System

Abstract

A system is provided.SOLUTION: A system comprising: an information transmitting unit that transmits information including an image and text data; an acquiring unit that acquires information from the information transmitting unit; a storing unit that stores the acquired data; a converting unit that analyzes the stored data and converts the data into a designated format; a generating unit that generates a web page based on the analyzed and converted data; and a publishing unit that publishes the web page generated by the generating unit.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] Many stores today use social media platforms to disseminate information. However, these platforms are weak in SEO (search engine optimization), making it difficult to attract customers from search engines. Additionally, creating and updating a dedicated homepage on the store side is time-consuming and laborious, making it difficult to keep the latest information updated. This poses a major challenge to store marketing strategies. [Means for solving the problem]

[0005] The present invention provides a system for automatically generating a homepage based on image and text data acquired from a social media platform and publishing a webpage with SEO measures. The system includes the following means:

[0006] 1. An information transmission means including image and text data;

[0007] 2. An acquisition means for acquiring information from the information transmission means;

[0008] 3. A storage means for storing the acquired data;

[0009] 4. A conversion means for analyzing the stored data and converting it into a specified format;

[0010] 5. A generating means for generating a web page based on the analyzed and converted data;

[0011] 6. Publishing means for publishing the web page generated by the generating means.

[0012] This allows stores to automatically update their homepages with the latest social media information, and also makes it easier to maintain pages that are SEO-optimized.

[0013] "Information dissemination means" refers to general digital information dissemination methods, such as social media platforms for posting and disseminating images and text data over the Internet.

[0014] "Acquisition means" refers to the process and means for acquiring data from information dissemination means, and includes, for example, the function of collecting data via an API.

[0015] The "storage means" refers to a storage device such as a database or storage for storing and holding acquired data.

[0016] "Transformation means" refers to processes and means for analyzing stored data and converting it into a specific format. Examples include data cleansing and format conversion.

[0017] "Generator" means a process or system for automatically generating web pages based on the analyzed and converted data. The generated web pages include content such as images, text, and video.

[0018] The "publication means" refers to the process and means by which the generated web page is made publicly available on the Internet. The published web page is subjected to SEO measures. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0040] The present invention provides a system that automatically generates web pages based on data acquired from social media platforms and publishes homepages that incorporate SEO (search engine optimization) measures. The following describes an embodiment of this system.

[0041] The system includes the following main components:

[0042] 1. Means of information dissemination

[0043] A social media platform (e.g., Instagram) is used as a means of disseminating information, through which users post content such as images, text, and videos.

[0044] 2. Acquisition method

[0045] The device automatically retrieves user post data using the API of the social media platform, including image URLs, text, hashtags, and posting dates and times.

[0046] 3. Preservation means

[0047] The server stores the acquired data in a database, which is used to efficiently manage the acquired data and use it in the analysis and generation process described below.

[0048] 4. Conversion Methods

[0049] The server then analyzes the stored data and converts it into a format that can be easily consumed by the generative AI model, which involves cleansing and reformatting the data, for example, by assigning image URLs to the appropriate fields and segmenting text data.

[0050] 5. Generation means

[0051] The generative AI model automatically generates web pages based on the converted data. Specifically, it analyzes social media posts and builds each section (new product introduction, gallery, etc.). It also generates keywords, meta tags, alt text, etc. for SEO purposes.

[0052] 6. Disclosure Methods

[0053] The server saves the generated web page in a public directory, making it available on the Internet, and notifies the user of the new URL.

[0054] Program processing explanation

[0055] 1. Users

[0056] A user posts a new post on a social media platform (e.g., Instagram), which can contain content such as images, text, and videos.

[0057] 2. Terminal

[0058] The device automatically calls the social media API to retrieve the user's latest posted data, which is then sent to the server in JSON format.

[0059] 3. Server

[0060] The server receives the data sent from the terminal and stores it in a database.

[0061] The data stored in the database is analyzed and converted into a format that is easy for the generative AI model to use using a conversion method.

[0062] 4. Generative AI Models

[0063] Based on the analyzed and converted data, each section of the web page is automatically generated, including content such as images, text, and videos.

[0064] Automatically generates appropriate keywords, meta tags, Alt text, etc. as SEO measures.

[0065] 5. Server

[0066] Receive the generated web page code and save it in a public directory.

[0067] Notify users of the new homepage URL via email or other notification services.

[0068] Specific examples

[0069] A cafe owner posts an Instagram post introducing a new product, a "berry smoothie." The following process takes place:

[0070] 1. Users

[0071] The owner publishes a post on Instagram with an image and text of "Berry Smoothie."

[0072] 2. Terminal

[0073] The device retrieves this new post data through Instagram's API and sends it to the server.

[0074] 3. Server

[0075] The data acquired by the server is stored in a database and analyzed and converted using a conversion means.

[0076] The converted data is passed to a generative AI model to instruct it to generate a web page.

[0077] 4. Generative AI Models

[0078] The model automatically generates a web page containing an introductory section for "berry smoothies."

[0079] Generate appropriate keywords and meta tags as part of your SEO strategy.

[0080] 5. Server

[0081] The server saves the generated web page in a public directory and notifies the cafe owner of the new URL.

[0082] In this way, an SEO-friendly homepage that reflects the latest information is automatically generated based on Instagram posts.

[0083] The processing flow will be explained below.

[0084] Step 1:

[0085] A user posts a new post on a social media platform (e.g., Instagram). The user uploads and publishes content such as images, text, and videos.

[0086] Step 2:

[0087] The device retrieves new post data using the social media platform's API. The API call retrieves data such as the image URL, text, hashtags, and post date and time.

[0088] Step 3:

[0089] The device converts the acquired data into an appropriate format, such as JSON, and sends the data to the server. Data transfer is performed using a secure protocol (e.g., HTTPS).

[0090] Step 4:

[0091] The server receives the data sent from the terminal and stores it in a database, which is used to efficiently manage and store user-submitted data.

[0092] Step 5:

[0093] The server parses the data stored in the database and converts it into a specific format. During the parsing process, data such as image URLs, text, and hashtags are segmented and cleansed.

[0094] Step 6:

[0095] The server then inputs the converted data into a generative AI model, which instructs it to generate a webpage. This input includes the analyzed image and text data, as well as meta information and keywords.

[0096] Step 7:

[0097] Based on the input data, the generative AI model automatically generates sections of a webpage, such as a new product introduction section, gallery section, etc. It also automatically generates keywords, meta tags, alt text, etc. for SEO purposes.

[0098] Step 8:

[0099] The generative AI model sends the generated web page code (HTML, CSS, JavaScript, etc.) back to the server.

[0100] Step 9:

[0101] The server receives the generated web page code and stores it in a public directory that is accessible on the Internet.

[0102] Step 10:

[0103] The server will notify the user of the new web page URL via email or a notification service.

[0104] Step 11:

[0105] The user checks the notified URL and checks whether the new homepage has been generated as intended. The user can then modify the homepage content as necessary.

[0106] Through the above processing steps, social media posts are automatically retrieved and an SEO-friendly homepage is generated and published.

[0107] Example 1

[0108] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0109] To automatically generate and publish web pages with SEO measures by effectively utilizing the vast amount of posted data on social media, and to provide web pages that always contain the latest information while reducing the user's workload.

[0110] 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.

[0111] In this invention, the server includes a terminal that includes an information acquisition process based on user posts, a server that places generated web pages in a storage directory, and a generation means that automatically builds web page sections using a generative AI model. This makes it possible to efficiently acquire and analyze social media post data and automatically generate and publish SEO-optimized web pages that include the latest information.

[0112] "Information transmission means" refers to a medium through which a user publishes information including images and text data.

[0113] "Acquisition means" refers to a device or program that has the function of automatically acquiring data from information transmission means.

[0114] "Storage means" refers to a mechanism for storing acquired data in a storage device such as a database.

[0115] "Conversion means" refers to a process or device that analyzes and converts stored data into a specified format.

[0116] "Generation means" refers to a program or system that automatically generates a web page based on the analyzed and converted data.

[0117] The "publication means" refers to a mechanism for publishing the generated web page on the Internet so that users can access it.

[0118] "Terminal" refers to a device or system that acquires information based on posts from users and transmits data to a server.

[0119] "Server" refers to a device or system that places generated web pages in a storage directory and makes them available on the Internet.

[0120] "Generative AI model" refers to an artificial intelligence model that performs a process of data analysis and automatic generation to build each section of a web page.

[0121] A "prompt sentence" refers to an input sentence that triggers a generative AI model.

[0122] This invention provides a system that automatically generates web pages based on data acquired from social media platforms and publishes homepages that incorporate SEO measures. The following describes in detail an embodiment of this system.

[0123] Key Components of the System

[0124] The system includes the following main components:

[0125] 1. Means of information dissemination

[0126] A social media platform is used as a means of disseminating information. Users can post content such as images, text, and videos through this platform. This information becomes the source data for the system.

[0127] 2. Acquisition method

[0128] The device automatically retrieves user post data using the API of the social media platform, including image URLs, text, hashtags, and posting dates and times.

[0129] 3. Preservation means

[0130] The server stores the acquired data in a database, which is used to efficiently manage the acquired data and utilize it in the analysis and production processes.

[0131] 4. Conversion Methods

[0132] The server then analyzes the stored data and converts it into a format that can be easily consumed by the generative AI model, which involves cleansing and reformatting the data, for example, by assigning image URLs to the appropriate fields and segmenting text data.

[0133] 5. Generation means

[0134] The generative AI model automatically generates web pages based on the converted data. Specifically, it analyzes social media posts and builds each section (new product introduction, gallery, etc.). It also generates keywords, meta tags, alt text, etc. for SEO purposes.

[0135] 6. Disclosure Methods

[0136] The server saves the generated web page in a public directory, making it available on the Internet, and notifies the user of the new URL via email or other notification service.

[0137] Specific examples

[0138] For example, when a cafe owner posts on social media to introduce a new product, a "berry smoothie," the system executes the following process:

[0139] 1. Users

[0140] The owner publishes a post on social media with an image of a "berry smoothie" and text, such as "Today's new menu item: Berry Smoothie! Loaded with fresh berries. New item at the cafe."

[0141] 2. Terminal

[0142] The device obtains this new post data through the social media API and sends it to the server.

[0143] 3. Server

[0144] The server stores the acquired data in a database, analyzes and converts it using a conversion method, and passes the converted data to a generative AI model to instruct it to generate a web page.

[0145] 4. Generative AI Models

[0146] The model automatically generates a web page with an introductory section for "Berry Smoothie," including images, text, and related videos, as well as appropriate keywords and meta tags for SEO.

[0147] 5. Server

[0148] The server saves the generated web page in a public directory and notifies the cafe owner of the new URL.

[0149] Prompt Sentence Examples

[0150] Examples of input prompts for generative AI models include:

[0151] "Create a webpage with a new product section based on your latest Instagram posts. Use the following data: image URL, text, and hashtags. For SEO purposes, please also generate appropriate keywords, meta tags, and alt text."

[0152] Using this prompt, the generative AI model can automatically generate a web page based on the request, allowing users to effectively transform their social media posts into up-to-date, SEO-optimized homepages.

[0153] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0154] Step 1: User Submission

[0155] Input: A user makes a post on a social media platform that includes an image, text, hashtags, video, etc.

[0156] Specific operation: The user enters the content of the post and presses the publish button. At that time, the necessary data (image URL, text, hashtags, posting date and time) is included in the post.

[0157] Output: Posts published on social media platforms.

[0158] Step 2: Data acquisition by device

[0159] Input: A new post on a social media platform.

[0160] Specific operation: The device periodically calls the API of the social media platform to obtain new post data, and processes the obtained data in JSON format, etc.

[0161] Data processing: Format the acquired RAW data and extract only the necessary information.

[0162] Output: Formatted post data (image URL, text, hashtags, post date and time).

[0163] Step 3: Save data to the server

[0164] Input: Formatted post data sent from the device.

[0165] Specific operation: The server receives the data sent from the device and saves it in the database. When saving, it checks the integrity of the data and assigns it to the appropriate fields.

[0166] Data processing: Checking the integrity of the received data and assigning it to fields.

[0167] Output: Post data saved in the database.

[0168] Step 4: Analyze and transform the data

[0169] Input: Post data stored in the database.

[0170] What it does: The server analyzes the data and performs data cleansing to remove unnecessary data and noise. After analysis, the data is converted into a format that is easy for the generative AI model to process. For example, it segments text and assigns image URLs to the appropriate fields.

[0171] Data processing: data cleansing, reformatting, field assignment.

[0172] Output: Parsed and transformed data.

[0173] Step 5: Generative AI model generates webpage

[0174] Input: Parsed and transformed data.

[0175] What it does: The server generates a prompt and inputs the converted data into the generative AI model. The prompt might look something like this: "Create a webpage with a new product introduction section based on the latest Instagram post data. Please use the following data: image URL, text, and hashtags. For SEO purposes, please also generate appropriate keywords, meta tags, and alt text."

