System

Abstract

To provide a system.SOLUTION: A system includes: means for acquiring and analyzing information on a Web site; means for prompting the Web site information acquired by the analysis means; means for visualizing an element of the Web site by using a prompt prompted by the prompting means; means for converting the element visualized by the visualizing means into a two-dimensional virtual space platform; means for automatizing creation of the two-dimensional virtual space platform in the converting means; and means for packaging a series of processing of the analysis means, the prompting means, the visualizing means, the converting means and the automatizing means and providing it as a service.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] Traditional website creation requires specialized knowledge and skills, is time-consuming and costly, and there is no way to represent a website as a 2D metaverse. [Means for solving the problem]

[0005] The present invention provides a means for acquiring and analyzing website information, a means for generating prompts from the acquired information, a means for visualizing website elements using the prompts, a means for converting the visualized elements into a 2D metaverse platform, a means for automating the creation of the 2D metaverse, and a means for packaging and selling the entire process under a SaaS model, thereby enabling the efficient creation of websites and their metaverse creation. [Brief explanation of the drawings]

[0006] [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. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] 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.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] 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.

[0011] 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.

[0012] 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.

[0013] 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."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 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.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] In one embodiment of the present invention, an HTML scraping tool is used to obtain website information. This tool obtains HTML information from a specified website and analyzes its structure and content. For example, it obtains the URL of each page of the website, as well as text information, image information, and link information within the page.

[0029] "Example 2"

[0030] Natural language processing (NLP) technology is used to generate prompts from the acquired website information. Specifically, the acquired text information is analyzed and keywords and phrases that represent its content are extracted. These keywords and phrases are then combined to generate prompts that represent the website content.

[0031] "Example 3"

[0032] Image generation AI is used as a means of visualizing website elements using prompts. Specifically, the image generation AI uses the generated prompt as input to generate visuals (images) that represent website elements. For example, if the prompt is "a large red button on a blue and white background," it will generate a visual that matches that.

[0033] "Example 4"

[0034] The 2D Metaverse Engine is used to convert the visualized elements into a 2D Metaverse platform. Specifically, the generated visuals are fed into the 2D Metaverse Engine, which then generates a 2D Metaverse world based on them. For example, if the generated visual is "a large red button on a blue and white background," the engine will generate a 2D Metaverse world that matches that.

[0035] "Example 5"

[0036] Automation scripts are used to automate the creation of 2D metaverses. Specifically, scripts are created to automate the operation of the 2D metaverse engine, and then executed to automate the creation of 2D metaverses. For example, the scripts call the API of the 2D metaverse engine to automatically perform operations such as visual placement, color settings, and button placement.

[0037] "Example 6"

[0038] A cloud-based service platform is used to package a series of processes and sell them under the SaaS model. Specifically, each of the above processes is bundled into a single software package and provided on the cloud. Users can access this service to transform their websites into a 2D metaverse.

[0039] The processing flow of each embodiment will be described below.

[0040] "Example 1"

[0041] Step 1: Use an HTML scraping tool to obtain the HTML information of the specified website. In this step, you will obtain the URL of each page of the website, as well as the text information, image information, link information, etc.

[0042] Step 2: Analyze the acquired HTML information. In this step, the structure and content of the HTML information are analyzed to understand the structure of the website.

[0043] "Example 2"

[0044] Step 1: Analyze the acquired text information. In this step, natural language processing (NLP) techniques are used to extract keywords and phrases from the text information.

[0045] Step 2: Combine the extracted keywords and phrases to generate prompts that represent the content of the website.

[0046] "Example 3"

[0047] Step 1: Input the generated prompt into the image generation AI.

[0048] Step 2: Image generation AI generates visuals (images) representing website elements based on the prompts.

[0049] "Example 4"

[0050] Step 1: Input the generated visuals into the 2D Metaverse engine.

[0051] Step 2: The 2D Metaverse Engine generates a 2D Metaverse world based on the visuals.

[0052] "Example 5"

[0053] Step 1: Create a script to automate the operation of the 2D Metaverse Engine.

[0054] Step 2: Automate the creation of your 2D metaverse by running the script you created.

[0055] "Example 6"

[0056] Step 1: Combine the above methods into a single software package.

[0057] Step 2: Provide the compiled software package on the cloud.

[0058] Step 3: Users access the service and transform their website into a 2D metaverse.

[0059] Example 1

[0060] Next, a description will be given of Example 1 of Form 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."

[0061] Conventional methods for acquiring and analyzing website information required a lot of manual operations and were inefficient. In addition, the process of visually representing the acquired information and then converting it into a two-dimensional virtual space was complicated and required specialized knowledge. This made it difficult for general users to easily utilize website information.

[0062] 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.

[0063] In this invention, the server includes a means for acquiring and analyzing website information, a means for converting the acquired website information into prompts, and a means for visualizing website elements using the prompts, thereby enabling website information to be efficiently acquired, visually represented, and further converted into a two-dimensional virtual space.

[0064] A "website" is a collection of information made public on the Internet and is constructed using technologies such as HTML, CSS, and JavaScript (registered trademark).

[0065] "Means of obtaining and analyzing information" refers to the methods and technologies used to collect the necessary data from a website and analyze that data.

[0066] "Prompting means" refers to methods or technologies that convert acquired website information into a format that can be input into a generative AI model.

[0067] "Visualization methods" refers to methods or techniques used to visually represent website elements using prompts.

[0068] A "two-dimensional virtual space platform" refers to the infrastructure or environment for providing two-dimensional virtual space.

[0069] "Means for automating production" refers to methods and technologies for automating the production process of two-dimensional virtual space.

[0070] "Means of providing as a service" refers to methods and technologies for packaging a series of processes and providing them to users via the Internet.

[0071] "HTML information" refers to data written in HTML, a markup language used to describe the structure and content of web pages.

[0072] "Image generation artificial intelligence" refers to systems and algorithms for generating images using artificial intelligence technology.

[0073] MODE FOR CARRYING OUT THE INVENTION

[0074] This invention is a system that efficiently acquires information from a website, visually represents it, and then converts it into a two-dimensional virtual space. A specific embodiment of this system will be described below.

[0075] System program generation

[0076] The user generates a program to retrieve website information. This program uses an HTML scraping tool, such as "BeautifulSoup" or "Scrapy," to collect website information.

[0077] Program processing explanation

[0078] The server accesses the specified website URL and retrieves its HTML information. The retrieved HTML information is then parsed by the server to analyze the structure and content of the website. Specifically, the following data is retrieved:

[0079] URL of each page

[0080] Text information on the page

[0081] Image information

[0082] Link information

[0083] The server prompts the acquired information and converts it into a format for input to the generative AI model. The prompted information is then used by the generative AI model to visualize website elements. The visualized elements are then converted into a two-dimensional virtual space platform.

[0084] Hardware and software used

[0085] This system uses the following hardware and software:

[0086] Server: A computer system that retrieves and analyzes website information

[0087] HTML scraping tools: "BeautifulSoup" and "Scrapy"

[0088] Generative AI Model: An artificial intelligence model for visualizing website elements using prompts

[0089] Specific examples

[0090] When a user enters the URL "https: / / example.com", the server does the following:

[0091] 1. The user enters a URL.

[0092] 2. The server accesses "https: / / example.com" and retrieves HTML information.

[0093] 3. The HTML information obtained by the server is analyzed using "BeautifulSoup" to extract text, image, and link information from the page.

[0094] 4. The server prompts the extracted information and inputs it into the generative AI model.

[0095] 5. The generative AI model uses the prompts to visualize website elements.

[0096] 6. Transform the visualized elements into a two-dimensional virtual space platform.

[0097] Prompt Sentence Examples

[0098] "Create a program that retrieves information from a specified website and extracts text, images, and links from the page."

[0099] By inputting this prompt into the generative AI model, the user can automatically generate the necessary program. For example, if a user wants to get information about the website "https: / / example.com," the generative AI model will output the appropriate code.

[0100] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0101] Step 1:

[0102] The user enters a website URL.

[0103] Input: The URL of the website you want to retrieve (e.g., "https: / / example.com")

[0104] Specific operation: The user enters the URL of a website into the system's input form and clicks the submit button.

[0105] Output: The entered URL is sent to the server.

[0106] Step 2:

[0107] The server accesses the specified URL.

[0108] Input: The URL entered by the user

[0109] What happens: The server uses the "requests" library to send an HTTP request to the specified URL.

[0110] Output: The HTML of the website is returned as a response.

[0111] Step 3:

[0112] The server retrieves the HTML information.

[0113] Input: HTML information returned as a response to an HTTP request

[0114] Specific operation: The server obtains HTML information from the content attribute of the response object.

[0115] Output: The retrieved HTML information is stored in memory.

[0116] Step 4:

[0117] The server parses the HTML information.

[0118] Input: Retrieved HTML information

[0119] Specific operation: The server uses the "BeautifulSoup" library to parse the HTML information and extract text information, image information, and link information within the page.

[0120] Output: The parsed text information, image information, and link information are stored as lists or strings.

[0121] Step 5:

[0122] Prompts for information retrieved by the server.

[0123] Input: Analyzed text information, image information, link information

[0124] Specific operation: The server converts this information into a format that can be input into the generative AI model.

[0125] Output: Prompted information is generated.

[0126] Step 6:

[0127] The server uses the generative AI model to visualize website elements.

[0128] Input: Prompted information

[0129] How it works: The server inputs prompted information into the generative AI model to create a visual representation of the website elements.

[0130] Output: The visualized elements are generated.

[0131] Step 7:

[0132] The server transforms the visualized elements into a two-dimensional virtual space platform.

[0133] Input: Visualized element

[0134] Specific operation: The server converts the visualized elements into a format suitable for the 2D virtual space platform.

[0135] Output: Data suitable for a 2D virtual space platform is generated.

[0136] Step 8:

[0137] The server provides the analysis results to the user.

[0138] Input: Data suitable for a two-dimensional virtual space platform

[0139] Specific operation: The server displays the generated data to the user.

[0140] Output: The user can check the analysis results.

[0141] (Application example 1)

[0142] Next, a description will be given of Application Example 1 of Embodiment 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."

[0143] Conventional website information acquisition systems take time to acquire and analyze information, making it difficult to compare prices efficiently. Furthermore, they lacked a function to send notifications when the price dropped below the user's set price, making it difficult for users to purchase products at the optimal time.

[0144] 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.

[0145] In this invention, the server includes means for acquiring and analyzing website information, means for prompting the acquired website information, means for visualizing website elements using the prompts, means for converting the visualized elements into a 2D metaverse platform, means for automating the creation of the 2D metaverse, means for packaging a series of processes and selling them under a SaaS model, means for acquiring product information from multiple online sales sites and comparing prices, means for presenting the lowest price, and means for sending a notification when the price drops below the price set by the user. This enables efficient price comparison and allows users to purchase products at the optimal time.

[0146] "Means for acquiring and analyzing website information" refers to technology for acquiring HTML information from a specified website and analyzing its structure and content.

[0147] "Means for converting acquired website information into prompts" refers to a technology that converts acquired website information into prompt sentences to be input into a generative AI model.

[0148] "Method for visualizing website elements using prompts" is a technology that uses a generative AI model to visually represent website elements from prompt statements.

[0149] "Means for converting visualized elements into a 2D metaverse platform" refers to a technology that converts visualized website elements into a two-dimensional virtual space.

[0150] "Means for automating the production of 2D metaverses" refers to technology that automates the production process of 2D metaverse platforms.

[0151] "A means of packaging a series of processes and selling them under the SaaS model" refers to a technology that brings together the above series of processes into a single package and provides it as a service.

[0152] "Means for obtaining product information from multiple online sales sites and comparing prices" refers to a technology for scraping product information from multiple online sales sites and comparing the obtained price information.

[0153] "Means for presenting the lowest price" refers to a technique for presenting the user with the lowest price from among the obtained price information.

[0154] The "means for sending a notification when the price falls below the price set by the user" is a technology for sending a notification to the user when the price falls below the price set by the user.

[0155] The following system configuration is used as an embodiment of the present invention.

[0156] System Configuration

[0157] 1. Server:

[0158] How to obtain website information: The server uses Python's requests and BeautifulSoup libraries to obtain HTML information from the specified website, allowing it to analyze the structure and content of the web page.

[0159] Prompt generation method: Converts the acquired website information into a prompt sentence to be input into the generative AI model. For example, a prompt sentence containing product name and price information is generated.

[0160] Visualization: Using a generative AI model, we create a visual representation of the website elements from the prompt text using image generation AI.

[0161] 2D Metaverse Transformation: Transforms visualized website elements into a 2D virtual space, allowing users to visually confirm website elements within the virtual space.

[0162] Automation: Automate the process of creating a 2D metaverse platform, which will allow for efficient generation of the 2D metaverse.

[0163] SaaS model sales method: The above series of processes are packaged and provided as a service, allowing users to easily use the system.

[0164] Price comparison tool: Obtain product information from multiple online retailers and compare prices using scraping technology.

[0165] Lowest price display method: The lowest price from the obtained price information is displayed to the user.

[0166] Notification method: Send a notification to the user when the price falls below the price set by the user.

[0167] Processing flow

[0168] 1. Data retrieval: The server retrieves HTML data from the specified URL using the requests library.

[0169] 2. Data analysis: Analyze the HTML data using the BeautifulSoup library and extract product name and price information.

[0170] 3. Prompt generation: Based on the extracted information, a prompt sentence is generated to be input into the generative AI model.

[0171] 4. Visualization: Using a generative AI model, we generate a visual representation of the website elements from the prompt.

[0172] 5. 2D Metaverse Transformation: Transforming visualized elements into a two-dimensional virtual space.

[0173] 6. Price Comparison: Compare price information obtained from multiple online retailers and offer the lowest price.

[0174] 7. Notification: Send a notification when the price drops below the user's set price.

[0175] Specific examples

[0176] If a user searches for "iPhone 13," the server will:

[0177] 1. The user types "iPhone 13."

[0178] 2. The server obtains price information for the iPhone 13 from multiple online sales sites.

[0179] 3. Generate a prompt sentence based on the obtained price information.

[0180] 4. Use a generative AI model to generate visual elements from the prompt.

[0181] 5. Transform the visualized elements into a 2D metaverse platform.

[0182] 6. Compare prices and offer the lowest price to users.

[0183] 7. Send a notification when the price drops below the price set by the user.

[0184] Prompt Sentence Examples

[0185] Write a Python program that retrieves pricing information from multiple online retailers and returns the lowest price when a user searches for "iPhone 13." Use BeautifulSoup and the requests library.

[0186] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0187] Step 1:

[0188] The server receives a product name from the user as input. For example, the user enters "iPhone 13." Based on this input, the server generates URLs for multiple online retailers.

[0189] Step 2:

[0190] The server uses the generated URLs to retrieve HTML data from each online sales site using the requests library. In this process, an HTTP request is sent to each URL and HTML data is received as a response. The input is a URL and the output is HTML data.

[0191] Step 3:

[0192] The server analyzes the retrieved HTML data using the BeautifulSoup library. Specifically, it extracts product names, prices, images, and other information from the HTML data. The input is the HTML data, and the output is the analyzed product information.

[0193] Step 4:

[0194] Based on the analyzed product information, the server generates a prompt to be input into the generative AI model. For example, it generates a prompt such as "Get price information for the iPhone 13 and display the lowest price." The input is the product information, and the output is the prompt.

[0195] Step 5:

[0196] The server uses a generative AI model to visually represent website elements from the prompt text. Specifically, the prompt text is input into the generative AI model, which then generates visual elements (images and text). The input is the prompt text, and the output is the visual elements.

[0197] Step 6:

[0198] The server converts the visual elements into a 2D metaverse platform. Specifically, the generated visual elements are placed in a 2D virtual space so that users can visually confirm them. The input is the visual elements, and the output is the 2D metaverse platform.

[0199] Step 7:

[0200] The server compares price information obtained from multiple online sales sites. Specifically, it compiles the price information obtained from each site into a list and finds the cheapest price. The input is multiple price information, and the output is the cheapest price.

[0201] Step 8:

[0202] The server presents the cheapest price to the user. Specifically, it displays the cheapest price information on the user's terminal. The input is the cheapest price, and the output is the information presented to the user.

[0203] Step 9:

[0204] The server sends a notification when the price falls below the price set by the user. Specifically, it compares the price with the price set by the user, and if the condition is met, it generates a notification and sends it to the user's terminal. The input is the user-set price and the current price, and the output is the notification.

[0205] Example 2

[0206] Next, a description will be given of Example 2 of Form 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."

[0207] Conventional methods for analyzing and visualizing website information often require a series of manual processes, from acquiring information to analyzing, generating prompts, visualizing, and converting to a metaverse platform, resulting in inefficiency. Furthermore, because there is no integrated system for automating these processes, users must combine multiple tools and technologies, making operations cumbersome. Furthermore, the quality and accuracy of the generated prompts can be low, preventing the full potential of the generative AI model.

[0208] 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.

[0209] In this invention, the server includes means for acquiring and analyzing website information, means for analyzing the acquired website information using natural language processing technology and extracting keywords and phrases, and means for generating prompt sentences by combining the extracted keywords and phrases. This automates the entire process from acquiring website information to analyzing it and generating prompt sentences, enabling efficient and high-quality generation of prompt sentences.

[0210] "Website information" refers to the content of web pages published on the Internet, including text, images, video, and audio.

[0211] "Means of analysis" refers to the technology and methods used to analyze the acquired website information and understand its content.

[0212] "Natural language processing technology" refers to technology that enables computers to understand and process human language, including text analysis and the extraction of keywords and phrases.

[0213] "Keywords and phrases" refer to important words or short sentences extracted from website information.

[0214] A "prompt" is a textual input to a generative AI model, containing a question or instruction to summarize the content of a website or elicit specific information.

[0215] "Visualization tools" refers to techniques and methods for visually representing textual information or data, including graphs, charts, images, etc.

[0216] A "2D Metaverse Platform" refers to a platform that provides a two-dimensional virtual space, providing an environment in which users can interact with the virtual space.

[0217] "Providing as a service" refers to a business model in which systems or software are provided to users via the Internet. Specifically, this includes the SaaS (Software as a Service) model.

[0218] "HTML information" refers to information written in a markup language used to construct a web page.

[0219] "Image generation technology" refers to technology that allows computers to generate images. Specifically, it includes image generation algorithms that use machine learning and deep learning.

[0220] The present invention relates to a system for acquiring and analyzing information from a website and generating a prompt sentence. A specific embodiment of this system will be described below.

[0221] First, the user enters the URL of a specific website on the terminal. For example, the user enters the URL "https: / / example.com". Then, the server accesses the URL entered by the user and obtains the HTML content of the web page. In this case, the server uses the "requests" library to obtain the HTML information.

[0222] To extract text information from the retrieved HTML content, the server uses the "BeautifulSoup" library, which allows it to strip HTML tags and obtain pure text information.

[0223] The server then uses the spaCy library to analyze the extracted text, specifically extracting important keywords and phrases from the text, providing valuable information for summarizing the website's content.

[0224] Based on the extracted keywords and phrases, the server generates a prompt sentence. The generated prompt sentence is used as input to a generative AI model (e.g., a natural language generation model). Examples of prompt sentences that may be generated include the following:

[0225] To summarize the content of "https: / / example.com," the main topics are "Latest Technology Trends," "AI Applications," and "Latest Research in Data Science." Please provide detailed information on these topics.

[0226] By inputting this prompt into a generative AI model, detailed information based on the website content can be generated, which is provided to the user as a reference for gaining a deeper understanding of the website content.

[0227] This system automates the entire process, from acquiring website information to analyzing it and generating prompts, enabling efficient and high-quality prompt generation. This simplifies operation and eliminates the need for users to combine multiple tools and technologies. It also improves the quality and accuracy of the generated prompts, fully utilizing the capabilities of the generative AI model.

[0228] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0229] Step 1:

[0230] The user enters a website URL.

[0231] The user inputs the URL of a specific website from the terminal. For example, the user inputs the URL "https: / / example.com". This input URL becomes the input data for the next processing step.

[0232] Step 2:

[0233] The server retrieves the HTML information from the specified URL.

[0234] The server accesses the URL entered by the user and retrieves the HTML content of the web page. To do this, the server uses the "requests" library. Specifically, it sends an HTTP request to the URL and receives HTML information as a response. This HTML information becomes the input data for the next processing step.

[0235] Step 3:

[0236] The server extracts text information from the HTML information it has obtained.

[0237] The server uses the "BeautifulSoup" library to extract text information from the retrieved HTML content. Specifically, it parses the HTML tags and removes them to obtain pure text information, which becomes the input data for the next processing step.

[0238] Step 4:

[0239] The server analyzes the text information and extracts keywords and phrases.

[0240] The server uses the "spaCy" library to analyze the extracted text information. Specifically, it analyzes the text information using natural language processing technology to extract important keywords and phrases. The keywords and phrases obtained through this analysis become the input data for the next processing step.

[0241] Step 5:

[0242] The server generates a prompt by combining extracted keywords and phrases.

[0243] The server generates a prompt sentence based on the extracted keywords and phrases. Specifically, it appropriately combines keywords and phrases to create a prompt sentence to input into the generative AI model. The generated prompt sentence becomes the output data for the next processing step.

[0244] Step 6:

[0245] Provides the user with a server-generated prompt.

[0246] The server returns the generated prompt to the user. Specifically, it sends the generated prompt to the user as an HTTP response. The user can use this prompt to input the generative AI model.

[0247] (Application example 2)

[0248] Next, a description will be given of Application Example 2 of Form 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."

[0249] Conventional website information analysis and content recommendation systems have difficulty accurately grasping users' interests and recommending appropriate content. In addition, there has been a lack of methods for efficiently analyzing website information and providing useful information to users.

[0250] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring and analyzing website information, means for generating prompts from the acquired website information, and means for visualizing website elements using the prompts. This enables means for analyzing the acquired website information and extracting keywords and phrases, means for generating prompts by combining the extracted keywords and phrases, and means for recommending content based on the generated prompts.

[0251] "Means of obtaining and analyzing website information" refers to technologies and methods for collecting and analyzing text and metadata from web pages on the Internet.

[0252] "Means for turning acquired website information into prompts" refers to techniques or methods for analyzing collected website text information using natural language processing technology, summarizing the content or converting it into keywords to generate prompts.

[0253] A "prompt-based visualization of website elements" is a technique or method for visually representing website content based on generated prompts.

[0254] "Means of transforming visualized elements into a 2D metaverse platform" refers to techniques and methods for transforming visually represented website elements into a two-dimensional virtual space.

[0255] "Methods for automating the creation of 2D metaverses" are technologies and methods for automatically generating and managing two-dimensional virtual spaces.

[0256] "Means of analyzing acquired website information and extracting keywords and phrases" refers to techniques and methods for analyzing collected website text information and extracting important keywords and phrases from it.

[0257] "Means for generating prompts by combining extracted keywords and phrases" refers to techniques or methods for combining extracted keywords and phrases to generate prompts that summarize the content of a website.

[0258] The "means for recommending content based on a generated prompt" refers to a technique or method for recommending relevant content to a user based on a generated prompt.

[0259] "Methods of packaging a series of processes and selling them under the SaaS model" refers to technologies and methods that bundle the above series of technologies and methods into a single service and provide and sell it as cloud-based software.

[0260] The system for implementing this invention consists of three main elements: a server, a terminal, and a user. Below we will explain how each element functions.

[0261] server

[0262] The server is responsible for retrieving and analyzing website information. Specifically, the server uses the following software and hardware:

[0263] The requests library: Used to retrieve HTML information from websites.

[0264] BeautifulSoup: Used to parse the retrieved HTML information and extract text information.

