" is an artificial intelligence technology that uses machine learning algorithms learned from large amounts of data to generate new data and content.

[0671] This invention is a system that generates optimal modification plans using website visit data and user emotion data, and reflects the modification plans in a virtual website. This system includes the following means.

[0672] 1. Collection of visit and sentiment data

[0673] The server collects user emotional data using smartphones and head-mounted displays. Specifically, it collects the user's facial expressions and tone of voice through the smartphone's camera and microphone, and analyzes them using emotion recognition technology. It also uses the Google Analytics API to collect website visit data (page views, number of visitors, duration of visits, click patterns, etc.).

[0674] 2. Data Analysis

[0675] The server integrates and analyzes the collected visit data and sentiment data, using TensorFlow and other machine learning algorithms to evaluate the usability and content performance of websites and identify problems on specific pages, particularly those with high bounce rates or negative user sentiment.

[0676] 3. Generation of renovation proposals

[0677] The server uses a generative AI model to automatically generate suggested improvements based on the analysis results, such as revising text, replacing images, or presenting special offers. For example, if a page's explanatory text is insufficient, a suggestion to add more detailed information will be generated.

[0678] 4. Automatic generation of virtual websites

[0679] The server creates a virtual website based on the proposed changes, including new text, images, and layouts that reflect the proposed changes, and generates a preview link for the virtual website and sends it to the operator.

[0680] 5. User Acknowledgment and Implementation

[0681] The user checks the preview link of the provided virtual website and visually confirms the contents of the proposed modifications. They then approve or request modifications, and the approved modification is confirmed as the final modification.

[0682] 6. Reflection on the actual website

[0683] After receiving user approval, the device uses automatically generated code to apply the proposed changes to the actual website, improving the user experience by incorporating new text, layout, images, and more.

[0684] Specific examples

[0685] When a customer visited a certain product page, emotions such as "dissatisfaction" and "anger" were detected via camera. Furthermore, Google Analytics data revealed that the page had a high bounce rate. Based on this data, the invented system automatically generated revisions to the product description text, added high-resolution images, and presented special offers, which were then reflected on the virtual website. The operator approved these revisions and applied them to the actual online shopping site. As a result, the bounce rate on the page decreased and user satisfaction improved.

[0686] Prompt Sentence Examples

[0687] "Collect visit data and user sentiment data for 7 days. Obtain click patterns and time spent on specific pages from Google Analytics, and collect sentiment data in real time using users' facial expressions and tone of voice. Analyze the collected data and automatically generate improvement proposals using a generative AI model. Generate a preview link of the virtual website and send it to the operator."

[0688] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0689] Step 1:

[0690] The server collects user emotional data using a smartphone or head-mounted display. The input includes the user's facial expressions and tone of voice captured by the smartphone's camera and microphone, which are then analyzed using emotion recognition technology to output data representing the user's emotional state (e.g., joy, dissatisfaction, anger, etc.).

[0691] Step 2:

[0692] The server uses the Google Analytics API to collect website visit data (page views, number of visitors, duration, click patterns, etc.) The server requires a Google Analytics API key and website URL as input, and using these, the server sends a request to the API, which outputs behavioral data such as page views and number of visitors.

[0693] Step 3:

[0694] The server integrates and analyzes the emotion data and visit data collected in steps 1 and 2. Emotion data and visit data are given as input, and by analyzing them using a machine learning algorithm (e.g., TensorFlow), an evaluation result of the website's usability and content performance is output. This evaluation result includes pages with high dropout rates and pages where users express negative emotions.

[0695] Step 4:

[0696] The server uses the generative AI model to automatically generate improvement proposals based on the analysis results from step 3. The analysis results are input, and the generative AI model generates improvement proposals (e.g., revising text, replacing images, presenting special offers, etc.) based on the analysis results, and specific improvement proposals are output.

[0697] Step 5:

[0698] The server automatically generates a virtual website that reflects the proposed modifications. The server takes the proposed modifications as input, automatically generates a page that reflects the proposed modifications in the virtual website template, and outputs a preview link for the virtual website.

[0699] Step 6:

[0700] The user checks the preview link of the virtual website provided. The preview link is given as input, and the user visually checks the pages of the virtual website, checks the contents of the proposed modifications, and approves or requests modifications. At this stage, the approved modifications are finalized.

[0701] Step 7:

[0702] The device applies the user-approved modifications to the actual website. It takes the approved modifications as input and processes them on the website using automatically generated code. As a result, new text, layout, images, etc. are reflected on the actual website, improving the user experience.

[0703] 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.

[0704] 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.

[0705] 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.

[0706] [Third embodiment]

[0707] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0708] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0709] 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).

[0710] 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.

[0711] 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.

[0712] 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).

[0713] 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.

[0714] 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.

[0715] 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.

[0716] 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.

[0717] 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.

[0718] 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."

[0719] This paper describes a system that collects website visitation data, generates optimal improvement plans, and reflects them in a virtual website. This system enables website operators to efficiently update their websites with high quality. Below, we will create a program for this system and explain its processing in natural language.

[0720] 1. Collection of visit data

[0721] The server uses the Google Analytics API to collect data such as website page views, number of visitors, and duration of visits. It also uses heat map tools to obtain data on user click patterns and scrolling behavior. It also uses contact forms and feedback functions to collect user comments and questions.

[0722] 2. Data Analysis

[0723] Based on the collected data, the server uses generative AI to perform a detailed analysis of the website's usability and content performance, identifying pages with high bounce rates and short viewing times and analyzing the causes.

[0724] 3. Content Generation

[0725] Based on the analysis results, the server identifies areas for improvement and automatically generates proposed modifications (text improvements, layout changes, image insertion, etc.) These modifications are aimed at improving usability and conversion rates.

[0726] 4. Automatic generation of virtual websites

[0727] The server creates a virtual renewal website based on the automatically generated revision plan, generates a preview of the virtual website, and provides its URL to the operator.

[0728] 5. User Acknowledgment and Implementation

[0729] The user sees a preview of the virtual website and approves the proposed changes.

[0730] After receiving user approval, the device applies the proposed modifications to the actual website using automatically generated code, so the new content and layout are instantly reflected on the real website.

[0731] Examples:

[0732] 1. Collection of visit data

[0733] The server uses the Google Analytics API to collect data on page views, number of visitors and time spent on the site over a period of time, as well as a heatmap tool to visualize click patterns on specific pages and collect comments from feedback forms.

[0734] 2. Data Analysis

[0735] The server analyzes data on pages with particularly high bounce rates, and then analyzes feedback comments and heat map data to identify the cause. For example, if the analysis shows that the bounce rate is high on the inquiry page, feedback such as "the form is too long" or "it's difficult to understand how to make an inquiry" can be extracted as the cause.

[0736] 3. Content Generation

[0737] Based on the analysis results, the server automatically generates improvement proposals such as "simplifying the form" and "adding an explanation of how to make an inquiry." It also generates appropriate images and guidance text.

[0738] 4. Automatic generation of virtual websites

[0739] The server creates a virtual renewal website incorporating the proposed modifications and sends a preview link to the operator.

[0740] 5. User Acknowledgment and Implementation

[0741] Users can open the preview link, view the virtual redesigned website, and approve the proposed changes.

[0742] After receiving user approval, the device applies the automatically generated code to the actual website and updates it, publishing a new page with a simplified form and additional instructions on how to contact the website.

[0743] As described above, the present invention provides a specific method for website operators to update their websites efficiently and with high quality, thereby improving usability and website quality.

[0744] The processing flow will be explained below.

[0745] Step 1:

[0746] The server uses the Google Analytics API to collect data such as website page views, number of visitors, and duration of visits. This allows basic website visit data to be obtained. This data is formatted by period and page and stored in a database.

[0747] Step 2:

[0748] The server uses the Heatmap Tool API to collect data on users' click patterns and scrolling behavior. Specifically, it visualizes user behavior on a web page and obtains that data. This makes it clear which areas of the page users are interested in. This data is also stored in a database.

[0749] Step 3:

[0750] The server collects comments and questions from users using inquiry forms and feedback functions. This text data is analyzed using natural language processing technology to extract frequently occurring problems and requests for improvement. The extracted results are stored in a database.

[0751] Step 4:

[0752] The server integrates the collected Google Analytics data and heat map data and performs a comprehensive analysis using generative AI. This analysis reveals the causes of high bounce rates on specific pages and usability issues.

[0753] Step 5:

[0754] Based on the analysis results, the server generates proposals for improvements, such as the text content of the pages that need to be improved, areas for layout improvement, and the addition or deletion of images. This generation process utilizes generative AI, which automatically generates specific proposals for improvements based on the areas that need improvement.

[0755] Step 6:

[0756] The server creates a virtual website based on the automatically generated revision proposals, which reflects the proposed changes, such as new text, layout, and images, and generates a preview link for the virtual website and sends it to the website operator.

[0757] Step 7:

[0758] The user can check the proposed changes by clicking the preview link of the virtual website provided. This preview allows the user to visually confirm how the proposed changes will actually be reflected on the website.

[0759] Step 8:

[0760] The user approves or requests modifications to the virtual website. The approved modification is confirmed as the final modification.

[0761] Step 9:

[0762] After receiving user approval, the device applies the proposed modifications to the actual website using automatically generated code, which reflects the new text, layout, and image content on the actual website.

[0763] Through the above steps, the present invention automates website modifications, allowing website operators to update the content of their websites efficiently and effectively.

[0764] Example 1

[0765] 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."

[0766] In the traditional website improvement process, collecting and analyzing visitor data and generating improvement proposals requires a significant amount of time and effort. Furthermore, applying the improvement proposals requires manual operations, which is inefficient and often results in inconsistent update quality. Therefore, a method for website operators to update their sites efficiently and with high quality is needed.

[0767] 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.

[0768] In this invention, the server includes means for collecting website visit data, means for analyzing the collected data and identifying improvements to the website, means for generating modification proposals using a generative AI model based on the identified improvements, means for automatically generating a virtual website that reflects the generated modification proposals, means for providing a preview of the virtual website, and means for applying the modification proposals to the actual website after receiving user approval, thereby enabling website operators to update their websites efficiently and with high quality.

[0769] A "website" is an information providing system that is published on the Internet and consists of multiple web pages that users can access via a browser.

[0770] "Visit Data" refers to records of access to a website, including various statistical information such as page views, number of visitors, duration of visit, click patterns, and scrolling behavior.

[0771] "Analysis" is the process of processing and analyzing collected visit data to identify specific performance indicators and issues.

[0772] "Areas for Improvement" means areas that need to be improved to improve the efficiency and usability of the website as identified through the analysis.

[0773] A "generative AI model" is an algorithm that uses machine learning and generative AI technology to automatically generate suggested text, image, and layout modifications based on collected data and analysis results.

[0774] "Proposed Fixes" refers to specific changes or correction plans proposed to address improvements identified using generative AI models.

[0775] A "virtual website" is a test site that is temporarily constructed to reflect proposed modifications to an actual website and allow users to check them in advance.

[0776] A "preview" is a display screen of a virtual website that allows users to check in advance the appearance and functionality of the website after the proposed modifications are applied.