[0176] Data processing: Building web pages using generative AI models, generating keywords, meta tags, and alt text.

[0177] Output: Auto-generated web page code.

[0178] Step 6: Publish and advertise your webpage

[0179] Input: The code for the generated web page.

[0180] What it does: The server receives the generated web page code and saves it in a public directory, then generates a new URL and notifies the user via email or other notification services.

[0181] Data processing: web page storage, URL generation, notification process.

[0182] Output: Web page saved to public directory, new URL, notified users.

[0183] (Application example 1)

[0184] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0185] Food delivery services are rapidly becoming more popular these days, requiring businesses to maintain their online presence and update it quickly with the latest information. However, doing this manually requires a great deal of effort and time, making it inefficient. Creating web pages with SEO optimization also requires specialized knowledge, placing a significant burden on many webmasters. Therefore, there is a need for a system that can automatically and efficiently generate web pages that reflect the latest information and publish them while incorporating SEO optimization.

[0186] 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.

[0187] In this invention, the server includes an information transmission means including image and text data, an acquisition means for acquiring information from the information transmission means, a storage means for saving the acquired data, a conversion means for analyzing the saved data and converting it into a specified format, a generation means for generating a web page based on the analyzed and converted data, a publishing means for updating the generated web page in an application and website of the electronic device, and a notification means for notifying a new URL. This enables operators of food delivery services to efficiently update their latest information and automatically generate and publish SEO-friendly web pages.

[0188] "Information dissemination means" refers to an online communication platform that allows users to provide content such as images and text data over the Internet.

[0189] "Acquisition means" refers to the means for automatically acquiring content provided by a user via the API of the online communication platform.

[0190] "Storage means" refers to a database or storage system that stores acquired data and keeps it in a state that allows it to be used for later processing.

[0191] "Conversion means" refers to the means for analyzing stored data and converting it into a format that is easy for the generative AI model to handle.

[0192] "Generating means" means a means including a generative AI model for automatically generating a web page based on the transformed data.

[0193] The "publication means" refers to a means for publishing the generated web page on the Internet and reflecting it in the application and website of the electronic device.

[0194] The "notification means" refers to a means for notifying the user of the URL of the newly generated web page.

[0195] The present invention provides a system that automatically generates web pages based on data posted on an online communication platform by operators of food delivery services, implements SEO measures, and publishes them. Specific embodiments of the system are described below.

[0196] System Configuration

[0197] The system includes the following main components:

[0198] 1. Means of information dissemination

[0199] Online communication platforms (e.g., Instagram) are used as a means of disseminating this information, and food delivery service operators post information about new menu items and promotions on these platforms.

[0200] 2. Acquisition method

[0201] The device automatically retrieves post data from food delivery service operators using the API of the online communication platform, including image URLs, text, hashtags, and posting dates and times.

[0202] 3. Preservation means

[0203] The server stores the acquired data in a database, which is used to efficiently manage the acquired data and use it in the analysis and generation process described below.

[0204] 4. Conversion Methods

[0205] The server then analyzes the stored data and converts it into a format that can be easily consumed by the generative AI model, which involves cleaning and reformatting the data, for example, by assigning image URLs to the appropriate fields and segmenting text data.

[0206] 5. Generation means

[0207] The generative AI model automatically generates web pages based on the converted data. Specifically, it analyzes posts on online communication platforms and builds each section (new product introduction, gallery, etc.). It also generates keywords, meta tags, alt text, etc. for SEO purposes.

[0208] 6. Disclosure Methods

[0209] The server saves the generated web page in a public directory, making it available on the Internet, and notifies the user of the new URL via email or other notification service.

[0210] System operation and concrete examples

[0211] The operation of the system will be explained below using a specific example.

[0212] example

[0213] A food delivery service operator posts an image and description of a new "specialty pizza" on an online communication platform.

[0214] 1. User Operation

[0215] The operator posts an image and description of the "special pizza" on an online communication platform, including hashtags and other text information.

[0216] 2. Data Acquisition

[0217] The device automatically calls the API of the online communication platform, obtains this new posting data, and sends it to the server.

[0218] 3. Data storage and analysis

[0219] The server stores the acquired data in a database and uses a conversion tool to analyze and convert it, for example, assigning image URLs and text to the appropriate fields.

[0220] 4. Web Page Generation

[0221] The generative AI model automatically generates a webpage containing an introductory section for the "Specialty Pizza" based on the converted data, as well as generating appropriate keywords and meta tags for SEO purposes.

[0222] 5. Webpage Publication and Notification

[0223] The server saves the generated web page in a public directory and notifies the operator of the new URL, possibly via email or other notification service.

[0224] Prompt Sentence Examples

[0225] Generate a page introducing the new menu item based on social media post data.

[0226] Post content:

[0227] Image URL: https: / / example.com / image.jpg

[0228] Text: Special pizza, only 999 yen now!

[0229] Hashtag: New Menu Pizza

[0230] Posting date: YYYY-MM-DD HH:MM

[0231] This allows food delivery service operators to efficiently reflect the latest information and automatically generate and publish web pages that are SEO-friendly.

[0232] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0233] Step 1:

[0234] A user posts new menu items or promotion information to an online communication platform. The input includes an image URL, text, hashtags, and posting date and time. This posting data is accepted by the online communication platform.

[0235] Step 2:

[0236] The terminal calls the API of the online communication platform and automatically obtains the user's posted data. The input is a posted data request, and the output includes the posted data in JSON format. This JSON format data is sent to the server.

[0237] Step 3:

[0238] The server stores the JSON-formatted post data received from the terminal in a database. The input is JSON-formatted post data, and the output is structured data stored in the database. This stored data is used in subsequent processes.

[0239] Step 4:

[0240] The server analyzes and preprocesses the data stored in the database. The input is the data stored in the database, and the output is data converted into a format that is easy for the generative AI model to use. Specific operations include cleaning the data, assigning fields, and segmenting the text data.

[0241] Step 5:

[0242] The server inputs the converted data into a generative AI model to generate a webpage. The input is preprocessed data, and the output is HTML code with the webpage structure. The generative AI model creates a new menu introduction page and applies SEO optimization.

[0243] Step 6:

[0244] The server saves the generated HTML code of the web page in a public directory. The input is the generated HTML code, and the output is a web page that can be accessed on the Internet. Specifically, an HTML file is placed in the destination directory.

[0245] Step 7:

[0246] The server notifies the user of the URL of the new web page. The input is the URL of the generated web page, and the output is a notification message to the user. Notification can occur via email or other notification service.

[0247] 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.

[0248] This invention is a system that automatically generates web pages based on data obtained from social media platforms and publishes homepages with SEO (search engine optimization) measures. This system incorporates an emotion engine that recognizes user emotions, and can display web page content more effectively based on emotional information.

[0249] The system includes the following main components:

[0250] 1. Means of information dissemination

[0251] A social media platform (e.g., Instagram) is used as a means of disseminating information, through which users post content such as images, text, and videos.

[0252] 2. Acquisition method

[0253] The device retrieves new post data using the social media platform's API. The API call retrieves data such as the image URL, text, hashtags, and post date and time.

[0254] 3. Preservation means

[0255] The server stores the acquired data in a database, which is used to efficiently manage user-submitted data and to use it in the analysis and generation process described below.

[0256] 4. Conversion Methods

[0257] The server analyzes the stored data and converts it into a format that can be easily consumed by the generative AI model. This process involves cleansing and reformatting the data, for example, by assigning image URLs to the appropriate fields and segmenting text data.

[0258] 5. Emotion Engine

[0259] The emotion engine built into the server recognizes the user's emotions from the analyzed data. The emotion engine uses an algorithm to analyze the posted text and images and determine the user's emotions (happiness, sadness, surprise, etc.).

[0260] 6. Generation means

[0261] A generative AI model automatically generates sections of a webpage based on the results of the emotion engine, for example by creating different designs and content placements depending on the emotion, as well as generating keywords, meta tags, and alt text for SEO purposes.

[0262] 7. Disclosure Methods

[0263] The server saves the generated web page in a public directory, makes it available on the Internet, and notifies the user of the new URL.

[0264] Program processing explanation

[0265] 1. Users

[0266] A user posts a new post on a social media platform (e.g., Instagram). The user uploads and publishes content such as images, text, and videos.

[0267] 2. Terminal

[0268] The device retrieves new post data using the social media platform's API. The API call retrieves data such as the image URL, text, hashtags, and post date and time.

[0269] 3. Terminal

[0270] It converts the acquired data into an appropriate format, such as JSON, and sends the data to the server using a secure protocol (e.g., HTTPS).

[0271] 4. Server

[0272] The server receives the data sent from the terminal and stores it in a database, which is used to efficiently manage and store user-submitted data.

[0273] 5. Server

[0274] The server parses the data stored in the database and converts it into a specific format. During the parsing process, the data is segmented into image URLs, text, hashtags, etc., and data cleansing is performed.

[0275] 6. Server

[0276] The converted data is passed to an emotion engine, which analyzes the text and image data to recognize the user's emotions. For example, it uses text emotion analysis algorithms and image analysis technology.

[0277] 7. Server

[0278] The emotion engine uses the emotional information it recognizes to direct the generation of web pages, for example, by highlighting upbeat design and positive content that matches the emotion of "joy."

[0279] 8. Generative AI Models

[0280] The generative AI model automatically generates sections of a webpage, including images, text, and video, taking into account emotion recognition results. It also automatically generates keywords, meta tags, and alt text for SEO optimization.

[0281] 9. Server

[0282] It takes the generated web page code and stores it in a public directory that is accessible on the Internet.

[0283] 10. Server

[0284] The new web page URL will be notified to the user via email or a notification service.

[0285] 11. Users

[0286] The user checks the notified URL and checks whether the new homepage has been generated as intended. The user can then modify the homepage content as necessary.

[0287] Specific examples

[0288] A cafe owner posts an Instagram post introducing a new product, a "berry smoothie." The following process takes place:

[0289] 1. Users

[0290] The owner publishes a post on Instagram with an image of a "berry smoothie" and text such as, "A new berry smoothie has arrived! Made with plenty of fresh berries. New berry smoothie menu item."

[0291] 2. Terminal

[0292] The device retrieves new post data through Instagram's API and sends it to the server.

[0293] 3. Server

[0294] The server stores the retrieved data in a database, where it performs analysis and data cleansing, properly formatting image URLs, text, hashtags, etc.

[0295] 4. Emotion Engine

[0296] The server uses an emotion engine to analyze the text and images and recognize the user's emotions. In this case, the emotion "joy" is extracted from the text "New berry smoothie has arrived!"

[0297] 5. Generative AI Models

[0298] The system generates web pages based on the results of the emotion engine. For example, because the user is expressing joy, it emphasizes content with a bright design and positive messages. It also applies SEO keywords like "berry smoothie," "new menu item," and "cafe."

[0299] 6. Server

[0300] Save the generated web page in a public directory and notify the cafe owner of the new URL.

[0301] In this way, user sentiment is analyzed from Instagram posts, and based on the results, an SEO-friendly homepage reflecting the latest information is automatically generated, improving the user experience.

[0302] The processing flow will be explained below.

[0303] Step 1:

[0304] A user posts a new post on a social media platform (e.g., Instagram). The user uploads and publishes content such as images, text, and videos.

[0305] Step 2:

[0306] The device retrieves new post data using the social media platform's API. The API call retrieves data such as the image URL, text, hashtags, and post date and time.

[0307] Step 3:

[0308] The device converts the acquired data into an appropriate format, such as JSON, and sends the data to the server. Data transfer is performed using a secure protocol (e.g., HTTPS).

[0309] Step 4:

[0310] The server receives the data sent from the terminal and stores it in a database, which is used to efficiently manage and store user-submitted data.

[0311] Step 5:

[0312] The server parses the data stored in the database and converts it into a specific format. During the parsing process, the data is segmented into image URLs, text, hashtags, etc., and data cleansing is performed.

[0313] Step 6:

[0314] The server passes the converted data to the emotion engine, which analyzes the text and image data to recognize the user's emotions. For example, it uses text emotion analysis algorithms and image analysis technology.

[0315] Step 7:

[0316] The emotion engine recognizes emotions such as "joy," "sadness," and "surprise" from user posts and passes the results to the generative AI model.

[0317] Step 8:

[0318] The server uses the results of the emotion engine to direct the generation of web pages, for example, if the user indicates the emotion "joy," it will emphasize upbeat design and positive content that matches that emotion.

[0319] Step 9:

[0320] The generative AI model automatically generates sections of a webpage, including images, text, and video, taking into account emotion recognition results. It also automatically generates keywords, meta tags, and alt text for SEO optimization.

[0321] Step 10:

[0322] The server receives the generated web page code and stores it in a public directory that is accessible on the Internet.

[0323] Step 11:

[0324] The server will notify the user of the new web page URL via email or a notification service.

[0325] Step 12:

[0326] The user checks the notified URL and checks whether the new homepage has been generated as intended. The user can then modify the homepage content as necessary.