[0265] The spacy library: Used to analyze the extracted text information and extract keywords and phrases.

[0266] The transformers library: Used to generate and recommend content based on prompts using a generative AI model (GPT-3®).

[0267] The server analyzes the retrieved website information to extract keywords and phrases. It then combines the extracted keywords and phrases to generate prompts. Based on these prompts, a generative AI model is used to recommend relevant content to the user.

[0268] Terminal

[0269] A Device is a device through which a User browses a website and receives recommended content. A Device uses the following software and hardware:

[0270] Web browser: Used by users to browse websites.

[0271] Smartphone application: Used to receive recommended content from the server and display it to the user.

[0272] The device sends the URL of the website the user has visited to the server and receives recommended content from the server, which is then displayed to the user.

[0273] User

[0274] A user is an entity that browses a website and receives recommended content. A user uses the system in the following steps:

[0275] 1. Browse the website using a web browser or smartphone application.

[0276] 2. The URL of the visited website is sent to the server.

[0277] 3. Receive the recommended content from the server and view it.

[0278] Specific examples

[0279] For example, if a user visits the URL "https: / / example.com" and the content of this URL is "Latest Technology News," the server will parse this information and generate a prompt like this:

[0280] Latest Technology News

[0281] Based on this prompt, the generative AI model is expected to recommend other news articles related to the latest technology. Users can receive and view these recommended content through a smartphone application.

[0282] As described above, the present invention provides a system that efficiently analyzes information on a website and recommends related content to a user.

[0283] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0284] Step 1:

[0285] The server receives the URL of the website visited by the user. The input is the URL sent by the user from the terminal, and the output is the HTML information obtained based on that URL. Specifically, the server uses the requests library to obtain the HTML information from the specified URL.

[0286] Step 2:

[0287] The server analyzes the obtained HTML information and extracts text information. The input is the HTML information obtained in step 1, and the output is the extracted text information. Specifically, the server uses BeautifulSoup to extract text information from HTML.

[0288] Step 3:

[0289] The server analyzes the extracted text information and extracts keywords and phrases. The input is the text information extracted in step 2, and the output is the extracted keywords and phrases. Specifically, the server uses the spacy library to analyze the text information and extract important keywords and phrases.

[0290] Step 4:

[0291] The server generates a prompt by combining the extracted keywords and phrases. The input is the keywords and phrases extracted in step 3, and the output is the generated prompt. Specifically, the server combines the extracted keywords and phrases to generate a prompt that summarizes the content of the website.

[0292] Step 5:

[0293] The server recommends content based on the generated prompt. The input is the prompt generated in step 4, and the output is the recommended content. Specifically, the server uses the transformers library to generate and recommend relevant content based on the prompt using a generative AI model (GPT-3).

[0294] Step 6:

[0295] The terminal receives the recommended content from the server and displays it to the user. The input is the recommended content sent from the server in step 5, and the output is the content displayed to the user. Specifically, the terminal receives the recommended content using a smartphone application and displays it to the user.

[0296] Step 7:

[0297] The user browses the recommended content through the device. The input is the content displayed on the device in step 6, and the output is the user's browsing behavior. Specifically, the user browses the recommended content using a smartphone application and explores further related content as needed.

[0298] Example 3

[0299] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0300] The process of visualizing traditional website elements and converting them into virtual space was often done manually, which required time and effort. Furthermore, there was no integrated system for automating these processes, making efficient production difficult. Furthermore, there was a lack of a way to provide these processes as cloud services, making it difficult for users to easily access them.

[0301] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means. In this invention, the server includes means for acquiring and analyzing website information, means for generating prompts from the acquired website information, means for visualizing website elements using the prompts, means for converting the visualized elements into a 2D virtual space platform, means for automating the creation of the 2D virtual space, and means for packaging a series of processes and selling them under a cloud service model. This makes it possible to efficiently visualize website elements, automate the process of converting them into a virtual space, and provide it as a cloud service that users can easily access.

[0302] "Means for acquiring and analyzing website information" refers to technology for collecting data from websites on the Internet and analyzing that data.

[0303] "Means for prompting acquired website information" refers to a technology that generates text-based instructions (prompts) to be input into a generative AI model based on collected website data.

[0304] "A means of visualizing website elements using prompts" is a technology that inputs prompts into a generative AI model and generates images and graphics based on those instructions.

[0305] "Means for converting visualized elements into a 2D virtual space platform" refers to a technology that inputs the generated visuals into a 2D virtual space engine and constructs a virtual space scene based on that.

[0306] "Means for automating the production of 2D virtual spaces" refers to technology that streamlines the production process of virtual spaces by using scripts and programs that automate the operation of 2D virtual space engines.

[0307] "A means of packaging a series of processes and selling them as a cloud service model" refers to a technology that integrates the above technologies, packages them as a service provided on the cloud, and makes them available to users via the Internet.

[0308] "Means for obtaining markup language information" refers to technology that collects data from languages ​​used to describe the structure of web pages (e.g., HTML and XML).

[0309] A "generative AI model" is an artificial intelligence model that generates images and other content based on input text or data.

[0310] This invention is a system for visualizing website elements and converting them into a 2D virtual space. A specific embodiment of this system will be described below.

[0311] First, the server receives the website URL and HTML data provided by the user. The server parses this data and obtains the markup language information to understand the structure and content of the website. This analysis is performed using software such as an HTML parser or a web scraping tool.

[0312] The server then generates prompts based on the acquired website information. The prompts are text instructions to be input to the generative AI model. For example, the server generates prompts containing information such as the website's background color and button placement.

[0313] Using the generated prompt, the server inputs the prompt into a generative AI model (e.g., DALL-E or MidJourney) to generate a visual (image). The generative AI model generates an image containing the specified elements based on the input prompt.

[0314] Next, the server inputs the generated visuals into a 2D virtual space platform (such as Unity or Godot), which then constructs a 2D virtual space scene based on the input visuals. For example, it generates a scene with a large red button on a blue and white background.

[0315] Furthermore, the server creates and executes scripts to automate the creation of the 2D virtual space. These scripts call the API of the 2D virtual space platform to automatically perform operations such as visual placement, color setting, and button placement.

[0316] Finally, the server packages the above process and provides it as a cloud service. Users can access the cloud-based service platform and convert their website into a 2D virtual space by entering prompts.

[0317] For example, if a user inputs the prompt "A big red button on a blue and white background," the generative AI model will generate a visual representation of that scene, and the 2D virtual world platform will build a scene based on that visual. The user can then view the generated 2D virtual world and edit it as needed.

[0318] This system makes it possible to efficiently visualize website elements, automate the process of converting them into a virtual space, and provide it as a cloud service that users can easily access. The flow of the specific processing in Example 3 will be described with reference to Fig. 15.

[0319] Step 1:

[0320] A user inputs a website URL or HTML data. The server receives this and retrieves website information. Specifically, the server uses an HTML parser or web scraping tool to analyze the structure and content of the website and obtain markup language information. The input is the website URL or HTML data, and the output is the parsed website information.

[0321] Step 2:

[0322] The server generates a prompt based on the retrieved website information. Specifically, it extracts information such as the website's background color, button placement, and text content, and converts it into a text prompt. The input is the parsed website information, and the output is the generated prompt.

[0323] Step 3:

[0324] The server inputs the generated prompt into the generative AI model. Specifically, it sends the prompt sentence as an API request to the generative AI model (e.g., DALL-E or MidJourney), which then generates a visual (image) based on the prompt. The input is the prompt sentence, and the output is the generated visual.

[0325] Step 4:

[0326] The server inputs the generated visuals into the 2D virtual space platform. Specifically, it sends the visual data to the API of the 2D virtual space engine (e.g., Unity or Godot), and builds a 2D virtual space scene based on that. The input is the generated visuals, and the output is a 2D virtual space scene.

[0327] Step 5:

[0328] The server creates and executes scripts to automate the creation of 2D virtual spaces. Specifically, it calls the API of the 2D virtual space engine to automatically perform operations such as visual placement, color settings, and button placement. The input is a 2D virtual space scene, and the output is the automated 2D virtual space scene.

[0329] Step 6:

[0330] The server packages the above series of processes and provides them as a cloud service model. Specifically, the service is hosted using cloud infrastructure and made accessible to users via the Internet. The input is an automated 2D virtual space scene, and the output is a system provided as a cloud-based service.

[0331] Step 7:

[0332] Users can access the cloud-based service platform and convert their websites into 2D virtual spaces by entering a prompt. Specifically, users enter a prompt and then view and edit the generated 2D virtual world. The input is the prompt, and the output is an editable 2D virtual world.

[0333] (Application example 3)

[0334] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0335] Traditionally, designing websites and building virtual stores required specialized knowledge and skills, making them difficult for average users. Changing or updating designs was also time-consuming, making it difficult to respond quickly. Furthermore, converting to a 2D metaverse platform and automating production were complex, requiring efficient operation.

[0336] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means for acquiring and analyzing website information, means for converting the acquired website information into prompts, means for visualizing website elements using the prompts, means for converting the visualized elements into a 2D metaverse platform, means for automating the creation of the 2D metaverse, means for packaging a series of processes and providing them in a cloud-based service model, means for automatically generating a virtual store design when a user inputs prompts, and means for placing the generated virtual store visuals on the 2D metaverse platform. This enables even general users to easily design websites and build virtual stores, and enables quick and efficient design changes and updates.

[0337] "Means for obtaining and analyzing website information" refers to a method for collecting data on web pages that exist on the Internet and analyzing their content.

[0338] A "means for generating prompts from acquired website information" is a method for generating text prompts that express specific instructions or requests based on collected web page data.

[0339] A "means for visualizing website elements using prompts" is a method that uses generated prompts as input to generate images or graphics that visually represent the components of a web page.

[0340] "Means for converting visualized elements into a 2D metaverse platform" refers to a method for applying visually represented web page elements to a two-dimensional virtual space platform.

[0341] "Methods for automating the creation of a 2D metaverse" refers to the use of scripts and programs to automatically construct a two-dimensional virtual space.

[0342] "Means of packaging a series of processes and providing them as a cloud-based service model" refers to a method of integrating the above-mentioned means and providing them as a service that can be used over the Internet.

[0343] The "means for automatically generating a virtual store design by a user inputting a prompt" is a method for automatically creating a virtual store design based on instructions input by a user.

[0344] "Means for placing the generated visuals of a virtual store on a 2D metaverse platform" refers to a method for placing an automatically generated virtual store design on a two-dimensional virtual space platform.

[0345] A system for implementing this invention includes means for acquiring and analyzing website information, means for prompting the acquired website information, means for visualizing website elements using the prompts, means for converting the visualized elements into a 2D metaverse platform, means for automating the creation of the 2D metaverse, means for packaging a series of processes and providing them in a cloud-based service model, means for automatically generating a virtual store design when a user inputs prompts, and means for placing the generated virtual store visuals on the 2D metaverse platform.

[0346] System program processing explanation

[0347] A means of obtaining and analyzing website information

[0348] The server retrieves HTML information from web pages on the Internet and analyzes the content. Specifically, it uses an HTML parser to analyze the structure of the web page and extract the necessary information.

[0349] A method to prompt for acquired website information

[0350] The server uses the parsed information from the web page to generate a text prompt that expresses a specific instruction or request, such as "There is a big red button on a blue and white background."

[0351] A way to visualize website elements using prompts

[0352] The server uses the generated prompt as input to generate an image that visually represents the components of the web page using an image generation AI (for example, OpenAI (registered trademark) API).

[0353] A way to transform visualizations into a 2D metaverse platform

[0354] The server applies the visually represented elements of the web page to a two-dimensional virtual space platform, specifically by using the API of the 2D metaverse engine to place the generated images in the virtual space.

[0355] A way to automate the creation of the 2D metaverse

[0356] The server uses scripts and programs to automatically construct a two-dimensional virtual space, which automatically performs operations such as visual placement, color setting, and button placement.

[0357] A means of packaging a series of processes and providing them as a cloud-based service model

[0358] The server integrates the above methods and provides them as a service available over the Internet, allowing users to convert their websites into a 2D metaverse.

[0359] A method for automatically generating virtual store designs by prompting users to enter information

[0360] Using a smartphone or head-mounted display, users input specific prompts, such as "a large red button on a blue and white background," and a virtual store design is automatically generated.

[0361] A means to place the generated virtual store visuals on a 2D metaverse platform

[0362] The server places the automatically generated virtual store design on a two-dimensional virtual space platform, allowing users to view and operate the virtual store in the virtual space.

[0363] Specific examples

[0364] As an example, use the following prompt:

[0365] Prompt: "A large red button on a blue and white background."

[0366] Prompt: "Yellow flowers bloom in the green field."

[0367] Prompt: "The word 'On Sale' is written in white letters on a black background."

[0368] By entering these prompts, a corresponding visual of a virtual store is generated and placed on the 2D metaverse platform.

[0369] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0370] Step 1:

[0371] The server retrieves HTML information from web pages on the Internet and analyzes its content. Specifically, it uses an HTML parser to analyze the structure of the web page and extract the necessary information. The input is the URL of the web page, and the output is the analyzed HTML information.

[0372] Step 2:

[0373] The server uses the parsed information from the web page to generate a text prompt that expresses a specific instruction or request, such as "There is a big red button on a blue and white background." The input is the parsed HTML information, and the output is the generated prompt text.

[0374] Step 3:

[0375] The server takes the generated prompt as input and uses image generation AI (e.g., OpenAI's API) to generate an image that visually represents the components of the web page. The input is the prompt text, and the output is the generated image.

[0376] Step 4:

[0377] The server applies the visually represented elements of the web page to a 2D virtual space platform. Specifically, it uses the API of the 2D metaverse engine to place the generated image in the virtual space. The input is the generated image, and the output is the visual placed on the 2D metaverse platform.

[0378] Step 5:

[0379] The server uses scripts and programs to automatically construct a 2D virtual space, which automatically performs operations such as visual placement, color setting, and button placement. The input is the visuals placed on the 2D metaverse platform, and the output is the automated 2D metaverse world.

[0380] Step 6:

[0381] The server integrates the above methods and provides them as a service available over the Internet. Users can access this service to convert their websites into 2D metaverses. The input is the user's access information, and the output is the 2D metaverse conversion service provided in a cloud-based service model.

[0382] Step 7:

[0383] The user inputs a specific prompt using a smartphone or head-mounted display. For example, by inputting a prompt such as "There is a large red button on a blue and white background," the design of a virtual store is automatically generated. The input is the prompt entered by the user, and the output is the generated design of the virtual store.

[0384] Step 8:

[0385] The server places the automatically generated virtual store design on a 2D virtual space platform, allowing users to view and operate the virtual store in the virtual space. The input is the generated virtual store design, and the output is a virtual store placed on the 2D metaverse platform.

[0386] 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.

[0387] "Example 1"

[0388] As one embodiment of the present invention, a system incorporating an emotion engine is provided. This emotion engine has the ability to recognize emotions from a user's facial expressions and tone of voice. Specifically, when a user uses the system, the system captures the user's facial expressions and voice using a camera and microphone, and analyzes the data. This analysis makes it possible to recognize the user's emotional state, such as joy, sadness, surprise, or anger.

[0389] "Example 2"

[0390] Additionally, a function is provided to adjust the display of the 2D Metaverse based on the recognized emotions. For example, if the user expresses joy, the environment in the 2D Metaverse will be changed to a bright and cheerful atmosphere. Conversely, if the user expresses sadness, the environment in the 2D Metaverse will be changed to a quiet and calm atmosphere. This allows for a more personalized 2D Metaverse experience that reflects the user's emotional state.

[0391] "Example 3"

[0392] The emotion engine can also track a user's emotional state over time and analyze the data. This allows it to understand the user's emotional patterns and optimize the 2D metaverse display accordingly. For example, if a user tends to feel tired in the afternoon, it can provide a relaxing 2D metaverse environment at that time.

[0393] The processing flow of each embodiment will be described below.

[0394] "Example 1"

[0395] Step 1: When a user uses the system, the user's facial expressions and voice are captured through a camera and microphone.

[0396] Step 2: The captured facial and voice data is sent to the emotion engine.

[0397] Step 3: The emotion engine analyzes the data and recognizes the user's emotional state.

[0398] "Example 2"

[0399] Step 1: The emotion engine recognizes the user's emotional state.

[0400] Step 2: Adjust the display of the 2D metaverse based on the perceived emotional state.

[0401] Step 3: Provide a 2D metaverse experience that responds to the user's emotional state.

[0402] "Example 3"

[0403] Step 1: The emotion engine tracks the user's emotional state over time.

[0404] Step 2: Analyze the tracked data and understand the changing patterns of users' emotions.

[0405] Step 3: Optimize the display of the 2D metaverse based on the emotional change patterns.

[0406] Example 1

[0407] Next, a description will be given of Example 1 of Form 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."

[0408] Conventional website information acquisition and analysis systems are limited to acquiring and analyzing information, and lack the means to effectively utilize the acquired information. They also lack the technology to recognize user emotions and optimize system responses. This makes it difficult to improve the user experience and utilize the acquired information in a multifaceted way.

[0409] 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. In this invention, the server includes means for acquiring and analyzing website information, means for converting the acquired website information into prompts, means for visualizing website elements using the prompts, means for converting the visualized elements into a 2D virtual space platform, means for automating the creation of the 2D virtual space, means for recognizing the user's facial expressions and tone of voice and analyzing their emotions, and means for packaging a series of processes and selling them as a service. This enables diversified use of website information and an improved user experience.

[0410] "Means for acquiring and analyzing website information" refers to technology for acquiring HTML information from a specified website and analyzing its structure and content.

[0411] "Means for prompting acquired website information" refers to a technology that generates prompt sentences to be input into a generative AI model based on acquired website information.

[0412] "Method for visualizing website elements using prompts" is a technology that inputs prompt statements into a generative AI model to visually represent website elements.

[0413] "Means for converting visualized elements into a 2D virtual space platform" refers to a technology for reproducing visualized website elements in a 2D virtual space.

[0414] "Means for automating the creation of 2D virtual spaces" refers to technology for automatically executing the process of constructing 2D virtual spaces.

[0415] "Means for recognizing a user's facial expression and tone of voice and analyzing emotions" refers to technology that uses a camera or microphone to capture a user's facial expression and tone of voice, and analyzes that data to recognize the user's emotional state.

[0416] "A means of packaging a series of processes and selling them as a service model" refers to a technology that brings together the above series of processes into a single package and provides it as a service.

[0417] MODE FOR CARRYING OUT THE INVENTION

[0418] This invention is a system that acquires and analyzes website information and improves the user experience based on that information. This system works in cooperation with three parties: a server, a terminal, and a user.

[0419] 1. Obtaining website information using an HTML scraping tool

[0420] The server uses an HTML scraping tool to obtain HTML information from a specified website. This tool is implemented using libraries such as BeautifulSoup or Scrapy. The server accesses the specified URL and obtains the HTML document. It then analyzes the structure of the HTML document and extracts text information, image information, link information, and other information from the page.

[0421] Examples:

[0422] The server accesses the URL "https: / / example.com" and retrieves all link information on the page.

[0423] Use BeautifulSoup to extract the URLs of all images on the page.

[0424] Example prompt sentence:

[0425] Get all link information for "https: / / example.com".

[0426] 2. User Emotion Recognition Using an Emotion Engine

[0427] The device uses a camera and microphone to capture the user's facial expressions and tone of voice, and sends the data acquired from these devices to a server.

[0428] The server uses an emotion engine to analyze the received data. This emotion engine is implemented using libraries such as OpenCV and TENSORFLOW (registered trademark). The server analyzes the user's facial expressions and tone of voice to recognize emotional states such as joy, sadness, surprise, and anger.

[0429] Examples:

[0430] The device captures the user's facial expressions through the camera and sends the data to the server.

[0431] The server uses TensorFlow to recognize the emotion of "happiness" from the user's facial expression.

[0432] Example prompt sentence:

[0433] Use the camera and microphone to recognize the user's emotions.

[0434] 3. Prompt and visualize website information

[0435] Based on the acquired website information, the server generates prompt sentences to be input into the generative AI model. These prompt sentences are used to visually represent the website elements. The generative AI model, for example, uses image generation AI to visualize the website elements.

[0436] Examples:

[0437] Based on the link information obtained by the server, a prompt sentence is generated for the generative AI model saying, "Please visually represent this link information."

[0438] A generative AI model generates visual elements based on link information.

[0439] Example prompt sentence:

[0440] Please provide a visual representation of this link information.

[0441] 4. Conversion to 2D virtual space platform and automation

[0442] The server converts the visualized elements into a 2D virtual space platform. This conversion process is automated, eliminating the need for users to do anything. The 2D virtual space is created using a game engine such as Unity or Unreal Engine.

[0443] Examples:

[0444] The server imports the generated visual elements into Unity and automatically constructs a 2D virtual space.

[0445] Allows users to interact with website elements in a 2D virtual space.

[0446] 5. Service model

[0447] The server packages a series of processes and provides them as a service model. This service is provided as a Software as a Service (SaaS) model, and users can access it via the Internet.

[0448] Examples:

[0449] The server bundles all the processes into one package and provides it to the user.

[0450] Users access the service through a web browser to retrieve, analyze, visualize, and convert website information into a 2D virtual space.

[0451] The above is an embodiment of the present invention. This system enables multifaceted use of website information and improves the user experience.

[0452] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0453] Program processing flow

[0454] Step 1: Obtaining website information

[0455] The server accesses the specified URL and retrieves the HTML document. As input, it receives the URL specified by the user (e.g., "https: / / example.com"). The server sends an HTTP request and retrieves the HTML document as a response. As output, it receives the retrieved HTML document.

[0456] Specific behavior:

[0457] The server uses the requests library to access the specified URL.

[0458] The server retrieves the HTML document from the response.

[0459] Step 2: Parsing the HTML document

[0460] The server parses the retrieved HTML document. As input, it receives the HTML document retrieved in step 1. The server parses the HTML document using the BeautifulSoup library and extracts text information, image information, link information, etc. from the page. As output, it obtains the parsed information.

[0461] Specific behavior:

[0462] The server uses BeautifulSoup to parse the HTML document.

[0463] The server extracts all link information within the page.

[0464] Step 3: Generate a prompt statement

[0465] Based on the analyzed information, the server generates a prompt sentence to be input to the generative AI model. As input, it receives the information analyzed in step 2. The server organizes the information and generates a prompt sentence. As output, it obtains the generated prompt sentence.

[0466] Specific behavior:

[0467] The server generates a prompt sentence such as "Please visually represent this link information" based on the link information.

[0468] Step 4: Visualization with generative AI models

[0469] The server inputs the generated prompt sentence into the generative AI model to visualize the website elements. As input, it receives the prompt sentence generated in step 3. The server inputs the prompt sentence into the generative AI model to generate the visual elements. As output, it obtains the visualized elements.

[0470] Specific behavior:

[0471] The server inputs prompts into the generative AI model to generate visual elements.

[0472] Step 5: Transforming into a 2D virtual space platform

[0473] The server converts the visualized elements into a 2D virtual space platform. As input, it receives the visualized elements from step 4. The server uses a game engine such as Unity or Unreal Engine to build the 2D virtual space. As output, it obtains a 2D virtual space.

[0474] Specific behavior:

[0475] The server imports the visualized elements into Unity and automatically constructs a 2D virtual space.

[0476] Step 6: Recognizing User Emotions

[0477] The device uses a camera and microphone to capture the user's facial expressions and tone of voice. As input, the device captures the user's facial expressions and voice in real time. The device sends the captured data to the server. As output, the device obtains the captured data.

[0478] Specific behavior:

[0479] The device captures the user's facial expressions through the camera.