[0777] "User" means the person or organization that manages and operates the website and is the entity that gives final approval to the proposed modifications.

[0778] A "means" refers to a method, technique, device, or part of a system used to achieve a particular purpose.

[0779] This invention relates to a system that collects website visitation data, analyzes that data, and generates optimal improvement plans. This system enables website operators to update their websites efficiently and with high quality.

[0780] System Overview

[0781] The basic configuration of this system is as follows: The system is composed of a server, terminals, and users, and each component operates in cooperation with each other.

[0782] 1. Collection of visit data

[0783] The server uses the Google Analytics API to collect data such as website page views, number of visitors, and duration of visits. It also uses heat map tools to obtain data on user click patterns and scrolling behavior. It also uses contact forms and feedback functions to collect user comments and questions.

[0784] 2. Data Analysis

[0785] Based on the collected data, the server uses generative AI to perform a detailed analysis of the website's usability and content performance, identifying pages with high bounce rates and short viewing times and analyzing the causes.

[0786] 3. Content Generation

[0787] Based on the analysis results, the server identifies areas for improvement and automatically generates proposed modifications (text improvements, layout changes, image insertion, etc.) using a generative AI model.These proposed modifications aim to improve usability and conversion rates.

[0788] 4. Automatic generation of virtual websites

[0789] The server builds a virtual renewal website based on the automatically generated revision plan, generates a preview of the virtual website, and provides its URL to the operator (user).

[0790] 5. User Acknowledgment and Implementation

[0791] The user sees a preview of the virtual website and approves the proposed changes.

[0792] After receiving user approval, the device applies the proposed modifications to the actual website using automatically generated code, so the new content and layout are instantly reflected on the real website.

[0793] Specific examples

[0794] 1. Collection of visit data

[0795] The server uses the Google Analytics API to collect data on page views, number of visitors and time spent on the site over a period of time, as well as a heatmap tool to visualize click patterns on specific pages and collect comments from feedback forms.

[0796] 2. Data Analysis

[0797] The server analyzes data on pages with particularly high bounce rates, and then analyzes feedback comments and heat map data to identify the cause. For example, if the analysis shows that the bounce rate is high on the inquiry page, feedback such as "the form is too long" or "it's difficult to understand how to make an inquiry" can be extracted as the cause.

[0798] 3. Content Generation

[0799] Based on the analysis results, the server automatically generates improvement proposals such as "simplifying the form" and "adding an explanation of how to make an inquiry." It also generates appropriate images and guidance text.

[0800] 4. Automatic generation of virtual websites

[0801] The server creates a virtual renewal website incorporating the proposed modifications and sends a preview link to the operator.

[0802] 5. User Acknowledgment and Implementation

[0803] Users can open the preview link, view the virtual redesigned website, and approve the proposed changes.

[0804] After receiving user approval, the device applies the automatically generated code to the actual website and updates it, publishing a new page with a simplified form and additional instructions on how to contact the website.

[0805] An example of an input prompt for a generative AI model is:

[0806] Input Prompt: "Analyze the usability and content performance of our website using data collected from the Google Analytics API and heatmap tools. Generate recommendations for improving pages with high bounce rates and poor performance."

[0807] Example response: "We're seeing high bounce rates on your contact page, so we suggest simplifying the form and adding detailed instructions on how to contact us. We'll also add appropriate images to make it easier to understand visually."

[0808] By the above means, the present invention provides a specific method for website operators to update their websites efficiently and with high quality, thereby improving usability and website quality.

[0809] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0810] Step 1: Collect visit data

[0811] The server sends a "GET" request to the Google Analytics API to retrieve data such as website page views, number of visitors, and time spent. The request includes any necessary authentication information and the period of time to retrieve. The input data is the Google Analytics API endpoint, and the output data is the collected statistics on page views, number of visitors, and time spent.

[0812] The server uses the heatmap tool's API to collect users' click patterns and scrolling behavior on a particular web page. The input data is the heatmap tool's endpoint, and the output data is a record of the click patterns and scrolling behavior.

[0813] The server collects user comments and questions using the inquiry form and feedback function installed on the website. The input data is text information from the inquiry form and feedback tool, and the output data is a list of comments and questions.

[0814] Step 2: Analyze the data

[0815] The server cleans the collected data and converts it into the appropriate format. Specifically, it executes data cleansing scripts, imputes missing values, and unifies data types. The input data is the collected raw data, and the output data is the cleaned data.

[0816] The server analyzes the formatted data using a generative AI model to identify pages with high bounce rates and pages with short viewing times. The input data is the formatted clean data, and the output data is a list of identified problem pages.

[0817] The server inputs the feedback comments into a text mining tool to extract key issues. The input data are the user feedback comments, and the output data is a list of extracted key issues.

[0818] Step 3: Generate content

[0819] The server identifies appropriate improvements based on the analysis results. Using a generative AI model, it sends prompts such as "List specific improvements." The input data is the analysis results, and the output data is a list of specific improvements.

[0820] The server automatically generates improvement proposals based on the identified improvements. Specifically, improvement proposals such as "simplify the form" and "add an explanation of how to contact us" are created using a generative AI model. The input data is a list of improvements, and the output data is a list of improvement proposals.

[0821] The server generates new content (text, images) as needed. It sends a prompt to the generative AI model saying, "Generate explanatory text for the inquiry page," and uses the resulting text. The input data is the prompt, and the output data is the new content (text and images).

[0822] Step 4: Automatically generate a virtual website

[0823] The server incorporates the automatically generated modification suggestions into the existing website code. The input data is a list of modification suggestions, and the output data is the website code with the modification suggestions incorporated.

[0824] The server builds a virtual website that reflects the proposed modifications and hosts it on a temporary preview server so that users can check it. The input data is the code incorporating the proposed modifications, and the output data is the preview virtual website.

[0825] The server generates a preview link of the virtual website and provides it to the user via email or dashboard. The input data is the URL of the virtual website, and the output data is the preview link.

[0826] Step 5: User Acceptance and Implementation

[0827] The user opens the preview link sent from the server and checks the virtual website. The input data is the preview link, and the output data is the user's evaluation and feedback.

[0828] If the user is satisfied with the preview, he or she clicks a button to approve the proposed modifications. The input data is the preview screen, and the output data is the approval information.

[0829] The terminal applies the automatically generated code to the actual website after receiving the user's approval. The input data is the approved code, and the output data is the website with the proposed modifications applied.

[0830] (Application example 1)

[0831] 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."

[0832] Improving user experience and optimizing conversion rates are extremely important issues in modern online shopping site operations. Improving usability and placing appropriate content requires effective collection and analysis of visit data, as well as rapid site modifications. However, these processes are time-consuming and require specialized knowledge, placing a heavy burden on many site operators. Therefore, there is a need for automation technology to achieve efficient, high-quality site modifications.

[0833] 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.

[0834] In this invention, the server includes means for collecting website visit data, means for analyzing the collected data and identifying improvements to the website, means for generating a modification plan based on the identified improvements, means for automatically generating a virtual website that reflects the generated modification plan, means for providing a preview of the virtual website, means for applying the modification plan to the actual website after receiving user approval, and means for notifying the user of the modification plan to a user terminal and providing a preview link until the operator approves it. This enables online shopping website operators to perform site modifications efficiently and with high quality, improving the user experience and optimizing the conversion rate.

[0835] "Visitation Data" means data about website visitors, such as page views, number of visitors, duration of visits, click patterns, and scrolling behavior.

[0836] "Analysis" is the process of analyzing collected data in detail to identify specific trends or problems.

[0837] "Areas for improvement" are areas that need to be fixed to improve the website's usability and conversion rate.

[0838] The "improvement proposal" is a proposal generated based on the improvement points, proposing specific improvement methods such as correcting text, changing the layout, and adding images.

[0839] A "virtual website" is a website in a virtual environment that reflects the generated modification proposal.

[0840] "Preview" means a display provided to allow the operator to check the generated virtual website in advance.

[0841] "User approval" refers to the act of the operator reviewing the generated modification plan and approving its application to the actual website.

[0842] "Automatic generation" refers to the process in which a system automatically generates data without manual intervention, using generative AI or other technologies.

[0843] A "heat map" is a tool that visually displays users' click patterns and scrolling depth.

[0844] "Natural language processing technology" is a computer science technology for analyzing text data and understanding its meaning and intent.

[0845] This invention is a system that collects website visitation data, generates optimal modification plans, and reflects them in a virtual website. This system can be used particularly by operators of online shopping sites to perform efficient, high-quality site modifications. A specific embodiment of this system is described below.

[0846] First, the server collects website visit data. Specifically, it uses the Google Analytics API to obtain data such as page views, number of visitors, and duration of visit. It also uses a heat map tool to collect data on user click patterns and scrolling behavior. Finally, it uses a feedback collection API to collect user comments and questions.

[0847] The server then analyzes the collected data, using generative AI models to perform detailed analysis of the website's usability and content performance. Pages with high drop-off rates and short viewing times are identified, and feedback comments and heat map data are also analyzed to identify the causes.

[0848] The server then automatically generates a proposal for modification based on the analysis results. By inputting the following prompts to the generative AI model, specific proposals (e.g., correcting text, changing layout, adding images, etc.) are generated:

[0849] Analytics data: {'pagePath': ' / contact', 'sessions': 120}

[0850] Heatmap data: {'clicks': {'submit_button': 30, 'scroll_depth': '50%'}}

[0851] Feedback data: {'comments': ['The form is too long', 'It's hard to understand how to contact us']}

[0852] Once the proposed modifications are generated, the server automatically generates a virtual website that reflects the modifications. This virtual website is provided as a link that allows the administrator to preview the modifications. By clicking the link, the administrator can check the preview of the virtual website and approve the modifications.

[0853] Once the user approves the proposed changes, the device applies them to the actual website using automatically generated code, so the new content and layout are instantly reflected on the live website.

[0854] As a concrete example, consider a case where the bounce rate on a contact page is high. In this case, feedback includes comments such as "the form is too long" and "it's hard to understand how to make an inquiry." Heat map data also confirms that the number of clicks on the form submit button is low. By analyzing this data, the server generates improvement proposals such as "simplify the form" and "add explanations on how to make an inquiry." These are reflected on the virtual website, and after the operator checks the preview link and approves it, they are finally applied to the actual website.

[0855] In this way, this system provides a concrete method for operators of online shopping sites to update their sites efficiently and with high quality, thereby improving usability and site quality.

[0856] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0857] Step 1:

[0858] The server collects website visit data. Specifically, it uses the Google Analytics API to obtain data such as page views, number of visitors, and duration of visit. The input is a request to the Google Analytics API, and the output is the collected visit data. Data processing involves converting the obtained data into a unified format and saving it.

[0859] Step 2:

[0860] The server uses a heat map tool to collect data on users' click patterns and scrolling behavior. This allows it to understand which areas are frequently clicked and how far the user has scrolled. The input is a request to the heat map tool, and the output is heat map data. The data is then analyzed for calculation purposes, including information such as the number of clicks and scroll depth.