[0327] Example 2

[0328] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0329] Conventional web page generation systems do not optimize content based on user sentiment, making it difficult to maximize user experience. In addition, SEO measures are often not implemented properly, making it difficult to attract new customers and rank highly in search engines.

[0330] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an information transmission means including image and text data, an acquisition means for acquiring information from the information transmission means, a storage means for saving the acquired data, a conversion means for analyzing the saved data and converting it into a specified format, a generation means for generating a web page based on the analyzed and converted data, a publishing means for publishing the web page generated by the generation means, an emotion recognition means for recognizing a user's emotion using an emotion engine, and an optimization means for optimizing the design and content of the web page based on the recognized emotion information. This makes it possible to automatically generate content optimized based on the user's emotion and provide a web page with SEO measures.

[0331] "Information dissemination means" refers to a means by which a user publishes information, including images and text data, and includes social media platforms.

[0332] "Means of acquisition" refers to the means for acquiring information made public from information dissemination means, and involves acquiring data via the API of the social media platform.

[0333] The "storage means" is a means for temporarily or continuously storing and managing the data acquired by the acquisition means.

[0334] "Conversion Means" means means for analyzing data stored by the Storage Means and converting it into a particular format, including data cleansing and reformatting.

[0335] The "generation means" is a means for automatically generating a new web page based on the analyzed and converted data.

[0336] The "publication means" is a means for publicizing the web page generated by the generation means on the Internet.

[0337] The "emotion recognition means" is a means for recognizing the user's emotions using an emotion engine, and analyzes text data and image data to determine emotions.

[0338] "Optimization measures" are measures for optimizing the design and content of web pages based on the recognized emotional information.

[0339] This invention is a system that automatically generates web pages based on data obtained from social media platforms and publishes homepages with SEO measures. This system incorporates an emotion engine that recognizes user emotions, and can display web page content more effectively based on emotional information.

[0340] Hardware and software used

[0341] 1. Server: The core of the system. The server is responsible for storing, analyzing, transforming, generating, and publishing data.

[0342] 2. Terminal: It is responsible for obtaining data using the API of the social media platform and sending it to the server.

[0343] 3. User: The entity that posts on a social media platform.

[0344] Data processing and calculation

[0345] 1. Means of information dissemination: A user posts a new content on a social media platform (e.g., Instagram). The user uploads and publishes content such as images, text, and videos.

[0346] 2. Acquisition method: The device retrieves new post data using the social media platform's API. The API call retrieves data such as image URL, text, hashtags, and post date and time. The retrieved data is converted into an appropriate format, such as JSON, and sent to the server using a secure protocol (e.g., HTTPS).

[0347] 3. Storage: The server receives the data sent from the terminal and stores it in a database. This database is used to efficiently manage and store the data posted by users.

[0348] 4. Transformation: The server parses the data stored in the database and transforms it into a specific format. During the parsing process, data such as image URLs, text, and hashtags are segmented and data cleansed.

[0349] 5. Emotion recognition means: The server passes the converted data to the emotion engine, which analyzes the text data and image data to recognize the user's emotions. For example, it uses text emotion analysis algorithms or image analysis technology.

[0350] 6. Generation: The generative AI model automatically generates each section of a webpage, taking into account the emotion recognition results. The generated webpage includes images, text, and video. It also automatically generates keywords, meta tags, and alt text appropriate for SEO.

[0351] 7. Optimization: Optimize the design and content of a web page based on the recognized emotion. For example, if a user expresses the emotion "happy," emphasize a bright design and positive content that matches that emotion.

[0352] 8. Publishing: The server receives the generated web page code and saves it in a public directory. This directory is set up to be accessible on the Internet. The new web page URL is notified to the user. Notification can be done via email or a notification service.

[0353] Specific examples

[0354] For example, if a cafe owner posts an Instagram post introducing a new product, a "berry smoothie," the following process takes place:

[0355] 1. A user publishes a post on Instagram that includes an image of a "berry smoothie" and text such as "A new berry smoothie has arrived! Made with plenty of fresh berries. New berry smoothie menu item."

[0356] 2. The device retrieves new post data through Instagram's API and sends it to the server.

[0357] 3. The server stores the retrieved data in a database, where it performs analysis and data cleansing, properly formatting image URLs, text, hashtags, etc.

[0358] 4. The emotion recognition unit analyzes the text and image to recognize the user’s emotion. In this case, the emotion “joy” is extracted from the text “New berry smoothie is here!”

[0359] 5. The generator generates a web page based on the results of the emotion engine. Because the user is expressing joy, content with a bright design and positive messages is emphasized. Additionally, keywords like "berry smoothie," "new menu," and "cafe" are applied as SEO keywords.

[0360] 6. The server saves the generated web page to a public directory and notifies the cafe owner of the new URL.

[0361] In this way, user sentiment is analyzed from Instagram posts, and based on the results, an SEO-friendly homepage reflecting the latest information is automatically generated, improving the user experience.

[0362] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0363] Step 1:

[0364] A user makes a new post on a social media platform (e.g., Instagram). The user uploads and publishes content such as images, text, and videos. The input is the image, text, and video created by the user. The output is the published post on the social media platform.

[0365] Step 2:

[0366] The device uses the API of the social media platform to obtain new post data. By making an API call, data such as the image URL, text, hashtags, and posting date and time is obtained. The input is the user's post information via the API call. The output is the obtained post data.

[0367] Step 3:

[0368] The data acquired by the terminal is converted into an appropriate format, such as JSON format, and sent to the server using a secure protocol (e.g., HTTPS). The input is the acquired post data. The output is the data converted into JSON format and the data sent securely.

[0369] Step 4:

[0370] The server receives the data sent from the terminal and stores it in a database. The stored data is used to efficiently manage user posted data. The input is securely transmitted JSON format data. The output is the posted data saved in the database.

[0371] Step 5:

[0372] The server parses the data stored in the database and converts it into a specific format. During the parsing process, data such as image URLs, text, and hashtags are segmented and cleansed. The input is the post data stored in the database. The output is the segmented and cleansed data.

[0373] Step 6:

[0374] The server passes the converted data to the emotion engine, which analyzes the text data and image data to recognize the user's emotions. For example, it uses text emotion analysis algorithms and image analysis techniques. The input is the segmented and cleansed data. The output is the user's emotional information (happiness, sadness, surprise, etc.).

[0375] Step 7:

[0376] The generative AI model automatically generates each section of a webpage, taking into account the emotion recognition results. The generated webpage includes images, text, and video. It also automatically generates appropriate keywords, meta tags, and alt text for SEO purposes. The input is the emotion-recognized user's emotion information. The output is the automatically generated webpage code.

[0377] Step 8:

[0378] The server receives the generated web page code and saves it in a public directory. The public directory is set up to be accessible on the Internet. It also notifies the user of the new web page URL. The input is the automatically generated web page code. The output is the web page saved in the public directory and the notified URL.

[0379] Step 9:

[0380] The user checks the notified URL and checks whether the new homepage has been generated as intended. The contents of the homepage can be modified as necessary. The input is the notified web page URL. The output is the confirmed and modified homepage.

[0381] (Application example 2)

[0382] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0383] There is a need for an effective method to provide real-time traffic information on the dashboard of an autonomous vehicle and display content tailored to the driver's emotions. Conventional systems do not provide information that takes the driver's emotions into consideration, and there is a lack of measures to reduce the driver's stress and anxiety. Against this background, the challenge is to develop a system that recognizes the driver's emotions and provides real-time information accordingly.

[0384] 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.

[0385] In this invention, the server includes an information transmission means including image and text data, an acquisition means for acquiring information from the information transmission means, a storage means for saving the acquired data, a conversion means for analyzing the saved data and converting it into a specified format, a generation means for generating a web page based on the analyzed and converted data, a publication means for publishing the web page generated by the generation means, an emotion engine for recognizing emotions and judging the driver's state based on the published web page, and a dashboard display means for displaying information customized based on the driver's emotions. This makes it possible to provide information according to the driver's emotions and a relaxing driving environment.

[0386] "Information dissemination means including images and text data" refers to means such as social media platforms that allow drivers or users to publish images and text data.

[0387] "Means of acquisition" refers to means such as APIs and communication protocols for collecting data made public from information dissemination means.

[0388] "Storage means" refers to a database or storage system that stores acquired data for a long period of time so that it can be analyzed or used later.

[0389] A "conversion means" is a program or algorithm that analyzes the stored data and converts it into a specified format.

[0390] "Generation means" refers to an AI model or automatic generation tool for automatically generating web pages based on the analyzed and converted data.

[0391] The "publication means" refers to a server or hosting service that makes the generated web page publicly available on the Internet and accessible to users.

[0392] An "emotion engine" is a program or algorithm that analyzes and recognizes the emotions of drivers or users from posted images and text data.

[0393] "Dashboard display means" refers to a display or interface system for displaying information on the dashboard of an autonomous vehicle according to the driver's emotional state.

[0394] An embodiment of the present invention will now be described. The present system is designed to provide real-time information according to the driver's emotions and to provide a relaxing driving environment.

[0395] The system mainly consists of the following components:

[0396] 1. Means of information dissemination

[0397] Drivers use social media platforms (e.g., Twitter and Instagram) to publish images and text data, which then becomes input into the system.

[0398] 2. Acquisition method

[0399] The device uses the API of the social media platform to retrieve new post data. The retrieved data includes image URLs, text, hashtags, and posting dates and times, allowing users to always obtain the latest information.

[0400] 3. Preservation means

[0401] The server stores the acquired data in a database, which is used to efficiently manage data over a long period of time.

[0402] 4. Conversion Methods

[0403] The server parses the stored data and converts it into the specified format. The parsing process involves data cleansing and reformatting.

[0404] 5. Generation means

[0405] Using a generative AI model, web pages are automatically generated based on the analyzed and converted data, incorporating the results of the emotion engine to arrange the design and content according to the user's emotions.

[0406] 6. Disclosure Methods

[0407] The generated web page is saved in a public directory for publishing on the Internet, making it accessible to users, and notifying users of the new URL.

[0408] 7. Emotion Engine

[0409] The server includes an emotion engine that analyzes text data and image data and recognizes the user's emotion, using, for example, a text emotion analysis algorithm or image analysis technology.

[0410] 8. Dashboard display method

[0411] The dashboard of an autonomous vehicle will display customized information based on the driver's emotions, reducing stress and anxiety for the driver and providing a more relaxed driving environment.

[0412] Operation example

[0413] For example, when a driver shares a post containing traffic congestion information or accident information on social media, the post is acquired by the acquisition means. The data stored in the storage means is analyzed and formatted by the conversion means, and the emotion engine identifies the driver's emotion.

[0414] Example prompts for generative AI models

[0415] "Design a driver dashboard application that collects the latest traffic information from social media and customizes it based on emotions. In stressful situations, provide relaxation suggestions, and for enjoyable driving, provide recommended spots."

[0416] As described above, the present invention is a system that provides real-time information according to the emotional state of the driver and optimizes the driving environment.

[0417] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0418] Step 1:

[0419] The device uses the API of a social media platform to obtain new post data. The input is the image URL, text, hashtag, and posting date and time data obtained by the API call, and the output is JSON format data. This data includes information such as the image and text posted by the user on social media.

[0420] Step 2:

[0421] The JSON format data acquired by the terminal is sent to the storage means, and the data is stored in the server's database. The input is the JSON format data to be stored, and the output is the data stored in the database. This database is used to efficiently manage user posted data.

[0422] Step 3:

[0423] The server parses the stored data and converts it into the specified format. The input is JSON format data retrieved from the database, and the parsed and converted format (e.g., segmented text and image URLs assigned to appropriate fields) is obtained as output. Specific operations include cleansing the text data and organizing image URLs.

[0424] Step 4:

[0425] The server passes the parsed and converted data to the emotion engine for emotion analysis. The input is the converted data, and the output is the user's emotional state (e.g., happy or sad). The emotion engine uses text analysis algorithms and image analysis techniques to determine the emotion.

[0426] Step 5:

[0427] The generation means generates a web page based on the results of the emotion engine. The input is emotion information and analyzed / converted data, and the output is an SEO-optimized web page. Specific operations include different designs and content placement depending on the emotion.

[0428] Step 6:

[0429] The server saves the generated web page in a public directory and makes it available on the net. The input is the generated web page code, and the output is a new URL that can be accessed on the internet. The server notifies the user of this URL.

[0430] Step 7:

[0431] The terminal displays information on the dashboard display means based on the published web page and emotional information. The input is the URL of the generated web page and emotional information, and the output is customized content that the driver can view on the dashboard. Specific operations include displaying information according to the driver's emotions and making suggestions to help them relax.

[0432] 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.

[0433] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0434] 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.

[0435] [Second embodiment]

[0436] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0437] 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.

[0438] 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).

[0439] 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.

[0440] 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.

[0441] 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).

[0442] 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.

[0443] 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.

[0444] 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.

[0445] 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.

[0446] 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.