[0480] The device captures the user's voice through a microphone.

[0481] Step 7: Emotion Engine Analysis

[0482] The server uses an emotion engine to analyze the received data. As input, it receives the data captured in step 6. The server analyzes the data using libraries such as OpenCV and TensorFlow to recognize the user's emotional state. As output, it obtains the recognized emotional state.

[0483] Specific behavior:

[0484] The server uses TensorFlow to analyze emotions from the user's facial expressions.

[0485] The server recognizes the user's emotional state based on the analysis results.

[0486] Step 8: Offering as a service model

[0487] The server packages a series of processes and provides them as a service model. As input, it receives the data and results obtained at all steps. The server compiles these into a single package and provides it to the user. As output, it obtains a package that is provided as a service.

[0488] Specific behavior:

[0489] The server bundles all the processes together as a single package.

[0490] The server provides it as a service to the user.

[0491] The above is the specific processing flow of the program of this system.

[0492] (Application example 1)

[0493] Next, a description will be given of Application Example 1 of Embodiment 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."

[0494] Conventional online shopping sites do not take user emotions into consideration when recommending products, and this does not sufficiently improve the user experience. Furthermore, there is a lack of methods for efficiently acquiring and analyzing website information, making it difficult to quickly provide users with products that are suitable for them. Furthermore, there is a lack of methods for effectively visualizing acquired information and presenting it to users in an easy-to-understand manner. To solve these issues, a system is needed that analyzes user emotions in real time and recommends products based on that analysis.

[0495] 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.

[0496] In this invention, the server includes means for acquiring and analyzing website information, means for converting the acquired website information into prompts, means for visualizing website elements using the prompts, means for recognizing emotions from a user's facial expressions and tone of voice, and means for recommending products based on the recognized emotions. This enables product recommendations that take the user's emotions into consideration, improving the user experience.

[0497] "Means for acquiring and analyzing website information" refers to technology for acquiring HTML information from a specified website and analyzing its structure and content.

[0498] "Means for prompting acquired website information" refers to a technology that generates prompt sentences to be input into a generative AI model based on acquired website information.

[0499] A "means for visualizing website elements using prompts" is a technique for visually representing website elements using generated prompt sentences.

[0500] "Means for transforming visualized elements into a 2D metaverse platform" refers to a technology that adapts visualized website elements into a 2D metaverse environment.

[0501] "Means for automating the creation of 2D metaverses" refers to technology that automates the process of building a 2D metaverse platform.

[0502] "Means for recognizing emotions from a user's facial expressions and tone of voice" refers to technology that uses a camera and microphone to analyze a user's facial expressions and voice to identify emotions.

[0503] "Means for recommending products based on recognized emotions" refers to a technology that selects and recommends appropriate products based on the user's emotional state.

[0504] "A means of packaging a series of processes and selling them under the SaaS model" refers to a technology that integrates the above technologies, creates a package to provide it as a service, and sells it under the SaaS (Software as a Service) model.

[0505] A system for implementing this invention has the following configuration: First, a server has means for acquiring and analyzing website information. This means acquires HTML information from a specified website and uses Python libraries such as BeautifulSoup and requests to analyze its structure and content.

[0506] Next, there is a means for converting the acquired website information into a prompt. This means generates a prompt sentence to be input into the generative AI model based on the acquired website information. The generated prompt sentence has the following format, for example:

[0507] Design a system that analyzes the user's facial expressions and voice through a camera and microphone while they are browsing an online shopping site, and recognize their emotions. Develop an application that can recommend suitable products based on the recognized emotions.

[0508] It also has a method for visualizing website elements using prompts, which uses image generation AI to visually represent website elements using generated prompt sentences, specifically using libraries such as OpenCV and Keras.

[0509] Furthermore, there is a means for converting visualized elements into a 2D metaverse platform, which performs the appropriate transformations to adapt the visualized website elements to the 2D metaverse environment.

[0510] Also included is a method for automating the creation of the 2D metaverse, which uses scripts and automation tools to automate the process of building a 2D metaverse platform.

[0511] It also has a means of recognizing emotions from the user's facial expressions and tone of voice, which uses a camera and microphone to analyze the user's facial expressions and voice and uses libraries such as OpenCV and Vosk to identify emotions.

[0512] It also includes a recognized emotion-based product recommendation solution, which uses a Python algorithm to select and recommend appropriate products based on the user's emotional state.

[0513] Finally, there is the option of packaging a series of processes and selling them under the SaaS model. This option integrates the above technologies, creates a package to provide it as a service, and uses a cloud platform to sell it under the SaaS (Software as a Service) model.

[0514] A specific example is a system that analyzes the user's emotions through a camera and microphone while browsing a shopping site on a smartphone, and recommends discounted products if the user is recognized as "happy." Another example is a system that recommends products that will provide comfort if the user's emotion is recognized as "sad" while browsing a shopping site using smart glasses.

[0515] In this way, product recommendations can be made that take the user's emotions into consideration, improving the user experience.

[0516] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0517] Step 1:

[0518] The server receives the URL of the specified website as input and retrieves the HTML information using the requests library. The retrieved HTML information is then analyzed using the BeautifulSoup library to extract the URLs, text information, image information, link information, and other information for each page of the website. This makes the structure and content of the website clear.

[0519] Step 2:

[0520] The server generates a prompt sentence to be input to the generative AI model based on the website information acquired in step 1. Specifically, it analyzes each element of the website (text, images, links, etc.) and converts them into a prompt sentence format. This prompt sentence is input to the generative AI model.

[0521] Step 3:

[0522] The server uses the generated prompt text to visually represent the elements of the website. Specifically, it uses image generation AI to generate images of each element of the website. This makes the content of the website easier to understand visually.

[0523] Step 4:

[0524] The server converts the visualized website elements into the 2D metaverse platform, specifically by performing appropriate transformations to adapt the visualized elements to the 2D metaverse environment, so that the website content is displayed in the 2D metaverse environment.

[0525] Step 5:

[0526] The server automates the creation of the 2D metaverse platform. Specifically, it uses scripts and automation tools to automate the process of building the 2D metaverse platform, thereby streamlining the creation of the 2D metaverse platform.

[0527] Step 6:

[0528] The device uses a camera and microphone to capture the user's facial expressions and voice. The captured data is analyzed using OpenCV and Vosk libraries to recognize the user's emotions, thereby identifying the user's emotional state.

[0529] Step 7:

[0530] The server recommends appropriate products based on the user's emotions recognized in step 6. Specifically, it uses a Python algorithm to select products that are appropriate for the user's emotional state and generate a recommendation list. This allows the user to be provided with products that match their emotions.

[0531] Step 8:

[0532] The server packages a series of processes and sells them under the SaaS model. Specifically, it integrates each of the above technologies, creates a package to provide it as a service, and sells it under the SaaS model using a cloud platform, allowing users to use the service.

[0533] Example 2

[0534] Next, a description will be given of Example 2 of Form 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."

[0535] Conventional website information acquisition and analysis systems have difficulty effectively prompting acquired information and providing a personalized 2D metaverse environment that responds to user emotions. Furthermore, there is a lack of a way to automate the process of visualizing website elements and converting them into a 2D metaverse platform. This has led to issues such as the inability to improve user experience and efficiently create a metaverse.

[0536] 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.

[0537] In this invention, the server includes means for acquiring and analyzing website information, means for analyzing the acquired website information using natural language processing technology and extracting keywords and phrases, means for combining the extracted keywords and phrases to generate prompts, means for recognizing a user's emotions and adjusting a 2D metaverse environment based on the emotions, means for visualizing website elements using the prompts, means for converting the visualized elements into a 2D metaverse platform, means for automating the creation of the 2D metaverse, and means for packaging the series of processes and selling them as a service. This enables effective prompting of website information and provision of a personalized 2D metaverse environment according to the user's emotions.

[0538] "Means for acquiring and analyzing website information" refers to the technology and methods for acquiring text and HTML information from websites on the Internet and analyzing their content.

[0539] "Means of analyzing using natural language processing technology and extracting keywords and phrases" refers to techniques or methods for analyzing acquired text information using natural language processing technology and identifying and extracting important keywords and phrases.

[0540] A "means for generating prompts" is a technique or method for combining extracted keywords and phrases to create prompts that meet a specific purpose.

[0541] "Means for recognizing a user's emotions and adjusting the environment of the 2D metaverse based on those emotions" refers to techniques or methods for using technology to recognize a user's emotions and changing the environment settings in the 2D metaverse based on the recognized emotions.

[0542] "Methods for visualizing website elements" are techniques and methods for visually representing information obtained from a website.

[0543] "Means for converting to a 2D Metaverse platform" means the techniques and methods for converting the visualized elements into a format usable within the 2D Metaverse.

[0544] "Means for automating the creation of 2D metaverses" refers to techniques and methods for automatically generating and arranging environments and elements in a 2D metaverse.

[0545] "A means of packaging a series of processes and selling them as a service model" refers to the technology and methods for bundling the above series of processes into a single package and providing and selling it as a service.

[0546] This invention is a system that acquires and analyzes information from websites and provides a 2D metaverse environment that responds to the user's emotions. A specific embodiment of this system will be described below.

[0547] The server first retrieves text information from the specified website. For this process, the server can use the Python "requests" library. For example, the server can retrieve the HTML of the web page using "requests.get(URL)" and then extract the text information using the "BeautifulSoup" library.

[0548] The server then analyzes the acquired text information using natural language processing (NLP) technology. This analysis is performed using tools such as "spaCy" and "NLTK." The server inputs the text information into "spaCy's nlp()" function to obtain the analysis results. From the analysis results, the server understands the structure of the sentence and the meaning of words, and extracts important keywords and phrases.

[0549] The server combines the extracted keywords and phrases to generate prompts, which are then input to the generative AI model. The server then sorts the keywords and phrases into the appropriate order and saves the generated prompts in text format.

[0550] The device runs software to recognize the user's emotions, such as using Microsoft® Azure® Cognitive Services or Google® Cloud Natural Language API. The device receives the user's facial expressions and voice as input, sends the acquired data to the emotion recognition API, and obtains an emotional analysis result.

[0551] Based on the recognized emotion, the device adjusts the 2D metaverse environment. For example, Unity or Unreal Engine can be used to build the 2D metaverse. If the user expresses joy, the device changes the background to a bright color and plays cheerful music. Conversely, if the user expresses sadness, the device changes the background to a calm color and plays quiet music.

[0552] For example, when a user is browsing a news site, the server retrieves the text information of the news article and analyzes it using NLP technology. Keywords such as "economy," "stock prices," and "rising" are extracted from the analysis results, and these are combined to generate a prompt such as "Economic News: Stock prices are rising."

[0553] Example prompt sentence:

[0554] "Analyze the latest articles on news sites, extract important keywords, and generate prompts."

[0555] "Recognize the user's emotions and adjust the 2D Metaverse environment based on those emotions."

[0556] In this way, it is possible to effectively prompt users with website information and provide a personalized 2D metaverse environment according to their emotions.

[0557] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0558] Step 1:

[0559] The server retrieves text information from the specified website.

[0560] Input: User-specified URL

[0561] Specific operation: The server retrieves the HTML of the web page using "requests.get(URL)" and extracts the text information using the "BeautifulSoup" library.

[0562] Output: Retrieved text information

[0563] Step 2:

[0564] The server analyzes the acquired text information using natural language processing technology.

[0565] Input: Obtained text information

[0566] Specific operation: The server inputs the text information into the "nlp()" function of "spaCy" and obtains the analysis results. From the analysis results, the structure of the sentence and the meaning of the words are understood.

[0567] Output: Analysis results (sentence structure and word meanings)

[0568] Step 3:

[0569] The server extracts important keywords and phrases from the analysis results.

[0570] Input: Analysis results

[0571] Specific operation: The server extracts noun phrases and proper nouns from the analysis results using the "noun_chunks" and "entity_recognition" functions of "spaCy."

[0572] Output: A list of extracted keywords and phrases

[0573] Step 4:

[0574] The server generates prompts by combining the extracted keywords and phrases.

[0575] Input: A list of extracted keywords or phrases

[0576] What happens: The server sorts the keywords and phrases into the appropriate order and saves the generated prompt in text format.

[0577] Output: Generated prompt

[0578] Step 5:

[0579] The terminal runs software for recognizing the user's emotions.

[0580] Input: User's facial expressions and voice

[0581] Specific operation: The device receives the user's facial expressions and voice as input, and uses Microsoft Azure Cognitive Services and Google Cloud Natural Language API to obtain emotion analysis results.

[0582] Output: Emotion analysis results

[0583] Step 6:

[0584] The device adjusts the 2D metaverse environment based on the emotions it recognizes.

[0585] Input: Emotion analysis results

[0586] Specific operation: The device changes the environment settings in the metaverse based on the emotion recognition results. If the user expresses joy, the device changes the background to a bright color and plays cheerful music. Conversely, if the user expresses sadness, the device changes the background to a calm color and plays quiet music.

[0587] Output: A tailored 2D Metaverse environment

[0588] (Application example 2)

[0589] Next, a description will be given of Application Example 2 of Form 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."

[0590] Conventional website information acquisition and analysis systems have struggled to provide a personalized experience that responds to user emotions. Furthermore, the process of effectively visualizing acquired information and converting it into a 2D metaverse platform is complex and difficult to automate. Furthermore, efficiently packaging this series of processes and providing them as a service model has also been a challenge.

[0591] 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.

[0592] In this invention, the server includes means for acquiring and analyzing website information, means for prompting the acquired website information, means for visualizing website elements using the prompts, means for converting the visualized elements into a 2D metaverse platform, means for automating the creation of the 2D metaverse, means for recognizing a user's emotion, means for adjusting the display of the 2D metaverse based on the recognized emotion, and means for packaging and selling the series of processes as a service model, thereby providing a personalized 2D metaverse experience according to the user's emotion and enabling efficient information acquisition and visualization.

[0593] "Means for acquiring and analyzing website information" refers to technology for collecting data from web pages on the Internet and analyzing their content.

[0594] The "means of generating prompts from acquired website information" is a technology that uses natural language processing technology to analyze collected web page data, extract important keywords and phrases, and generate prompts.

[0595] "A method for visualizing website elements using prompts" is a technique for visually representing the content of a web page based on generated prompts.

[0596] "Means for converting visualized elements into a 2D metaverse platform" is a technology that converts visually represented elements of a web page into a two-dimensional virtual space.

[0597] "Means for automating the creation of 2D metaverses" refers to technology for automatically generating and managing two-dimensional virtual spaces.

[0598] "Means for recognizing user emotions" refers to technology for detecting emotions from the user's facial expressions, voice, etc.

[0599] "Means for adjusting the display of the 2D metaverse based on recognized emotions" is a technology that dynamically changes the display of a two-dimensional virtual space according to the detected emotions of the user.

[0600] "A means of packaging a series of processes and selling them as a service model" refers to technology that integrates the series of technologies mentioned above and provides them as a service.

[0601] A system for implementing this invention has the following configuration. First, a server acquires website information, collects data from web pages on the Internet, and analyzes the content using a means for analyzing the information. Specifically, to obtain structured document information, it acquires the HTML of the web page using the requests library.

[0602] Next, a natural language processing library (e.g., spaCy) is used to analyze the collected text information and extract important keywords and phrases to provide the user with prompts about the website information.

[0603] To visualize website elements using prompts, an image generation algorithm (e.g., DALL-E) is used to visually represent the contents of the web page based on the generated prompts. The visualized elements are then transformed into a 2D metaverse platform.

[0604] As a means of converting the visualized elements into a 2D metaverse platform, a 2D metaverse engine (e.g., Unity) is used to convert the visually represented elements of a web page into a 2D virtual space, thereby automating the creation of the 2D metaverse.

[0605] To recognize the user's emotions, we use emotion recognition libraries (e.g., OpenCV and TensorFlow) to detect emotions from the user's facial expressions and voice.To adjust the display of the 2D metaverse based on the recognized emotions, we use the 2D metaverse engine to dynamically change the display of the virtual space.

[0606] These technologies will be integrated and offered as cloud services, as a means of packaging a series of processes and selling them under a service model, allowing users to access them over the internet and enjoy a personalized 2D metaverse experience.

[0607] As a concrete example, consider a user searching for new clothes in a virtual store. The server retrieves information from the product's web page accessed by the user and uses NLP techniques to generate prompts such as, "This outfit is the latest trend and can be worn both casually and formally." Furthermore, if the user expresses happiness, the server changes the background in the 2D metaverse to a brighter, more cheerful atmosphere.

[0608] Examples of prompts include:

[0609] "This product is the latest trend and can be worn casually or formally."

[0610] "The user is expressing joy. Change the background of the virtual store to a bright and cheerful atmosphere."

[0611] In this way, it is possible to provide a personalized shopping experience that responds to the user's emotions.

[0612] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0613] Step 1:

[0614] The server receives the URL of the web page accessed by the user as input, then uses the requests library to retrieve the HTML information of the web page, which becomes the output.

[0615] Step 2:

[0616] The server uses the acquired HTML information as input and performs text analysis using the spaCy library. As a result of the analysis, important keywords and phrases are extracted. These keywords and phrases are the output.

[0617] Step 3:

[0618] The server takes the extracted keywords and phrases as input and generates a prompt. Using a generative AI model (e.g., GPT-3), it creates a natural language prompt based on these keywords and phrases. This prompt is the output.

[0619] Step 4:

[0620] The server uses the generated prompt sentence as input and visualizes the elements of the web page using an image generation algorithm (e.g., DALL-E), and the visualized image is the output.

[0621] Step 5:

[0622] The server takes the visualized image as input and converts it into a 2D virtual space using a 2D metaverse engine (e.g. Unity), which then outputs the virtual space.

[0623] Step 6:

[0624] The device takes the user's facial expressions and voice as input and recognizes their emotions using emotion recognition libraries (e.g., OpenCV and TensorFlow). The recognized emotions are the output.

[0625] Step 7:

[0626] The server uses the recognized emotion as input and adjusts the display of the virtual space using a 2D metaverse engine. For example, if the user expresses joy, the background of the virtual space is changed to a bright and cheerful atmosphere. This adjusted virtual space is the output.

[0627] Step 8:

[0628] The server packages the above series of processes and provides them as a cloud service. Users can access this service via the Internet and enjoy a personalized 2D metaverse experience.

[0629] Example 3

[0630] Next, a third embodiment of the third embodiment will be described. 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."

[0631] Modern websites contain a wide variety of information, making it difficult to effectively visualize that information and then convert it into a virtual space. There is also a need to optimize the display of the virtual space according to the user's emotional state, but there is a lack of efficient means to achieve this. Furthermore, there is a need for a system that automates the entire process and provides it as a cloud service, making it easy for users to use.

[0632] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0633] In this invention, the server includes means for acquiring and analyzing website information, means for prompting the acquired website information, means for visualizing website elements using the prompts, means for converting the visualized elements into a 2D virtual space platform, means for automating the creation of the 2D virtual space, means for packaging a series of processes and providing them in a cloud service model, and means for tracking the user's emotional state and optimizing the display of the 2D virtual space, thereby enabling the website information to be effectively visualized and converted into a virtual space, and further providing an optimal display according to the user's emotional state.

[0634] "Website information" refers to data about the content and structure of web pages published on the Internet.

[0635] "Means of analysis" refers to methods and techniques for analyzing acquired website information and extracting data for specific purposes.

[0636] "Prompting means" refers to methods or technologies that generate instructions (prompts) in a format that can be understood by image generation AI, based on the information obtained from the website.

[0637] A "prompt" is textual input that instructs the image generation AI to generate a specific visual.

[0638] A "visualization tool" is a method or technique that uses prompts to generate images that visually represent website elements.

[0639] A "2D virtual space platform" is software or a system for building and managing a two-dimensional virtual environment.

[0640] "Means for automating the creation of 2D virtual spaces" are scripts or programs for automating the operation of 2D virtual space platforms.

[0641] The "cloud service model" is a form of providing software and services via the Internet.

[0642] "User's emotional state" refers to the emotional state that a user feels at a particular time or in a particular situation.

[0643] "Means for tracking emotional states" refers to methods and technologies for monitoring changes in a user's emotions over the long term and collecting and analyzing the data.

[0644] The "means for optimizing the display" refers to a method or technology that provides an optimal visual display for the user based on the collected emotion data.

[0645] This invention is a system that acquires and analyzes website information, visualizes website elements using prompts, converts the visualization into a 2D virtual space platform, automates the creation of the 2D virtual space, provides a series of processes in a cloud service model, and further tracks the user's emotional state to optimize the display of the 2D virtual space.

[0646] Hardware and software used

[0647] The server uses the following hardware and software:

[0648] Hardware: A server machine with a powerful processor, sufficient memory, storage, and network interfaces.

[0649] Software: Web server software, database management system, image generation AI (e.g., DALL-E, Stable Diffusion), 2D virtual space platform (e.g., Unity 2D, Godot), automation scripts (e.g., Python scripts), cloud service platform (e.g., AWS (registered trademark), Google Cloud), emotion engine.

[0650] The user uses the following hardware and software:

[0651] Hardware: Devices such as personal computers, tablets, and smartphones.

[0652] Software: Web browser, dedicated application.

[0653] Data processing and calculation

[0654] 1. Website information collection and analysis

[0655] The server retrieves and analyzes the HTML information of the website using an HTML parser or scraping tool.

[0656] Based on the acquired information, website elements (text, images, buttons, etc.) are identified and prompt sentences are generated.

[0657] 2. Visualization with prompts

[0658] The server sends the generated prompt text to the image generation AI, which then generates a visual (image) based on the prompt text.

[0659] For example, if the prompt is "There is a big red button on a blue and white background," it will generate a visual that matches that.

[0660] 3. Transforming visuals into 2D virtual space

[0661] The server transmits the generated visuals to the 2D virtual space platform, which then generates a 2D virtual space world based on the visuals.

[0662] 4. Automation of 2D Virtual Spaces

[0663] The server executes automation scripts to automate the operation of the 2D virtual space platform, such as visual placement, color setting, and button placement.

[0664] 5. Cloud service model

[0665] The server packages a series of processes and provides them on a cloud service platform, allowing users to access the service and transform their websites into 2D virtual spaces.

[0666] 6. Emotional Engine Optimization

[0667] The server uses an emotion engine to track the user's emotional state over time and analyzes the data, thereby understanding the user's emotional change patterns and optimizing the display of the 2D virtual space accordingly.

[0668] For example, if a user tends to feel tired every afternoon, a 2D virtual space environment can be provided that helps them relax during that time.

[0669] Specific examples

[0670] Example prompt sentence:

[0671] "There's a big red button on a blue and white background."

[0672] "Yellow flowers blooming in the green grassland"

[0673] "Starry night sky"

[0674] Using these prompts, the image generation AI generates corresponding visuals, and then converts these visuals into a 2D virtual space platform, allowing users to create a virtual space that is intuitively easy to understand.

[0675] The above is an embodiment of the present invention. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0676] Step 1:

[0677] The user enters a prompt statement.

[0678] Input: The user enters the prompt text through a web browser or a dedicated application.

[0679] What happens: The user types "A big red button on a blue and white background" into the input field and clicks the submit button.

[0680] Output: The prompt text is sent to the server.

[0681] Step 2:

[0682] The server sends the prompt text to the image generation AI.

[0683] Input: The server receives the prompt text received from the user.

[0684] Specific operation: The server converts the prompt text into JSON format and sends a POST request to the image generation AI's API endpoint.

[0685] Output: The prompt is sent to the image generation AI.

[0686] Step 3:

[0687] Image generation AI generates the visuals.

[0688] Input: The image generation AI receives a prompt sent from the server.

[0689] Specific operation: The image generation AI generates visuals (images) based on the prompt text.