[0861] Step 3:

[0862] The server uses a feedback collection API to collect user comments and questions. This information is used to identify areas for usability improvement. The input is a request to the feedback collection API, and the output is the collected feedback data. Data processing involves organizing the text data and formatting it into a form suitable for analysis.

[0863] Step 4:

[0864] The server analyzes the collected data. It uses a generative AI model to perform a detailed analysis of the website's usability and content performance. It identifies pages with high bounce rates and short viewing times, and analyzes feedback comments and heat map data to find the causes. The inputs are visit data, heat map data, and feedback data, and the output is the analysis results. Data calculations include multivariate analysis and clustering.

[0865] Step 5:

[0866] The server automatically generates a modification plan based on the analysis results. A specific modification plan is generated by inputting a prompt statement to the generation AI model. The input is the analysis results and the prompt statement, and the output is the generated modification plan. Specifically, a prompt statement of the following format is input to the generation AI:

[0867] Analytics data: {'pagePath': ' / contact', 'sessions': 120}

[0868] Heatmap data: {'clicks': {'submit_button': 30, 'scroll_depth': '50%'}}

[0869] Feedback data: {'comments': ['The form is too long', 'It's hard to understand how to contact us']}

[0870] Step 6:

[0871] The server automatically generates a virtual website that reflects the generated modification proposal. This virtual website is used by the administrator to preview the modification proposal. The input is the generated modification proposal, and the output is the preview URL of the virtual website. Data processing involves generating HTML and CSS code based on the modification proposal.

[0872] Step 7:

[0873] The user checks the preview link of the virtual website provided by the server and approves the proposed modifications. The input is the preview URL of the virtual website, and the output is the user's approval. Specifically, the user checks the preview using a browser and clicks the approve button.

[0874] Step 8:

[0875] The device receives user approval and applies the generated code to the actual website. This allows the new content and layout to be instantly reflected on the actual website. The input is the user approval and the generated code, and the output is the updated website. The code application process is performed on the server side as a data calculation.

[0876] 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.

[0877] This paper describes a system that collects website visit data and user emotion data, generates optimal modification proposals, and reflects them in a virtual website. This system enables website operators to efficiently and efficiently update their websites with high quality, while taking user emotions into consideration. Below, we will create a program for this system and explain its processing in natural language.

[0878] 1. Collection of visit and sentiment data

[0879] The server uses the Google Analytics API to collect data such as website page views, number of visitors, and duration of visits. It also uses a heat map tool to obtain data on users' click patterns and scrolling behavior. It also uses an emotion engine that recognizes users' emotions to collect emotional data when users use the website.

[0880] 2. Data Analysis

[0881] The server analyzes the collected visit data and sentiment data. Generative AI analyzes this data comprehensively and performs a detailed evaluation of the website's usability and content performance. This analysis identifies pages with high drop-off rates and pages where users express negative sentiment.

[0882] 3. Content Generation

[0883] Based on the analysis results, the server identifies areas for improvement and automatically generates proposals (such as text improvements, layout changes, and image insertion). This generation process utilizes generative AI, which creates specific proposals based on the identified areas for improvement. Furthermore, it also takes into account the user's emotional data and generates customized proposals based on their emotions.

[0884] 4. Automatic generation of virtual websites

[0885] The server builds a virtual website based on the automatically generated revision proposals. This virtual website reflects the proposed changes, such as new text, layout, and images. It also provides a customized preview based on the user's emotions. A preview link for the virtual website is generated and sent to the website operator.

[0886] 5. User Acknowledgment and Implementation

[0887] The user can check the proposed changes by clicking the preview link of the virtual website provided. This preview allows the user to visually confirm how the proposed changes will actually be reflected on the website, and confirm that the suggestions based on user sentiment are reflected.

[0888] The user approves or requests modifications to the virtual website. The approved modification is confirmed as the final modification.

[0889] 6. Reflection on the actual website

[0890] After receiving user approval, the device applies the proposed modifications to the actual website using automatically generated code. This application process reflects the new text, layout, images, etc. on the actual website. Furthermore, customization based on the user's emotions is also taken into consideration.

[0891] Examples:

[0892] 1. Collection of visit and sentiment data

[0893] The server collects Google Analytics data over a period of time, visualizes click patterns on specific pages as heat maps, and uses an emotion engine to collect user emotions (e.g., satisfaction, dissatisfaction, joy, anger, etc.) in real time.

[0894] 2. Data Analysis

[0895] The server focuses on pages with high drop-off rates and analyzes the visit and sentiment data for those pages. For example, the analysis may reveal that "the drop-off rate is high on product detail pages, and users are dissatisfied."

[0896] 3. Content Generation

[0897] The server identifies specific areas for improvement, such as "the description text on the product detail page is insufficient" or "users are looking closely at the images but are not satisfied," and generates suggestions for improvements based on these. Specifically, it generates suggestions for adding more detailed description text or inserting high-resolution images.

[0898] 4. Automatic generation of virtual websites

[0899] The server then creates a virtual website that reflects the proposed modifications and sends a preview link to the operator, allowing the operator to see the customizations based on the user's emotions (for example, special offers to improve satisfaction).

[0900] 5. User Acknowledgment and Implementation

[0901] The user opens the preview link, checks the virtual website, and approves or requests modifications. Approved modifications are confirmed as the final modifications.

[0902] 6. Reflection on the actual website

[0903] The device then applies the proposed changes to the live website using automatically generated code, after which the product detail page will reflect the new text, images, and customizations, improving the user experience.

[0904] As described above, the present invention, which combines user emotion data, allows website operators to make more effective website improvements that take user emotions into consideration.

[0905] The processing flow will be explained below.

[0906] Step 1:

[0907] The server uses the Google Analytics API to collect data such as website page views, number of visitors, and length of stay, and then formats this data by period and page and stores it in a database.

[0908] Step 2:

[0909] The server uses the Heatmap Tool API to collect data on users' click patterns and scrolling behavior, which is then visualized and stored in a database.

[0910] Step 3:

[0911] The server collects comments and questions from users using inquiry forms and feedback functions. The collected text data is analyzed using natural language processing technology to extract frequently occurring problems and requests for improvement.

[0912] Step 4:

[0913] The server uses an emotion engine to collect user emotion data. It recognizes the emotions (e.g., satisfaction, dissatisfaction, joy, anger, etc.) of users when they use the website in real time and stores them in a database.

[0914] Step 5:

[0915] The server integrates the collected visit data and sentiment data and uses generative AI to perform detailed analysis of this data, specifically identifying pages with high dropout rates and pages where users express negative sentiment.

[0916] Step 6:

[0917] The server identifies areas for improvement based on the analysis results and automatically generates proposals (such as text improvements, layout changes, and image insertion) that also take into account user emotional data.

[0918] Step 7:

[0919] The server builds a virtual website based on the automatically generated modification proposals, generates a preview link for the virtual website, and sends it to the website operator. This preview also includes customizations based on the user's emotions.

[0920] Step 8:

[0921] The user checks the preview link of the virtual website provided and approves or requests corrections. The approved proposal is confirmed as the final modification content.

[0922] Step 9:

[0923] After receiving user approval, the device applies the proposed changes to the live website using automatically generated code, resulting in the live website being updated with the new text, layout, images, and customizations.

[0924] Through the above steps, the present invention automates website modifications, allowing website operators to update their website content efficiently and effectively. By combining user emotion data, the modifications will be more satisfying to users.

[0925] Example 2

[0926] 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."

[0927] Conventional website modification methods do not fully utilize user behavioral and emotional data, limiting the accuracy and effectiveness of modification proposals. It is particularly difficult to identify pages with high bounce rates or pages where users express negative emotions, and provide appropriate modification proposals. Furthermore, the process of reviewing modification proposals on a virtual website and obtaining user approval is time-consuming, requiring efficient operation.

[0928] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0929] In this invention, the server includes means for collecting website visit data and emotion data, means for analyzing the collected data and identifying improvements to the website, means for generating modification proposals based on the identified improvements, means for automatically generating a virtual website that reflects the generated modification proposals, means for providing a preview of the virtual website, and means for applying the modification proposals to the actual website after receiving user approval. This enables the generation of highly accurate modification proposals based on user behavior and emotion data, allowing operators to modify their websites efficiently and effectively.

[0930] A "website" is a collection of web pages that provide information or functionality available on the Internet.

[0931] "Visit data" refers to data about the behavior of users when they browse a website, such as page views, number of visitors, and length of stay.

[0932] "Emotional data" is data that indicates the emotional state (e.g., satisfaction, dissatisfaction, joy, anger) of a user when using a website.

[0933] A "heat map" is a data representation that visually shows users' click patterns and scrolling behavior.

[0934] A "virtual website" is a virtual website that reflects proposed modifications and is provided as a preview before the actual modifications are made.

[0935] "Preview" is a feature that allows you to visually check how the proposed changes will be reflected on the website before actually making the changes.

[0936] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and automatically generate specific renovation proposals.

[0937] "User" means a website operator or administrator who is responsible for reviewing the preview of the virtual website and approving or modifying the proposed changes.

[0938] A "server" is a computer system that collects and analyzes website data, generates modification proposals, and automatically generates virtual websites.

[0939] This invention relates to a system that collects website visit data and user emotion data, generates optimal modification proposals, and reflects them in a virtual website. This system enables website operators to efficiently and efficiently update their websites with high quality, while taking user emotions into consideration.

[0940] The server performs the process in the following procedure.

[0941] Collection of visit and sentiment data

[0942] The server automatically collects data such as website page views, number of visitors, and visit duration using the Google Analytics API. It also uses tools like Hotjar to obtain data on user click patterns and scrolling behavior using heat map tools. It also uses an emotion engine to collect emotional data (e.g., satisfaction, dissatisfaction, joy, anger) as users use the website. It uses the Emotion API to obtain this emotional data in real time and store it in a database.

[0943] Data analysis

[0944] The server integrates the collected visit data and sentiment data and performs a comprehensive analysis using a generative AI model. This involves using methods such as correlation analysis, regression analysis, and clustering to identify pages with particularly high drop-off rates and pages where users express negative sentiment. This analysis allows for a detailed evaluation of the site's usability and content performance.

[0945] Content generation

[0946] The server identifies areas for improvement based on the analysis results and automatically generates specific improvement proposals using a generative AI model. For example, it suggests text improvements, layout changes, image insertion, etc. for identified issues. It also considers user emotional data and creates customized improvement proposals based on emotions.

[0947] Automatic generation of virtual websites

[0948] The server then creates a virtual website based on the proposed changes, incorporating the new text, layout, and images, and generates a preview link that can be sent to the website owner, allowing them to visually inspect the proposed changes.

[0949] User approval and implementation

[0950] The user opens the provided preview link, checks the proposed modifications to the virtual website, and then approves the proposed modifications or requests modifications as necessary. The approved modifications are confirmed as the final modifications.

[0951] Reflection on the actual website

[0952] The device then applies the user-approved modifications to the actual website, using automatically generated code to update the website with new text, layout, and images, as well as customizations based on the user's emotions.

[0953] Examples:

[0954] Collection of visit and sentiment data

[0955] The server collects Google Analytics data for the past month and visualizes click patterns on product detail pages as heat maps. It also uses an emotion engine to collect user emotion data (e.g., satisfaction, dissatisfaction) in real time.