[0447] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0448] The present invention provides a system that automatically generates web pages based on data acquired from social media platforms and publishes homepages that incorporate SEO (search engine optimization) measures. The following describes an embodiment of this system.

[0449] The system includes the following main components:

[0450] 1. Means of information dissemination

[0451] A social media platform (e.g., Instagram) is used as a means of disseminating information, through which users post content such as images, text, and videos.

[0452] 2. Acquisition method

[0453] The device automatically retrieves user post data using the API of the social media platform, including image URLs, text, hashtags, and posting dates and times.

[0454] 3. Preservation means

[0455] The server stores the acquired data in a database, which is used to efficiently manage the acquired data and use it in the analysis and generation process described below.

[0456] 4. Conversion Methods

[0457] The server then analyzes the stored data and converts it into a format that can be easily consumed by the generative AI model, which involves cleansing and reformatting the data, for example, by assigning image URLs to the appropriate fields and segmenting text data.

[0458] 5. Generation means

[0459] The generative AI model automatically generates web pages based on the converted data. Specifically, it analyzes social media posts and builds each section (new product introduction, gallery, etc.). It also generates keywords, meta tags, alt text, etc. for SEO purposes.

[0460] 6. Disclosure Methods

[0461] The server saves the generated web page in a public directory, making it available on the Internet, and notifies the user of the new URL.

[0462] Program processing explanation

[0463] 1. Users

[0464] A user posts a new post on a social media platform (e.g., Instagram), which can contain content such as images, text, and videos.

[0465] 2. Terminal

[0466] The device automatically calls the social media API to retrieve the user's latest posted data, which is then sent to the server in JSON format.

[0467] 3. Server

[0468] The server receives the data sent from the terminal and stores it in a database.

[0469] The data stored in the database is analyzed and converted into a format that is easy for the generative AI model to use using a conversion method.

[0470] 4. Generative AI Models

[0471] Based on the analyzed and converted data, each section of the web page is automatically generated, including content such as images, text, and videos.

[0472] Automatically generates appropriate keywords, meta tags, Alt text, etc. as SEO measures.

[0473] 5. Server

[0474] Receive the generated web page code and save it in a public directory.

[0475] Notify users of the new homepage URL via email or other notification services.

[0476] Specific examples

[0477] A cafe owner posts an Instagram post introducing a new product, a "berry smoothie." The following process takes place:

[0478] 1. Users

[0479] The owner publishes a post on Instagram with an image and text of "Berry Smoothie."

[0480] 2. Terminal

[0481] The device retrieves this new post data through Instagram's API and sends it to the server.

[0482] 3. Server

[0483] The data acquired by the server is stored in a database and analyzed and converted using a conversion means.

[0484] The converted data is passed to a generative AI model to instruct it to generate a web page.

[0485] 4. Generative AI Models

[0486] The model automatically generates a web page containing an introductory section for "berry smoothies."

[0487] Generate appropriate keywords and meta tags as part of your SEO strategy.

[0488] 5. Server

[0489] The server saves the generated web page in a public directory and notifies the cafe owner of the new URL.

[0490] In this way, an SEO-friendly homepage that reflects the latest information is automatically generated based on Instagram posts.

[0491] The processing flow will be explained below.

[0492] Step 1:

[0493] A user posts a new post on a social media platform (e.g., Instagram). The user uploads and publishes content such as images, text, and videos.

[0494] Step 2:

[0495] The device retrieves new post data using the social media platform's API. The API call retrieves data such as the image URL, text, hashtags, and post date and time.

[0496] Step 3:

[0497] The device converts the acquired data into an appropriate format, such as JSON, and sends the data to the server. Data transfer is performed using a secure protocol (e.g., HTTPS).

[0498] Step 4:

[0499] The server receives the data sent from the terminal and stores it in a database, which is used to efficiently manage and store user-submitted data.

[0500] Step 5:

[0501] The server parses the data stored in the database and converts it into a specific format. During the parsing process, data such as image URLs, text, and hashtags are segmented and cleansed.

[0502] Step 6:

[0503] The server then inputs the converted data into a generative AI model, which instructs it to generate a webpage. This input includes the analyzed image and text data, as well as meta information and keywords.

[0504] Step 7:

[0505] Based on the input data, the generative AI model automatically generates sections of a webpage, such as a new product introduction section, gallery section, etc. It also automatically generates keywords, meta tags, alt text, etc. for SEO purposes.

[0506] Step 8:

[0507] The generative AI model sends the generated web page code (HTML, CSS, JavaScript, etc.) back to the server.

[0508] Step 9:

[0509] The server receives the generated web page code and stores it in a public directory that is accessible on the Internet.

[0510] Step 10:

[0511] The server will notify the user of the new web page URL via email or a notification service.

[0512] Step 11:

[0513] The user checks the notified URL and checks whether the new homepage has been generated as intended. The user can then modify the homepage content as necessary.

[0514] Through the above processing steps, social media posts are automatically retrieved and an SEO-friendly homepage is generated and published.

[0515] Example 1

[0516] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0517] To automatically generate and publish web pages with SEO measures by effectively utilizing a huge amount of posted data on social media, and to provide web pages that always contain the latest information while reducing the user's workload.

[0518] 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.

[0519] In this invention, the server includes a terminal that includes an information acquisition process based on user posts, a server that places generated web pages in a storage directory, and a generation means that automatically builds web page sections using a generative AI model. This makes it possible to efficiently acquire and analyze social media post data and automatically generate and publish SEO-optimized web pages that include the latest information.

[0520] "Information transmission means" refers to a medium through which a user publishes information including images and text data.

[0521] "Acquisition means" refers to a device or program that has the function of automatically acquiring data from information transmission means.

[0522] "Storage means" refers to a mechanism for storing acquired data in a storage device such as a database.

[0523] "Conversion means" refers to a process or device that analyzes and converts stored data into a specified format.

[0524] "Generation means" refers to a program or system that automatically generates a web page based on the analyzed and converted data.

[0525] The "publication means" refers to a mechanism for publishing the generated web page on the Internet so that users can access it.

[0526] "Terminal" refers to a device or system that acquires information based on posts from users and transmits data to a server.

[0527] "Server" refers to a device or system that places generated web pages in a storage directory and makes them available on the Internet.

[0528] "Generative AI model" refers to an artificial intelligence model that performs a process of data analysis and automatic generation to build each section of a web page.

[0529] A "prompt sentence" refers to an input sentence that triggers a generative AI model.

[0530] This invention provides a system that automatically generates web pages based on data acquired from social media platforms and publishes homepages that incorporate SEO measures. The following describes in detail an embodiment of this system.

[0531] Key Components of the System

[0532] The system includes the following main components:

[0533] 1. Means of information dissemination

[0534] A social media platform is used as a means of disseminating information. Users can post content such as images, text, and videos through this platform. This information becomes the source data for the system.

[0535] 2. Acquisition method

[0536] The device automatically retrieves user post data using the API of the social media platform, including image URLs, text, hashtags, and posting dates and times.

[0537] 3. Preservation means

[0538] The server stores the acquired data in a database, which is used to efficiently manage the acquired data and utilize it in the analysis and production processes.

[0539] 4. Conversion Methods

[0540] The server then analyzes the stored data and converts it into a format that can be easily consumed by the generative AI model, which involves cleansing and reformatting the data, for example, by assigning image URLs to the appropriate fields and segmenting text data.

[0541] 5. Generation means

[0542] The generative AI model automatically generates web pages based on the converted data. Specifically, it analyzes social media posts and builds each section (new product introduction, gallery, etc.). It also generates keywords, meta tags, alt text, etc. for SEO purposes.

[0543] 6. Disclosure Methods

[0544] The server saves the generated web page in a public directory, making it available on the Internet, and notifies the user of the new URL via email or other notification service.

[0545] Specific examples

[0546] For example, when a cafe owner posts on social media to introduce a new product, a "berry smoothie," the system executes the following process:

[0547] 1. Users

[0548] The owner publishes a post on social media with an image of a "berry smoothie" and text, such as "Today's new menu item: Berry Smoothie! Loaded with fresh berries. New item at the cafe."

[0549] 2. Terminal

[0550] The device obtains this new post data through the social media API and sends it to the server.

[0551] 3. Server

[0552] The server stores the acquired data in a database, analyzes and converts it using a conversion method, and passes the converted data to a generative AI model to instruct it to generate a web page.

[0553] 4. Generative AI Models

[0554] The model automatically generates a web page with an introductory section for "Berry Smoothie," including images, text, and related videos, as well as appropriate keywords and meta tags for SEO.

[0555] 5. Server

[0556] The server saves the generated web page in a public directory and notifies the cafe owner of the new URL.

[0557] Prompt Sentence Examples

[0558] Examples of input prompts for generative AI models include:

[0559] "Create a webpage with a new product section based on your latest Instagram posts. Use the following data: image URL, text, and hashtags. For SEO purposes, please also generate appropriate keywords, meta tags, and alt text."

[0560] Using this prompt, the generative AI model can automatically generate a web page based on the request, allowing users to effectively transform their social media posts into up-to-date, SEO-optimized homepages.

[0561] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0562] Step 1: User Submission

[0563] Input: A user makes a post on a social media platform that includes an image, text, hashtags, video, etc.

[0564] Specific operation: The user enters the content of the post and presses the publish button. At that time, the necessary data (image URL, text, hashtags, posting date and time) is included in the post.

[0565] Output: Posts published on social media platforms.

[0566] Step 2: Data acquisition by device

[0567] Input: A new post on a social media platform.

[0568] Specific operation: The device periodically calls the API of the social media platform to obtain new post data, and processes the obtained data in JSON format, etc.

[0569] Data processing: Format the acquired RAW data and extract only the necessary information.

[0570] Output: Formatted post data (image URL, text, hashtags, post date and time).

[0571] Step 3: Save data to the server

[0572] Input: Formatted post data sent from the device.

[0573] Specific operation: The server receives the data sent from the device and saves it in the database. When saving, it checks the integrity of the data and assigns it to the appropriate fields.

[0574] Data processing: Checking the integrity of the received data and assigning it to fields.

[0575] Output: Post data saved in the database.

[0576] Step 4: Analyze and transform the data

[0577] Input: Post data stored in the database.

[0578] What it does: The server analyzes the data and performs data cleansing to remove unnecessary data and noise. After analysis, the data is converted into a format that is easy for the generative AI model to process. For example, it segments text and assigns image URLs to the appropriate fields.

[0579] Data processing: data cleansing, reformatting, field assignment.

[0580] Output: Parsed and transformed data.

[0581] Step 5: Generative AI model generates webpage

[0582] Input: Parsed and transformed data.

[0583] What it does: The server generates a prompt and inputs the converted data into the generative AI model. The prompt might look something like this: "Create a webpage with a new product introduction section based on the latest Instagram post data. Please use the following data: image URL, text, and hashtags. For SEO purposes, please also generate appropriate keywords, meta tags, and alt text."

[0584] Data processing: Building web pages using generative AI models, generating keywords, meta tags, and alt text.

[0585] Output: Auto-generated web page code.

[0586] Step 6: Publish and advertise your webpage

[0587] Input: The code for the generated web page.

[0588] What it does: The server receives the generated web page code and saves it in a public directory, then generates a new URL and notifies the user via email or other notification services.

[0589] Data processing: web page storage, URL generation, notification process.

[0590] Output: Web page saved to public directory, new URL, notified users.

[0591] (Application example 1)

[0592] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0593] Food delivery services are rapidly becoming more popular these days, requiring businesses to maintain their online presence and update it quickly with the latest information. However, doing this manually requires a great deal of effort and time, making it inefficient. Creating web pages with SEO optimization also requires specialized knowledge, placing a significant burden on many webmasters. Therefore, there is a need for a system that can automatically and efficiently generate web pages that reflect the latest information and publish them while incorporating SEO optimization.

[0594] 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.

[0595] In this invention, the server includes an information transmission means including image and text data, an acquisition means for acquiring information from the information transmission means, a storage means for saving the acquired data, a conversion means for analyzing the saved data and converting it into a specified format, a generation means for generating a web page based on the analyzed and converted data, a publishing means for updating the generated web page in an application and website of the electronic device, and a notification means for notifying a new URL. This enables operators of food delivery services to efficiently update their latest information and automatically generate and publish SEO-friendly web pages.

[0596] "Information dissemination means" refers to an online communication platform that allows users to provide content such as images and text data over the Internet.

[0597] "Acquisition means" refers to the means for automatically acquiring content provided by a user via the API of the online communication platform.

[0598] "Storage means" refers to a database or storage system that stores acquired data and keeps it in a state that allows it to be used for later processing.

[0599] "Conversion means" refers to the means for analyzing stored data and converting it into a format that is easy for the generative AI model to handle.

[0600] "Generating means" means a means including a generative AI model for automatically generating a web page based on the transformed data.

[0601] The "publication means" refers to a means for publishing the generated web page on the Internet and reflecting it in the application and website of the electronic device.

[0602] The "notification means" refers to a means for notifying the user of the URL of the newly generated web page.