[0690] Output: The generated visuals are sent back to the server.

[0691] Step 4:

[0692] The server sends the generated visuals to the 2D virtual space platform.

[0693] Input: The server receives the visuals received from the image generation AI.

[0694] Specific operation: The server converts the visuals into PNG format and uploads them to the API of the 2D virtual space platform.

[0695] Output: Visuals are sent to a 2D virtual world platform.

[0696] Step 5:

[0697] A 2D virtual space platform generates a 2D virtual space world.

[0698] Input: The 2D virtual space platform receives visuals sent from the server.

[0699] Specific operation: The 2D virtual space platform analyzes the visual and generates corresponding 2D virtual space objects.

[0700] Output: The generated 2D virtual world is sent back to the server.

[0701] Step 6:

[0702] The server runs the automation script.

[0703] Input: The server receives the 2D virtual world received from the 2D virtual world platform.

[0704] Specific operation: The server executes Python scripts and calls the API of the 2D virtual space platform to automate the placement and color settings of visuals.

[0705] Output: An automated 2D virtual world is generated.

[0706] Step 7:

[0707] The server provides a series of processes in a cloud service model.

[0708] Input: The server receives an automated 2D virtual world.

[0709] Specific operation: The server deploys the generated 2D virtual world to a cloud-based service platform, making it accessible to users.

[0710] Output: A 2D virtual world provided as a cloud service is provided to the user.

[0711] Step 8:

[0712] Users access the cloud service and use the 2D virtual space.

[0713] Input: A user accesses a cloud-based service platform.

[0714] Specific operation: The user logs into the provided 2D virtual space and reflects elements of their website in the 2D virtual space.

[0715] Output: A 2D virtual world available to the user is displayed.

[0716] Step 9:

[0717] The server uses an emotion engine to track the user's emotional state and optimize the 2D virtual space.

[0718] Input: The server collects the user's emotion data.

[0719] How it works: The server uses an emotion engine to track the user's emotional state over time and analyzes the data using machine learning algorithms.

[0720] Output: The 2D virtual environment is dynamically modified based on the analysis results.

[0721] (Application example 3)

[0722] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0723] Conventional website visualization and metaverse processes have been largely manual, requiring time and effort. Furthermore, it is difficult to provide an optimal display that takes into account the user's emotional state, making improving the user experience a challenge. Furthermore, there was a lack of a way to centrally manage these processes and provide them efficiently.

[0724] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0725] In this invention, the server includes means for acquiring and analyzing website information, means for prompting the acquired website information, means for visualizing website elements using the prompts, means for converting the visualized elements into a 2D metaverse platform, means for automating the creation of the 2D metaverse, means for packaging a series of processes and selling them in a SaaS model, means for tracking a user's emotional state and analyzing the data, and means for optimizing the display of the 2D metaverse based on the user's emotional state. This makes it possible to efficiently automate the website visualization and metaverse creation process and provide an optimal display based on the user's emotional state.

[0726] "Means for obtaining and analyzing website information" refers to methods for collecting data from websites on the Internet and analyzing that data.

[0727] "Means of converting acquired website information into prompts" refers to a method of converting collected website data into instructions (prompts) in a format that can be understood by image generation AI.

[0728] "Method for visualizing website elements using prompts" refers to a method for visually representing website elements using image generation AI, using generated prompts as input.

[0729] "Means for converting visualized elements into a 2D metaverse platform" refers to a method for converting visualized website elements into a 2D metaverse virtual space.

[0730] "Means for automating the creation of 2D metaverses" refers to a method for efficiently creating virtual spaces using scripts and programs that automate the operation of 2D metaverse platforms.

[0731] "A method of packaging a series of processes and selling them under the SaaS model" refers to a method of combining each of the above processes into a single software package and providing it as a service on the cloud.

[0732] "Means for tracking a user's emotional state and analyzing the data" refers to a method for monitoring a user's emotions over the long term and analyzing the data.

[0733] "Means for optimizing the display of the 2D metaverse based on the user's emotional state" refers to a method for adjusting the display content of the 2D metaverse based on the user's emotional data to provide an optimal user experience.

[0734] A system for implementing the present invention has the following configuration.

[0735] First, the server obtains and analyzes website information. Specifically, it collects HTML information from websites on the Internet and analyzes that data. Based on the results of this analysis, it prompts website elements. The prompted data is then input into an image generation AI, which generates images to visualize the website elements.

[0736] Next, the visualized elements are converted into a 2D metaverse platform. Using the 2D metaverse engine, a virtual space is constructed based on the generated visuals. This virtual space is efficiently created using automation scripts. The automation scripts call the 2D metaverse engine's API to automatically perform operations such as visual placement, color settings, and button placement.

[0737] Furthermore, the entire process is packaged and provided under the SaaS model. Users can access the cloud-based service and convert their websites into 2D metaverses. The service also has the ability to track users' emotional states and analyze the data. It uses an emotion engine to monitor users' emotions over the long term and optimizes the display of the 2D metaverse based on that data.

[0738] For example, if a user inputs the prompt "A big red button on a blue and white background," a visual representation of that image is generated. This visual is then fed into the 2D metaverse engine, which then constructs a virtual space. Furthermore, the emotion engine tracks the user's emotional state. For example, if the user feels like relaxing, the display of the virtual space will be optimized based on that emotional state.

[0739] The hardware used includes a server, smartphone, head-mounted display, etc. The software uses OpenAI's image generation API, 2D metaverse engine, and emotion engine.

[0740] Examples of prompts include:

[0741] "There's a big red button on a blue and white background."

[0742] "Green plants are placed on the wood-grain floor."

[0743] "Shelves of merchandise are lined up against a luxurious black and gold background."

[0744] This allows users to easily create a virtual store that suits their preferences and enhance their shopping experience.

[0745] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0746] Step 1:

[0747] The server acquires and analyzes website information. Specifically, the server collects HTML information from websites on the Internet and analyzes the data. The input is the website URL, and the output is the analyzed website element information.

[0748] Step 2:

[0749] The server converts the acquired website information into prompts. Specifically, it converts the analyzed website element information into instructions (prompts) in a format that can be understood by the image generation AI, based on the analyzed website element information. The input is the analyzed website element information, and the output is the prompt.

[0750] Step 3:

[0751] The server uses the prompt to visualize the website elements. Specifically, the generated prompt is input into an image generation AI to generate an image that visually represents the website element. The input is the prompt text, and the output is the generated image.

[0752] Step 4:

[0753] The server converts the visualized elements into a 2D metaverse platform. Specifically, the generated image is input into the 2D metaverse engine, and a virtual space is constructed based on it. The input is the generated image, and the output is the 2D metaverse virtual space.

[0754] Step 5:

[0755] The server automates the creation of the 2D metaverse. Specifically, it uses automation scripts to call the API of the 2D metaverse engine and automatically performs operations such as visual placement, color settings, and button placement. The input is the virtual space of the 2D metaverse, and the output is the automated virtual space of the 2D metaverse.

[0756] Step 6:

[0757] The server packages a series of processes and provides them under the SaaS model. Specifically, each of the above processes is compiled into a single software package and provided as a service on the cloud. The input is the result of each process, and the output is the service under the SaaS model.

[0758] Step 7:

[0759] The server tracks the user's emotional state and analyzes the data. Specifically, it uses an emotion engine to monitor the user's emotions over time and analyzes the data. The input is the user's emotional data, and the output is the analyzed emotional data.

[0760] Step 8:

[0761] The server optimizes the display of the 2D metaverse based on the user's emotional state. Specifically, it adjusts the display content of the 2D metaverse based on the analyzed emotional data to provide an optimal user experience. The input is the analyzed emotional data, and the output is the optimized display of the 2D metaverse.

[0762] 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.

[0763] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating 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.

[0764] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0765] 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.

[0766] [Second embodiment]

[0767] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0768] 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.

[0769] 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).

[0770] 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.

[0771] 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.

[0772] The camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0773] For example, an imaging range defined by an angle of view equivalent to the width of the field of view of a typical healthy person is captured.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0780] "Example 1"

[0781] In one embodiment of the present invention, an HTML scraping tool is used to obtain website information. This tool obtains HTML information from a specified website and analyzes its structure and content. For example, it obtains the URL of each page of the website, as well as text information, image information, and link information within the page.

[0782] "Example 2"

[0783] Natural language processing (NLP) technology is used to generate prompts from the acquired website information. Specifically, the acquired text information is analyzed and keywords and phrases that represent its content are extracted. These keywords and phrases are then combined to generate prompts that represent the website content.

[0784] "Example 3"

[0785] Image generation AI is used as a means of visualizing website elements using prompts. Specifically, the image generation AI uses the generated prompt as input to generate visuals (images) that represent website elements. For example, if the prompt is "a large red button on a blue and white background," it will generate a visual that matches that.

[0786] "Example 4"

[0787] The 2D Metaverse Engine is used to convert the visualized elements into a 2D Metaverse platform. Specifically, the generated visuals are fed into the 2D Metaverse Engine, which then generates a 2D Metaverse world based on them. For example, if the generated visual is "a large red button on a blue and white background," the engine will generate a 2D Metaverse world that matches that.

[0788] "Example 5"

[0789] Automation scripts are used to automate the creation of 2D metaverses. Specifically, scripts are created to automate the operation of the 2D metaverse engine, and then executed to automate the creation of 2D metaverses. For example, the scripts call the API of the 2D metaverse engine to automatically perform operations such as visual placement, color settings, and button placement.

[0790] "Example 6"

[0791] A cloud-based service platform is used to package a series of processes and sell them under the SaaS model. Specifically, each of the above processes is bundled into a single software package and provided on the cloud. Users can access this service to transform their websites into a 2D metaverse.

[0792] The processing flow of each embodiment will be described below.

[0793] "Example 1"

[0794] Step 1: Use an HTML scraping tool to obtain the HTML information of the specified website. In this step, you will obtain the URL of each page of the website, as well as the text information, image information, link information, etc.

[0795] Step 2: Analyze the acquired HTML information. In this step, the structure and content of the HTML information are analyzed to understand the structure of the website.

[0796] "Example 2"

[0797] Step 1: Analyze the acquired text information. In this step, natural language processing (NLP) techniques are used to extract keywords and phrases from the text information.

[0798] Step 2: Combine the extracted keywords and phrases to generate prompts that represent the content of the website.

[0799] "Example 3"

[0800] Step 1: Input the generated prompt into the image generation AI.

[0801] Step 2: Image generation AI generates visuals (images) representing website elements based on the prompts.

[0802] "Example 4"

[0803] Step 1: Input the generated visuals into the 2D Metaverse engine.

[0804] Step 2: The 2D Metaverse Engine generates a 2D Metaverse world based on the visuals.

[0805] "Example 5"

[0806] Step 1: Create a script to automate the operation of the 2D Metaverse Engine.

[0807] Step 2: Automate the creation of your 2D metaverse by running the script you created.

[0808] "Example 6"

[0809] Step 1: Combine the above methods into a single software package.

[0810] Step 2: Provide the compiled software package on the cloud.

[0811] Step 3: Users access the service and transform their website into a 2D metaverse.

[0812] Example 1

[0813] Next, a description will be given of Example 1 of Form 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."

[0814] Conventional methods for acquiring and analyzing website information required a lot of manual operations and were inefficient. In addition, the process of visually representing the acquired information and then converting it into a two-dimensional virtual space was complicated and required specialized knowledge. This made it difficult for general users to easily utilize website information.

[0815] 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.

[0816] In this invention, the server includes a means for acquiring and analyzing website information, a means for converting the acquired website information into prompts, and a means for visualizing website elements using the prompts, thereby enabling website information to be efficiently acquired, visually represented, and further converted into a two-dimensional virtual space.

[0817] A "website" is a collection of information that is made public on the Internet and is constructed using technologies such as HTML, CSS, and JavaScript.

[0818] "Means of obtaining and analyzing information" refers to the methods and technologies used to collect the necessary data from a website and analyze that data.

[0819] "Prompting means" refers to methods or technologies that convert acquired website information into a format that can be input into a generative AI model.

[0820] "Visualization methods" refers to methods or techniques used to visually represent website elements using prompts.

[0821] A "two-dimensional virtual space platform" refers to the infrastructure or environment for providing two-dimensional virtual space.

[0822] "Means for automating production" refers to methods and technologies for automating the production process of two-dimensional virtual space.

[0823] "Means of providing as a service" refers to methods and technologies for packaging a series of processes and providing them to users via the Internet.

[0824] "HTML information" refers to data written in HTML, a markup language used to describe the structure and content of web pages.

[0825] "Image generation artificial intelligence" refers to systems and algorithms for generating images using artificial intelligence technology.

[0826] MODE FOR CARRYING OUT THE INVENTION

[0827] This invention is a system that efficiently acquires information from a website, visually represents it, and then converts it into a two-dimensional virtual space. A specific embodiment of this system will be described below.

[0828] System program generation

[0829] The user generates a program to retrieve website information. This program uses an HTML scraping tool, such as "BeautifulSoup" or "Scrapy," to collect website information.

[0830] Program processing explanation

[0831] The server accesses the specified website URL and retrieves its HTML information. The retrieved HTML information is then parsed by the server to analyze the structure and content of the website. Specifically, the following data is retrieved:

[0832] URL of each page

[0833] Text information on the page

[0834] Image information

[0835] Link information

[0836] The server prompts the acquired information and converts it into a format for input to the generative AI model. The prompted information is then used by the generative AI model to visualize website elements. The visualized elements are then converted into a two-dimensional virtual space platform.

[0837] Hardware and software used

[0838] This system uses the following hardware and software:

[0839] Server: A computer system that retrieves and analyzes website information

[0840] HTML scraping tools: "BeautifulSoup" and "Scrapy"

[0841] Generative AI Model: An artificial intelligence model for visualizing website elements using prompts

[0842] Specific examples

[0843] When a user enters the URL "https: / / example.com", the server does the following:

[0844] 1. The user enters a URL.

[0845] 2. The server accesses "https: / / example.com" and retrieves HTML information.

[0846] 3. The HTML information obtained by the server is analyzed using "BeautifulSoup" to extract text, image, and link information from the page.

[0847] 4. The server prompts the extracted information and inputs it into the generative AI model.

[0848] 5. The generative AI model uses the prompts to visualize website elements.

[0849] 6. Transform the visualized elements into a two-dimensional virtual space platform.

[0850] Prompt Sentence Examples

[0851] "Create a program that retrieves information from a specified website and extracts text, images, and links from the page."

[0852] By inputting this prompt into the generative AI model, the user can automatically generate the necessary program. For example, if a user wants to get information about the website "https: / / example.com," the generative AI model will output the appropriate code.

[0853] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0854] Step 1:

[0855] The user enters a website URL.

[0856] Input: The URL of the website you want to retrieve (e.g., "https: / / example.com")

[0857] Specific operation: The user enters the URL of a website into the system's input form and clicks the submit button.

[0858] Output: The entered URL is sent to the server.

[0859] Step 2:

[0860] The server accesses the specified URL.

[0861] Input: The URL entered by the user

[0862] What happens: The server uses the "requests" library to send an HTTP request to the specified URL.

[0863] Output: The HTML of the website is returned as a response.

[0864] Step 3:

[0865] The server retrieves the HTML information.

[0866] Input: HTML information returned as a response to an HTTP request

[0867] Specific operation: The server obtains HTML information from the content attribute of the response object.

[0868] Output: The retrieved HTML information is stored in memory.

[0869] Step 4:

[0870] The server parses the HTML information.

[0871] Input: Retrieved HTML information

[0872] Specific operation: The server uses the "BeautifulSoup" library to parse the HTML information and extract text information, image information, and link information within the page.

[0873] Output: The parsed text information, image information, and link information are stored as lists or strings.

[0874] Step 5:

[0875] Prompts for information retrieved by the server.

[0876] Input: Analyzed text information, image information, link information

[0877] Specific operation: The server converts this information into a format that can be input into the generative AI model.

[0878] Output: Prompted information is generated.

[0879] Step 6:

[0880] The server uses the generative AI model to visualize website elements.

[0881] Input: Prompted information

[0882] How it works: The server inputs prompted information into the generative AI model to create a visual representation of the website elements.

[0883] Output: The visualized elements are generated.

[0884] Step 7:

[0885] The server converts the visualized elements into a two-dimensional virtual space platform.

[0886] Input: Visualized element

[0887] Specific operation: The server converts the visualized elements into a format suitable for the 2D virtual space platform.

[0888] Output: Data suitable for a 2D virtual space platform is generated.

[0889] Step 8:

[0890] The server provides the analysis results to the user.

[0891] Input: Data suitable for a two-dimensional virtual space platform

[0892] Specific operation: The server displays the generated data to the user.

[0893] Output: The user can check the analysis results.

[0894] (Application example 1)

[0895] Next, a description will be given of Application Example 1 of Form 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."

[0896] Conventional website information acquisition systems take time to acquire and analyze information, making it difficult to compare prices efficiently. Furthermore, they lacked a function to send notifications when the price dropped below the user's set price, making it difficult for users to purchase products at the optimal time.

[0897] 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.

[0898] In this invention, the server includes means for acquiring and analyzing website information, means for prompting the acquired website information, means for visualizing website elements using the prompts, means for converting the visualized elements into a 2D metaverse platform, means for automating the creation of the 2D metaverse, means for packaging a series of processes and selling them under a SaaS model, means for acquiring product information from multiple online sales sites and comparing prices, means for presenting the lowest price, and means for sending a notification when the price drops below the price set by the user. This enables efficient price comparison and allows users to purchase products at the optimal time.

[0899] "Means for acquiring and analyzing website information" refers to technology for acquiring HTML information from a specified website and analyzing its structure and content.

[0900] "Means for converting acquired website information into prompts" refers to a technology that converts acquired website information into prompt sentences to be input into a generative AI model.

[0901] "Method for visualizing website elements using prompts" is a technology that uses a generative AI model to visually represent website elements from prompt statements.

[0902] "Means for converting visualized elements into a 2D metaverse platform" refers to a technology that converts visualized website elements into a two-dimensional virtual space.

[0903] "Means for automating the production of 2D metaverses" refers to technology that automates the production process of 2D metaverse platforms.

[0904] "A means of packaging a series of processes and selling them under the SaaS model" refers to a technology that brings together the above series of processes into a single package and provides it as a service.

[0905] "Means for obtaining product information from multiple online sales sites and comparing prices" refers to a technology for scraping product information from multiple online sales sites and comparing the obtained price information.

[0906] "Means for presenting the lowest price" refers to a technique for presenting the user with the lowest price from among the obtained price information.

[0907] The "means for sending a notification when the price falls below the price set by the user" is a technology for sending a notification to the user when the price falls below the price set by the user.

[0908] The following system configuration is used as an embodiment of the present invention.

[0909] System Configuration

[0910] 1. Server:

[0911] How to obtain website information: The server uses Python's requests and BeautifulSoup libraries to obtain HTML information from the specified website, allowing it to analyze the structure and content of the web page.

[0912] Prompt generation method: Converts the acquired website information into a prompt sentence to be input into the generative AI model. For example, a prompt sentence containing product name and price information is generated.

[0913] Visualization: Using a generative AI model, we create a visual representation of the website elements from the prompt text using image generation AI.

[0914] 2D Metaverse Transformation: Transforms visualized website elements into a 2D virtual space, allowing users to visually confirm website elements within the virtual space.

[0915] Automation: Automate the process of creating a 2D metaverse platform, which will allow for efficient generation of the 2D metaverse.

[0916] SaaS model sales method: The above series of processes are packaged and provided as a service, allowing users to easily use the system.

[0917] Price comparison tool: Obtain product information from multiple online retailers and compare prices using scraping technology.

[0918] Lowest price display method: The lowest price from the obtained price information is displayed to the user.

[0919] Notification method: Send a notification to the user when the price falls below the price set by the user.

[0920] Processing flow

[0921] 1. Data retrieval: The server retrieves HTML data from the specified URL using the requests library.

[0922] 2. Data analysis: Analyze the HTML data using the BeautifulSoup library and extract product name and price information.

[0923] 3. Prompt generation: Based on the extracted information, a prompt sentence is generated to be input into the generative AI model.

[0924] 4. Visualization: Using a generative AI model, we generate a visual representation of the website elements from the prompt.

[0925] 5. 2D Metaverse Transformation: Transforming visualized elements into a two-dimensional virtual space.

[0926] 6. Price Comparison: Compare price information obtained from multiple online retailers and offer the lowest price.

[0927] 7. Notification: Send a notification when the price drops below the user's set price.

[0928] Specific examples

[0929] If a user searches for "iPhone 13," the server will:

[0930] 1. The user types "iPhone 13."

[0931] 2. The server obtains price information for the iPhone 13 from multiple online sales sites.

[0932] 3. Generate a prompt sentence based on the obtained price information.

[0933] 4. Use a generative AI model to generate visual elements from the prompt.

[0934] 5. Transform the visualized elements into a 2D metaverse platform.

[0935] 6. Compare prices and offer the lowest price to users.

[0936] 7. Send a notification when the price drops below the price set by the user.

[0937] Prompt Sentence Examples

[0938] Write a Python program that retrieves pricing information from multiple online retailers and returns the lowest price when a user searches for "iPhone 13." Use BeautifulSoup and the requests library.

[0939] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0940] Step 1:

[0941] The server receives a product name from the user as input. For example, the user enters "iPhone 13." Based on this input, the server generates URLs for multiple online retailers.

[0942] Step 2:

[0943] The server uses the generated URLs to retrieve HTML data from each online sales site using the requests library. In this process, an HTTP request is sent to each URL and HTML data is received as a response. The input is a URL and the output is HTML data.

[0944] Step 3:

[0945] The server analyzes the retrieved HTML data using the BeautifulSoup library. Specifically, it extracts product names, prices, images, and other information from the HTML data. The input is the HTML data, and the output is the analyzed product information.

[0946] Step 4:

[0947] Based on the analyzed product information, the server generates a prompt to be input into the generative AI model. For example, it generates a prompt such as "Get price information for the iPhone 13 and display the lowest price." The input is the product information, and the output is the prompt.

[0948] Step 5:

[0949] The server uses a generative AI model to visually represent website elements from the prompt text. Specifically, the prompt text is input into the generative AI model, which then generates visual elements (images and text). The input is the prompt text, and the output is the visual elements.

[0950] Step 6:

[0951] The server converts the visual elements into a 2D metaverse platform. Specifically, the generated visual elements are placed in a 2D virtual space so that users can visually confirm them. The input is the visual elements, and the output is the 2D metaverse platform.

[0952] Step 7:

[0953] The server compares price information obtained from multiple online sales sites. Specifically, it compiles the price information obtained from each site into a list and finds the cheapest price. The input is multiple price information, and the output is the cheapest price.

[0954] Step 8:

[0955] The server presents the cheapest price to the user. Specifically, it displays the cheapest price information on the user's terminal. The input is the cheapest price, and the output is the information presented to the user.

[0956] Step 9:

[0957] The server sends a notification when the price falls below the price set by the user. Specifically, it compares the price with the price set by the user, and if the condition is met, it generates a notification and sends it to the user's terminal. The input is the user-set price and the current price, and the output is the notification.

[0958] Example 2

[0959] Next, a description will be given of Example 2 of Form 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."

[0960] Conventional methods for analyzing and visualizing website information often require a series of manual processes, from acquiring information to analyzing, generating prompts, visualizing, and converting to a metaverse platform, resulting in inefficiency. Furthermore, because there is no integrated system for automating these processes, users must combine multiple tools and technologies, making operations cumbersome. Furthermore, the quality and accuracy of the generated prompts can be low, preventing the full potential of the generative AI model.

[0961] 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.