[0956] Data analysis

[0957] The server analyzes the visit data and emotion data on the product detail page and finds that "the dropout rate is high and users are dissatisfied."

[0958] Content generation

[0959] The server generates specific suggestions for modifications to the product detail page, such as adding detailed description text or inserting high-resolution images.

[0960] Automatic generation of virtual websites

[0961] The server then creates a virtual website that reflects the proposed changes and sends the operator a preview link, which includes customizations based on the user's emotions.

[0962] User approval and implementation

[0963] The user opens the preview link, checks the virtual website, and approves or requests modifications. Approved modifications are confirmed as the final modifications.

[0964] Reflection on the actual website

[0965] The device then uses the auto-generated code to apply the proposed changes to the live website, where the product detail page will then reflect the new text, images, and customizations, improving the user experience.

[0966] Example prompt sentence:

[0967] "Analyze 30 days of visit data and user sentiment data to generate specific improvement recommendations for product detail pages."

[0968] "Generate specific suggestions to improve the text and images on pages with high bounce rates and dissatisfied comments."

[0969] As described above, the present invention is a system that enables website operators to efficiently and effectively improve their websites by utilizing user behavior and emotion data.

[0970] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0971] Step 1: Collect visit and sentiment data

[0972] The server uses the Google Analytics API to obtain data such as website page views, number of visitors, and duration of visits. Google Analytics account information and API authentication key are required as input. The obtained data is saved in a database (output).

[0973] The server uses a heatmap tool (e.g., Hotjar) to collect data on users' click patterns and scrolling behavior. The inputs require Hotjar account information and the URLs of the pages to be collected. The collected data is visualized and stored in a database (output).

[0974] The server uses the Emotion API to collect user emotion data in real time. The input requires the Emotion API authentication key and the user's image or text data. The acquired emotion data is stored in a database (output).

[0975] Step 2: Integrate the data

[0976] The server aggregates visit data, heatmap data, and sentiment data collected from databases, and requires multiple datasets from each data source as input.

[0977] The integration process merges data based on the same timestamp or user ID to create a single dataset (output), allowing information from different data sources to be analyzed in a unified manner.

[0978] Step 3: Analyze the data

[0979] The server uses the integrated dataset and performs analysis using a generative AI model, with the integrated dataset and the analytical model as inputs.

[0980] The generative AI model performs correlation analysis, regression analysis, and clustering to evaluate the usability and content performance of each page, and outputs an analysis report that identifies pages with high dropoff rates and pages that evoke negative sentiment.

[0981] Step 4: Identify areas for improvement

[0982] The server identifies specific improvements based on the analysis result report, and the analysis result report is used as input.

[0983] For example, based on information such as "the bounce rate on a particular product detail page is high, causing users to feel dissatisfied," areas for improvement are listed (output).

[0984] Step 5: Generate renovation proposals

[0985] The server generates a proposed fix based on the identified improvements, taking the list of improvements as input.

[0986] Using a generative AI model, specific improvement proposals (e.g., text improvement, layout changes, image addition) are automatically generated. The output is a specific improvement proposal.

[0987] Step 6: Automatically Generate Virtual Websites

[0988] The server constructs a virtual website based on the generated modification proposals. The input is a set of modification proposals.

[0989] The system builds a virtual website based on the automatically generated revision proposals, and then generates a preview link and sends it to the website operator (output).

[0990] Step 7: User Acceptance and Implementation

[0991] The user opens the provided preview link and checks the proposed modifications to the virtual website. The input is the preview link.

[0992] The user either approves the proposed revision or requests a revision. The approved revision is saved in the database as the final version (output).

[0993] Step 8: Applying to the actual website

[0994] The terminal applies the proposed modifications approved by the user to the actual website, and the final approved modification is used as input.

[0995] The new text, layout, and images are applied to the website using the automatically generated code, and finally, the changes are verified (output).

[0996] Through these steps, the present invention utilizes user behavioral and emotional data to enable the generation and implementation of highly accurate modification proposals.

[0997] (Application example 2)

[0998] 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."

[0999] Conventional website modification methods do not take user emotional data into account, making it difficult to improve usability and modify websites effectively. Furthermore, the lack of a system that can integrate and analyze emotional data and visitor data makes it difficult to understand users' true needs and dissatisfaction. Furthermore, the lack of virtual website generation and preview functionality means that site managers are unable to visually confirm the effects of proposed modifications.

[1000] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1001] In this invention, the server includes means for collecting website visit data and user emotion data, means for analyzing the collected visit data and emotion data to identify site improvements, means for generating modification proposals based on the identified improvements, means for automatically generating a virtual website that reflects the generated modification proposals, means for providing a preview of the virtual website, means for applying the modification proposals to the actual website after receiving user approval, means for collecting user emotion data using a smartphone or a head-mounted display, and means for generating modification proposals using a generative AI model based on the collected data, thereby enabling effective site modifications that take user emotions into consideration.

[1002] "Website visit data" refers to behavioral data such as page views, time spent, and click patterns when a user accesses a website.

[1003] "User emotional data" refers to data on the emotional state of a user, derived from facial expressions, tone of voice, etc., collected using emotion recognition technology.

[1004] "Means of collection" refers to the technical means for acquiring data using servers, sensors, etc.

[1005] "Analytical means" means the technical means used to analyze collected data using statistical or machine learning techniques to derive patterns and trends.

[1006] "Means for identifying areas for improvement" refers to technical means for identifying problems with the usability and performance of a website from the analysis results and identifying areas that need improvement.

[1007] "Means for generating improvement proposals" refers to technical means for creating specific improvement measures (such as changes to text, images, or layout) based on the identified improvement points.

[1008] "Means for automatically generating a virtual website" refers to a technical means for constructing a virtual website based on the generated modification plan.

[1009] "Means for providing a preview" refers to a technical means for presenting proposed modifications to a virtual website so that the operator can visually check them.

[1010] "Means for applying proposed modifications to the actual website after receiving user approval" refers to the technical means for reflecting proposed modifications that have been approved by the operator on the actual website.

[1011] A "smartphone" is a portable information terminal that has Internet access, various sensor functions, and can run a variety of applications.

[1012] A "head-mounted display" is a display device that a user wears on their head to display visual information.

[1013] A "generative AI model" is an artificial intelligence technology that uses machine learning algorithms learned from large amounts of data to generate new data and content.

[1014] This invention is a system that generates optimal modification plans using website visit data and user emotion data, and reflects the modification plans in a virtual website. This system includes the following means.

[1015] 1. Collection of visit and sentiment data

[1016] The server collects user emotional data using smartphones and head-mounted displays. Specifically, it collects the user's facial expressions and tone of voice through the smartphone's camera and microphone, and analyzes them using emotion recognition technology. It also uses the Google Analytics API to collect website visit data (page views, number of visitors, duration of visits, click patterns, etc.).

[1017] 2. Data Analysis

[1018] The server integrates and analyzes the collected visit data and sentiment data, using TensorFlow and other machine learning algorithms to evaluate the usability and content performance of websites and identify problems on specific pages, particularly those with high bounce rates or negative user sentiment.

[1019] 3. Generation of renovation proposals

[1020] The server uses a generative AI model to automatically generate suggested improvements based on the analysis results, such as revising text, replacing images, or presenting special offers. For example, if a page's explanatory text is insufficient, a suggestion to add more detailed information will be generated.

[1021] 4. Automatic generation of virtual websites

[1022] The server creates a virtual website based on the proposed changes, including new text, images, and layouts that reflect the proposed changes, and generates a preview link for the virtual website and sends it to the operator.

[1023] 5. User Acknowledgment and Implementation

[1024] The user checks the preview link of the provided virtual website and visually confirms the contents of the proposed modifications. They then approve or request modifications, and the approved modification is confirmed as the final modification.

[1025] 6. Reflection on the actual website

[1026] After receiving user approval, the device uses automatically generated code to apply the proposed changes to the actual website, improving the user experience by incorporating new text, layout, images, and more.

[1027] Specific examples

[1028] When a customer visited a certain product page, emotions such as "dissatisfaction" and "anger" were detected via camera. Furthermore, Google Analytics data revealed that the page had a high bounce rate. Based on this data, the invented system automatically generated revisions to the product description text, added high-resolution images, and presented special offers, which were then reflected on the virtual website. The operator approved these revisions and applied them to the actual online shopping site. As a result, the bounce rate on the page decreased and user satisfaction improved.

[1029] Prompt Sentence Examples

[1030] "Collect visit data and user sentiment data for 7 days. Obtain click patterns and time spent on specific pages from Google Analytics, and collect sentiment data in real time using users' facial expressions and tone of voice. Analyze the collected data and automatically generate improvement proposals using a generative AI model. Generate a preview link of the virtual website and send it to the operator."

[1031] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1032] Step 1:

[1033] The server collects user emotional data using a smartphone or head-mounted display. The input includes the user's facial expressions and tone of voice captured by the smartphone's camera and microphone, which are then analyzed using emotion recognition technology to output data representing the user's emotional state (e.g., joy, dissatisfaction, anger, etc.).

[1034] Step 2:

[1035] The server uses the Google Analytics API to collect website visit data (page views, number of visitors, duration, click patterns, etc.) The server requires a Google Analytics API key and website URL as input, and using these, the server sends a request to the API, which outputs behavioral data such as page views and number of visitors.

[1036] Step 3:

[1037] The server integrates and analyzes the emotion data and visit data collected in steps 1 and 2. Emotion data and visit data are given as input, and by analyzing them using a machine learning algorithm (e.g., TensorFlow), an evaluation result of the website's usability and content performance is output. This evaluation result includes pages with high dropout rates and pages where users express negative emotions.

[1038] Step 4:

[1039] The server uses the generative AI model to automatically generate improvement proposals based on the analysis results from step 3. The analysis results are input, and the generative AI model generates improvement proposals (e.g., revising text, replacing images, presenting special offers, etc.) based on the analysis results, and specific improvement proposals are output.

[1040] Step 5:

[1041] The server automatically generates a virtual website that reflects the proposed modifications. The server takes the proposed modifications as input, automatically generates a page that reflects the proposed modifications in the virtual website template, and outputs a preview link for the virtual website.

[1042] Step 6:

[1043] The user checks the preview link of the virtual website provided. The preview link is given as input, and the user visually checks the pages of the virtual website, checks the contents of the proposed modifications, and approves or requests modifications. At this stage, the approved modifications are finalized.

[1044] Step 7:

[1045] The device applies the user-approved modifications to the actual website. It takes the approved modifications as input and processes them on the website using automatically generated code. As a result, new text, layout, images, etc. are reflected on the actual website, improving the user experience.

[1046] 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.

[1047] 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.

[1048] 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.

[1049] [Fourth embodiment]

[1050] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1051] 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.

[1052] 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).

[1053] 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.

[1054] 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.

[1055] 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).

[1056] 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.

[1057] 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.

[1058] 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.

[1059] 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.

[1060] 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.

[1061] 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.

[1062] 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."

[1063] This paper describes a system that collects website visitation data, generates optimal improvement plans, and reflects them in a virtual website. This system enables website operators to efficiently update their websites with high quality. Below, we will create a program for this system and explain its processing in natural language.