[0603] The present invention provides a system that automatically generates web pages based on data posted on an online communication platform by operators of food delivery services, implements SEO measures, and publishes them. Specific embodiments of the system are described below.

[0604] System Configuration

[0605] The system includes the following main components:

[0606] 1. Means of information dissemination

[0607] Online communication platforms (e.g., Instagram) are used as a means of disseminating this information, and food delivery service operators post information about new menu items and promotions on these platforms.

[0608] 2. Acquisition method

[0609] The device automatically retrieves post data from food delivery service operators using the API of the online communication platform, including image URLs, text, hashtags, and posting dates and times.

[0610] 3. Preservation means

[0611] The server stores the acquired data in a database, which is used to efficiently manage the acquired data and use it in the analysis and generation process described below.

[0612] 4. Conversion Methods

[0613] The server then analyzes the stored data and converts it into a format that can be easily consumed by the generative AI model, which involves cleaning and reformatting the data, for example, by assigning image URLs to the appropriate fields and segmenting text data.

[0614] 5. Generation means

[0615] The generative AI model automatically generates web pages based on the converted data. Specifically, it analyzes posts on online communication platforms and builds each section (new product introduction, gallery, etc.). It also generates keywords, meta tags, alt text, etc. for SEO purposes.

[0616] 6. Disclosure Methods

[0617] The server saves the generated web page in a public directory, making it available on the Internet, and notifies the user of the new URL via email or other notification service.

[0618] System operation and concrete examples

[0619] The operation of the system will be explained below using a specific example.

[0620] example

[0621] A food delivery service operator posts an image and description of a new "specialty pizza" on an online communication platform.

[0622] 1. User Operation

[0623] The operator posts an image and description of the "special pizza" on an online communication platform, including hashtags and other text information.

[0624] 2. Data Acquisition

[0625] The device automatically calls the API of the online communication platform, obtains this new posting data, and sends it to the server.

[0626] 3. Data storage and analysis

[0627] The server stores the acquired data in a database and uses a conversion tool to analyze and convert it, for example, assigning image URLs and text to the appropriate fields.

[0628] 4. Web Page Generation

[0629] The generative AI model automatically generates a webpage containing an introductory section for the "Specialty Pizza" based on the converted data, as well as generating appropriate keywords and meta tags for SEO purposes.

[0630] 5. Webpage Publication and Notification

[0631] The server saves the generated web page in a public directory and notifies the operator of the new URL, possibly via email or other notification service.

[0632] Prompt Sentence Examples

[0633] Generate a page introducing the new menu item based on social media post data.

[0634] Post content:

[0635] Image URL: https: / / example.com / image.jpg

[0636] Text: Special pizza, only 999 yen now!

[0637] Hashtag: New Menu Pizza

[0638] Posting date: YYYY-MM-DD HH:MM

[0639] This allows food delivery service operators to efficiently reflect the latest information and automatically generate and publish web pages that are SEO-friendly.

[0640] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0641] Step 1:

[0642] A user posts new menu items or promotion information to an online communication platform. The input includes an image URL, text, hashtags, and posting date and time. This posting data is accepted by the online communication platform.

[0643] Step 2:

[0644] The terminal calls the API of the online communication platform and automatically obtains the user's posted data. The input is a posted data request, and the output includes the posted data in JSON format. This JSON format data is sent to the server.

[0645] Step 3:

[0646] The server stores the JSON-formatted post data received from the terminal in a database. The input is JSON-formatted post data, and the output is structured data stored in the database. This stored data is used in subsequent processes.

[0647] Step 4:

[0648] The server analyzes and preprocesses the data stored in the database. The input is the data stored in the database, and the output is data converted into a format that is easy for the generative AI model to use. Specific operations include cleaning the data, assigning fields, and segmenting the text data.

[0649] Step 5:

[0650] The server inputs the converted data into a generative AI model to generate a webpage. The input is preprocessed data, and the output is HTML code with the webpage structure. The generative AI model creates a new menu introduction page and applies SEO optimization.

[0651] Step 6:

[0652] The server saves the generated HTML code of the web page in a public directory. The input is the generated HTML code, and the output is a web page that can be accessed on the Internet. Specifically, an HTML file is placed in the destination directory.

[0653] Step 7:

[0654] The server notifies the user of the URL of the new web page. The input is the URL of the generated web page, and the output is a notification message to the user. Notification can occur via email or other notification service.

[0655] 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.

[0656] This invention is a system that automatically generates web pages based on data obtained from social media platforms and publishes homepages with SEO (search engine optimization) measures. This system incorporates an emotion engine that recognizes user emotions, and can display web page content more effectively based on emotional information.

[0657] The system includes the following main components:

[0658] 1. Means of information dissemination

[0659] A social media platform (e.g., Instagram) is used as a means of disseminating information, through which users post content such as images, text, and videos.

[0660] 2. Acquisition method

[0661] The device retrieves new post data using the social media platform's API. The API call retrieves data such as the image URL, text, hashtags, and post date and time.

[0662] 3. Preservation means

[0663] The server stores the acquired data in a database, which is used to efficiently manage user-submitted data and to use it in the analysis and generation process described below.

[0664] 4. Conversion Methods

[0665] The server analyzes the stored data and converts it into a format that can be easily consumed by the generative AI model. This process involves cleansing and reformatting the data, for example, by assigning image URLs to the appropriate fields and segmenting text data.

[0666] 5. Emotion Engine

[0667] The emotion engine built into the server recognizes the user's emotions from the analyzed data. The emotion engine uses an algorithm to analyze the posted text and images and determine the user's emotions (happiness, sadness, surprise, etc.).

[0668] 6. Generation means

[0669] A generative AI model automatically generates sections of a webpage based on the results of the emotion engine, for example by creating different designs and content placements depending on the emotion, as well as generating keywords, meta tags, and alt text for SEO purposes.

[0670] 7. Disclosure Methods

[0671] The server saves the generated web page in a public directory, makes it available on the Internet, and notifies the user of the new URL.

[0672] Program processing explanation

[0673] 1. Users

[0674] A user posts a new post on a social media platform (e.g., Instagram). The user uploads and publishes content such as images, text, and videos.

[0675] 2. Terminal

[0676] The device retrieves new post data using the social media platform's API. The API call retrieves data such as the image URL, text, hashtags, and post date and time.

[0677] 3. Terminal

[0678] It converts the acquired data into an appropriate format, such as JSON, and sends the data to the server using a secure protocol (e.g., HTTPS).

[0679] 4. Server

[0680] The server receives the data sent from the terminal and stores it in a database, which is used to efficiently manage and store user-submitted data.

[0681] 5. Server

[0682] The server parses the data stored in the database and converts it into a specific format. During the parsing process, the data is segmented into image URLs, text, hashtags, etc., and data cleansing is performed.

[0683] 6. Server

[0684] The converted data is passed to an emotion engine, which analyzes the text and image data to recognize the user's emotions. For example, it uses text emotion analysis algorithms and image analysis technology.

[0685] 7. Server

[0686] The emotion engine uses the emotional information it recognizes to direct the generation of web pages, for example, by highlighting upbeat design and positive content that matches the emotion of "joy."

[0687] 8. Generative AI Models

[0688] The generative AI model automatically generates sections of a webpage, including images, text, and video, taking into account emotion recognition results. It also automatically generates keywords, meta tags, and alt text for SEO optimization.

[0689] 9. Server

[0690] It takes the generated web page code and stores it in a public directory that is accessible on the Internet.

[0691] 10. Server

[0692] The new web page URL will be notified to the user via email or a notification service.

[0693] 11. Users

[0694] The user checks the notified URL and checks whether the new homepage has been generated as intended. The user can then modify the homepage content as necessary.

[0695] Specific examples

[0696] A cafe owner posts an Instagram post introducing a new product, a "berry smoothie." The following process takes place:

[0697] 1. Users

[0698] The owner publishes a post on Instagram with an image of a "berry smoothie" and text such as, "A new berry smoothie has arrived! Made with plenty of fresh berries. New berry smoothie menu item."

[0699] 2. Terminal

[0700] The device retrieves new post data through Instagram's API and sends it to the server.

[0701] 3. Server

[0702] The server stores the retrieved data in a database, where it performs analysis and data cleansing, properly formatting image URLs, text, hashtags, etc.

[0703] 4. Emotion Engine

[0704] The server uses an emotion engine to analyze the text and images and recognize the user's emotions. In this case, the emotion "joy" is extracted from the text "New berry smoothie has arrived!"

[0705] 5. Generative AI Models

[0706] The system generates web pages based on the results of the emotion engine. For example, because the user is expressing joy, it emphasizes content with a bright design and positive messages. It also applies SEO keywords like "berry smoothie," "new menu item," and "cafe."

[0707] 6. Server

[0708] Save the generated web page in a public directory and notify the cafe owner of the new URL.

[0709] In this way, user sentiment is analyzed from Instagram posts, and based on the results, an SEO-friendly homepage reflecting the latest information is automatically generated, improving the user experience.

[0710] The processing flow will be explained below.

[0711] Step 1:

[0712] A user posts a new post on a social media platform (e.g., Instagram). The user uploads and publishes content such as images, text, and videos.

[0713] Step 2:

[0714] The device retrieves new post data using the social media platform's API. The API call retrieves data such as the image URL, text, hashtags, and post date and time.

[0715] Step 3:

[0716] The device converts the acquired data into an appropriate format, such as JSON, and sends the data to the server. Data transfer is performed using a secure protocol (e.g., HTTPS).

[0717] Step 4:

[0718] The server receives the data sent from the terminal and stores it in a database, which is used to efficiently manage and store user-submitted data.

[0719] Step 5:

[0720] The server parses the data stored in the database and converts it into a specific format. During the parsing process, the data is segmented into image URLs, text, hashtags, etc., and data cleansing is performed.

[0721] Step 6:

[0722] The server passes the converted data to the emotion engine, which analyzes the text and image data to recognize the user's emotions. For example, it uses text emotion analysis algorithms and image analysis technology.

[0723] Step 7:

[0724] The emotion engine recognizes emotions such as "joy," "sadness," and "surprise" from user posts and passes the results to the generative AI model.

[0725] Step 8:

[0726] The server uses the results of the emotion engine to direct the generation of web pages, for example, if the user indicates the emotion "joy," it will emphasize upbeat design and positive content that matches that emotion.

[0727] Step 9:

[0728] The generative AI model automatically generates sections of a webpage, including images, text, and video, taking into account emotion recognition results. It also automatically generates keywords, meta tags, and alt text for SEO optimization.

[0729] Step 10:

[0730] The server receives the generated web page code and stores it in a public directory that is accessible on the Internet.

[0731] Step 11:

[0732] The server will notify the user of the new web page URL via email or a notification service.

[0733] Step 12:

[0734] The user checks the notified URL and checks whether the new homepage has been generated as intended. The user can then modify the homepage content as necessary.

[0735] Example 2

[0736] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0737] Conventional web page generation systems do not optimize content based on user sentiment, making it difficult to maximize user experience. In addition, SEO measures are often not implemented properly, making it difficult to attract new customers and rank highly in search engines.

[0738] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an information transmission means including image and text data, an acquisition means for acquiring information from the information transmission means, a storage means for saving the acquired data, a conversion means for analyzing the saved data and converting it into a specified format, a generation means for generating a web page based on the analyzed and converted data, a publishing means for publishing the web page generated by the generation means, an emotion recognition means for recognizing a user's emotion using an emotion engine, and an optimization means for optimizing the design and content of the web page based on the recognized emotion information. This makes it possible to automatically generate content optimized based on the user's emotion and provide a web page with SEO measures.

[0739] "Information dissemination means" refers to a means by which a user publishes information, including images and text data, and includes social media platforms.

[0740] "Means of acquisition" refers to the means for acquiring information made public from information dissemination means, and involves acquiring data via the API of the social media platform.

[0741] The "storage means" is a means for temporarily or continuously storing and managing the data acquired by the acquisition means.

[0742] "Conversion Means" means means for analyzing data stored by the Storage Means and converting it into a particular format, including data cleansing and reformatting.

[0743] The "generation means" is a means for automatically generating a new web page based on the analyzed and converted data.

[0744] The "publication means" is a means for publicizing the web page generated by the generation means on the Internet.

[0745] The "emotion recognition means" is a means for recognizing the user's emotions using an emotion engine, and analyzes text data and image data to determine emotions.

[0746] "Optimization measures" are measures for optimizing the design and content of web pages based on the recognized emotional information.

[0747] This invention is a system that automatically generates web pages based on data obtained from social media platforms and publishes homepages with SEO measures. This system incorporates an emotion engine that recognizes user emotions, and can display web page content more effectively based on emotional information.

[0748] Hardware and software used

[0749] 1. Server: The core of the system. The server is responsible for storing, analyzing, transforming, generating, and publishing data.

[0750] 2. Terminal: It is responsible for obtaining data using the API of the social media platform and sending it to the server.

[0751] 3. User: The entity that posts on a social media platform.