[0962] In this invention, the server includes means for acquiring and analyzing website information, means for analyzing the acquired website information using natural language processing technology and extracting keywords and phrases, and means for generating prompt sentences by combining the extracted keywords and phrases. This automates the entire process from acquiring website information to analyzing it and generating prompt sentences, enabling efficient and high-quality generation of prompt sentences.

[0963] "Website information" refers to the content of web pages published on the Internet, including text, images, video, and audio.

[0964] "Means of analysis" refers to the technology and methods used to analyze the acquired website information and understand its content.

[0965] "Natural language processing technology" refers to technology that enables computers to understand and process human language, including text analysis and the extraction of keywords and phrases.

[0966] "Keywords and phrases" refer to important words or short sentences extracted from website information.

[0967] A "prompt" is a textual input to a generative AI model, containing a question or instruction to summarize the content of a website or elicit specific information.

[0968] "Visualization tools" refers to techniques and methods for visually representing textual information or data, including graphs, charts, images, etc.

[0969] A "2D Metaverse Platform" refers to a platform that provides a two-dimensional virtual space, providing an environment in which users can interact with the virtual space.

[0970] "Providing as a service" refers to a business model in which systems or software are provided to users via the Internet. Specifically, this includes the SaaS (Software as a Service) model.

[0971] "HTML information" refers to information written in a markup language used to construct a web page.

[0972] "Image generation technology" refers to technology that allows computers to generate images. Specifically, it includes image generation algorithms that use machine learning and deep learning.

[0973] The present invention relates to a system for acquiring and analyzing information from a website and generating a prompt sentence. A specific embodiment of this system will be described below.

[0974] First, the user enters the URL of a specific website on the terminal. For example, the user enters the URL "https: / / example.com". Then, the server accesses the URL entered by the user and obtains the HTML content of the web page. In this case, the server uses the "requests" library to obtain the HTML information.

[0975] To extract text information from the retrieved HTML content, the server uses the "BeautifulSoup" library, which allows it to strip HTML tags and obtain pure text information.

[0976] The server then uses the spaCy library to analyze the extracted text, specifically extracting important keywords and phrases from the text, providing valuable information for summarizing the website's content.

[0977] Based on the extracted keywords and phrases, the server generates a prompt sentence. The generated prompt sentence is used as input to a generative AI model (e.g., a natural language generation model). Examples of prompt sentences that may be generated include the following:

[0978] To summarize the content of "https: / / example.com," the main topics are "Latest Technology Trends," "AI Applications," and "Latest Research in Data Science." Please provide detailed information on these topics.

[0979] By inputting this prompt into a generative AI model, detailed information based on the website content can be generated, which is provided to the user as a reference for gaining a deeper understanding of the website content.

[0980] This system automates the entire process, from acquiring website information to analyzing it and generating prompts, enabling efficient and high-quality prompt generation. This simplifies operation and eliminates the need for users to combine multiple tools and technologies. It also improves the quality and accuracy of the generated prompts, fully utilizing the capabilities of the generative AI model.

[0981] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0982] Step 1:

[0983] The user enters a website URL.

[0984] The user inputs the URL of a specific website from the terminal. For example, the user inputs the URL "https: / / example.com". This input URL becomes the input data for the next processing step.

[0985] Step 2:

[0986] The server retrieves the HTML information from the specified URL.

[0987] The server accesses the URL entered by the user and retrieves the HTML content of the web page. To do this, the server uses the "requests" library. Specifically, it sends an HTTP request to the URL and receives HTML information as a response. This HTML information becomes the input data for the next processing step.

[0988] Step 3:

[0989] The server extracts text information from the HTML information it has obtained.

[0990] The server uses the "BeautifulSoup" library to extract text information from the retrieved HTML content. Specifically, it parses the HTML tags and removes them to obtain pure text information, which becomes the input data for the next processing step.

[0991] Step 4:

[0992] The server analyzes the text information and extracts keywords and phrases.

[0993] The server uses the "spaCy" library to analyze the extracted text information. Specifically, it analyzes the text information using natural language processing technology to extract important keywords and phrases. The keywords and phrases obtained through this analysis become the input data for the next processing step.

[0994] Step 5:

[0995] The server generates a prompt by combining extracted keywords and phrases.

[0996] The server generates a prompt sentence based on the extracted keywords and phrases. Specifically, it appropriately combines keywords and phrases to create a prompt sentence to input into the generative AI model. The generated prompt sentence becomes the output data for the next processing step.

[0997] Step 6:

[0998] Provides the user with a server-generated prompt.

[0999] The server returns the generated prompt to the user. Specifically, it sends the generated prompt to the user as an HTTP response. The user can use this prompt to input the generative AI model.

[1000] (Application example 2)

[1001] Next, a description will be given of Application Example 2 of Form 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."

[1002] Conventional website information analysis and content recommendation systems have difficulty accurately grasping users' interests and recommending appropriate content. In addition, there has been a lack of methods for efficiently analyzing website information and providing useful information to users.

[1003] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring and analyzing website information, means for generating prompts from the acquired website information, and means for visualizing website elements using the prompts. This enables means for analyzing the acquired website information and extracting keywords and phrases, means for generating prompts by combining the extracted keywords and phrases, and means for recommending content based on the generated prompts.

[1004] "Means of obtaining and analyzing website information" refers to technologies and methods for collecting and analyzing text and metadata from web pages on the Internet.

[1005] "Means for turning acquired website information into prompts" refers to techniques or methods for analyzing collected website text information using natural language processing technology, summarizing the content or converting it into keywords to generate prompts.

[1006] A "prompt-based visualization of website elements" is a technique or method for visually representing website content based on generated prompts.

[1007] "Means of transforming visualized elements into a 2D metaverse platform" refers to techniques and methods for transforming visually represented website elements into a two-dimensional virtual space.

[1008] "Methods for automating the creation of 2D metaverses" are technologies and methods for automatically generating and managing two-dimensional virtual spaces.

[1009] "Means of analyzing acquired website information and extracting keywords and phrases" refers to techniques and methods for analyzing collected website text information and extracting important keywords and phrases from it.

[1010] "Means for generating prompts by combining extracted keywords and phrases" refers to techniques or methods for combining extracted keywords and phrases to generate prompts that summarize the content of a website.

[1011] The "means for recommending content based on a generated prompt" refers to a technique or method for recommending relevant content to a user based on a generated prompt.

[1012] "Methods of packaging a series of processes and selling them under the SaaS model" refers to technologies and methods that bundle the above series of technologies and methods into a single service and provide and sell it as cloud-based software.

[1013] The system for implementing this invention consists of three main elements: a server, a terminal, and a user. Below we will explain how each element functions.

[1014] server

[1015] The server is responsible for retrieving and analyzing website information. Specifically, the server uses the following software and hardware:

[1016] The requests library: Used to retrieve HTML information from websites.

[1017] BeautifulSoup: Used to parse the retrieved HTML information and extract text information.

[1018] The spacy library: Used to analyze the extracted text information and extract keywords and phrases.

[1019] The transformers library: Used to generate and recommend content based on prompts using a generative AI model (GPT-3).

[1020] The server analyzes the retrieved website information to extract keywords and phrases. It then combines the extracted keywords and phrases to generate prompts. Based on these prompts, a generative AI model is used to recommend relevant content to the user.

[1021] Terminal

[1022] A Device is a device through which a User browses a website and receives recommended content. A Device uses the following software and hardware:

[1023] Web browser: Used by users to browse websites.

[1024] Smartphone application: Used to receive recommended content from the server and display it to the user.

[1025] The device sends the URL of the website the user has visited to the server and receives recommended content from the server, which is then displayed to the user.

[1026] User

[1027] A user is an entity that browses a website and receives recommended content. A user uses the system in the following steps:

[1028] 1. Browse the website using a web browser or smartphone application.

[1029] 2. The URL of the visited website is sent to the server.

[1030] 3. Receive the recommended content from the server and view it.

[1031] Specific examples

[1032] For example, if a user visits the URL "https: / / example.com" and the content of this URL is "Latest Technology News," the server will parse this information and generate a prompt like this:

[1033] Latest Technology News

[1034] Based on this prompt, the generative AI model is expected to recommend other news articles related to the latest technology. Users can receive and view these recommended content through a smartphone application.

[1035] As described above, the present invention provides a system that efficiently analyzes information on a website and recommends related content to a user.

[1036] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1037] Step 1:

[1038] The server receives the URL of the website visited by the user. The input is the URL sent by the user from the terminal, and the output is the HTML information obtained based on that URL. Specifically, the server uses the requests library to obtain the HTML information from the specified URL.

[1039] Step 2:

[1040] The server analyzes the obtained HTML information and extracts text information. The input is the HTML information obtained in step 1, and the output is the extracted text information. Specifically, the server uses BeautifulSoup to extract text information from HTML.

[1041] Step 3:

[1042] The server analyzes the extracted text information and extracts keywords and phrases. The input is the text information extracted in step 2, and the output is the extracted keywords and phrases. Specifically, the server uses the spacy library to analyze the text information and extract important keywords and phrases.

[1043] Step 4:

[1044] The server generates a prompt by combining the extracted keywords and phrases. The input is the keywords and phrases extracted in step 3, and the output is the generated prompt. Specifically, the server combines the extracted keywords and phrases to generate a prompt that summarizes the content of the website.

[1045] Step 5:

[1046] The server recommends content based on the generated prompt. The input is the prompt generated in step 4, and the output is the recommended content. Specifically, the server uses the transformers library to generate and recommend relevant content based on the prompt using a generative AI model (GPT-3).

[1047] Step 6:

[1048] The terminal receives the recommended content from the server and displays it to the user. The input is the recommended content sent from the server in step 5, and the output is the content displayed to the user. Specifically, the terminal receives the recommended content using a smartphone application and displays it to the user.

[1049] Step 7:

[1050] The user browses the recommended content through the device. The input is the content displayed on the device in step 6, and the output is the user's browsing behavior. Specifically, the user browses the recommended content using a smartphone application and explores further related content as needed.

[1051] Example 3

[1052] Next, a description will be given of Example 3 of Form Example 3. 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."

[1053] The process of visualizing traditional website elements and converting them into virtual space was often done manually, which required time and effort. Furthermore, there was no integrated system for automating these processes, making efficient production difficult. Furthermore, there was a lack of a way to provide these processes as cloud services, making it difficult for users to easily access them.

[1054] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means. In this invention, the server includes means for acquiring and analyzing website information, means for generating prompts from the acquired website information, means for visualizing website elements using the prompts, means for converting the visualized elements into a 2D virtual space platform, means for automating the creation of the 2D virtual space, and means for packaging a series of processes and selling them under a cloud service model. This makes it possible to efficiently visualize website elements, automate the process of converting them into a virtual space, and provide it as a cloud service that users can easily access.

[1055] "Means for acquiring and analyzing website information" refers to technology for collecting data from websites on the Internet and analyzing that data.

[1056] "Means for prompting acquired website information" refers to a technology that generates text-based instructions (prompts) to be input into a generative AI model based on collected website data.

[1057] "A means of visualizing website elements using prompts" is a technology that inputs prompts into a generative AI model and generates images and graphics based on those instructions.

[1058] "Means for converting visualized elements into a 2D virtual space platform" refers to a technology that inputs the generated visuals into a 2D virtual space engine and constructs a virtual space scene based on that.

[1059] "Means for automating the production of 2D virtual spaces" refers to technology that streamlines the production process of virtual spaces by using scripts and programs that automate the operation of 2D virtual space engines.

[1060] "A means of packaging a series of processes and selling them as a cloud service model" refers to a technology that integrates the above technologies, packages them as a service provided on the cloud, and makes them available to users via the Internet.

[1061] "Means for obtaining markup language information" refers to technology that collects data from languages ​​used to describe the structure of web pages (e.g., HTML and XML).

[1062] A "generative AI model" is an artificial intelligence model that generates images and other content based on input text or data.

[1063] This invention is a system for visualizing website elements and converting them into a 2D virtual space. A specific embodiment of this system will be described below.

[1064] First, the server receives the website URL and HTML data provided by the user. The server parses this data and obtains the markup language information to understand the structure and content of the website. This analysis is performed using software such as an HTML parser or a web scraping tool.

[1065] The server then generates prompts based on the acquired website information. The prompts are text instructions to be input to the generative AI model. For example, the server generates prompts containing information such as the website's background color and button placement.

[1066] Using the generated prompt, the server inputs the prompt into a generative AI model (e.g., DALL-E or MidJourney) to generate a visual (image). The generative AI model generates an image containing the specified elements based on the input prompt.

[1067] Next, the server inputs the generated visuals into a 2D virtual space platform (such as Unity or Godot), which then constructs a 2D virtual space scene based on the input visuals. For example, it generates a scene with a large red button on a blue and white background.

[1068] Furthermore, the server creates and executes scripts to automate the creation of the 2D virtual space. These scripts call the API of the 2D virtual space platform to automatically perform operations such as visual placement, color setting, and button placement.

[1069] Finally, the server packages the above process and provides it as a cloud service. Users can access the cloud-based service platform and convert their website into a 2D virtual space by entering prompts.

[1070] For example, if a user inputs the prompt "A big red button on a blue and white background," the generative AI model will generate a visual representation of that scene, and the 2D virtual world platform will build a scene based on that visual. The user can then view the generated 2D virtual world and edit it as needed.

[1071] This system makes it possible to efficiently visualize website elements, automate the process of converting them into a virtual space, and provide it as a cloud service that users can easily access. The flow of the specific processing in Example 3 will be described with reference to Fig. 15.

[1072] Step 1:

[1073] A user inputs a website URL or HTML data. The server receives this and retrieves website information. Specifically, the server uses an HTML parser or web scraping tool to analyze the structure and content of the website and obtain markup language information. The input is the website URL or HTML data, and the output is the parsed website information.

[1074] Step 2:

[1075] The server generates a prompt based on the retrieved website information. Specifically, it extracts information such as the website's background color, button placement, and text content, and converts it into a text prompt. The input is the parsed website information, and the output is the generated prompt.

[1076] Step 3:

[1077] The server inputs the generated prompt into the generative AI model. Specifically, it sends the prompt sentence as an API request to the generative AI model (e.g., DALL-E or MidJourney), which then generates a visual (image) based on the prompt. The input is the prompt sentence, and the output is the generated visual.

[1078] Step 4:

[1079] The server inputs the generated visuals into the 2D virtual space platform. Specifically, it sends the visual data to the API of the 2D virtual space engine (e.g., Unity or Godot), and builds a 2D virtual space scene based on that. The input is the generated visuals, and the output is a 2D virtual space scene.

[1080] Step 5:

[1081] The server creates and executes scripts to automate the creation of 2D virtual spaces. Specifically, it calls the API of the 2D virtual space engine to automatically perform operations such as visual placement, color settings, and button placement. The input is a 2D virtual space scene, and the output is the automated 2D virtual space scene.

[1082] Step 6:

[1083] The server packages the above series of processes and provides them as a cloud service model. Specifically, the service is hosted using cloud infrastructure and made accessible to users via the Internet. The input is an automated 2D virtual space scene, and the output is a system provided as a cloud-based service.

[1084] Step 7:

[1085] Users can access the cloud-based service platform and convert their websites into 2D virtual spaces by entering a prompt. Specifically, users enter a prompt and then view and edit the generated 2D virtual world. The input is the prompt, and the output is an editable 2D virtual world.

[1086] (Application example 3)

[1087] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1088] Traditionally, designing websites and building virtual stores required specialized knowledge and skills, making them difficult for average users. Changing or updating designs was also time-consuming, making it difficult to respond quickly. Furthermore, converting to a 2D metaverse platform and automating production were complex, requiring efficient operation.

[1089] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means for acquiring and analyzing website information, means for converting the acquired website information into prompts, means for visualizing website elements using the prompts, means for converting the visualized elements into a 2D metaverse platform, means for automating the creation of the 2D metaverse, means for packaging a series of processes and providing them in a cloud-based service model, means for automatically generating a virtual store design when a user inputs prompts, and means for placing the generated virtual store visuals on the 2D metaverse platform. This enables even general users to easily design websites and build virtual stores, and enables quick and efficient design changes and updates.

[1090] "Means for obtaining and analyzing website information" refers to a method for collecting data on web pages that exist on the Internet and analyzing their content.

[1091] A "means for generating prompts from acquired website information" is a method for generating text prompts that express specific instructions or requests based on collected web page data.

[1092] A "means for visualizing website elements using prompts" is a method that uses generated prompts as input to generate images or graphics that visually represent the components of a web page.

[1093] "Means for converting visualized elements into a 2D metaverse platform" refers to a method for applying visually represented web page elements to a two-dimensional virtual space platform.

[1094] "Methods for automating the creation of a 2D metaverse" refers to the use of scripts and programs to automatically construct a two-dimensional virtual space.

[1095] "Means of packaging a series of processes and providing them as a cloud-based service model" refers to a method of integrating the above-mentioned means and providing them as a service that can be used over the Internet.

[1096] The "means for automatically generating a virtual store design by a user inputting a prompt" is a method for automatically creating a virtual store design based on instructions input by a user.

[1097] "Means for placing the generated visuals of a virtual store on a 2D metaverse platform" refers to a method for placing an automatically generated virtual store design on a two-dimensional virtual space platform.

[1098] A system for implementing this invention includes means for acquiring and analyzing website information, means for prompting the acquired website information, means for visualizing website elements using the prompts, means for converting the visualized elements into a 2D metaverse platform, means for automating the creation of the 2D metaverse, means for packaging a series of processes and providing them in a cloud-based service model, means for automatically generating a virtual store design when a user inputs prompts, and means for placing the generated virtual store visuals on the 2D metaverse platform.

[1099] System program processing explanation

[1100] A means of obtaining and analyzing website information

[1101] The server retrieves HTML information from web pages on the Internet and analyzes the content. Specifically, it uses an HTML parser to analyze the structure of the web page and extract the necessary information.

[1102] A method to prompt for acquired website information

[1103] The server uses the parsed information from the web page to generate a text prompt that expresses a specific instruction or request, such as "There is a big red button on a blue and white background."

[1104] A way to visualize website elements using prompts

[1105] The server uses the generated prompt as input to generate an image that visually represents the components of the web page using image generation AI (e.g., OpenAI's API).

[1106] A way to transform visualizations into a 2D metaverse platform

[1107] The server applies the visually represented elements of the web page to a two-dimensional virtual space platform, specifically by using the API of the 2D metaverse engine to place the generated images in the virtual space.

[1108] A way to automate the creation of the 2D metaverse

[1109] The server uses scripts and programs to automatically construct a two-dimensional virtual space, which automatically performs operations such as visual placement, color setting, and button placement.

[1110] A means of packaging a series of processes and providing them as a cloud-based service model

[1111] The server integrates the above methods and provides them as a service available over the Internet, allowing users to convert their websites into a 2D metaverse.

[1112] A method for automatically generating virtual store designs by prompting users to enter information

[1113] Using a smartphone or head-mounted display, users input specific prompts, such as "a large red button on a blue and white background," and a virtual store design is automatically generated.

[1114] A means to place the generated virtual store visuals on a 2D metaverse platform

[1115] The server places the automatically generated virtual store design on a two-dimensional virtual space platform, allowing users to view and operate the virtual store in the virtual space.

[1116] Specific examples

[1117] As an example, use the following prompt:

[1118] Prompt: "A large red button on a blue and white background."

[1119] Prompt: "Yellow flowers bloom in the green field."

[1120] Prompt: "The word 'On Sale' is written in white letters on a black background."

[1121] By entering these prompts, a corresponding visual of a virtual store is generated and placed on the 2D metaverse platform.

[1122] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1123] Step 1:

[1124] The server retrieves HTML information from web pages on the Internet and analyzes its content. Specifically, it uses an HTML parser to analyze the structure of the web page and extract the necessary information. The input is the URL of the web page, and the output is the analyzed HTML information.

[1125] Step 2:

[1126] The server uses the parsed information from the web page to generate a text prompt that expresses a specific instruction or request, such as "There is a big red button on a blue and white background." The input is the parsed HTML information, and the output is the generated prompt text.

[1127] Step 3:

[1128] The server takes the generated prompt as input and uses image generation AI (e.g., OpenAI's API) to generate an image that visually represents the components of the web page. The input is the prompt text, and the output is the generated image.

[1129] Step 4:

[1130] The server applies the visually represented elements of the web page to a 2D virtual space platform. Specifically, it uses the API of the 2D metaverse engine to place the generated image in the virtual space. The input is the generated image, and the output is the visual placed on the 2D metaverse platform.

[1131] Step 5:

[1132] The server uses scripts and programs to automatically construct a 2D virtual space, which automatically performs operations such as visual placement, color setting, and button placement. The input is the visuals placed on the 2D metaverse platform, and the output is the automated 2D metaverse world.

[1133] Step 6:

[1134] The server integrates the above methods and provides them as a service available over the Internet. Users can access this service to convert their websites into 2D metaverses. The input is the user's access information, and the output is the 2D metaverse conversion service provided in a cloud-based service model.

[1135] Step 7:

[1136] The user inputs a specific prompt using a smartphone or head-mounted display. For example, by inputting a prompt such as "There is a large red button on a blue and white background," the design of a virtual store is automatically generated. The input is the prompt entered by the user, and the output is the generated design of the virtual store.

[1137] Step 8:

[1138] The server places the automatically generated virtual store design on a 2D virtual space platform, allowing users to view and operate the virtual store in the virtual space. The input is the generated virtual store design, and the output is a virtual store placed on the 2D metaverse platform.

[1139] 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.

[1140] "Example 1"

[1141] As one embodiment of the present invention, a system incorporating an emotion engine is provided. This emotion engine has the ability to recognize emotions from a user's facial expressions and tone of voice. Specifically, when a user uses the system, the system captures the user's facial expressions and voice using a camera and microphone, and analyzes the data. This analysis makes it possible to recognize the user's emotional state, such as joy, sadness, surprise, or anger.

[1142] "Example 2"

[1143] Additionally, a function is provided to adjust the display of the 2D Metaverse based on the recognized emotions. For example, if the user expresses joy, the environment in the 2D Metaverse will be changed to a bright and cheerful atmosphere. Conversely, if the user expresses sadness, the environment in the 2D Metaverse will be changed to a quiet and calm atmosphere. This allows for a more personalized 2D Metaverse experience that reflects the user's emotional state.

[1144] "Example 3"

[1145] The emotion engine can also track a user's emotional state over time and analyze the data. This allows it to understand the user's emotional patterns and optimize the 2D metaverse display accordingly. For example, if a user tends to feel tired in the afternoon, it can provide a relaxing 2D metaverse environment at that time.

[1146] The processing flow of each embodiment will be described below.

[1147] "Example 1"

[1148] Step 1: When a user uses the system, the user's facial expressions and voice are captured through a camera and microphone.

[1149] Step 2: The captured facial and voice data is sent to the emotion engine.

[1150] Step 3: The emotion engine analyzes the data and recognizes the user's emotional state.

[1151] "Example 2"

[1152] Step 1: The emotion engine recognizes the user's emotional state.

[1153] Step 2: Adjust the display of the 2D metaverse based on the perceived emotional state.

[1154] Step 3: Provide a 2D metaverse experience that responds to the user's emotional state.

[1155] "Example 3"

[1156] Step 1: The emotion engine tracks the user's emotional state over time.

[1157] Step 2: Analyze the tracked data and understand the changing patterns of users' emotions.

[1158] Step 3: Optimize the display of the 2D metaverse based on the emotional change patterns.

[1159] Example 1

[1160] Next, a description will be given of Example 1 of Form 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."