[1064] 1. Collection of visit data

[1065] The server uses the Google Analytics API to collect data such as website page views, number of visitors, and duration of visits. It also uses heat map tools to obtain data on user click patterns and scrolling behavior. It also uses contact forms and feedback functions to collect user comments and questions.

[1066] 2. Data Analysis

[1067] Based on the collected data, the server uses generative AI to perform a detailed analysis of the website's usability and content performance, identifying pages with high bounce rates and short viewing times and analyzing the causes.

[1068] 3. Content Generation

[1069] Based on the analysis results, the server identifies areas for improvement and automatically generates proposed modifications (text improvements, layout changes, image insertion, etc.) These modifications are aimed at improving usability and conversion rates.

[1070] 4. Automatic generation of virtual websites

[1071] The server creates a virtual renewal website based on the automatically generated revision plan, generates a preview of the virtual website, and provides its URL to the operator.

[1072] 5. User Acknowledgment and Implementation

[1073] The user sees a preview of the virtual website and approves the proposed changes.

[1074] After receiving user approval, the device applies the proposed modifications to the actual website using automatically generated code, so the new content and layout are instantly reflected on the real website.

[1075] Examples:

[1076] 1. Collection of visit data

[1077] The server uses the Google Analytics API to collect data on page views, number of visitors and time spent on the site over a period of time, as well as a heatmap tool to visualize click patterns on specific pages and collect comments from feedback forms.

[1078] 2. Data Analysis

[1079] The server analyzes data on pages with particularly high bounce rates, and then analyzes feedback comments and heat map data to identify the cause. For example, if the analysis shows that the bounce rate is high on the inquiry page, feedback such as "the form is too long" or "it's difficult to understand how to make an inquiry" can be extracted as the cause.

[1080] 3. Content Generation

[1081] Based on the analysis results, the server automatically generates improvement proposals such as "simplifying the form" and "adding an explanation of how to make an inquiry." It also generates appropriate images and guidance text.

[1082] 4. Automatic generation of virtual websites

[1083] The server creates a virtual renewal website incorporating the proposed modifications and sends a preview link to the operator.

[1084] 5. User Acknowledgment and Implementation

[1085] Users can open the preview link, view the virtual redesigned website, and approve the proposed changes.

[1086] After receiving user approval, the device applies the automatically generated code to the actual website and updates it, publishing a new page with a simplified form and additional instructions on how to contact the website.

[1087] As described above, the present invention provides a specific method for website operators to update their websites efficiently and with high quality, thereby improving usability and website quality.

[1088] The processing flow will be explained below.

[1089] Step 1:

[1090] The server uses the Google Analytics API to collect data such as website page views, number of visitors, and duration of visits. This allows basic website visit data to be obtained. This data is formatted by period and page and stored in a database.

[1091] Step 2:

[1092] The server uses the Heatmap Tool API to collect data on users' click patterns and scrolling behavior. Specifically, it visualizes user behavior on a web page and obtains that data. This makes it clear which areas of the page users are interested in. This data is also stored in a database.

[1093] Step 3:

[1094] The server collects comments and questions from users using inquiry forms and feedback functions. This text data is analyzed using natural language processing technology to extract frequently occurring problems and requests for improvement. The extracted results are stored in a database.

[1095] Step 4:

[1096] The server integrates the collected Google Analytics data and heat map data and performs a comprehensive analysis using generative AI. This analysis reveals the causes of high bounce rates on specific pages and usability issues.

[1097] Step 5:

[1098] Based on the analysis results, the server generates proposals for improvements, such as the text content of the pages that need to be improved, areas for layout improvement, and the addition or deletion of images. This generation process utilizes generative AI, which automatically generates specific proposals for improvements based on the areas that need improvement.

[1099] Step 6:

[1100] The server creates a virtual website based on the automatically generated revision proposals, which reflects the proposed changes, such as new text, layout, and images, and generates a preview link for the virtual website and sends it to the website operator.

[1101] Step 7:

[1102] The user can check the proposed changes by clicking the preview link of the virtual website provided. This preview allows the user to visually confirm how the proposed changes will actually be reflected on the website.

[1103] Step 8:

[1104] The user approves or requests modifications to the virtual website. The approved modification is confirmed as the final modification.

[1105] Step 9:

[1106] After receiving user approval, the device applies the proposed modifications to the actual website using automatically generated code, which reflects the new text, layout, and image content on the actual website.

[1107] Through the above steps, the present invention automates website modifications, allowing website operators to update the content of their websites efficiently and effectively.

[1108] Example 1

[1109] 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."

[1110] In the traditional website improvement process, collecting and analyzing visitor data and generating improvement proposals requires a significant amount of time and effort. Furthermore, applying the improvement proposals requires manual operations, which is inefficient and often results in inconsistent update quality. Therefore, a method for website operators to update their sites efficiently and with high quality is needed.

[1111] 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.

[1112] In this invention, the server includes means for collecting website visit data, means for analyzing the collected data and identifying improvements to the website, means for generating modification proposals using a generative AI model based on the identified improvements, means for automatically generating a virtual website that reflects the generated modification proposals, means for providing a preview of the virtual website, and means for applying the modification proposals to the actual website after receiving user approval, thereby enabling website operators to update their websites efficiently and with high quality.

[1113] A "website" is an information providing system that is published on the Internet and consists of multiple web pages that users can access via a browser.

[1114] "Visit Data" refers to records of access to a website, including various statistical information such as page views, number of visitors, duration of visit, click patterns, and scrolling behavior.

[1115] "Analysis" is the process of processing and analyzing collected visit data to identify specific performance indicators and issues.

[1116] "Areas for Improvement" means areas that need to be improved to improve the efficiency and usability of the website as identified through the analysis.

[1117] A "generative AI model" is an algorithm that uses machine learning and generative AI technology to automatically generate suggested text, image, and layout modifications based on collected data and analysis results.

[1118] "Proposed Fixes" refers to specific changes or correction plans proposed to address improvements identified using generative AI models.

[1119] A "virtual website" is a test site that is temporarily constructed to reflect proposed modifications to an actual website and allow users to check them in advance.

[1120] A "preview" is a display screen of a virtual website that allows users to check in advance the appearance and functionality of the website after the proposed modifications are applied.

[1121] "User" means the person or organization that manages and operates the website and is the entity that gives final approval to the proposed modifications.

[1122] A "means" refers to a method, technique, device, or part of a system used to achieve a particular purpose.

[1123] This invention relates to a system that collects website visitation data, analyzes that data, and generates optimal improvement plans. This system enables website operators to update their websites efficiently and with high quality.

[1124] System Overview

[1125] The basic configuration of this system is as follows: The system is composed of a server, terminals, and users, and each component operates in cooperation with each other.

[1126] 1. Collection of visit data

[1127] The server uses the Google Analytics API to collect data such as website page views, number of visitors, and duration of visits. It also uses heat map tools to obtain data on user click patterns and scrolling behavior. It also uses contact forms and feedback functions to collect user comments and questions.

[1128] 2. Data Analysis

[1129] Based on the collected data, the server uses generative AI to perform a detailed analysis of the website's usability and content performance, identifying pages with high bounce rates and short viewing times and analyzing the causes.

[1130] 3. Content Generation

[1131] Based on the analysis results, the server identifies areas for improvement and automatically generates proposed modifications (text improvements, layout changes, image insertion, etc.) using a generative AI model.These proposed modifications aim to improve usability and conversion rates.

[1132] 4. Automatic generation of virtual websites

[1133] The server builds a virtual renewal website based on the automatically generated revision plan, generates a preview of the virtual website, and provides its URL to the operator (user).

[1134] 5. User Acknowledgment and Implementation

[1135] The user sees a preview of the virtual website and approves the proposed changes.

[1136] After receiving user approval, the device applies the proposed modifications to the actual website using automatically generated code, so the new content and layout are instantly reflected on the real website.

[1137] Specific examples

[1138] 1. Collection of visit data

[1139] The server uses the Google Analytics API to collect data on page views, number of visitors and time spent on the site over a period of time, as well as a heatmap tool to visualize click patterns on specific pages and collect comments from feedback forms.

[1140] 2. Data Analysis

[1141] The server analyzes data on pages with particularly high bounce rates, and then analyzes feedback comments and heat map data to identify the cause. For example, if the analysis shows that the bounce rate is high on the inquiry page, feedback such as "the form is too long" or "it's difficult to understand how to make an inquiry" can be extracted as the cause.

[1142] 3. Content Generation

[1143] Based on the analysis results, the server automatically generates improvement proposals such as "simplifying the form" and "adding an explanation of how to make an inquiry." It also generates appropriate images and guidance text.

[1144] 4. Automatic generation of virtual websites

[1145] The server creates a virtual renewal website incorporating the proposed modifications and sends a preview link to the operator.

[1146] 5. User Acknowledgment and Implementation

[1147] Users can open the preview link, view the virtual redesigned website, and approve the proposed changes.

[1148] After receiving user approval, the device applies the automatically generated code to the actual website and updates it, publishing a new page with a simplified form and additional instructions on how to contact the website.

[1149] An example of an input prompt for a generative AI model is:

[1150] Input Prompt: "Analyze the usability and content performance of our website using data collected from the Google Analytics API and heatmap tools. Generate recommendations for improving pages with high bounce rates and poor performance."

[1151] Example response: "We're seeing high bounce rates on your contact page, so we suggest simplifying the form and adding detailed instructions on how to contact us. We'll also add appropriate images to make it easier to understand visually."

[1152] By the above means, the present invention provides a specific method for website operators to update their websites efficiently and with high quality, thereby improving usability and website quality.

[1153] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1154] Step 1: Collect visit data

[1155] The server sends a "GET" request to the Google Analytics API to retrieve data such as website page views, number of visitors, and time spent. The request includes any necessary authentication information and the period of time to retrieve. The input data is the Google Analytics API endpoint, and the output data is the collected statistics on page views, number of visitors, and time spent.

[1156] The server uses the heatmap tool's API to collect users' click patterns and scrolling behavior on a particular web page. The input data is the heatmap tool's endpoint, and the output data is a record of the click patterns and scrolling behavior.

[1157] The server collects user comments and questions using the inquiry form and feedback function installed on the website. The input data is text information from the inquiry form and feedback tool, and the output data is a list of comments and questions.

[1158] Step 2: Analyze the data

[1159] The server cleans the collected data and converts it into the appropriate format. Specifically, it executes data cleansing scripts, imputes missing values, and unifies data types. The input data is the collected raw data, and the output data is the cleaned data.

[1160] The server analyzes the formatted data using a generative AI model to identify pages with high bounce rates and pages with short viewing times. The input data is the formatted clean data, and the output data is a list of identified problem pages.

[1161] The server inputs the feedback comments into a text mining tool to extract key issues. The input data are the user feedback comments, and the output data is a list of extracted key issues.

[1162] Step 3: Generate content

[1163] The server identifies appropriate improvements based on the analysis results. Using a generative AI model, it sends prompts such as "List specific improvements." The input data is the analysis results, and the output data is a list of specific improvements.