[0752] Data processing and calculation

[0753] 1. Means of information dissemination: A user posts a new content on a social media platform (e.g., Instagram). The user uploads and publishes content such as images, text, and videos.

[0754] 2. Acquisition method: The device retrieves new post data using the social media platform's API. The API call retrieves data such as image URL, text, hashtags, and post date and time. The retrieved data is converted into an appropriate format, such as JSON, and sent to the server using a secure protocol (e.g., HTTPS).

[0755] 3. Storage: The server receives the data sent from the terminal and stores it in a database. This database is used to efficiently manage and store the data posted by users.

[0756] 4. Transformation: The server parses the data stored in the database and transforms it into a specific format. During the parsing process, data such as image URLs, text, and hashtags are segmented and data cleansed.

[0757] 5. Emotion recognition means: The server passes the converted data to the emotion engine, which analyzes the text data and image data to recognize the user's emotions. For example, it uses text emotion analysis algorithms or image analysis technology.

[0758] 6. Generation: The generative AI model automatically generates each section of a webpage, taking into account the emotion recognition results. The generated webpage includes images, text, and video. It also automatically generates keywords, meta tags, and alt text appropriate for SEO.

[0759] 7. Optimization: Optimize the design and content of a web page based on the recognized emotion. For example, if a user expresses the emotion "happy," emphasize a bright design and positive content that matches that emotion.

[0760] 8. Publishing: The server receives the generated web page code and saves it in a public directory. This directory is set up to be accessible on the Internet. The new web page URL is notified to the user. Notification can be done via email or a notification service.

[0761] Specific examples

[0762] For example, if a cafe owner posts an Instagram post introducing a new product, a "berry smoothie," the following process takes place:

[0763] 1. A user publishes a post on Instagram that includes an image of a "berry smoothie" and text such as "A new berry smoothie has arrived! Made with plenty of fresh berries. New berry smoothie menu item."

[0764] 2. The device retrieves new post data through Instagram's API and sends it to the server.

[0765] 3. The server stores the retrieved data in a database, where it performs analysis and data cleansing, properly formatting image URLs, text, hashtags, etc.

[0766] 4. The emotion recognition unit analyzes the text and image to recognize the user’s emotion. In this case, the emotion “joy” is extracted from the text “New berry smoothie is here!”

[0767] 5. The generator generates a web page based on the results of the emotion engine. Because the user is expressing joy, content with a bright design and positive messages is emphasized. Additionally, keywords like "berry smoothie," "new menu," and "cafe" are applied as SEO keywords.

[0768] 6. The server saves the generated web page to a public directory and notifies the cafe owner of the new URL.

[0769] In this way, user sentiment is analyzed from Instagram posts, and based on the results, an SEO-friendly homepage reflecting the latest information is automatically generated, improving the user experience.

[0770] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0771] Step 1:

[0772] A user makes a new post on a social media platform (e.g., Instagram). The user uploads and publishes content such as images, text, and videos. The input is the image, text, and video created by the user. The output is the published post on the social media platform.

[0773] Step 2:

[0774] The device uses the API of the social media platform to obtain new post data. By making an API call, data such as the image URL, text, hashtags, and posting date and time is obtained. The input is the user's post information via the API call. The output is the obtained post data.

[0775] Step 3:

[0776] The data acquired by the terminal is converted into an appropriate format, such as JSON format, and sent to the server using a secure protocol (e.g., HTTPS). The input is the acquired post data. The output is the data converted into JSON format and the data sent securely.

[0777] Step 4:

[0778] The server receives the data sent from the terminal and stores it in a database. The stored data is used to efficiently manage user posted data. The input is securely transmitted JSON format data. The output is the posted data saved in the database.

[0779] Step 5:

[0780] The server parses the data stored in the database and converts it into a specific format. During the parsing process, data such as image URLs, text, and hashtags are segmented and cleansed. The input is the post data stored in the database. The output is the segmented and cleansed data.

[0781] Step 6:

[0782] The server passes the converted data to the emotion engine, which analyzes the text data and image data to recognize the user's emotions. For example, it uses text emotion analysis algorithms and image analysis techniques. The input is the segmented and cleansed data. The output is the user's emotional information (happiness, sadness, surprise, etc.).

[0783] Step 7:

[0784] The generative AI model automatically generates each section of a webpage, taking into account the emotion recognition results. The generated webpage includes images, text, and video. It also automatically generates appropriate keywords, meta tags, and alt text for SEO purposes. The input is the emotion-recognized user's emotion information. The output is the automatically generated webpage code.

[0785] Step 8:

[0786] The server receives the generated web page code and saves it in a public directory. The public directory is set up to be accessible on the Internet. It also notifies the user of the new web page URL. The input is the automatically generated web page code. The output is the web page saved in the public directory and the notified URL.

[0787] Step 9:

[0788] The user checks the notified URL and checks whether the new homepage has been generated as intended. The contents of the homepage can be modified as necessary. The input is the notified web page URL. The output is the confirmed and modified homepage.

[0789] (Application example 2)

[0790] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0791] There is a need for an effective method to provide real-time traffic information on the dashboard of an autonomous vehicle and display content tailored to the driver's emotions. Conventional systems do not provide information that takes the driver's emotions into consideration, and there is a lack of measures to reduce the driver's stress and anxiety. Against this background, the challenge is to develop a system that recognizes the driver's emotions and provides real-time information accordingly.

[0792] 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.

[0793] In this invention, the server includes an information transmission means including image and text data, an acquisition means for acquiring information from the information transmission means, a storage means for saving the acquired data, a conversion means for analyzing the saved data and converting it into a specified format, a generation means for generating a web page based on the analyzed and converted data, a publication means for publishing the web page generated by the generation means, an emotion engine for recognizing emotions and judging the driver's state based on the published web page, and a dashboard display means for displaying information customized based on the driver's emotions. This makes it possible to provide information according to the driver's emotions and a relaxing driving environment.

[0794] "Information dissemination means including images and text data" refers to means such as social media platforms that allow drivers or users to publish images and text data.

[0795] "Means of acquisition" refers to means such as APIs and communication protocols for collecting data made public from information dissemination means.

[0796] "Storage means" refers to a database or storage system that stores acquired data for a long period of time so that it can be analyzed or used later.

[0797] A "conversion means" is a program or algorithm that analyzes the stored data and converts it into a specified format.

[0798] "Generation means" refers to an AI model or automatic generation tool for automatically generating web pages based on the analyzed and converted data.

[0799] The "publication means" refers to a server or hosting service that makes the generated web page publicly available on the Internet and accessible to users.

[0800] An "emotion engine" is a program or algorithm that analyzes and recognizes the emotions of drivers or users from posted images and text data.

[0801] "Dashboard display means" refers to a display or interface system for displaying information on the dashboard of an autonomous vehicle according to the driver's emotional state.

[0802] An embodiment of the present invention will now be described. The present system is designed to provide real-time information according to the driver's emotions and to provide a relaxing driving environment.

[0803] The system mainly consists of the following components:

[0804] 1. Means of information dissemination

[0805] Drivers use social media platforms (e.g., Twitter and Instagram) to publish images and text data, which then becomes input into the system.

[0806] 2. Acquisition method

[0807] The device uses the API of the social media platform to retrieve new post data. The retrieved data includes image URLs, text, hashtags, and posting dates and times, allowing users to always obtain the latest information.

[0808] 3. Preservation means

[0809] The server stores the acquired data in a database, which is used to efficiently manage data over a long period of time.

[0810] 4. Conversion Methods

[0811] The server parses the stored data and converts it into the specified format. The parsing process involves data cleansing and reformatting.

[0812] 5. Generation means

[0813] Using a generative AI model, web pages are automatically generated based on the analyzed and converted data, incorporating the results of the emotion engine to arrange the design and content according to the user's emotions.

[0814] 6. Disclosure Methods

[0815] The generated web page is saved in a public directory for publishing on the Internet, making it accessible to users, and notifying users of the new URL.

[0816] 7. Emotion Engine

[0817] The server includes an emotion engine that analyzes text data and image data and recognizes the user's emotion, using, for example, a text emotion analysis algorithm or image analysis technology.

[0818] 8. Dashboard display method

[0819] The dashboard of an autonomous vehicle will display customized information based on the driver's emotions, reducing stress and anxiety for the driver and providing a more relaxed driving environment.

[0820] Operation example

[0821] For example, when a driver shares a post containing traffic congestion information or accident information on social media, the post is acquired by the acquisition means. The data stored in the storage means is analyzed and formatted by the conversion means, and the emotion engine identifies the driver's emotion.

[0822] Example prompts for generative AI models

[0823] "Design a driver dashboard application that collects the latest traffic information from social media and customizes it based on emotions. In stressful situations, provide relaxation suggestions, and for enjoyable driving, provide recommended spots."

[0824] As described above, the present invention is a system that provides real-time information according to the emotional state of the driver and optimizes the driving environment.

[0825] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0826] Step 1:

[0827] The device uses the API of a social media platform to obtain new post data. The input is the image URL, text, hashtag, and posting date and time data obtained by the API call, and the output is JSON format data. This data includes information such as the image and text posted by the user on social media.

[0828] Step 2:

[0829] The JSON format data acquired by the terminal is sent to the storage means, and the data is stored in the server's database. The input is the JSON format data to be stored, and the output is the data stored in the database. This database is used to efficiently manage user posted data.

[0830] Step 3:

[0831] The server parses the stored data and converts it into the specified format. The input is JSON format data retrieved from the database, and the parsed and converted format (e.g., segmented text and image URLs assigned to appropriate fields) is obtained as output. Specific operations include cleansing the text data and organizing image URLs.

[0832] Step 4:

[0833] The server passes the parsed and converted data to the emotion engine for emotion analysis. The input is the converted data, and the output is the user's emotional state (e.g., happy or sad). The emotion engine uses text analysis algorithms and image analysis techniques to determine the emotion.

[0834] Step 5:

[0835] The generation means generates a web page based on the results of the emotion engine. The input is emotion information and analyzed / converted data, and the output is an SEO-optimized web page. Specific operations include different designs and content placement depending on the emotion.

[0836] Step 6:

[0837] The server saves the generated web page in a public directory and makes it available on the net. The input is the generated web page code, and the output is a new URL that can be accessed on the internet. The server notifies the user of this URL.

[0838] Step 7:

[0839] The terminal displays information on the dashboard display means based on the published web page and emotional information. The input is the URL of the generated web page and emotional information, and the output is customized content that the driver can view on the dashboard. Specific operations include displaying information according to the driver's emotions and making suggestions to help them relax.

[0840] 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.

[0841] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0842] 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.

[0843] [Third embodiment]

[0844] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0845] 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.

[0846] 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).

[0847] 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.

[0848] 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.

[0849] 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).

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0856] The present invention provides a system that automatically generates web pages based on data acquired from social media platforms and publishes homepages that incorporate SEO (search engine optimization) measures. The following describes an embodiment of this system.

[0857] The system includes the following main components:

[0858] 1. Means of information dissemination

[0859] A social media platform (e.g., Instagram) is used as a means of disseminating information, through which users post content such as images, text, and videos.

[0860] 2. Acquisition method

[0861] The device automatically retrieves user post data using the API of the social media platform, including image URLs, text, hashtags, and posting dates and times.

[0862] 3. Preservation means

[0863] The server stores the acquired data in a database, which is used to efficiently manage the acquired data and use it in the analysis and generation process described below.

[0864] 4. Conversion Methods

[0865] The server then analyzes the stored data and converts it into a format that can be easily consumed by the generative AI model, which involves cleansing and reformatting the data, for example, by assigning image URLs to the appropriate fields and segmenting text data.

[0866] 5. Generation means

[0867] The generative AI model automatically generates web pages based on the converted data. Specifically, it analyzes social media posts and builds each section (new product introduction, gallery, etc.). It also generates keywords, meta tags, alt text, etc. for SEO purposes.

[0868] 6. Disclosure Methods

[0869] The server saves the generated web page in a public directory, making it available on the Internet, and notifies the user of the new URL.

[0870] Program processing explanation

[0871] 1. Users

[0872] A user posts a new post on a social media platform (e.g., Instagram), which can contain content such as images, text, and videos.

[0873] 2. Terminal

[0874] The device automatically calls the social media API to retrieve the user's latest posted data, which is then sent to the server in JSON format.

[0875] 3. Server

[0876] The server receives the data sent from the terminal and stores it in a database.

[0877] The data stored in the database is analyzed and converted into a format that is easy for the generative AI model to use using a conversion method.

[0878] 4. Generative AI Models

[0879] Based on the analyzed and converted data, each section of the web page is automatically generated, including content such as images, text, and videos.

[0880] Automatically generates appropriate keywords, meta tags, Alt text, etc. as SEO measures.

[0881] 5. Server

[0882] Receive the generated web page code and save it in a public directory.

[0883] Notify users of the new homepage URL via email or other notification services.

[0884] Specific examples

[0885] A cafe owner posts an Instagram post introducing a new product, a "berry smoothie." The following process takes place:

[0886] 1. Users

[0887] The owner publishes a post on Instagram with an image and text of "Berry Smoothie."