[1161] Conventional website information acquisition and analysis systems are limited to acquiring and analyzing information, and lack the means to effectively utilize the acquired information. They also lack the technology to recognize user emotions and optimize system responses. This makes it difficult to improve the user experience and utilize the acquired information in a multifaceted way.

[1162] 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. In this invention, the server includes means for acquiring and analyzing website information, means for converting the acquired website information into prompts, means for visualizing website elements using the prompts, means for converting the visualized elements into a 2D virtual space platform, means for automating the creation of the 2D virtual space, means for recognizing the user's facial expressions and tone of voice and analyzing their emotions, and means for packaging a series of processes and selling them as a service. This enables diversified use of website information and an improved user experience.

[1163] "Means for acquiring and analyzing website information" refers to technology for acquiring HTML information from a specified website and analyzing its structure and content.

[1164] "Means for prompting acquired website information" refers to a technology that generates prompt sentences to be input into a generative AI model based on acquired website information.

[1165] "Method for visualizing website elements using prompts" is a technology that inputs prompt statements into a generative AI model to visually represent website elements.

[1166] "Means for converting visualized elements into a 2D virtual space platform" refers to a technology for reproducing visualized website elements in a 2D virtual space.

[1167] "Means for automating the creation of 2D virtual spaces" refers to technology for automatically executing the process of constructing 2D virtual spaces.

[1168] "Means for recognizing a user's facial expression and tone of voice and analyzing emotions" refers to technology that uses a camera or microphone to capture a user's facial expression and tone of voice, and analyzes that data to recognize the user's emotional state.

[1169] "A means of packaging a series of processes and selling them as a service model" refers to a technology that brings together the above series of processes into a single package and provides it as a service.

[1170] MODE FOR CARRYING OUT THE INVENTION

[1171] This invention is a system that acquires and analyzes website information and improves the user experience based on that information. This system works in cooperation with three parties: a server, a terminal, and a user.

[1172] 1. Obtaining website information using an HTML scraping tool

[1173] The server uses an HTML scraping tool to obtain HTML information from a specified website. This tool is implemented using libraries such as BeautifulSoup or Scrapy. The server accesses the specified URL and obtains the HTML document. It then analyzes the structure of the HTML document and extracts text information, image information, link information, and other information from the page.

[1174] Examples:

[1175] The server accesses the URL "https: / / example.com" and retrieves all link information on the page.

[1176] Use BeautifulSoup to extract the URLs of all images on the page.

[1177] Example prompt sentence:

[1178] Get all link information for "https: / / example.com".

[1179] 2. User Emotion Recognition Using an Emotion Engine

[1180] The device uses a camera and microphone to capture the user's facial expressions and tone of voice, and sends the data acquired from these devices to a server.

[1181] The server uses an emotion engine to analyze the received data. This emotion engine is implemented using libraries such as OpenCV and TensorFlow. The server analyzes the user's facial expressions and tone of voice to recognize emotional states such as joy, sadness, surprise, and anger.

[1182] Examples:

[1183] The device captures the user's facial expressions through the camera and sends the data to the server.

[1184] The server uses TensorFlow to recognize the emotion of "happiness" from the user's facial expression.

[1185] Example prompt sentence:

[1186] Use the camera and microphone to recognize the user's emotions.

[1187] 3. Prompt and visualize website information

[1188] Based on the acquired website information, the server generates prompt sentences to be input into the generative AI model. These prompt sentences are used to visually represent the website elements. The generative AI model, for example, uses image generation AI to visualize the website elements.

[1189] Examples:

[1190] Based on the link information obtained by the server, a prompt sentence is generated for the generative AI model saying, "Please visually represent this link information."

[1191] A generative AI model generates visual elements based on link information.

[1192] Example prompt sentence:

[1193] Please provide a visual representation of this link information.

[1194] 4. Conversion to 2D virtual space platform and automation

[1195] The server converts the visualized elements into a 2D virtual space platform. This conversion process is automated, eliminating the need for users to do anything. The 2D virtual space is created using a game engine such as Unity or Unreal Engine.

[1196] Examples:

[1197] The server imports the generated visual elements into Unity and automatically constructs a 2D virtual space.

[1198] Allows users to interact with website elements in a 2D virtual space.

[1199] 5. Service model

[1200] The server packages a series of processes and provides them as a service model. This service is provided as a Software as a Service (SaaS) model, and users can access it via the Internet.

[1201] Examples:

[1202] The server bundles all the processes into one package and provides it to the user.

[1203] Users access the service through a web browser to retrieve, analyze, visualize, and convert website information into a 2D virtual space.

[1204] The above is an embodiment of the present invention. This system enables multifaceted use of website information and improves the user experience.

[1205] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1206] Program processing flow

[1207] Step 1: Obtaining website information

[1208] The server accesses the specified URL and retrieves the HTML document. As input, it receives the URL specified by the user (e.g., "https: / / example.com"). The server sends an HTTP request and retrieves the HTML document as a response. As output, it receives the retrieved HTML document.

[1209] Specific behavior:

[1210] The server uses the requests library to access the specified URL.

[1211] The server retrieves the HTML document from the response.

[1212] Step 2: Parsing the HTML document

[1213] The server parses the retrieved HTML document. As input, it receives the HTML document retrieved in step 1. The server parses the HTML document using the BeautifulSoup library and extracts text information, image information, link information, etc. from the page. As output, it obtains the parsed information.

[1214] Specific behavior:

[1215] The server uses BeautifulSoup to parse the HTML document.

[1216] The server extracts all link information within the page.

[1217] Step 3: Generate a prompt statement

[1218] Based on the analyzed information, the server generates a prompt sentence to be input to the generative AI model. As input, it receives the information analyzed in step 2. The server organizes the information and generates a prompt sentence. As output, it obtains the generated prompt sentence.

[1219] Specific behavior:

[1220] The server generates a prompt sentence saying "Please visually express this link information" based on the link information.

[1221] Step 4: Visualization with generative AI models

[1222] The server inputs the generated prompt sentence into the generative AI model to visualize the website elements. As input, it receives the prompt sentence generated in step 3. The server inputs the prompt sentence into the generative AI model to generate the visual elements. As output, it obtains the visualized elements.

[1223] Specific behavior:

[1224] The server inputs prompts into the generative AI model to generate visual elements.

[1225] Step 5: Transforming into a 2D virtual space platform

[1226] The server converts the visualized elements into a 2D virtual space platform. As input, it receives the visualized elements from step 4. The server uses a game engine such as Unity or Unreal Engine to build the 2D virtual space. As output, it obtains a 2D virtual space.

[1227] Specific behavior:

[1228] The server imports the visualized elements into Unity and automatically constructs a 2D virtual space.

[1229] Step 6: Recognizing User Emotions

[1230] The device uses a camera and microphone to capture the user's facial expressions and tone of voice. As input, the device captures the user's facial expressions and voice in real time. The device sends the captured data to the server. As output, the device obtains the captured data.

[1231] Specific behavior:

[1232] The device captures the user's facial expressions through the camera.

[1233] The device captures the user's voice through a microphone.

[1234] Step 7: Emotion Engine Analysis

[1235] The server uses an emotion engine to analyze the received data. As input, it receives the data captured in step 6. The server analyzes the data using libraries such as OpenCV and TensorFlow to recognize the user's emotional state. As output, it obtains the recognized emotional state.

[1236] Specific behavior:

[1237] The server uses TensorFlow to analyze emotions from the user's facial expressions.

[1238] The server recognizes the user's emotional state based on the analysis results.

[1239] Step 8: Offering as a service model

[1240] The server packages a series of processes and provides them as a service model. As input, it receives the data and results obtained at all steps. The server compiles these into a single package and provides it to the user. As output, it obtains a package that is provided as a service.

[1241] Specific behavior:

[1242] The server bundles all the processes together as a single package.

[1243] The server provides it as a service to the user.

[1244] The above is the specific processing flow of the program of this system.

[1245] (Application example 1)

[1246] Next, a description will be given of Application Example 1 of Form 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."

[1247] Conventional online shopping sites do not take user emotions into consideration when recommending products, and this does not sufficiently improve the user experience. Furthermore, there is a lack of methods for efficiently acquiring and analyzing website information, making it difficult to quickly provide users with products that are suitable for them. Furthermore, there is a lack of methods for effectively visualizing acquired information and presenting it to users in an easy-to-understand manner. To solve these issues, a system is needed that analyzes user emotions in real time and recommends products based on that analysis.

[1248] 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.

[1249] In this invention, the server includes means for acquiring and analyzing website information, means for converting the acquired website information into prompts, means for visualizing website elements using the prompts, means for recognizing emotions from a user's facial expressions and tone of voice, and means for recommending products based on the recognized emotions. This enables product recommendations that take the user's emotions into consideration, improving the user experience.

[1250] "Means for acquiring and analyzing website information" refers to technology for acquiring HTML information from a specified website and analyzing its structure and content.

[1251] "Means for prompting acquired website information" refers to a technology that generates prompt sentences to be input into a generative AI model based on acquired website information.

[1252] A "means for visualizing website elements using prompts" is a technique for visually representing website elements using generated prompt sentences.

[1253] "Means for transforming visualized elements into a 2D metaverse platform" refers to a technology that adapts visualized website elements into a 2D metaverse environment.

[1254] "Means for automating the creation of 2D metaverses" refers to technology that automates the process of building a 2D metaverse platform.

[1255] "Means for recognizing emotions from a user's facial expressions and tone of voice" refers to technology that uses a camera and microphone to analyze a user's facial expressions and voice to identify emotions.

[1256] "Means for recommending products based on recognized emotions" refers to a technology that selects and recommends appropriate products based on the user's emotional state.

[1257] "A means of packaging a series of processes and selling them under the SaaS model" refers to a technology that integrates the above technologies, creates a package to provide it as a service, and sells it under the SaaS (Software as a Service) model.

[1258] A system for implementing this invention has the following configuration: First, a server has means for acquiring and analyzing website information. This means acquires HTML information from a specified website and uses Python libraries such as BeautifulSoup and requests to analyze its structure and content.

[1259] Next, there is a means for converting the acquired website information into a prompt. This means generates a prompt sentence to be input into the generative AI model based on the acquired website information. The generated prompt sentence has the following format, for example:

[1260] Design a system that analyzes the user's facial expressions and voice through a camera and microphone while they are browsing an online shopping site, and recognize their emotions. Develop an application that can recommend suitable products based on the recognized emotions.

[1261] It also has a method for visualizing website elements using prompts, which uses image generation AI to visually represent website elements using generated prompt sentences, specifically using libraries such as OpenCV and Keras.

[1262] Furthermore, there is a means for converting visualized elements into a 2D metaverse platform, which performs the appropriate transformations to adapt the visualized website elements to the 2D metaverse environment.

[1263] Also included is a method for automating the creation of the 2D metaverse, which uses scripts and automation tools to automate the process of building a 2D metaverse platform.

[1264] It also has a means of recognizing emotions from the user's facial expressions and tone of voice, which uses a camera and microphone to analyze the user's facial expressions and voice and uses libraries such as OpenCV and Vosk to identify emotions.

[1265] It also includes a recognized emotion-based product recommendation solution, which uses a Python algorithm to select and recommend appropriate products based on the user's emotional state.

[1266] Finally, there is the option of packaging a series of processes and selling them under the SaaS model. This option integrates the above technologies, creates a package to provide it as a service, and uses a cloud platform to sell it under the SaaS (Software as a Service) model.

[1267] A specific example is a system that analyzes the user's emotions through a camera and microphone while browsing a shopping site on a smartphone, and recommends discounted products if the user is recognized as "happy." Another example is a system that recommends products that will provide comfort if the user's emotion is recognized as "sad" while browsing a shopping site using smart glasses.

[1268] In this way, product recommendations can be made that take the user's emotions into consideration, improving the user experience.

[1269] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1270] Step 1:

[1271] The server receives the URL of the specified website as input and retrieves the HTML information using the requests library. The retrieved HTML information is then analyzed using the BeautifulSoup library to extract the URLs, text information, image information, link information, and other information for each page of the website. This makes the structure and content of the website clear.

[1272] Step 2:

[1273] The server generates a prompt sentence to be input to the generative AI model based on the website information acquired in step 1. Specifically, it analyzes each element of the website (text, images, links, etc.) and converts them into a prompt sentence format. This prompt sentence is input to the generative AI model.

[1274] Step 3:

[1275] The server uses the generated prompt text to visually represent the elements of the website. Specifically, it uses image generation AI to generate images of each element of the website. This makes the content of the website easier to understand visually.

[1276] Step 4:

[1277] The server converts the visualized website elements into the 2D metaverse platform, specifically by performing appropriate transformations to adapt the visualized elements to the 2D metaverse environment, so that the website content is displayed in the 2D metaverse environment.

[1278] Step 5:

[1279] The server automates the creation of the 2D metaverse platform. Specifically, it uses scripts and automation tools to automate the process of building the 2D metaverse platform, thereby streamlining the creation of the 2D metaverse platform.

[1280] Step 6:

[1281] The device uses a camera and microphone to capture the user's facial expressions and voice. The captured data is analyzed using OpenCV and Vosk libraries to recognize the user's emotions, thereby identifying the user's emotional state.

[1282] Step 7:

[1283] The server recommends appropriate products based on the user's emotions recognized in step 6. Specifically, it uses a Python algorithm to select products that are appropriate for the user's emotional state and generate a recommendation list. This allows the user to be provided with products that match their emotions.

[1284] Step 8:

[1285] The server packages a series of processes and sells them under the SaaS model. Specifically, it integrates each of the above technologies, creates a package to provide it as a service, and sells it under the SaaS model using a cloud platform, allowing users to use the service.

[1286] Example 2

[1287] Next, a description will be given of Example 2 of Form 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."

[1288] Conventional website information acquisition and analysis systems have difficulty effectively prompting acquired information and providing a personalized 2D metaverse environment that responds to user emotions. Furthermore, there is a lack of a way to automate the process of visualizing website elements and converting them into a 2D metaverse platform. This has led to issues such as the inability to improve user experience and efficiently create a metaverse.

[1289] 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.

[1290] In this invention, the server includes means for acquiring and analyzing website information, means for analyzing the acquired website information using natural language processing technology and extracting keywords and phrases, means for combining the extracted keywords and phrases to generate prompts, means for recognizing a user's emotions and adjusting a 2D metaverse environment based on the emotions, means for visualizing website elements using the prompts, means for converting the visualized elements into a 2D metaverse platform, means for automating the creation of the 2D metaverse, and means for packaging the series of processes and selling them as a service. This enables effective prompting of website information and provision of a personalized 2D metaverse environment according to the user's emotions.

[1291] "Means for acquiring and analyzing website information" refers to the technology and methods for acquiring text and HTML information from websites on the Internet and analyzing their content.

[1292] "Means of analyzing using natural language processing technology and extracting keywords and phrases" refers to techniques or methods for analyzing acquired text information using natural language processing technology and identifying and extracting important keywords and phrases.

[1293] A "means for generating prompts" is a technique or method for combining extracted keywords and phrases to create prompts that meet a specific purpose.

[1294] "Means for recognizing a user's emotions and adjusting the environment of the 2D metaverse based on those emotions" refers to techniques or methods for using technology to recognize a user's emotions and changing the environment settings in the 2D metaverse based on the recognized emotions.

[1295] "Methods for visualizing website elements" are techniques and methods for visually representing information obtained from a website.

[1296] "Means for converting to a 2D Metaverse platform" means the techniques and methods for converting the visualized elements into a format usable within the 2D Metaverse.

[1297] "Means for automating the creation of 2D metaverses" refers to techniques and methods for automatically generating and arranging environments and elements in a 2D metaverse.

[1298] "A means of packaging a series of processes and selling them as a service model" refers to the technology and methods for bundling the above series of processes into a single package and providing and selling it as a service.

[1299] This invention is a system that acquires and analyzes information from websites and provides a 2D metaverse environment that responds to the user's emotions. A specific embodiment of this system will be described below.

[1300] The server first retrieves text information from the specified website. For this process, the server can use the Python "requests" library. For example, the server can retrieve the HTML of the web page using "requests.get(URL)" and then extract the text information using the "BeautifulSoup" library.

[1301] The server then analyzes the acquired text information using natural language processing (NLP) technology. This analysis is performed using tools such as "spaCy" and "NLTK." The server inputs the text information into "spaCy's nlp()" function to obtain the analysis results. From the analysis results, the server understands the structure of the sentence and the meaning of words, and extracts important keywords and phrases.

[1302] The server combines the extracted keywords and phrases to generate prompts, which are then input to the generative AI model. The server then sorts the keywords and phrases into the appropriate order and saves the generated prompts in text format.

[1303] The device runs software to recognize the user's emotions, using, for example, Microsoft Azure Cognitive Services or Google Cloud Natural Language API. The device receives the user's facial expressions and voice as input, sends the acquired data to the emotion recognition API, and obtains an emotional analysis result.

[1304] Based on the recognized emotion, the device adjusts the 2D metaverse environment. For example, Unity or Unreal Engine can be used to build the 2D metaverse. If the user expresses joy, the device changes the background to a bright color and plays cheerful music. Conversely, if the user expresses sadness, the device changes the background to a calm color and plays quiet music.

[1305] For example, when a user is browsing a news site, the server retrieves the text information of the news article and analyzes it using NLP technology. Keywords such as "economy," "stock prices," and "rising" are extracted from the analysis results, and these are combined to generate a prompt such as "Economic News: Stock prices are rising."

[1306] Example prompt sentence:

[1307] "Analyze the latest articles on news sites, extract important keywords, and generate prompts."

[1308] "Recognize the user's emotions and adjust the 2D Metaverse environment based on those emotions."

[1309] In this way, it is possible to effectively prompt users with website information and provide a personalized 2D metaverse environment according to their emotions.

[1310] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1311] Step 1:

[1312] The server retrieves text information from the specified website.

[1313] Input: User-specified URL

[1314] Specific operation: The server retrieves the HTML of the web page using "requests.get(URL)" and extracts the text information using the "BeautifulSoup" library.

[1315] Output: Retrieved text information

[1316] Step 2:

[1317] The server analyzes the acquired text information using natural language processing technology.

[1318] Input: Obtained text information

[1319] Specific operation: The server inputs the text information into the "nlp()" function of "spaCy" and obtains the analysis results. From the analysis results, the structure of the sentence and the meaning of the words are understood.

[1320] Output: Analysis results (sentence structure and word meanings)

[1321] Step 3:

[1322] The server extracts important keywords and phrases from the analysis results.

[1323] Input: Analysis results

[1324] Specific operation: The server extracts noun phrases and proper nouns from the analysis results using the "noun_chunks" and "entity_recognition" functions of "spaCy."

[1325] Output: A list of extracted keywords and phrases

[1326] Step 4:

[1327] The server generates prompts by combining the extracted keywords and phrases.

[1328] Input: A list of extracted keywords or phrases

[1329] What happens: The server sorts the keywords and phrases into the appropriate order and saves the generated prompt in text format.

[1330] Output: Generated prompt

[1331] Step 5:

[1332] The terminal runs software for recognizing the user's emotions.

[1333] Input: User's facial expressions and voice

[1334] Specific operation: The device receives the user's facial expressions and voice as input, and uses Microsoft Azure Cognitive Services and Google Cloud Natural Language API to obtain emotion analysis results.

[1335] Output: Emotion analysis results

[1336] Step 6:

[1337] The device adjusts the 2D metaverse environment based on the emotions it recognizes.

[1338] Input: Emotion analysis results

[1339] Specific operation: The device changes the environment settings in the metaverse based on the emotion recognition results. If the user expresses joy, the device changes the background to a bright color and plays cheerful music. Conversely, if the user expresses sadness, the device changes the background to a calm color and plays quiet music.

[1340] Output: A tailored 2D Metaverse environment

[1341] (Application example 2)

[1342] Next, a description will be given of Application Example 2 of Form 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."

[1343] Conventional website information acquisition and analysis systems have struggled to provide a personalized experience that responds to user emotions. Furthermore, the process of effectively visualizing acquired information and converting it into a 2D metaverse platform is complex and difficult to automate. Furthermore, efficiently packaging this series of processes and providing them as a service model has also been a challenge.

[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 acquiring and analyzing website information, means for prompting the acquired website information, means for visualizing website elements using the prompts, means for converting the visualized elements into a 2D metaverse platform, means for automating the creation of the 2D metaverse, means for recognizing a user's emotion, means for adjusting the display of the 2D metaverse based on the recognized emotion, and means for packaging and selling the series of processes as a service model, thereby providing a personalized 2D metaverse experience according to the user's emotion and enabling efficient information acquisition and visualization.

[1346] "Means for acquiring and analyzing website information" refers to technology for collecting data from web pages on the Internet and analyzing their content.

[1347] The "means of generating prompts from acquired website information" is a technology that uses natural language processing technology to analyze collected web page data, extract important keywords and phrases, and generate prompts.

[1348] "A method for visualizing website elements using prompts" is a technique for visually representing the content of a web page based on generated prompts.

[1349] "Means for converting visualized elements into a 2D metaverse platform" is a technology that converts visually represented elements of a web page into a two-dimensional virtual space.

[1350] "Means for automating the creation of 2D metaverses" refers to technology for automatically generating and managing two-dimensional virtual spaces.

[1351] "Means for recognizing user emotions" refers to technology for detecting emotions from the user's facial expressions, voice, etc.

[1352] "Means for adjusting the display of the 2D metaverse based on recognized emotions" is a technology that dynamically changes the display of a two-dimensional virtual space according to the detected emotions of the user.

[1353] "A means of packaging a series of processes and selling them as a service model" refers to technology that integrates the series of technologies mentioned above and provides them as a service.

[1354] A system for implementing this invention has the following configuration. First, a server acquires website information, collects data from web pages on the Internet, and analyzes the content using a means for analyzing the information. Specifically, to obtain structured document information, it acquires the HTML of the web page using the requests library.

[1355] Next, a natural language processing library (e.g., spaCy) is used to analyze the collected text information and extract important keywords and phrases to provide the user with prompts about the website information.

[1356] To visualize website elements using prompts, an image generation algorithm (e.g., DALL-E) is used to visually represent the contents of the web page based on the generated prompts. The visualized elements are then transformed into a 2D metaverse platform.

[1357] As a means of converting the visualized elements into a 2D metaverse platform, a 2D metaverse engine (e.g., Unity) is used to convert the visually represented elements of a web page into a 2D virtual space, thereby automating the creation of the 2D metaverse.

[1358] To recognize the user's emotions, we use emotion recognition libraries (e.g., OpenCV and TensorFlow) to detect emotions from the user's facial expressions and voice.To adjust the display of the 2D metaverse based on the recognized emotions, we use the 2D metaverse engine to dynamically change the display of the virtual space.

[1359] These technologies will be integrated and offered as cloud services, as a means of packaging a series of processes and selling them under a service model, allowing users to access them over the internet and enjoy a personalized 2D metaverse experience.

[1360] As a concrete example, consider a user searching for new clothes in a virtual store. The server retrieves information from the product's web page accessed by the user and uses NLP techniques to generate prompts such as, "This outfit is the latest trend and can be worn both casually and formally." Furthermore, if the user expresses happiness, the server changes the background in the 2D metaverse to a brighter, more cheerful atmosphere.

[1361] Examples of prompts include:

[1362] "This product is the latest trend and can be worn casually or formally."

[1363] "The user is expressing joy. Change the background of the virtual store to a bright and cheerful atmosphere."

[1364] In this way, it is possible to provide a personalized shopping experience that responds to the user's emotions.

[1365] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1366] Step 1:

[1367] The server receives the URL of the web page accessed by the user as input, then uses the requests library to retrieve the HTML information of the web page, which becomes the output.