[1164] The server automatically generates improvement proposals based on the identified improvements. Specifically, improvement proposals such as "simplify the form" and "add an explanation of how to contact us" are created using a generative AI model. The input data is a list of improvements, and the output data is a list of improvement proposals.

[1165] The server generates new content (text, images) as needed. It sends a prompt to the generative AI model saying, "Generate explanatory text for the inquiry page," and uses the resulting text. The input data is the prompt, and the output data is the new content (text and images).

[1166] Step 4: Automatically generate a virtual website

[1167] The server incorporates the automatically generated modification suggestions into the existing website code. The input data is a list of modification suggestions, and the output data is the website code with the modification suggestions incorporated.

[1168] The server builds a virtual website that reflects the proposed modifications and hosts it on a temporary preview server so that users can check it. The input data is the code incorporating the proposed modifications, and the output data is the preview virtual website.

[1169] The server generates a preview link of the virtual website and provides it to the user via email or dashboard. The input data is the URL of the virtual website, and the output data is the preview link.

[1170] Step 5: User Acceptance and Implementation

[1171] The user opens the preview link sent from the server and checks the virtual website. The input data is the preview link, and the output data is the user's evaluation and feedback.

[1172] If the user is satisfied with the preview, he or she clicks a button to approve the proposed modifications. The input data is the preview screen, and the output data is the approval information.

[1173] The terminal applies the automatically generated code to the actual website after receiving the user's approval. The input data is the approved code, and the output data is the website with the proposed modifications applied.

[1174] (Application example 1)

[1175] 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."

[1176] Improving user experience and optimizing conversion rates are extremely important issues in modern online shopping site operations. Improving usability and placing appropriate content requires effective collection and analysis of visit data, as well as rapid site modifications. However, these processes are time-consuming and require specialized knowledge, placing a heavy burden on many site operators. Therefore, there is a need for automation technology to achieve efficient, high-quality site modifications.

[1177] 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.

[1178] In this invention, the server includes means for collecting website visit data, means for analyzing the collected data and identifying improvements to the website, means for generating a modification plan based on the identified improvements, means for automatically generating a virtual website that reflects the generated modification plan, means for providing a preview of the virtual website, means for applying the modification plan to the actual website after receiving user approval, and means for notifying the user of the modification plan to a user terminal and providing a preview link until the operator approves it. This enables online shopping website operators to perform site modifications efficiently and with high quality, improving the user experience and optimizing the conversion rate.

[1179] "Visitation Data" means data about website visitors, such as page views, number of visitors, duration of visits, click patterns, and scrolling behavior.

[1180] "Analysis" is the process of analyzing collected data in detail to identify specific trends or problems.

[1181] "Areas for improvement" are areas that need to be fixed to improve the website's usability and conversion rate.

[1182] The "improvement proposal" is a proposal generated based on the improvement points, proposing specific improvement methods such as correcting text, changing the layout, and adding images.

[1183] A "virtual website" is a website in a virtual environment that reflects the generated modification proposal.

[1184] "Preview" means a display provided to allow the operator to check the generated virtual website in advance.

[1185] "User approval" refers to the act of the operator reviewing the generated modification plan and approving its application to the actual website.

[1186] "Automatic generation" refers to the process in which a system automatically generates data without manual intervention, using generative AI or other technologies.

[1187] A "heat map" is a tool that visually displays users' click patterns and scrolling depth.

[1188] "Natural language processing technology" is a computer science technology for analyzing text data and understanding its meaning and intent.

[1189] This invention is a system that collects website visitation data, generates optimal modification plans, and reflects them in a virtual website. This system can be used particularly by operators of online shopping sites to perform efficient, high-quality site modifications. A specific embodiment of this system is described below.

[1190] First, the server collects website visit data. Specifically, it uses the Google Analytics API to obtain data such as page views, number of visitors, and duration of visit. It also uses a heat map tool to collect data on user click patterns and scrolling behavior. Finally, it uses a feedback collection API to collect user comments and questions.

[1191] The server then analyzes the collected data, using generative AI models to perform detailed analysis of the website's usability and content performance. Pages with high drop-off rates and short viewing times are identified, and feedback comments and heat map data are also analyzed to identify the causes.

[1192] The server then automatically generates a proposal for modification based on the analysis results. By inputting the following prompts to the generative AI model, specific proposals (e.g., correcting text, changing layout, adding images, etc.) are generated:

[1193] Analytics data: {'pagePath': ' / contact', 'sessions': 120}

[1194] Heatmap data: {'clicks': {'submit_button': 30, 'scroll_depth': '50%'}}

[1195] Feedback data: {'comments': ['The form is too long', 'It's hard to understand how to contact us']}

[1196] Once the proposed modifications are generated, the server automatically generates a virtual website that reflects the modifications. This virtual website is provided as a link that allows the administrator to preview the modifications. By clicking the link, the administrator can check the preview of the virtual website and approve the modifications.

[1197] Once the user approves the proposed changes, the device applies them to the actual website using automatically generated code, so the new content and layout are instantly reflected on the live website.

[1198] As a concrete example, consider a case where the bounce rate on a contact page is high. In this case, feedback includes comments such as "the form is too long" and "it's hard to understand how to make an inquiry." Heat map data also confirms that the number of clicks on the form submit button is low. By analyzing this data, the server generates improvement proposals such as "simplify the form" and "add explanations on how to make an inquiry." These are reflected on the virtual website, and after the operator checks the preview link and approves it, they are finally applied to the actual website.

[1199] In this way, this system provides a concrete method for operators of online shopping sites to update their sites efficiently and with high quality, thereby improving usability and site quality.

[1200] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1201] Step 1:

[1202] The server collects website visit data. Specifically, it uses the Google Analytics API to obtain data such as page views, number of visitors, and duration of visit. The input is a request to the Google Analytics API, and the output is the collected visit data. Data processing involves converting the obtained data into a unified format and saving it.

[1203] Step 2:

[1204] The server uses a heat map tool to collect data on users' click patterns and scrolling behavior. This allows it to understand which areas are frequently clicked and how far the user has scrolled. The input is a request to the heat map tool, and the output is heat map data. The data is then analyzed for calculation purposes, including information such as the number of clicks and scroll depth.

[1205] Step 3:

[1206] The server uses a feedback collection API to collect user comments and questions. This information is used to identify areas for usability improvement. The input is a request to the feedback collection API, and the output is the collected feedback data. Data processing involves organizing the text data and formatting it into a form suitable for analysis.

[1207] Step 4:

[1208] The server analyzes the collected data. It uses a generative AI model to perform a detailed analysis of the website's usability and content performance. It identifies pages with high bounce rates and short viewing times, and analyzes feedback comments and heat map data to find the causes. The inputs are visit data, heat map data, and feedback data, and the output is the analysis results. Data calculations include multivariate analysis and clustering.

[1209] Step 5:

[1210] The server automatically generates a modification plan based on the analysis results. A specific modification plan is generated by inputting a prompt statement to the generation AI model. The input is the analysis results and the prompt statement, and the output is the generated modification plan. Specifically, a prompt statement of the following format is input to the generation AI:

[1211] Analytics data: {'pagePath': ' / contact', 'sessions': 120}

[1212] Heatmap data: {'clicks': {'submit_button': 30, 'scroll_depth': '50%'}}

[1213] Feedback data: {'comments': ['The form is too long', 'It's hard to understand how to contact us']}

[1214] Step 6:

[1215] The server automatically generates a virtual website that reflects the generated modification proposal. This virtual website is used by the administrator to preview the modification proposal. The input is the generated modification proposal, and the output is the preview URL of the virtual website. Data processing involves generating HTML and CSS code based on the modification proposal.

[1216] Step 7:

[1217] The user checks the preview link of the virtual website provided by the server and approves the proposed modifications. The input is the preview URL of the virtual website, and the output is the user's approval. Specifically, the user checks the preview using a browser and clicks the approve button.

[1218] Step 8:

[1219] The device receives user approval and applies the generated code to the actual website. This allows the new content and layout to be instantly reflected on the actual website. The input is the user approval and the generated code, and the output is the updated website. The code application process is performed on the server side as a data calculation.

[1220] 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.

[1221] This paper describes a system that collects website visit data and user emotion data, generates optimal modification proposals, and reflects them in a virtual website. This system enables website operators to efficiently and efficiently update their websites with high quality, while taking user emotions into consideration. Below, we will create a program for this system and explain its processing in natural language.

[1222] 1. Collection of visit and sentiment data

[1223] The server uses the Google Analytics API to collect data such as website page views, number of visitors, and duration of visits. It also uses a heat map tool to obtain data on users' click patterns and scrolling behavior. It also uses an emotion engine that recognizes users' emotions to collect emotional data when users use the website.

[1224] 2. Data Analysis

[1225] The server analyzes the collected visit data and sentiment data. Generative AI analyzes this data comprehensively and performs a detailed evaluation of the website's usability and content performance. This analysis identifies pages with high drop-off rates and pages where users express negative sentiment.

[1226] 3. Content Generation

[1227] Based on the analysis results, the server identifies areas for improvement and automatically generates proposals (such as text improvements, layout changes, and image insertion). This generation process utilizes generative AI, which creates specific proposals based on the identified areas for improvement. Furthermore, it also takes into account the user's emotional data and generates customized proposals based on their emotions.

[1228] 4. Automatic generation of virtual websites

[1229] The server builds a virtual website based on the automatically generated revision proposals. This virtual website reflects the proposed changes, such as new text, layout, and images. It also provides a customized preview based on the user's emotions. A preview link for the virtual website is generated and sent to the website operator.

[1230] 5. User Acknowledgment and Implementation

[1231] The user can check the proposed changes by clicking the preview link of the virtual website provided. This preview allows the user to visually confirm how the proposed changes will actually be reflected on the website, and confirm that the suggestions based on user sentiment are reflected.

[1232] The user approves or requests modifications to the virtual website. The approved modification is confirmed as the final modification.

[1233] 6. Reflection on the actual website

[1234] After receiving user approval, the device applies the proposed modifications to the actual website using automatically generated code. This application process reflects the new text, layout, images, etc. on the actual website. Furthermore, customization based on the user's emotions is also taken into consideration.

[1235] Examples:

[1236] 1. Collection of visit and sentiment data

[1237] The server collects Google Analytics data over a period of time, visualizes click patterns on specific pages as heat maps, and uses an emotion engine to collect user emotions (e.g., satisfaction, dissatisfaction, joy, anger, etc.) in real time.

[1238] 2. Data Analysis

[1239] The server focuses on pages with high drop-off rates and analyzes the visit and sentiment data for those pages. For example, the analysis may reveal that "the drop-off rate is high on product detail pages, and users are dissatisfied."

[1240] 3. Content Generation

[1241] The server identifies specific areas for improvement, such as "the description text on the product detail page is insufficient" or "users are looking closely at the images but are not satisfied," and generates suggestions for improvements based on these. Specifically, it generates suggestions for adding more detailed description text or inserting high-resolution images.

[1242] 4. Automatic generation of virtual websites

[1243] The server then creates a virtual website that reflects the proposed modifications and sends a preview link to the operator, allowing the operator to see the customizations based on the user's emotions (for example, special offers to improve satisfaction).