[0888] 2. Terminal

[0889] The device retrieves this new post data through Instagram's API and sends it to the server.

[0890] 3. Server

[0891] The data acquired by the server is stored in a database and analyzed and converted using a conversion means.

[0892] The converted data is passed to a generative AI model to instruct it to generate a web page.

[0893] 4. Generative AI Models

[0894] The model automatically generates a web page containing an introductory section for "berry smoothies."

[0895] Generate appropriate keywords and meta tags as part of your SEO strategy.

[0896] 5. Server

[0897] The server saves the generated web page in a public directory and notifies the cafe owner of the new URL.

[0898] In this way, an SEO-friendly homepage that reflects the latest information is automatically generated based on Instagram posts.

[0899] The processing flow will be explained below.

[0900] Step 1:

[0901] A user posts a new post on a social media platform (e.g., Instagram). The user uploads and publishes content such as images, text, and videos.

[0902] Step 2:

[0903] The device retrieves new post data using the social media platform's API. The API call retrieves data such as the image URL, text, hashtags, and post date and time.

[0904] Step 3:

[0905] The device converts the acquired data into an appropriate format, such as JSON, and sends the data to the server. Data transfer is performed using a secure protocol (e.g., HTTPS).

[0906] Step 4:

[0907] The server receives the data sent from the terminal and stores it in a database, which is used to efficiently manage and store user-submitted data.

[0908] Step 5:

[0909] The server parses the data stored in the database and converts it into a specific format. During the parsing process, data such as image URLs, text, and hashtags are segmented and cleansed.

[0910] Step 6:

[0911] The server then inputs the converted data into a generative AI model, which instructs it to generate a webpage. This input includes the analyzed image and text data, as well as meta information and keywords.

[0912] Step 7:

[0913] Based on the input data, the generative AI model automatically generates sections of a webpage, such as a new product introduction section, gallery section, etc. It also automatically generates keywords, meta tags, alt text, etc. for SEO purposes.

[0914] Step 8:

[0915] The generative AI model sends the generated web page code (HTML, CSS, JavaScript, etc.) back to the server.

[0916] Step 9:

[0917] The server receives the generated web page code and stores it in a public directory that is accessible on the Internet.

[0918] Step 10:

[0919] The server will notify the user of the new web page URL via email or a notification service.

[0920] Step 11:

[0921] The user checks the notified URL and checks whether the new homepage has been generated as intended. The user can then modify the homepage content as necessary.

[0922] Through the above processing steps, social media posts are automatically retrieved and an SEO-friendly homepage is generated and published.

[0923] Example 1

[0924] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0925] To automatically generate and publish web pages with SEO measures by effectively utilizing a huge amount of posted data on social media, and to provide web pages that always contain the latest information while reducing the user's workload.

[0926] 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.

[0927] In this invention, the server includes a terminal that includes an information acquisition process based on user posts, a server that places generated web pages in a storage directory, and a generation means that automatically builds web page sections using a generative AI model. This makes it possible to efficiently acquire and analyze social media post data and automatically generate and publish SEO-optimized web pages that include the latest information.

[0928] "Information transmission means" refers to a medium through which a user publishes information including images and text data.

[0929] "Acquisition means" refers to a device or program that has the function of automatically acquiring data from information transmission means.

[0930] "Storage means" refers to a mechanism for storing acquired data in a storage device such as a database.

[0931] "Conversion means" refers to a process or device that analyzes and converts stored data into a specified format.

[0932] "Generation means" refers to a program or system that automatically generates a web page based on the analyzed and converted data.

[0933] The "publication means" refers to a mechanism for publishing the generated web page on the Internet so that users can access it.

[0934] "Terminal" refers to a device or system that acquires information based on posts from users and transmits data to a server.

[0935] "Server" refers to a device or system that places generated web pages in a storage directory and makes them available on the Internet.

[0936] "Generative AI model" refers to an artificial intelligence model that performs a process of data analysis and automatic generation to build each section of a web page.

[0937] A "prompt sentence" refers to an input sentence that triggers a generative AI model.

[0938] This invention provides a system that automatically generates web pages based on data acquired from social media platforms and publishes homepages that incorporate SEO measures. The following describes in detail an embodiment of this system.

[0939] Key Components of the System

[0940] The system includes the following main components:

[0941] 1. Means of information dissemination

[0942] A social media platform is used as a means of disseminating information. Users can post content such as images, text, and videos through this platform. This information becomes the source data for the system.

[0943] 2. Acquisition method

[0944] The device automatically retrieves user post data using the API of the social media platform, including image URLs, text, hashtags, and posting dates and times.

[0945] 3. Preservation means

[0946] The server stores the acquired data in a database, which is used to efficiently manage the acquired data and utilize it in the analysis and production processes.

[0947] 4. Conversion Methods

[0948] The server then analyzes the stored data and converts it into a format that can be easily consumed by the generative AI model, which involves cleansing and reformatting the data, for example, by assigning image URLs to the appropriate fields and segmenting text data.

[0949] 5. Generation means

[0950] The generative AI model automatically generates web pages based on the converted data. Specifically, it analyzes social media posts and builds each section (new product introduction, gallery, etc.). It also generates keywords, meta tags, alt text, etc. for SEO purposes.

[0951] 6. Disclosure Methods

[0952] The server saves the generated web page in a public directory, making it available on the Internet, and notifies the user of the new URL via email or other notification service.

[0953] Specific examples

[0954] For example, when a cafe owner posts on social media to introduce a new product, a "berry smoothie," the system executes the following process:

[0955] 1. Users

[0956] The owner publishes a post on social media with an image of a "berry smoothie" and text, such as "Today's new menu item: Berry Smoothie! Loaded with fresh berries. New item at the cafe."

[0957] 2. Terminal

[0958] The device obtains this new post data through the social media API and sends it to the server.

[0959] 3. Server

[0960] The server stores the acquired data in a database, analyzes and converts it using a conversion method, and passes the converted data to a generative AI model to instruct it to generate a web page.

[0961] 4. Generative AI Models

[0962] The model automatically generates a web page with an introductory section for "Berry Smoothie," including images, text, and related videos, as well as appropriate keywords and meta tags for SEO.

[0963] 5. Server

[0964] The server saves the generated web page in a public directory and notifies the cafe owner of the new URL.

[0965] Prompt Sentence Examples

[0966] Examples of input prompts for generative AI models include:

[0967] "Create a webpage with a new product section based on your latest Instagram posts. Use the following data: image URL, text, and hashtags. For SEO purposes, please also generate appropriate keywords, meta tags, and alt text."

[0968] Using this prompt, the generative AI model can automatically generate a web page based on the request, allowing users to effectively transform their social media posts into up-to-date, SEO-optimized homepages.

[0969] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0970] Step 1: User Submission

[0971] Input: A user makes a post on a social media platform that includes an image, text, hashtags, video, etc.

[0972] Specific operation: The user enters the content of the post and presses the publish button. At that time, the necessary data (image URL, text, hashtags, posting date and time) is included in the post.

[0973] Output: Posts published on social media platforms.

[0974] Step 2: Data acquisition by device

[0975] Input: A new post on a social media platform.

[0976] Specific operation: The device periodically calls the API of the social media platform to obtain new post data, and processes the obtained data in JSON format, etc.

[0977] Data processing: Format the acquired RAW data and extract only the necessary information.

[0978] Output: Formatted post data (image URL, text, hashtags, post date and time).

[0979] Step 3: Save data to the server

[0980] Input: Formatted post data sent from the device.

[0981] Specific operation: The server receives the data sent from the device and saves it in the database. When saving, it checks the integrity of the data and assigns it to the appropriate fields.

[0982] Data processing: Checking the integrity of the received data and assigning it to fields.

[0983] Output: Post data saved in the database.

[0984] Step 4: Analyze and transform the data

[0985] Input: Post data stored in the database.

[0986] What it does: The server analyzes the data and performs data cleansing to remove unnecessary data and noise. After analysis, the data is converted into a format that is easy for the generative AI model to process. For example, it segments text and assigns image URLs to the appropriate fields.

[0987] Data processing: data cleansing, reformatting, field assignment.

[0988] Output: Parsed and transformed data.

[0989] Step 5: Generative AI model generates webpage

[0990] Input: Parsed and transformed data.

[0991] What it does: The server generates a prompt and inputs the converted data into the generative AI model. The prompt might look something like this: "Create a webpage with a new product introduction section based on the latest Instagram post data. Please use the following data: image URL, text, and hashtags. For SEO purposes, please also generate appropriate keywords, meta tags, and alt text."

[0992] Data processing: Building web pages using generative AI models, generating keywords, meta tags, and alt text.

[0993] Output: Auto-generated web page code.

[0994] Step 6: Publish and advertise your webpage

[0995] Input: The code for the generated web page.

[0996] What it does: The server receives the generated web page code and saves it in a public directory, then generates a new URL and notifies the user via email or other notification services.

[0997] Data processing: web page storage, URL generation, notification process.

[0998] Output: Web page saved to public directory, new URL, notified users.

[0999] (Application example 1)

[1000] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1001] Food delivery services are rapidly becoming more popular these days, requiring businesses to maintain their online presence and update it quickly with the latest information. However, doing this manually requires a great deal of effort and time, making it inefficient. Creating web pages with SEO optimization also requires specialized knowledge, placing a significant burden on many webmasters. Therefore, there is a need for a system that can automatically and efficiently generate web pages that reflect the latest information and publish them while incorporating SEO optimization.

[1002] 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.

[1003] In this invention, the server includes an information transmission means including image and text data, an acquisition means for acquiring information from the information transmission means, a storage means for saving the acquired data, a conversion means for analyzing the saved data and converting it into a specified format, a generation means for generating a web page based on the analyzed and converted data, a publishing means for updating the generated web page in an application and website of the electronic device, and a notification means for notifying a new URL. This enables operators of food delivery services to efficiently update their latest information and automatically generate and publish SEO-friendly web pages.

[1004] "Information dissemination means" refers to an online communication platform that allows users to provide content such as images and text data over the Internet.

[1005] "Acquisition means" refers to the means for automatically acquiring content provided by a user via the API of the online communication platform.

[1006] "Storage means" refers to a database or storage system that stores acquired data and keeps it in a state that allows it to be used for later processing.

[1007] "Conversion means" refers to the means for analyzing stored data and converting it into a format that is easy for the generative AI model to handle.

[1008] "Generating means" means a means including a generative AI model for automatically generating a web page based on the transformed data.

[1009] The "publication means" refers to a means for publishing the generated web page on the Internet and reflecting it in the application and website of the electronic device.

[1010] The "notification means" refers to a means for notifying the user of the URL of the newly generated web page.

[1011] The present invention provides a system that automatically generates web pages based on data posted on an online communication platform by operators of food delivery services, implements SEO measures, and publishes them. Specific embodiments of the system are described below.

[1012] System Configuration

[1013] The system includes the following main components:

[1014] 1. Means of information dissemination

[1015] Online communication platforms (e.g., Instagram) are used as a means of disseminating this information, and food delivery service operators post information about new menu items and promotions on these platforms.

[1016] 2. Acquisition method

[1017] The device automatically retrieves post data from food delivery service operators using the API of the online communication platform, including image URLs, text, hashtags, and posting dates and times.

[1018] 3. Preservation means

[1019] The server stores the acquired data in a database, which is used to efficiently manage the acquired data and use it in the analysis and generation process described below.

[1020] 4. Conversion Methods

[1021] The server then analyzes the stored data and converts it into a format that can be easily consumed by the generative AI model, which involves cleaning and reformatting the data, for example, by assigning image URLs to the appropriate fields and segmenting text data.

[1022] 5. Generation means

[1023] The generative AI model automatically generates web pages based on the converted data. Specifically, it analyzes posts on online communication platforms and builds each section (new product introduction, gallery, etc.). It also generates keywords, meta tags, alt text, etc. for SEO purposes.

[1024] 6. Disclosure Methods

[1025] The server saves the generated web page in a public directory, making it available on the Internet, and notifies the user of the new URL via email or other notification service.

[1026] System operation and concrete examples

[1027] The operation of the system will be explained below using a specific example.

[1028] example

[1029] A food delivery service operator posts an image and description of a new "specialty pizza" on an online communication platform.

[1030] 1. User Operation

[1031] The operator posts an image and description of the "special pizza" on an online communication platform, including hashtags and other text information.

[1032] 2. Data Acquisition

[1033] The device automatically calls the API of the online communication platform, obtains this new posting data, and sends it to the server.

[1034] 3. Data storage and analysis

[1035] The server stores the acquired data in a database and uses a conversion tool to analyze and convert it, for example, assigning image URLs and text to the appropriate fields.

[1036] 4. Web Page Generation

[1037] The generative AI model automatically generates a webpage containing an introductory section for the "Specialty Pizza" based on the converted data, as well as generating appropriate keywords and meta tags for SEO purposes.

[1038] 5. Webpage Publication and Notification

[1039] The server saves the generated web page in a public directory and notifies the operator of the new URL, possibly via email or other notification service.