[1368] Step 2:

[1369] The server uses the acquired HTML information as input and performs text analysis using the spaCy library. As a result of the analysis, important keywords and phrases are extracted. These keywords and phrases are the output.

[1370] Step 3:

[1371] The server takes the extracted keywords and phrases as input and generates a prompt. Using a generative AI model (e.g., GPT-3), it creates a natural language prompt based on these keywords and phrases. This prompt is the output.

[1372] Step 4:

[1373] The server uses the generated prompt sentence as input and visualizes the elements of the web page using an image generation algorithm (e.g., DALL-E), and the visualized image is the output.

[1374] Step 5:

[1375] The server takes the visualized image as input and converts it into a 2D virtual space using a 2D metaverse engine (e.g. Unity), which then outputs the virtual space.

[1376] Step 6:

[1377] The device takes the user's facial expressions and voice as input and recognizes their emotions using emotion recognition libraries (e.g., OpenCV and TensorFlow). The recognized emotions are the output.

[1378] Step 7:

[1379] The server uses the recognized emotion as input and adjusts the display of the virtual space using a 2D metaverse engine. For example, if the user expresses joy, the background of the virtual space is changed to a bright and cheerful atmosphere. This adjusted virtual space is the output.

[1380] Step 8:

[1381] The server packages the above series of processes and provides them as a cloud service. Users can access this service via the Internet and enjoy a personalized 2D metaverse experience.

[1382] Example 3

[1383] Next, a description will be given of Example 3 of Form Example 3. 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."

[1384] Modern websites contain a wide variety of information, making it difficult to effectively visualize that information and then convert it into a virtual space. There is also a need to optimize the display of the virtual space according to the user's emotional state, but there is a lack of efficient means to achieve this. Furthermore, there is a need for a system that automates the entire process and provides it as a cloud service, making it easy for users to use.

[1385] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1386] In this invention, the server includes means for acquiring and analyzing website information, means for prompting the acquired website information, means for visualizing website elements using the prompts, means for converting the visualized elements into a 2D virtual space platform, means for automating the creation of the 2D virtual space, means for packaging a series of processes and providing them in a cloud service model, and means for tracking the user's emotional state and optimizing the display of the 2D virtual space, thereby enabling the website information to be effectively visualized and converted into a virtual space, and further providing an optimal display according to the user's emotional state.

[1387] "Website information" refers to data about the content and structure of web pages published on the Internet.

[1388] "Means of analysis" refers to methods and techniques for analyzing acquired website information and extracting data for specific purposes.

[1389] "Prompting means" refers to methods or technologies that generate instructions (prompts) in a format that can be understood by image generation AI, based on the information obtained from the website.

[1390] A "prompt" is textual input that instructs the image generation AI to generate a specific visual.

[1391] A "visualization tool" is a method or technique that uses prompts to generate images that visually represent website elements.

[1392] A "2D virtual space platform" is software or a system for building and managing a two-dimensional virtual environment.

[1393] "Means for automating the creation of 2D virtual spaces" are scripts or programs for automating the operation of 2D virtual space platforms.

[1394] The "cloud service model" is a form of providing software and services via the Internet.

[1395] "User's emotional state" refers to the emotional state that a user feels at a particular time or in a particular situation.

[1396] "Means for tracking emotional states" refers to methods and technologies for monitoring changes in a user's emotions over the long term and collecting and analyzing the data.

[1397] The "means for optimizing the display" refers to a method or technology that provides an optimal visual display for the user based on the collected emotion data.

[1398] This invention is a system that acquires and analyzes website information, visualizes website elements using prompts, converts the visualization into a 2D virtual space platform, automates the creation of the 2D virtual space, provides a series of processes in a cloud service model, and further tracks the user's emotional state to optimize the display of the 2D virtual space.

[1399] Hardware and software used

[1400] The server uses the following hardware and software:

[1401] Hardware: A server machine with a powerful processor, sufficient memory, storage, and network interfaces.

[1402] Software: Web server software, database management system, image generation AI (e.g., DALL-E, Stable Diffusion), 2D virtual space platform (e.g., Unity 2D, Godot), automation scripts (e.g., Python scripts), cloud service platform (e.g., AWS, Google Cloud), emotion engine.

[1403] The user uses the following hardware and software:

[1404] Hardware: Devices such as personal computers, tablets, and smartphones.

[1405] Software: Web browser, dedicated application.

[1406] Data processing and calculation

[1407] 1. Website information collection and analysis

[1408] The server retrieves and analyzes the HTML information of the website using an HTML parser or scraping tool.

[1409] Based on the acquired information, website elements (text, images, buttons, etc.) are identified and prompt sentences are generated.

[1410] 2. Visualization with prompts

[1411] The server sends the generated prompt text to the image generation AI, which then generates a visual (image) based on the prompt text.

[1412] For example, if the prompt is "There is a big red button on a blue and white background," it will generate a visual that matches that.

[1413] 3. Transforming visuals into 2D virtual space

[1414] The server transmits the generated visuals to the 2D virtual space platform, which then generates a 2D virtual space world based on the visuals.

[1415] 4. Automation of 2D Virtual Spaces

[1416] The server executes automation scripts to automate the operation of the 2D virtual space platform, such as visual placement, color setting, and button placement.

[1417] 5. Cloud service model

[1418] The server packages a series of processes and provides them on a cloud service platform, allowing users to access the service and transform their websites into 2D virtual spaces.

[1419] 6. Emotional Engine Optimization

[1420] The server uses an emotion engine to track the user's emotional state over time and analyzes the data, thereby understanding the user's emotional change patterns and optimizing the display of the 2D virtual space accordingly.

[1421] For example, if a user tends to feel tired every afternoon, a 2D virtual space environment can be provided that helps them relax during that time.

[1422] Specific examples

[1423] Example prompt sentence:

[1424] "There's a big red button on a blue and white background."

[1425] "Yellow flowers blooming in the green grassland"

[1426] "Starry night sky"

[1427] Using these prompts, the image generation AI generates corresponding visuals, and then converts these visuals into a 2D virtual space platform, allowing users to create a virtual space that is intuitively easy to understand.

[1428] The above is an embodiment of the present invention. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1429] Step 1:

[1430] The user enters a prompt statement.

[1431] Input: The user enters the prompt text through a web browser or a dedicated application.

[1432] What happens: The user types "A big red button on a blue and white background" into the input field and clicks the submit button.

[1433] Output: The prompt text is sent to the server.

[1434] Step 2:

[1435] The server sends the prompt text to the image generation AI.

[1436] Input: The server receives the prompt text received from the user.

[1437] Specific operation: The server converts the prompt text into JSON format and sends a POST request to the image generation AI's API endpoint.

[1438] Output: The prompt is sent to the image generation AI.

[1439] Step 3:

[1440] Image generation AI generates the visuals.

[1441] Input: The image generation AI receives a prompt sent from the server.

[1442] Specific operation: The image generation AI generates visuals (images) based on the prompt text.

[1443] Output: The generated visuals are sent back to the server.

[1444] Step 4:

[1445] The server sends the generated visuals to the 2D virtual space platform.

[1446] Input: The server receives the visuals received from the image generation AI.

[1447] Specific operation: The server converts the visuals into PNG format and uploads them to the API of the 2D virtual space platform.

[1448] Output: Visuals are sent to a 2D virtual world platform.

[1449] Step 5:

[1450] A 2D virtual space platform generates a 2D virtual space world.

[1451] Input: The 2D virtual space platform receives visuals sent from the server.

[1452] Specific operation: The 2D virtual space platform analyzes the visual and generates corresponding 2D virtual space objects.

[1453] Output: The generated 2D virtual world is sent back to the server.

[1454] Step 6:

[1455] The server runs the automation script.

[1456] Input: The server receives the 2D virtual world received from the 2D virtual world platform.

[1457] Specific operation: The server executes Python scripts and calls the API of the 2D virtual space platform to automate the placement and color settings of visuals.

[1458] Output: An automated 2D virtual world is generated.

[1459] Step 7:

[1460] The server provides a series of processes in a cloud service model.

[1461] Input: The server receives an automated 2D virtual world.

[1462] Specific operation: The server deploys the generated 2D virtual world to a cloud-based service platform, making it accessible to users.

[1463] Output: A 2D virtual world provided as a cloud service is provided to the user.

[1464] Step 8:

[1465] Users access the cloud service and use the 2D virtual space.

[1466] Input: A user accesses a cloud-based service platform.

[1467] Specific operation: The user logs into the provided 2D virtual space and reflects elements of their website in the 2D virtual space.

[1468] Output: A 2D virtual world available to the user is displayed.

[1469] Step 9:

[1470] The server uses an emotion engine to track the user's emotional state and optimize the 2D virtual space.

[1471] Input: The server collects the user's emotion data.

[1472] How it works: The server uses an emotion engine to track the user's emotional state over time and analyzes the data using machine learning algorithms.

[1473] Output: The 2D virtual environment is dynamically modified based on the analysis results.

[1474] (Application example 3)

[1475] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1476] Conventional website visualization and metaverse processes have been largely manual, requiring time and effort. Furthermore, it is difficult to provide an optimal display that takes into account the user's emotional state, making improving the user experience a challenge. Furthermore, there was a lack of a way to centrally manage these processes and provide them efficiently.

[1477] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1478] In this invention, the server includes means for acquiring and analyzing website information, means for prompting the acquired website information, means for visualizing website elements using the prompts, means for converting the visualized elements into a 2D metaverse platform, means for automating the creation of the 2D metaverse, means for packaging a series of processes and selling them in a SaaS model, means for tracking a user's emotional state and analyzing the data, and means for optimizing the display of the 2D metaverse based on the user's emotional state. This makes it possible to efficiently automate the website visualization and metaverse creation process and provide an optimal display based on the user's emotional state.

[1479] "Means for obtaining and analyzing website information" refers to methods for collecting data from websites on the Internet and analyzing that data.

[1480] "Means of converting acquired website information into prompts" refers to a method of converting collected website data into instructions (prompts) in a format that can be understood by image generation AI.

[1481] "Method for visualizing website elements using prompts" refers to a method for visually representing website elements using image generation AI, using generated prompts as input.

[1482] "Means for converting visualized elements into a 2D metaverse platform" refers to a method for converting visualized website elements into a 2D metaverse virtual space.

[1483] "Means for automating the creation of 2D metaverses" refers to a method for efficiently creating virtual spaces using scripts and programs that automate the operation of 2D metaverse platforms.

[1484] "A method of packaging a series of processes and selling them under the SaaS model" refers to a method of combining each of the above processes into a single software package and providing it as a service on the cloud.

[1485] "Means for tracking a user's emotional state and analyzing the data" refers to a method for monitoring a user's emotions over the long term and analyzing the data.

[1486] "Means for optimizing the display of the 2D metaverse based on the user's emotional state" refers to a method for adjusting the display content of the 2D metaverse based on the user's emotional data to provide an optimal user experience.

[1487] A system for implementing the present invention has the following configuration.

[1488] First, the server obtains and analyzes website information. Specifically, it collects HTML information from websites on the Internet and analyzes that data. Based on the results of this analysis, it prompts website elements. The prompted data is then input into an image generation AI, which generates images to visualize the website elements.

[1489] Next, the visualized elements are converted into a 2D metaverse platform. Using the 2D metaverse engine, a virtual space is constructed based on the generated visuals. This virtual space is efficiently created using automation scripts. The automation scripts call the 2D metaverse engine's API to automatically perform operations such as visual placement, color settings, and button placement.

[1490] Furthermore, the entire process is packaged and provided under the SaaS model. Users can access the cloud-based service and convert their websites into 2D metaverses. The service also has the ability to track users' emotional states and analyze the data. It uses an emotion engine to monitor users' emotions over the long term and optimizes the display of the 2D metaverse based on that data.

[1491] For example, if a user inputs the prompt "A big red button on a blue and white background," a visual representation of that image is generated. This visual is then fed into the 2D metaverse engine, which then constructs a virtual space. Furthermore, the emotion engine tracks the user's emotional state. For example, if the user feels like relaxing, the display of the virtual space will be optimized based on that emotional state.

[1492] The hardware used includes a server, smartphone, head-mounted display, etc. The software uses OpenAI's image generation API, 2D metaverse engine, and emotion engine.

[1493] Examples of prompts include:

[1494] "There's a big red button on a blue and white background."

[1495] "Green plants are placed on the wood-grain floor."

[1496] "Shelves of merchandise are lined up against a luxurious black and gold background."

[1497] This allows users to easily create a virtual store that suits their preferences and enhance their shopping experience.

[1498] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1499] Step 1:

[1500] The server acquires and analyzes website information. Specifically, the server collects HTML information from websites on the Internet and analyzes the data. The input is the website URL, and the output is the analyzed website element information.

[1501] Step 2:

[1502] The server converts the acquired website information into prompts. Specifically, it converts the analyzed website element information into instructions (prompts) in a format that can be understood by the image generation AI, based on the analyzed website element information. The input is the analyzed website element information, and the output is the prompt.

[1503] Step 3:

[1504] The server uses the prompt to visualize the website elements. Specifically, the generated prompt is input into an image generation AI to generate an image that visually represents the website element. The input is the prompt text, and the output is the generated image.

[1505] Step 4:

[1506] The server converts the visualized elements into a 2D metaverse platform. Specifically, the generated image is input into the 2D metaverse engine, and a virtual space is constructed based on it. The input is the generated image, and the output is the 2D metaverse virtual space.

[1507] Step 5:

[1508] The server automates the creation of the 2D metaverse. Specifically, it uses automation scripts to call the API of the 2D metaverse engine and automatically performs operations such as visual placement, color settings, and button placement. The input is the virtual space of the 2D metaverse, and the output is the automated virtual space of the 2D metaverse.

[1509] Step 6:

[1510] The server packages a series of processes and provides them under the SaaS model. Specifically, each of the above processes is compiled into a single software package and provided as a service on the cloud. The input is the result of each process, and the output is the service under the SaaS model.

[1511] Step 7:

[1512] The server tracks the user's emotional state and analyzes the data. Specifically, it uses an emotion engine to monitor the user's emotions over time and analyzes the data. The input is the user's emotional data, and the output is the analyzed emotional data.

[1513] Step 8:

[1514] The server optimizes the display of the 2D metaverse based on the user's emotional state. Specifically, it adjusts the display content of the 2D metaverse based on the analyzed emotional data to provide an optimal user experience. The input is the analyzed emotional data, and the output is the optimized display of the 2D metaverse.

[1515] 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.

[1516] 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> ) 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.

[1517] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1518] 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.

[1519] [Third embodiment]

[1520] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1521] 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.

[1522] 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).

[1523] 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.

[1524] 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.

[1525] 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).

[1526] 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.

[1527] 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.

[1528] 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.

[1529] 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.

[1530] 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.

[1531] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1532] "Example 1"

[1533] In one embodiment of the present invention, an HTML scraping tool is used to obtain website information. This tool obtains HTML information from a specified website and analyzes its structure and content. For example, it obtains the URL of each page of the website, as well as text information, image information, and link information within the page.

[1534] "Example 2"

[1535] Natural language processing (NLP) technology is used to generate prompts from the acquired website information. Specifically, the acquired text information is analyzed and keywords and phrases that represent its content are extracted. These keywords and phrases are then combined to generate prompts that represent the website content.

[1536] "Example 3"

[1537] Image generation AI is used as a means of visualizing website elements using prompts. Specifically, the image generation AI uses the generated prompt as input to generate visuals (images) that represent website elements. For example, if the prompt is "a large red button on a blue and white background," it will generate a visual that matches that.

[1538] "Example 4"

[1539] The 2D Metaverse Engine is used to convert the visualized elements into a 2D Metaverse platform. Specifically, the generated visuals are fed into the 2D Metaverse Engine, which then generates a 2D Metaverse world based on them. For example, if the generated visual is "a large red button on a blue and white background," the engine will generate a 2D Metaverse world that matches that.

[1540] "Example 5"

[1541] Automation scripts are used to automate the creation of 2D metaverses. Specifically, scripts are created to automate the operation of the 2D metaverse engine, and then executed to automate the creation of 2D metaverses. For example, the scripts call the API of the 2D metaverse engine to automatically perform operations such as visual placement, color settings, and button placement.

[1542] "Example 6"

[1543] A cloud-based service platform is used to package a series of processes and sell them under the SaaS model. Specifically, each of the above processes is bundled into a single software package and provided on the cloud. Users can access this service to transform their websites into a 2D metaverse.

[1544] The processing flow of each embodiment will be described below.

[1545] "Example 1"

[1546] Step 1: Use an HTML scraping tool to obtain the HTML information of the specified website. In this step, you will obtain the URL of each page of the website, as well as the text information, image information, link information, etc.

[1547] Step 2: Analyze the acquired HTML information. In this step, the structure and content of the HTML information are analyzed to understand the structure of the website.

[1548] "Example 2"

[1549] Step 1: Analyze the acquired text information. In this step, natural language processing (NLP) techniques are used to extract keywords and phrases from the text information.

[1550] Step 2: Combine the extracted keywords and phrases to generate prompts that represent the content of the website.

[1551] "Example 3"

[1552] Step 1: Input the generated prompt into the image generation AI.

[1553] Step 2: Image generation AI generates visuals (images) representing website elements based on the prompts.

[1554] "Example 4"

[1555] Step 1: Input the generated visuals into the 2D Metaverse engine.

[1556] Step 2: The 2D Metaverse Engine generates a 2D Metaverse world based on the visuals.

[1557] "Example 5"

[1558] Step 1: Create a script to automate the operation of the 2D Metaverse Engine.

[1559] Step 2: Automate the creation of your 2D metaverse by running the script you created.

[1560] "Example 6"

[1561] Step 1: Combine the above methods into a single software package.

[1562] Step 2: Provide the compiled software package on the cloud.

[1563] Step 3: Users access the service and transform their website into a 2D metaverse.

[1564] Example 1

[1565] Next, a description will be given of Example 1 of Form 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."

[1566] Conventional methods for acquiring and analyzing website information required a lot of manual operations and were inefficient. In addition, the process of visually representing the acquired information and then converting it into a two-dimensional virtual space was complicated and required specialized knowledge. This made it difficult for general users to easily utilize website information.

[1567] 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.

[1568] In this invention, the server includes a means for acquiring and analyzing website information, a means for converting the acquired website information into prompts, and a means for visualizing website elements using the prompts, thereby enabling website information to be efficiently acquired, visually represented, and further converted into a two-dimensional virtual space.

[1569] A "website" is a collection of information that is made public on the Internet and is constructed using technologies such as HTML, CSS, and JavaScript.

[1570] "Means of obtaining and analyzing information" refers to the methods and technologies used to collect the necessary data from a website and analyze that data.

[1571] "Prompting means" refers to methods or technologies that convert acquired website information into a format that can be input into a generative AI model.

[1572] "Visualization methods" refers to methods or techniques used to visually represent website elements using prompts.

[1573] A "two-dimensional virtual space platform" refers to the infrastructure or environment for providing two-dimensional virtual space.

[1574] "Means for automating production" refers to methods and technologies for automating the production process of two-dimensional virtual space.

[1575] "Means of providing as a service" refers to methods and technologies for packaging a series of processes and providing them to users via the Internet.

[1576] "HTML information" refers to data written in HTML, a markup language used to describe the structure and content of web pages.

[1577] "Image generation artificial intelligence" refers to systems and algorithms for generating images using artificial intelligence technology.

[1578] MODE FOR CARRYING OUT THE INVENTION

[1579] This invention is a system that efficiently acquires information from a website, visually represents it, and then converts it into a two-dimensional virtual space. A specific embodiment of this system will be described below.

[1580] System program generation

[1581] The user generates a program to retrieve website information. This program uses an HTML scraping tool, such as "BeautifulSoup" or "Scrapy," to collect website information.

[1582] Program processing explanation

[1583] The server accesses the specified website URL and retrieves its HTML information. The retrieved HTML information is then parsed by the server to analyze the structure and content of the website. Specifically, the following data is retrieved:

[1584] URL of each page

[1585] Text information on the page

[1586] Image information

[1587] Link information

[1588] The server prompts the acquired information and converts it into a format for input to the generative AI model. The prompted information is then used by the generative AI model to visualize website elements. The visualized elements are then converted into a two-dimensional virtual space platform.

[1589] Hardware and software used

[1590] This system uses the following hardware and software:

[1591] Server: A computer system that retrieves and analyzes website information

[1592] HTML scraping tools: "BeautifulSoup" and "Scrapy"

[1593] Generative AI Model: An artificial intelligence model for visualizing website elements using prompts

[1594] Specific examples

[1595] When a user enters the URL "https: / / example.com", the server does the following:

[1596] 1. The user enters a URL.

[1597] 2. The server accesses "https: / / example.com" and retrieves HTML information.

[1598] 3. The HTML information obtained by the server is analyzed using "BeautifulSoup" to extract text, image, and link information from the page.

[1599] 4. The server prompts the extracted information and inputs it into the generative AI model.

[1600] 5. The generative AI model uses the prompts to visualize website elements.

[1601] 6. Transform the visualized elements into a two-dimensional virtual space platform.

[1602] Prompt Sentence Examples

[1603] "Create a program that retrieves information from a specified website and extracts text, images, and links from the page."

[1604] By inputting this prompt into the generative AI model, the user can automatically generate the necessary program. For example, if a user wants to get information about the website "https: / / example.com," the generative AI model will output the appropriate code.

[1605] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1606] Step 1:

[1607] The user enters a website URL.

[1608] Input: The URL of the website you want to retrieve (e.g., "https: / / example.com")

[1609] Specific operation: The user enters the URL of a website into the system's input form and clicks the submit button.

[1610] Output: The entered URL is sent to the server.

[1611] Step 2:

[1612] The server accesses the specified URL.

[1613] Input: The URL entered by the user

[1614] What happens: The server uses the "requests" library to send an HTTP request to the specified URL.

[1615] Output: The HTML of the website is returned as a response.

[1616] Step 3:

[1617] The server retrieves the HTML information.

[1618] Input: HTML information returned as a response to an HTTP request

[1619] Specific operation: The server obtains HTML information from the content attribute of the response object.

[1620] Output: The retrieved HTML information is stored in memory.

[1621] Step 4:

[1622] The server parses the HTML information.

[1623] Input: Retrieved HTML information

[1624] Specific operation: The server uses the "BeautifulSoup" library to parse the HTML information and extract text information, image information, and link information within the page.

[1625] Output: The parsed text information, image information, and link information are stored as lists or strings.

[1626] Step 5:

[1627] Prompts for information retrieved by the server.

[1628] Input: Analyzed text information, image information, link information

[1629] Specific operation: The server converts this information into a format that can be input into the generative AI model.

[1630] Output: Prompted information is generated.

[1631] Step 6:

[1632] The server uses the generative AI model to visualize website elements.

[1633] Input: Prompted information

[1634] How it works: The server inputs prompted information into the generative AI model to create a visual representation of the website elements.

[1635] Output: The visualized elements are generated.

[1636] Step 7:

[1637] The server converts the visualized elements into a two-dimensional virtual space platform.

[1638] Input: Visualized element

[1639] Specific operation: The server converts the visualized elements into a format suitable for the 2D virtual space platform.

[1640] Output: Data suitable for a 2D virtual space platform is generated.

[1641] Step 8:

[1642] The server provides the analysis results to the user.

[1643] Input: Data suitable for a two-dimensional virtual space platform

[1644] Specific operation: The server displays the generated data to the user.

[1645] Output: The user can check the analysis results.

[1646] (Application example 1)

[1647] Next, a description will be given of Application Example 1 of Form 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."

[1648] Conventional website information acquisition systems take time to acquire and analyze information, making it difficult to compare prices efficiently. Furthermore, they lacked a function to send notifications when the price dropped below the user's set price, making it difficult for users to purchase products at the optimal time.