[1244] 5. User Acknowledgment and Implementation

[1245] The user opens the preview link, checks the virtual website, and approves or requests modifications. Approved modifications are confirmed as the final modifications.

[1246] 6. Reflection on the actual website

[1247] The device then applies the proposed changes to the live website using automatically generated code, after which the product detail page will reflect the new text, images, and customizations, improving the user experience.

[1248] As described above, the present invention, which combines user emotion data, allows website operators to make more effective website improvements that take user emotions into consideration.

[1249] The processing flow will be explained below.

[1250] Step 1:

[1251] The server uses the Google Analytics API to collect data such as website page views, number of visitors, and length of stay, and then formats this data by period and page and stores it in a database.

[1252] Step 2:

[1253] The server uses the Heatmap Tool API to collect data on users' click patterns and scrolling behavior, which is then visualized and stored in a database.

[1254] Step 3:

[1255] The server collects comments and questions from users using inquiry forms and feedback functions. The collected text data is analyzed using natural language processing technology to extract frequently occurring problems and requests for improvement.

[1256] Step 4:

[1257] The server uses an emotion engine to collect user emotion data. It recognizes the emotions (e.g., satisfaction, dissatisfaction, joy, anger, etc.) of users when they use the website in real time and stores them in a database.

[1258] Step 5:

[1259] The server integrates the collected visit data and sentiment data and uses generative AI to perform detailed analysis of this data, specifically identifying pages with high dropout rates and pages where users express negative sentiment.

[1260] Step 6:

[1261] The server identifies areas for improvement based on the analysis results and automatically generates proposals (such as text improvements, layout changes, and image insertion) that also take into account user emotional data.

[1262] Step 7:

[1263] The server builds a virtual website based on the automatically generated modification proposals, generates a preview link for the virtual website, and sends it to the website operator. This preview also includes customizations based on the user's emotions.

[1264] Step 8:

[1265] The user checks the preview link of the virtual website provided and approves or requests corrections. The approved proposal is confirmed as the final modification content.

[1266] Step 9:

[1267] After receiving user approval, the device applies the proposed changes to the live website using automatically generated code, resulting in the live website being updated with the new text, layout, images, and customizations.

[1268] Through the above steps, the present invention automates website modifications, allowing website operators to update their website content efficiently and effectively. By combining user emotion data, the modifications will be more satisfying to users.

[1269] Example 2

[1270] 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."

[1271] Conventional website modification methods do not fully utilize user behavioral and emotional data, limiting the accuracy and effectiveness of modification proposals. It is particularly difficult to identify pages with high bounce rates or pages where users express negative emotions, and provide appropriate modification proposals. Furthermore, the process of reviewing modification proposals on a virtual website and obtaining user approval is time-consuming, requiring efficient operation.

[1272] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1273] In this invention, the server includes means for collecting website visit data and emotion data, means for analyzing the collected data and identifying improvements to the website, means for generating modification proposals based on the identified improvements, means for automatically generating a virtual website that reflects the generated modification proposals, means for providing a preview of the virtual website, and means for applying the modification proposals to the actual website after receiving user approval. This enables the generation of highly accurate modification proposals based on user behavior and emotion data, allowing operators to modify their websites efficiently and effectively.

[1274] A "website" is a collection of web pages that provide information or functionality available on the Internet.

[1275] "Visit data" refers to data about the behavior of users when they browse a website, such as page views, number of visitors, and length of stay.

[1276] "Emotional data" is data that indicates the emotional state (e.g., satisfaction, dissatisfaction, joy, anger) of a user when using a website.

[1277] A "heat map" is a data representation that visually shows users' click patterns and scrolling behavior.

[1278] A "virtual website" is a virtual website that reflects proposed modifications and is provided as a preview before the actual modifications are made.

[1279] "Preview" is a feature that allows you to visually check how the proposed changes will be reflected on the website before actually making the changes.

[1280] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and automatically generate specific renovation proposals.

[1281] "User" means a website operator or administrator who is responsible for reviewing the preview of the virtual website and approving or modifying the proposed changes.

[1282] A "server" is a computer system that collects and analyzes website data, generates modification proposals, and automatically generates virtual websites.

[1283] This invention relates to a system that collects website visit data and user emotion data, generates optimal modification proposals, and reflects them in a virtual website. This system enables website operators to efficiently and efficiently update their websites with high quality, while taking user emotions into consideration.

[1284] The server performs the process in the following procedure.

[1285] Collection of visit and sentiment data

[1286] The server automatically collects data such as website page views, number of visitors, and visit duration using the Google Analytics API. It also uses tools like Hotjar to obtain data on user click patterns and scrolling behavior using heat map tools. It also uses an emotion engine to collect emotional data (e.g., satisfaction, dissatisfaction, joy, anger) as users use the website. It uses the Emotion API to obtain this emotional data in real time and store it in a database.

[1287] Data analysis

[1288] The server integrates the collected visit data and sentiment data and performs a comprehensive analysis using a generative AI model. This involves using methods such as correlation analysis, regression analysis, and clustering to identify pages with particularly high drop-off rates and pages where users express negative sentiment. This analysis allows for a detailed evaluation of the site's usability and content performance.

[1289] Content generation

[1290] The server identifies areas for improvement based on the analysis results and automatically generates specific improvement proposals using a generative AI model. For example, it suggests text improvements, layout changes, image insertion, etc. for identified issues. It also considers user emotional data and creates customized improvement proposals based on emotions.

[1291] Automatic generation of virtual websites

[1292] The server then creates a virtual website based on the proposed changes, incorporating the new text, layout, and images, and generates a preview link that can be sent to the website owner, allowing them to visually inspect the proposed changes.

[1293] User approval and implementation

[1294] The user opens the provided preview link, checks the proposed modifications to the virtual website, and then approves the proposed modifications or requests modifications as necessary. The approved modifications are confirmed as the final modifications.

[1295] Reflection on the actual website

[1296] The device then applies the user-approved modifications to the actual website, using automatically generated code to update the website with new text, layout, and images, as well as customizations based on the user's emotions.

[1297] Examples:

[1298] Collection of visit and sentiment data

[1299] The server collects Google Analytics data for the past month and visualizes click patterns on product detail pages as heat maps. It also uses an emotion engine to collect user emotion data (e.g., satisfaction, dissatisfaction) in real time.

[1300] Data analysis

[1301] The server analyzes the visit data and emotion data on the product detail page and finds that "the dropout rate is high and users are dissatisfied."

[1302] Content generation

[1303] The server generates specific suggestions for modifications to the product detail page, such as adding detailed description text or inserting high-resolution images.

[1304] Automatic generation of virtual websites

[1305] The server then creates a virtual website that reflects the proposed changes and sends the operator a preview link, which includes customizations based on the user's emotions.

[1306] User approval and implementation

[1307] The user opens the preview link, checks the virtual website, and approves or requests modifications. Approved modifications are confirmed as the final modifications.

[1308] Reflection on the actual website

[1309] The device then uses the auto-generated code to apply the proposed changes to the live website, where the product detail page will then reflect the new text, images, and customizations, improving the user experience.

[1310] Example prompt sentence:

[1311] "Analyze 30 days of visit data and user sentiment data to generate specific improvement recommendations for product detail pages."

[1312] "Generate specific suggestions to improve the text and images on pages with high bounce rates and dissatisfied comments."

[1313] As described above, the present invention is a system that enables website operators to efficiently and effectively improve their websites by utilizing user behavior and emotion data.

[1314] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1315] Step 1: Collect visit and sentiment data

[1316] The server uses the Google Analytics API to obtain data such as website page views, number of visitors, and duration of visits. Google Analytics account information and API authentication key are required as input. The obtained data is saved in a database (output).

[1317] The server uses a heatmap tool (e.g., Hotjar) to collect data on users' click patterns and scrolling behavior. The inputs require Hotjar account information and the URLs of the pages to be collected. The collected data is visualized and stored in a database (output).

[1318] The server uses the Emotion API to collect user emotion data in real time. The input requires the Emotion API authentication key and the user's image or text data. The acquired emotion data is stored in a database (output).

[1319] Step 2: Integrate the data

[1320] The server aggregates visit data, heatmap data, and sentiment data collected from databases, and requires multiple datasets from each data source as input.

[1321] The integration process merges data based on the same timestamp or user ID to create a single dataset (output), allowing information from different data sources to be analyzed in a unified manner.

[1322] Step 3: Analyze the data

[1323] The server uses the integrated dataset and performs analysis using a generative AI model, with the integrated dataset and the analytical model as inputs.

[1324] The generative AI model performs correlation analysis, regression analysis, and clustering to evaluate the usability and content performance of each page, and outputs an analysis report that identifies pages with high dropoff rates and pages that evoke negative sentiment.

[1325] Step 4: Identify areas for improvement

[1326] The server identifies specific improvements based on the analysis result report, and the analysis result report is used as input.

[1327] For example, based on information such as "the bounce rate on a particular product detail page is high, causing users to feel dissatisfied," areas for improvement are listed (output).

[1328] Step 5: Generate renovation proposals

[1329] The server generates a proposed fix based on the identified improvements, taking the list of improvements as input.

[1330] Using a generative AI model, specific improvement proposals (e.g., text improvement, layout changes, image addition) are automatically generated. The output is a specific improvement proposal.

[1331] Step 6: Automatically Generate Virtual Websites

[1332] The server constructs a virtual website based on the generated modification proposals. The input is a set of modification proposals.

[1333] The system builds a virtual website based on the automatically generated revision proposals, and then generates a preview link and sends it to the website operator (output).

[1334] Step 7: User Acceptance and Implementation

[1335] The user opens the provided preview link and checks the proposed modifications to the virtual website. The input is the preview link.

[1336] The user either approves the proposed revision or requests a revision. The approved revision is saved in the database as the final version (output).

[1337] Step 8: Applying to the actual website

[1338] The terminal applies the proposed modifications approved by the user to the actual website, and the final approved modification is used as input.

[1339] The new text, layout, and images are applied to the website using the automatically generated code, and finally, the changes are verified (output).

[1340] Through these steps, the present invention utilizes user behavioral and emotional data to enable the generation and implementation of highly accurate modification proposals.

[1341] (Application example 2)

[1342] 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."

[1343] Conventional website modification methods do not take user emotional data into account, making it difficult to improve usability and modify websites effectively. Furthermore, the lack of a system that can integrate and analyze emotional data and visitor data makes it difficult to understand users' true needs and dissatisfaction. Furthermore, the lack of virtual website generation and preview functionality means that site managers are unable to visually confirm the effects of proposed modifications.

[1344] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1345] In this invention, the server includes means for collecting website visit data and user emotion data, means for analyzing the collected visit data and emotion data to identify site improvements, means for generating modification proposals based on the identified improvements, means for automatically generating a virtual website that reflects the generated modification proposals, means for providing a preview of the virtual website, means for applying the modification proposals to the actual website after receiving user approval, means for collecting user emotion data using a smartphone or a head-mounted display, and means for generating modification proposals using a generative AI model based on the collected data, thereby enabling effective site modifications that take user emotions into consideration.