[1649] 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.

[1650] In this invention, the server includes means for acquiring and analyzing website information, means for prompting the acquired website information, means for visualizing website elements using the prompts, means for converting the visualized elements into a 2D metaverse platform, means for automating the creation of the 2D metaverse, means for packaging a series of processes and selling them under a SaaS model, means for acquiring product information from multiple online sales sites and comparing prices, means for presenting the lowest price, and means for sending a notification when the price drops below the price set by the user. This enables efficient price comparison and allows users to purchase products at the optimal time.

[1651] "Means for acquiring and analyzing website information" refers to technology for acquiring HTML information from a specified website and analyzing its structure and content.

[1652] "Means for converting acquired website information into prompts" refers to a technology that converts acquired website information into prompt sentences to be input into a generative AI model.

[1653] "Method for visualizing website elements using prompts" is a technology that uses a generative AI model to visually represent website elements from prompt statements.

[1654] "Means for converting visualized elements into a 2D metaverse platform" refers to a technology that converts visualized website elements into a two-dimensional virtual space.

[1655] "Means for automating the production of 2D metaverses" refers to technology that automates the production process of 2D metaverse platforms.

[1656] "A means of packaging a series of processes and selling them under the SaaS model" refers to a technology that brings together the above series of processes into a single package and provides it as a service.

[1657] "Means for obtaining product information from multiple online sales sites and comparing prices" refers to a technology for scraping product information from multiple online sales sites and comparing the obtained price information.

[1658] "Means for presenting the lowest price" refers to a technique for presenting the user with the lowest price from among the obtained price information.

[1659] The "means for sending a notification when the price falls below the price set by the user" is a technology for sending a notification to the user when the price falls below the price set by the user.

[1660] The following system configuration is used as an embodiment of the present invention.

[1661] System Configuration

[1662] 1. Server:

[1663] How to obtain website information: The server uses Python's requests and BeautifulSoup libraries to obtain HTML information from the specified website, allowing it to analyze the structure and content of the web page.

[1664] Prompt generation method: Converts the acquired website information into a prompt sentence to be input into the generative AI model. For example, a prompt sentence containing product name and price information is generated.

[1665] Visualization: Using a generative AI model, we create a visual representation of the website elements from the prompt text using image generation AI.

[1666] 2D Metaverse Transformation: Transforms visualized website elements into a 2D virtual space, allowing users to visually confirm website elements within the virtual space.

[1667] Automation: Automate the process of creating a 2D metaverse platform, which will allow for efficient generation of the 2D metaverse.

[1668] SaaS model sales method: The above series of processes are packaged and provided as a service, allowing users to easily use the system.

[1669] Price comparison tool: Obtain product information from multiple online retailers and compare prices using scraping technology.

[1670] Lowest price display method: The lowest price from the obtained price information is displayed to the user.

[1671] Notification method: Send a notification to the user when the price falls below the price set by the user.

[1672] Processing flow

[1673] 1. Data retrieval: The server retrieves HTML data from the specified URL using the requests library.

[1674] 2. Data analysis: Analyze the HTML data using the BeautifulSoup library and extract product name and price information.

[1675] 3. Prompt generation: Based on the extracted information, a prompt sentence is generated to be input into the generative AI model.

[1676] 4. Visualization: Using a generative AI model, we generate a visual representation of the website elements from the prompt.

[1677] 5. 2D Metaverse Transformation: Transforming visualized elements into a two-dimensional virtual space.

[1678] 6. Price Comparison: Compare price information obtained from multiple online retailers and offer the lowest price.

[1679] 7. Notification: Send a notification when the price drops below the user's set price.

[1680] Specific examples

[1681] If a user searches for "iPhone 13," the server will:

[1682] 1. The user types "iPhone 13."

[1683] 2. The server obtains price information for the iPhone 13 from multiple online sales sites.

[1684] 3. Generate a prompt sentence based on the obtained price information.

[1685] 4. Use a generative AI model to generate visual elements from the prompt.

[1686] 5. Transform the visualized elements into a 2D metaverse platform.

[1687] 6. Compare prices and offer the lowest price to users.

[1688] 7. Send a notification when the price drops below the price set by the user.

[1689] Prompt Sentence Examples

[1690] Write a Python program that retrieves pricing information from multiple online retailers and returns the lowest price when a user searches for "iPhone 13." Use BeautifulSoup and the requests library.

[1691] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1692] Step 1:

[1693] The server receives a product name from the user as input. For example, the user enters "iPhone 13." Based on this input, the server generates URLs for multiple online retailers.

[1694] Step 2:

[1695] The server uses the generated URLs to retrieve HTML data from each online sales site using the requests library. In this process, an HTTP request is sent to each URL and HTML data is received as a response. The input is a URL and the output is HTML data.

[1696] Step 3:

[1697] The server analyzes the retrieved HTML data using the BeautifulSoup library. Specifically, it extracts product names, prices, images, and other information from the HTML data. The input is the HTML data, and the output is the analyzed product information.

[1698] Step 4:

[1699] Based on the analyzed product information, the server generates a prompt to be input into the generative AI model. For example, it generates a prompt such as "Get price information for the iPhone 13 and display the lowest price." The input is the product information, and the output is the prompt.

[1700] Step 5:

[1701] The server uses a generative AI model to visually represent website elements from the prompt text. Specifically, the prompt text is input into the generative AI model, which then generates visual elements (images and text). The input is the prompt text, and the output is the visual elements.

[1702] Step 6:

[1703] The server converts the visual elements into a 2D metaverse platform. Specifically, the generated visual elements are placed in a 2D virtual space so that users can visually confirm them. The input is the visual elements, and the output is the 2D metaverse platform.

[1704] Step 7:

[1705] The server compares price information obtained from multiple online sales sites. Specifically, it compiles the price information obtained from each site into a list and finds the cheapest price. The input is multiple price information, and the output is the cheapest price.

[1706] Step 8:

[1707] The server presents the cheapest price to the user. Specifically, it displays the cheapest price information on the user's terminal. The input is the cheapest price, and the output is the information presented to the user.

[1708] Step 9:

[1709] The server sends a notification when the price falls below the price set by the user. Specifically, it compares the price with the price set by the user, and if the condition is met, it generates a notification and sends it to the user's terminal. The input is the user-set price and the current price, and the output is the notification.

[1710] Example 2

[1711] Next, a description will be given of Example 2 of Form 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."

[1712] Conventional methods for analyzing and visualizing website information often require a series of manual processes, from acquiring information to analyzing, generating prompts, visualizing, and converting to a metaverse platform, resulting in inefficiency. Furthermore, because there is no integrated system for automating these processes, users must combine multiple tools and technologies, making operations cumbersome. Furthermore, the quality and accuracy of the generated prompts can be low, preventing the full potential of the generative AI model.

[1713] 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.

[1714] In this invention, the server includes means for acquiring and analyzing website information, means for analyzing the acquired website information using natural language processing technology and extracting keywords and phrases, and means for generating prompt sentences by combining the extracted keywords and phrases. This automates the entire process from acquiring website information to analyzing it and generating prompt sentences, enabling efficient and high-quality generation of prompt sentences.

[1715] "Website information" refers to the content of web pages published on the Internet, including text, images, video, and audio.

[1716] "Means of analysis" refers to the technology and methods used to analyze the acquired website information and understand its content.

[1717] "Natural language processing technology" refers to technology that enables computers to understand and process human language, including text analysis and the extraction of keywords and phrases.

[1718] "Keywords and phrases" refer to important words or short sentences extracted from website information.

[1719] A "prompt" is a textual input to a generative AI model, containing a question or instruction to summarize the content of a website or elicit specific information.

[1720] "Visualization tools" refers to techniques and methods for visually representing textual information or data, including graphs, charts, images, etc.

[1721] A "2D Metaverse Platform" refers to a platform that provides a two-dimensional virtual space, providing an environment in which users can interact with the virtual space.

[1722] "Providing as a service" refers to a business model in which systems or software are provided to users via the Internet. Specifically, this includes the SaaS (Software as a Service) model.

[1723] "HTML information" refers to information written in a markup language used to construct a web page.

[1724] "Image generation technology" refers to technology that allows computers to generate images. Specifically, it includes image generation algorithms that use machine learning and deep learning.

[1725] The present invention relates to a system for acquiring and analyzing information from a website and generating a prompt sentence. A specific embodiment of this system will be described below.

[1726] First, the user enters the URL of a specific website on the terminal. For example, the user enters the URL "https: / / example.com". Then, the server accesses the URL entered by the user and obtains the HTML content of the web page. In this case, the server uses the "requests" library to obtain the HTML information.

[1727] To extract text information from the retrieved HTML content, the server uses the "BeautifulSoup" library, which allows it to strip HTML tags and obtain pure text information.

[1728] The server then uses the spaCy library to analyze the extracted text, specifically extracting important keywords and phrases from the text, providing valuable information for summarizing the website's content.

[1729] Based on the extracted keywords and phrases, the server generates a prompt sentence. The generated prompt sentence is used as input to a generative AI model (e.g., a natural language generation model). Examples of prompt sentences that may be generated include the following:

[1730] To summarize the content of "https: / / example.com," the main topics are "Latest Technology Trends," "AI Applications," and "Latest Research in Data Science." Please provide detailed information on these topics.

[1731] By inputting this prompt into a generative AI model, detailed information based on the website content can be generated, which is provided to the user as a reference for gaining a deeper understanding of the website content.

[1732] This system automates the entire process, from acquiring website information to analyzing it and generating prompts, enabling efficient and high-quality prompt generation. This simplifies operation and eliminates the need for users to combine multiple tools and technologies. It also improves the quality and accuracy of the generated prompts, fully utilizing the capabilities of the generative AI model.

[1733] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1734] Step 1:

[1735] The user enters a website URL.

[1736] The user inputs the URL of a specific website from the terminal. For example, the user inputs the URL "https: / / example.com". This input URL becomes the input data for the next processing step.

[1737] Step 2:

[1738] The server retrieves the HTML information from the specified URL.

[1739] The server accesses the URL entered by the user and retrieves the HTML content of the web page. To do this, the server uses the "requests" library. Specifically, it sends an HTTP request to the URL and receives HTML information as a response. This HTML information becomes the input data for the next processing step.

[1740] Step 3:

[1741] The server extracts text information from the HTML information it has obtained.

[1742] The server uses the "BeautifulSoup" library to extract text information from the retrieved HTML content. Specifically, it parses the HTML tags and removes them to obtain pure text information, which becomes the input data for the next processing step.

[1743] Step 4:

[1744] The server analyzes the text information and extracts keywords and phrases.

[1745] The server uses the "spaCy" library to analyze the extracted text information. Specifically, it analyzes the text information using natural language processing technology to extract important keywords and phrases. The keywords and phrases obtained through this analysis become the input data for the next processing step.

[1746] Step 5:

[1747] The server generates a prompt by combining extracted keywords and phrases.

[1748] The server generates a prompt sentence based on the extracted keywords and phrases. Specifically, it appropriately combines keywords and phrases to create a prompt sentence to input into the generative AI model. The generated prompt sentence becomes the output data for the next processing step.

[1749] Step 6:

[1750] Provides the user with a server-generated prompt.

[1751] The server returns the generated prompt to the user. Specifically, it sends the generated prompt to the user as an HTTP response. The user can use this prompt to input the generative AI model.

[1752] (Application example 2)

[1753] Next, a description will be given of Application Example 2 of Form 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."

[1754] Conventional website information analysis and content recommendation systems have difficulty accurately grasping users' interests and recommending appropriate content. In addition, there has been a lack of methods for efficiently analyzing website information and providing useful information to users.

[1755] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring and analyzing website information, means for generating prompts from the acquired website information, and means for visualizing website elements using the prompts. This enables means for analyzing the acquired website information and extracting keywords and phrases, means for generating prompts by combining the extracted keywords and phrases, and means for recommending content based on the generated prompts.

[1756] "Means of obtaining and analyzing website information" refers to technologies and methods for collecting and analyzing text and metadata from web pages on the Internet.

[1757] "Means for turning acquired website information into prompts" refers to techniques or methods for analyzing collected website text information using natural language processing technology, summarizing the content or converting it into keywords to generate prompts.

[1758] A "prompt-based visualization of website elements" is a technique or method for visually representing website content based on generated prompts.

[1759] "Means of transforming visualized elements into a 2D metaverse platform" refers to techniques and methods for transforming visually represented website elements into a two-dimensional virtual space.

[1760] "Methods for automating the creation of 2D metaverses" are technologies and methods for automatically generating and managing two-dimensional virtual spaces.

[1761] "Means of analyzing acquired website information and extracting keywords and phrases" refers to techniques and methods for analyzing collected website text information and extracting important keywords and phrases from it.

[1762] "Means for generating prompts by combining extracted keywords and phrases" refers to techniques or methods for combining extracted keywords and phrases to generate prompts that summarize the content of a website.

[1763] The "means for recommending content based on a generated prompt" refers to a technique or method for recommending relevant content to a user based on a generated prompt.

[1764] "Methods of packaging a series of processes and selling them under the SaaS model" refers to technologies and methods that bundle the above series of technologies and methods into a single service and provide and sell it as cloud-based software.

[1765] The system for implementing this invention consists of three main elements: a server, a terminal, and a user. Below we will explain how each element functions.

[1766] server

[1767] The server is responsible for retrieving and analyzing website information. Specifically, the server uses the following software and hardware:

[1768] The requests library: Used to retrieve HTML information from websites.

[1769] BeautifulSoup: Used to parse the retrieved HTML information and extract text information.

[1770] The spacy library: Used to analyze the extracted text information and extract keywords and phrases.

[1771] The transformers library: Used to generate and recommend content based on prompts using a generative AI model (GPT-3).

[1772] The server analyzes the retrieved website information to extract keywords and phrases. It then combines the extracted keywords and phrases to generate prompts. Based on these prompts, a generative AI model is used to recommend relevant content to the user.

[1773] Terminal

[1774] A Device is a device through which a User browses a website and receives recommended content. A Device uses the following software and hardware:

[1775] Web browser: Used by users to browse websites.

[1776] Smartphone application: Used to receive recommended content from the server and display it to the user.

[1777] The device sends the URL of the website the user has visited to the server and receives recommended content from the server, which is then displayed to the user.

[1778] User

[1779] A user is an entity that browses a website and receives recommended content. A user uses the system in the following steps:

[1780] 1. Browse the website using a web browser or smartphone application.

[1781] 2. The URL of the visited website is sent to the server.

[1782] 3. Receive the recommended content from the server and view it.

[1783] Specific examples

[1784] For example, if a user visits the URL "https: / / example.com" and the content of this URL is "Latest Technology News," the server will parse this information and generate a prompt like this:

[1785] Latest Technology News

[1786] Based on this prompt, the generative AI model is expected to recommend other news articles related to the latest technology. Users can receive and view these recommended content through a smartphone application.

[1787] As described above, the present invention provides a system that efficiently analyzes information on a website and recommends related content to a user.

[1788] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1789] Step 1:

[1790] The server receives the URL of the website visited by the user. The input is the URL sent by the user from the terminal, and the output is the HTML information obtained based on that URL. Specifically, the server uses the requests library to obtain the HTML information from the specified URL.

[1791] Step 2:

[1792] The server analyzes the obtained HTML information and extracts text information. The input is the HTML information obtained in step 1, and the output is the extracted text information. Specifically, the server uses BeautifulSoup to extract text information from HTML.

[1793] Step 3:

[1794] The server analyzes the extracted text information and extracts keywords and phrases. The input is the text information extracted in step 2, and the output is the extracted keywords and phrases. Specifically, the server uses the spacy library to analyze the text information and extract important keywords and phrases.

[1795] Step 4:

[1796] The server generates a prompt by combining the extracted keywords and phrases. The input is the keywords and phrases extracted in step 3, and the output is the generated prompt. Specifically, the server combines the extracted keywords and phrases to generate a prompt that summarizes the content of the website.

[1797] Step 5:

[1798] The server recommends content based on the generated prompt. The input is the prompt generated in step 4, and the output is the recommended content. Specifically, the server uses the transformers library to generate and recommend relevant content based on the prompt using a generative AI model (GPT-3).

[1799] Step 6:

[1800] The terminal receives the recommended content from the server and displays it to the user. The input is the recommended content sent from the server in step 5, and the output is the content displayed to the user. Specifically, the terminal receives the recommended content using a smartphone application and displays it to the user.

[1801] Step 7:

[1802] The user browses the recommended content through the device. The input is the content displayed on the device in step 6, and the output is the user's browsing behavior. Specifically, the user browses the recommended content using a smartphone application and explores further related content as needed.

[1803] Example 3

[1804] Next, a third embodiment of the third embodiment will be described. 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."

[1805] The process of visualizing traditional website elements and converting them into virtual space was often done manually, which required time and effort. Furthermore, there was no integrated system for automating these processes, making efficient production difficult. Furthermore, there was a lack of a way to provide these processes as cloud services, making it difficult for users to easily access them.

[1806] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means. In this invention, the server includes means for acquiring and analyzing website information, means for generating prompts from the acquired website information, means for visualizing website elements using the prompts, means for converting the visualized elements into a 2D virtual space platform, means for automating the creation of the 2D virtual space, and means for packaging a series of processes and selling them under a cloud service model. This makes it possible to efficiently visualize website elements, automate the process of converting them into a virtual space, and provide it as a cloud service that users can easily access.

[1807] "Means for acquiring and analyzing website information" refers to technology for collecting data from websites on the Internet and analyzing that data.

[1808] "Means for prompting acquired website information" refers to a technology that generates text-based instructions (prompts) to be input into a generative AI model based on collected website data.

[1809] "A means of visualizing website elements using prompts" is a technology that inputs prompts into a generative AI model and generates images and graphics based on those instructions.

[1810] "Means for converting visualized elements into a 2D virtual space platform" refers to a technology that inputs the generated visuals into a 2D virtual space engine and constructs a virtual space scene based on that.

[1811] "Means for automating the production of 2D virtual spaces" refers to technology that streamlines the production process of virtual spaces by using scripts and programs that automate the operation of 2D virtual space engines.

[1812] "A means of packaging a series of processes and selling them as a cloud service model" refers to a technology that integrates the above technologies, packages them as a service provided on the cloud, and makes them available to users via the Internet.

[1813] "Means for obtaining markup language information" refers to technology that collects data from languages ​​used to describe the structure of web pages (e.g., HTML and XML).

[1814] A "generative AI model" is an artificial intelligence model that generates images and other content based on input text or data.

[1815] This invention is a system for visualizing website elements and converting them into a 2D virtual space. A specific embodiment of this system will be described below.

[1816] First, the server receives the website URL and HTML data provided by the user. The server parses this data and obtains the markup language information to understand the structure and content of the website. This analysis is performed using software such as an HTML parser or a web scraping tool.

[1817] The server then generates prompts based on the acquired website information. The prompts are text instructions to be input to the generative AI model. For example, the server generates prompts containing information such as the website's background color and button placement.

[1818] Using the generated prompt, the server inputs the prompt into a generative AI model (e.g., DALL-E or MidJourney) to generate a visual (image). The generative AI model generates an image containing the specified elements based on the input prompt.

[1819] Next, the server inputs the generated visuals into a 2D virtual space platform (such as Unity or Godot), which then constructs a 2D virtual space scene based on the input visuals. For example, it generates a scene with a large red button on a blue and white background.

[1820] Furthermore, the server creates and executes scripts to automate the creation of the 2D virtual space. These scripts call the API of the 2D virtual space platform to automatically perform operations such as visual placement, color setting, and button placement.

[1821] Finally, the server packages the above process and provides it as a cloud service. Users can access the cloud-based service platform and convert their website into a 2D virtual space by entering prompts.

[1822] For example, if a user inputs the prompt "A big red button on a blue and white background," the generative AI model will generate a visual representation of that scene, and the 2D virtual world platform will build a scene based on that visual. The user can then view the generated 2D virtual world and edit it as needed.

[1823] This system makes it possible to efficiently visualize website elements, automate the process of converting them into a virtual space, and provide it as a cloud service that users can easily access. The flow of the specific processing in Example 3 will be described with reference to Fig. 15.

[1824] Step 1:

[1825] A user inputs a website URL or HTML data. The server receives this and retrieves website information. Specifically, the server uses an HTML parser or web scraping tool to analyze the structure and content of the website and obtain markup language information. The input is the website URL or HTML data, and the output is the parsed website information.

[1826] Step 2:

[1827] The server generates a prompt based on the retrieved website information. Specifically, it extracts information such as the website's background color, button placement, and text content, and converts it into a text prompt. The input is the parsed website information, and the output is the generated prompt.

[1828] Step 3:

[1829] The server inputs the generated prompt into the generative AI model. Specifically, it sends the prompt sentence as an API request to the generative AI model (e.g., DALL-E or MidJourney), which then generates a visual (image) based on the prompt. The input is the prompt sentence, and the output is the generated visual.

[1830] Step 4:

[1831] The server inputs the generated visuals into the 2D virtual space platform. Specifically, it sends the visual data to the API of the 2D virtual space engine (e.g., Unity or Godot), and builds a 2D virtual space scene based on that. The input is the generated visuals, and the output is a 2D virtual space scene.

[1832] Step 5:

[1833] The server creates and executes scripts to automate the creation of 2D virtual spaces. Specifically, it calls the API of the 2D virtual space engine to automatically perform operations such as visual placement, color settings, and button placement. The input is a 2D virtual space scene, and the output is the automated 2D virtual space scene.

[1834] Step 6:

[1835] The server packages the above series of processes and provides them as a cloud service model. Specifically, the service is hosted using cloud infrastructure and made accessible to users via the Internet. The input is an automated 2D virtual space scene, and the output is a system provided as a cloud-based service.

[1836] Step 7:

[1837] Users can access the cloud-based service platform and convert their websites into 2D virtual spaces by entering a prompt. Specifically, users enter a prompt and then view and edit the generated 2D virtual world. The input is the prompt, and the output is an editable 2D virtual world.

[1838] (Application example 3)

[1839] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1840] Traditionally, designing websites and building virtual stores required specialized knowledge and skills, making them difficult for average users. Changing or updating designs was also time-consuming, making it difficult to respond quickly. Furthermore, converting to a 2D metaverse platform and automating production were complex, requiring efficient operation.

[1841] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means for acquiring and analyzing website information, means for converting the acquired website information into prompts, means for visualizing website elements using the prompts, means for converting the visualized elements into a 2D metaverse platform, means for automating the creation of the 2D metaverse, means for packaging a series of processes and providing them in a cloud-based service model, means for automatically generating a virtual store design when a user inputs prompts, and means for placing the generated virtual store visuals on the 2D metaverse platform. This enables even general users to easily design websites and build virtual stores, and enables quick and efficient design changes and updates.

[1842] "Means for obtaining and analyzing website information" refers to a method for collecting data on web pages that exist on the Internet and analyzing their content.

[1843] A "means for generating prompts from acquired website information" is a method ...

Claims

1. A means of acquiring and analyzing information on the website; A means for prompting the website information acquired by the analyzing means; means for visualizing an element of a website using the prompts generated by the means for prompting; a means for converting the elements visualized by the visualization means into a two-dimensional virtual space platform; means for automating the creation of the two-dimensional virtual space platform in the converting means; a means for packaging a series of processes of the analyzing means, the prompting means, the visualizing means, the converting means, and the automating means, and providing the same as a service; A system including:

2. 2. The system of claim 1, wherein the means for obtaining and analyzing web site information comprises means for obtaining HTML information.

3. The system of claim 1 , wherein the means for visualizing comprises means for using image generation artificial intelligence.

Images