[1346] "Website visit data" refers to behavioral data such as page views, time spent, and click patterns when a user accesses a website.

[1347] "User emotional data" refers to data on the emotional state of a user, derived from facial expressions, tone of voice, etc., collected using emotion recognition technology.

[1348] "Means of collection" refers to the technical means for acquiring data using servers, sensors, etc.

[1349] "Analytical means" means the technical means used to analyze collected data using statistical or machine learning techniques to derive patterns and trends.

[1350] "Means for identifying areas for improvement" refers to technical means for identifying problems with the usability and performance of a website from the analysis results and identifying areas that need improvement.

[1351] "Means for generating improvement proposals" refers to technical means for creating specific improvement measures (such as changes to text, images, or layout) based on the identified improvement points.

[1352] "Means for automatically generating a virtual website" refers to a technical means for constructing a virtual website based on the generated modification plan.

[1353] "Means for providing a preview" refers to a technical means for presenting proposed modifications to a virtual website so that the operator can visually check them.

[1354] "Means for applying proposed modifications to the actual website after receiving user approval" refers to the technical means for reflecting proposed modifications that have been approved by the operator on the actual website.

[1355] A "smartphone" is a portable information terminal that has Internet access, various sensor functions, and can run a variety of applications.

[1356] A "head-mounted display" is a display device that a user wears on their head to display visual information.

[1357] A "generative AI model" is an artificial intelligence technology that uses machine learning algorithms learned from large amounts of data to generate new data and content.

[1358] This invention is a system that generates optimal modification plans using website visit data and user emotion data, and reflects the modification plans in a virtual website. This system includes the following means.

[1359] 1. Collection of visit and sentiment data

[1360] The server collects user emotional data using smartphones and head-mounted displays. Specifically, it collects the user's facial expressions and tone of voice through the smartphone's camera and microphone, and analyzes them using emotion recognition technology. It also uses the Google Analytics API to collect website visit data (page views, number of visitors, duration of visits, click patterns, etc.).

[1361] 2. Data Analysis

[1362] The server integrates and analyzes the collected visit data and sentiment data, using TensorFlow and other machine learning algorithms to evaluate the usability and content performance of websites and identify problems on specific pages, particularly those with high bounce rates or negative user sentiment.

[1363] 3. Generation of renovation proposals

[1364] The server uses a generative AI model to automatically generate suggested improvements based on the analysis results, such as revising text, replacing images, or presenting special offers. For example, if a page's explanatory text is insufficient, a suggestion to add more detailed information will be generated.

[1365] 4. Automatic generation of virtual websites

[1366] The server creates a virtual website based on the proposed changes, including new text, images, and layouts that reflect the proposed changes, and generates a preview link for the virtual website and sends it to the operator.

[1367] 5. User Acknowledgment and Implementation

[1368] The user checks the preview link of the provided virtual website and visually confirms the contents of the proposed modifications. They then approve or request modifications, and the approved modification is confirmed as the final modification.

[1369] 6. Reflection on the actual website

[1370] After receiving user approval, the device uses automatically generated code to apply the proposed changes to the actual website, improving the user experience by incorporating new text, layout, images, and more.

[1371] Specific examples

[1372] When a customer visited a certain product page, emotions such as "dissatisfaction" and "anger" were detected via camera. Furthermore, Google Analytics data revealed that the page had a high bounce rate. Based on this data, the invented system automatically generated revisions to the product description text, added high-resolution images, and presented special offers, which were then reflected on the virtual website. The operator approved these revisions and applied them to the actual online shopping site. As a result, the bounce rate on the page decreased and user satisfaction improved.

[1373] Prompt Sentence Examples

[1374] "Collect visit data and user sentiment data for 7 days. Obtain click patterns and time spent on specific pages from Google Analytics, and collect sentiment data in real time using users' facial expressions and tone of voice. Analyze the collected data and automatically generate improvement proposals using a generative AI model. Generate a preview link of the virtual website and send it to the operator."

[1375] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1376] Step 1:

[1377] The server collects user emotional data using a smartphone or head-mounted display. The input includes the user's facial expressions and tone of voice captured by the smartphone's camera and microphone, which are then analyzed using emotion recognition technology to output data representing the user's emotional state (e.g., joy, dissatisfaction, anger, etc.).

[1378] Step 2:

[1379] The server uses the Google Analytics API to collect website visit data (page views, number of visitors, duration, click patterns, etc.) The server requires a Google Analytics API key and website URL as input, and using these, the server sends a request to the API, which outputs behavioral data such as page views and number of visitors.

[1380] Step 3:

[1381] The server integrates and analyzes the emotion data and visit data collected in steps 1 and 2. Emotion data and visit data are given as input, and by analyzing them using a machine learning algorithm (e.g., TensorFlow), an evaluation result of the website's usability and content performance is output. This evaluation result includes pages with high dropout rates and pages where users express negative emotions.

[1382] Step 4:

[1383] The server uses the generative AI model to automatically generate improvement proposals based on the analysis results from step 3. The analysis results are input, and the generative AI model generates improvement proposals (e.g., revising text, replacing images, presenting special offers, etc.) based on the analysis results, and specific improvement proposals are output.

[1384] Step 5:

[1385] The server automatically generates a virtual website that reflects the proposed modifications. The server takes the proposed modifications as input, automatically generates a page that reflects the proposed modifications in the virtual website template, and outputs a preview link for the virtual website.

[1386] Step 6:

[1387] The user checks the preview link of the virtual website provided. The preview link is given as input, and the user visually checks the pages of the virtual website, checks the contents of the proposed modifications, and approves or requests modifications. At this stage, the approved modifications are finalized.

[1388] Step 7:

[1389] The device applies the user-approved modifications to the actual website. It takes the approved modifications as input and processes them on the website using automatically generated code. As a result, new text, layout, images, etc. are reflected on the actual website, improving the user experience.

[1390] 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.

[1391] 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.

[1392] 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.

[1393] 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.

[1394] FIG. 9 is a diagram illustrating 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 actions 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.

[1395] 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.

[1396] 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).

[1397] 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.

[1398] 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."

[1399] 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.

[1400] 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).

[1401] 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.

[1402] 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.

[1403] 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.

[1404] 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.

[1405] 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.

[1406] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1407] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1408] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1409] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1410] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1411] The following is further disclosed regarding the above embodiment.

[1412] (Claim 1)

[1413] means of collecting website visitation data;

[1414] A means of analyzing the collected data and identifying areas for improvement on the Site;

[1415] a means for generating a modification proposal based on the identified improvements;

[1416] A means for automatically generating a virtual website that reflects the generated modification plan;

[1417] a means for providing a preview of the virtual website;

[1418] A method for applying the proposed changes to the actual website after receiving user approval;

[1419] A system including:

[1420] (Claim 2)

[1421] The system of claim 1, wherein the system visualizes user behavior data within a site using a heat map.

[1422] (Claim 3)

[1423] 2. The system of claim 1, wherein the collected data is used to analyze user feedback using natural language processing techniques.

[1424] (Claim 4)

[1425] The system of claim 1, which uses a generation AI to generate renovation proposals.

[1426] (Claim 5)

[1427] 2. The system according to claim 1, further comprising a management means for automating and collectively managing a series of processing steps.

[1428] "Example 1"

[1429] (Claim 1)

[1430] An information processing device includes a means for collecting website visit data;

[1431] A means of analyzing the collected data and identifying areas for improvement on the Site;

[1432] a means for generating a modification proposal using a generative AI model based on the identified improvements;

[1433] A means for automatically generating a virtual website that reflects the generated modification plan;

[1434] a means for providing a preview of the virtual website;

[1435] A method for applying the proposed changes to the actual website after receiving user approval;

[1436] A system including:

[1437] (Claim 2)

[1438] The system of claim 1, wherein a heat map is generated using a data collection means to visualize user behavior data within a site.

[1439] (Claim 3)

[1440] 10. The system of claim 1, wherein the collected data and user feedback are analyzed using natural language processing techniques.

[1441] "Application Example 1"

[1442] (Claim 1)

[1443] means of collecting website visitation data;

[1444] A means of analyzing the collected data and identifying areas for improvement on the Site;

[1445] a means for generating a modification proposal based on the identified improvements;

[1446] A means for automatically generating a virtual website that reflects the generated modification plan;

[1447] a means for providing a preview of the virtual website;

[1448] A method for applying the proposed changes to the actual website after receiving user approval;

[1449] A means to notify users of proposed modifications and provide a preview link until the administrator approves them.

[1450] A system including:

[1451] (Claim 2)

[1452] The system of claim 1, wherein the system visualizes user behavior data within a site using a heat map.

[1453] (Claim 3)

[1454] 2. The system of claim 1, wherein the collected data is used to analyze user feedback using natural language processing techniques.

[1455] "Example 2: Combining Emotion Engines"

[1456] (Claim 1)

[1457] a means of collecting website visitation and sentiment data;

[1458] A means of analyzing the collected data and identifying areas for improvement on the Site;

[1459] a means for generating a modification proposal based on the identified improvements;

[1460] A means for automatically generating a virtual website that reflects the generated modification plan;

[1461] a means for providing a preview of the virtual website;

[1462] A method for applying the proposed changes to the actual website after receiving user approval;

[1463] A system including:

[1464] (Claim 2)

[1465] 10. The system of claim 1, for visualizing user behavior data within a site.

[1466] (Claim 3)

[1467] 2. The system according to claim 1, wherein the collected data is used to analyze the user's emotional data using natural language processing technology.

[1468] "Application example 2 when combining emotion engines"

[1469] (Claim 1)

[1470] A means of collecting website visit data and user sentiment data;

[1471] A means of analyzing the collected visitation and sentiment data to identify areas for improvement on the site; and

[1472] a means for generating a modification proposal based on the identified improvements;

[1473] A means for automatically generating a virtual website that reflects the generated modification plan;

[1474] a means for providing a preview of the virtual website;

[1475] A method for applying the proposed changes to the actual website after receiving user approval;

[1476] A means of collecting user emotional data using smartphones and head-mounted displays,

[1477] A means for generating a repair plan using a generative AI model based on the collected data;

[1478] A system including:

[1479] (Claim 2)

[1480] The system of claim 1, wherein the system visualizes user behavioral data and sentiment data within a site using heat maps.

[1481] (Claim 3)

[1482] 2. The system of claim 1, wherein the collected data is used to analyze user feedback using natural language processing techniques. [Explanation of symbols]

[1483] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means of collecting website visitation data; A means of analyzing the collected data and identifying areas for improvement on the Site; a means for generating a modification proposal based on the identified improvements; A means for automatically generating a virtual website that reflects the generated modification plan; a means for providing a preview of the virtual website; A method for applying the proposed changes to the actual website after receiving user approval; A system including:

2. The system according to claim 1 , wherein the user behavior data within the site is visualized using a heat map.

3. The system of claim 1, further comprising: analyzing user feedback based on the collected data using natural language processing techniques.

4. The system according to claim 1, wherein a generation AI is used to generate the modification plan.

5. 2. The system according to claim 1, further comprising a management means for automating and collectively managing a series of processing steps.

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