[1040] Prompt Sentence Examples

[1041] Generate a page introducing the new menu item based on social media post data.

[1042] Post content:

[1043] Image URL: https: / / example.com / image.jpg

[1044] Text: Special pizza, only 999 yen now!

[1045] Hashtag: New Menu Pizza

[1046] Posting date: YYYY-MM-DD HH:MM

[1047] This allows food delivery service operators to efficiently reflect the latest information and automatically generate and publish web pages that are SEO-friendly.

[1048] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1049] Step 1:

[1050] A user posts new menu items or promotion information to an online communication platform. The input includes an image URL, text, hashtags, and posting date and time. This posting data is accepted by the online communication platform.

[1051] Step 2:

[1052] The terminal calls the API of the online communication platform and automatically obtains the user's posted data. The input is a posted data request, and the output includes the posted data in JSON format. This JSON format data is sent to the server.

[1053] Step 3:

[1054] The server stores the JSON-formatted post data received from the terminal in a database. The input is JSON-formatted post data, and the output is structured data stored in the database. This stored data is used in subsequent processes.

[1055] Step 4:

[1056] The server analyzes and preprocesses the data stored in the database. The input is the data stored in the database, and the output is data converted into a format that is easy for the generative AI model to use. Specific operations include cleaning the data, assigning fields, and segmenting the text data.

[1057] Step 5:

[1058] The server inputs the converted data into a generative AI model to generate a webpage. The input is preprocessed data, and the output is HTML code with the webpage structure. The generative AI model creates a new menu introduction page and applies SEO optimization.

[1059] Step 6:

[1060] The server saves the generated HTML code of the web page in a public directory. The input is the generated HTML code, and the output is a web page that can be accessed on the Internet. Specifically, an HTML file is placed in the destination directory.

[1061] Step 7:

[1062] The server notifies the user of the URL of the new web page. The input is the URL of the generated web page, and the output is a notification message to the user. Notification can occur via email or other notification service.

[1063] 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.

[1064] This invention is a system that automatically generates web pages based on data obtained from social media platforms and publishes homepages with SEO (search engine optimization) measures. This system incorporates an emotion engine that recognizes user emotions, and can display web page content more effectively based on emotional information.

[1065] The system includes the following main components:

[1066] 1. Means of information dissemination

[1067] A social media platform (e.g., Instagram) is used as a means of disseminating information, through which users post content such as images, text, and videos.

[1068] 2. Acquisition method

[1069] The device retrieves new post data using the social media platform's API. The API call retrieves data such as the image URL, text, hashtags, and post date and time.

[1070] 3. Preservation means

[1071] The server stores the acquired data in a database, which is used to efficiently manage user-submitted data and to use it in the analysis and generation process described below.

[1072] 4. Conversion Methods

[1073] The server analyzes the stored data and converts it into a format that can be easily consumed by the generative AI model. This process involves cleansing and reformatting the data, for example, by assigning image URLs to the appropriate fields and segmenting text data.

[1074] 5. Emotion Engine

[1075] The emotion engine built into the server recognizes the user's emotions from the analyzed data. The emotion engine uses an algorithm to analyze the posted text and images and determine the user's emotions (happiness, sadness, surprise, etc.).

[1076] 6. Generation means

[1077] A generative AI model automatically generates sections of a webpage based on the results of the emotion engine, for example by creating different designs and content placements depending on the emotion, as well as generating keywords, meta tags, and alt text for SEO purposes.

[1078] 7. Disclosure Methods

[1079] The server saves the generated web page in a public directory, makes it available on the Internet, and notifies the user of the new URL.

[1080] Program processing explanation

[1081] 1. Users

[1082] A user posts a new post on a social media platform (e.g., Instagram). The user uploads and publishes content such as images, text, and videos.

[1083] 2. Terminal

[1084] The device retrieves new post data using the social media platform's API. The API call retrieves data such as the image URL, text, hashtags, and post date and time.

[1085] 3. Terminal

[1086] It converts the acquired data into an appropriate format, such as JSON, and sends the data to the server using a secure protocol (e.g., HTTPS).

[1087] 4. Server

[1088] The server receives the data sent from the terminal and stores it in a database, which is used to efficiently manage and store user-submitted data.

[1089] 5. Server

[1090] The server parses the data stored in the database and converts it into a specific format. During the parsing process, the data is segmented into image URLs, text, hashtags, etc., and data cleansing is performed.

[1091] 6. Server

[1092] The converted data is passed to an emotion engine, which analyzes the text and image data to recognize the user's emotions. For example, it uses text emotion analysis algorithms and image analysis technology.

[1093] 7. Server

[1094] The emotion engine uses the emotional information it recognizes to direct the generation of web pages, for example, by highlighting upbeat design and positive content that matches the emotion of "joy."

[1095] 8. Generative AI Models

[1096] The generative AI model automatically generates sections of a webpage, including images, text, and video, taking into account emotion recognition results. It also automatically generates keywords, meta tags, and alt text for SEO optimization.

[1097] 9. Server

[1098] It takes the generated web page code and stores it in a public directory that is accessible on the Internet.

[1099] 10. Server

[1100] The new web page URL will be notified to the user via email or a notification service.

[1101] 11. Users

[1102] The user checks the notified URL and checks whether the new homepage has been generated as intended. The user can then modify the homepage content as necessary.

[1103] Specific examples

[1104] A cafe owner posts an Instagram post introducing a new product, a "berry smoothie." The following process takes place:

[1105] 1. Users

[1106] The owner publishes a post on Instagram with an image of a "berry smoothie" and text such as, "A new berry smoothie has arrived! Made with plenty of fresh berries. New berry smoothie menu item."

[1107] 2. Terminal

[1108] The device retrieves new post data through Instagram's API and sends it to the server.

[1109] 3. Server

[1110] The server stores the retrieved data in a database, where it performs analysis and data cleansing, properly formatting image URLs, text, hashtags, etc.

[1111] 4. Emotion Engine

[1112] The server uses an emotion engine to analyze the text and images and recognize the user's emotions. In this case, the emotion "joy" is extracted from the text "New berry smoothie has arrived!"

[1113] 5. Generative AI Models

[1114] The system generates web pages based on the results of the emotion engine. For example, because the user is expressing joy, it emphasizes content with a bright design and positive messages. It also applies SEO keywords like "berry smoothie," "new menu item," and "cafe."

[1115] 6. Server

[1116] Save the generated web page in a public directory and notify the cafe owner of the new URL.

[1117] In this way, user sentiment is analyzed from Instagram posts, and based on the results, an SEO-friendly homepage reflecting the latest information is automatically generated, improving the user experience.

[1118] The processing flow will be explained below.

[1119] Step 1:

[1120] A user posts a new post on a social media platform (e.g., Instagram). The user uploads and publishes content such as images, text, and videos.

[1121] Step 2:

[1122] The device retrieves new post data using the social media platform's API. The API call retrieves data such as the image URL, text, hashtags, and post date and time.

[1123] Step 3:

[1124] The device converts the acquired data into an appropriate format, such as JSON, and sends the data to the server. Data transfer is performed using a secure protocol (e.g., HTTPS).

[1125] Step 4:

[1126] The server receives the data sent from the terminal and stores it in a database, which is used to efficiently manage and store user-submitted data.

[1127] Step 5:

[1128] The server parses the data stored in the database and converts it into a specific format. During the parsing process, the data is segmented into image URLs, text, hashtags, etc., and data cleansing is performed.

[1129] Step 6:

[1130] The server passes the converted data to the emotion engine, which analyzes the text and image data to recognize the user's emotions. For example, it uses text emotion analysis algorithms and image analysis technology.

[1131] Step 7:

[1132] The emotion engine recognizes emotions such as "joy," "sadness," and "surprise" from user posts and passes the results to the generative AI model.

[1133] Step 8:

[1134] The server uses the results of the emotion engine to direct the generation of web pages, for example, if the user indicates the emotion "joy," it will emphasize upbeat design and positive content that matches that emotion.

[1135] Step 9:

[1136] The generative AI model automatically generates sections of a webpage, including images, text, and video, taking into account emotion recognition results. It also automatically generates keywords, meta tags, and alt text for SEO optimization.

[1137] Step 10:

[1138] The server receives the generated web page code and stores it in a public directory that is accessible on the Internet.

[1139] Step 11:

[1140] The server will notify the user of the new web page URL via email or a notification service.

[1141] Step 12:

[1142] The user checks the notified URL and checks whether the new homepage has been generated as intended. The user can then modify the homepage content as necessary.

[1143] Example 2

[1144] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1145] Conventional web page generation systems do not optimize content based on user sentiment, making it difficult to maximize user experience. In addition, SEO measures are often not implemented properly, making it difficult to attract new customers and rank highly in search engines.

[1146] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an information transmission means including image and text data, an acquisition means for acquiring information from the information transmission means, a storage means for saving the acquired data, a conversion means for analyzing the saved data and converting it into a specified format, a generation means for generating a web page based on the analyzed and converted data, a publishing means for publishing the web page generated by the generation means, an emotion recognition means for recognizing a user's emotion using an emotion engine, and an optimization means for optimizing the design and content of the web page based on the recognized emotion information. This makes it possible to automatically generate content optimized based on the user's emotion and provide a web page with SEO measures.

[1147] "Information dissemination means" refers to a means by which a user publishes information, including images and text data, and includes social media platforms.

[1148] "Means of acquisition" refers to the means for acquiring information made public from information dissemination means, and involves acquiring data via the API of the social media platform.

[1149] The "storage means" is a means for temporarily or continuously storing and managing the data acquired by the acquisition means.

[1150] "Conversion Means" means means for analyzing data stored by the Storage Means and converting it into a particular format, including data cleansing and reformatting.

[1151] The "generation means" is a means for automatically generating a new web page based on the analyzed and converted data.

[1152] The "publication means" is a means for publicizing the web page generated by the generation means on the Internet.

[1153] The "emotion recognition means" is a means for recognizing the user's emotions using an emotion engine, and analyzes text data and image data to determine emotions.

[1154] "Optimization measures" are measures for optimizing the design and content of web pages based on the recognized emotional information.

[1155] This invention is a system that automatically generates web pages based on data obtained from social media platforms and publishes homepages with SEO measures. This system incorporates an emotion engine that recognizes user emotions, and can display web page content more effectively based on emotional information.

[1156] Hardware and software used

[1157] 1. Server: The core of the system. The server is responsible for storing, analyzing, transforming, generating, and publishing data.

[1158] 2. Terminal: It is responsible for obtaining data using the API of the social media platform and sending it to the server.

[1159] 3. User: The entity that posts on a social media platform.

[1160] Data processing and calculation

[1161] 1. Means of information dissemination: A user posts a new content on a social media platform (e.g., Instagram). The user uploads and publishes content such as images, text, and videos.

[1162] 2. Acquisition method: The device retrieves new post data using the social media platform's API. The API call retrieves data such as image URL, text, hashtags, and post date and time. The retrieved data is converted into an appropriate format, such as JSON, and sent to the server using a secure protocol (e.g., HTTPS).

[1163] 3. Storage: The server receives the data sent from the terminal and stores it in a database. This database is used to efficiently manage and store the data posted by users.

[1164] 4. Transformation: The server parses the data stored in the database and transforms it into a specific format. During the parsing process, data such as image URLs, text, and hashtags are segmented and data cleansed.

[1165] 5. Emotion recognition means: The server passes the converted data to the emotion engine, which analyzes the text data and image data to recognize the user's emotions. For example, it uses text emotion analysis algorithms or image analysis technology.

[1166] 6. Generation: The generative AI model automatically generates each section of a webpage, taking into account the emotion recognition results. The generated webpage includes images, text, and video. It also automatically generates keywords, meta tags, and alt text appropriate for SEO.

[1167] 7. Optimization: Optimize the design and content of a web page based on the recognized emotion. For example, if a user expresses the emotion "happy," emphasize a bright design and positive content that matches that emotion.

[1168] 8. Publishing: The server receives the generated web page code and saves it in a public directory. This directory is set up to be accessible on the Internet. The new web page URL is notified to the user. Notification can be done via email or a notification service.

[1169] Specific examples

[1170] For example, if a cafe owner posts an Instagram post introducing a new product, a "berry smoothie," the following process takes place:

[1171] 1. A user publishes a post on Instagram that includes an image of a "berry smoothie" and text such as "A new berry smoothie has arrived! Made with plenty of fresh berries. New berry smoothie menu item."

[1172] 2. The device retrieves new post data through Instagram's API and sends it to the server.

[1173] 3. The server stores the retrieved data in a database, where it performs analysis and data cleansing, properly formatting image URLs, text, hashtags, etc.

[1174] 4. The emotion recognition unit analyzes the text and image to recognize the user’s emotion. In this case, the emotion “joy” is extracted from the text “New berry smoothie is here!”

[1175] 5. The generator generates a web page based on the results of the emotion engine. Because the user is expressing joy, content with a bright design and positive messages is emphasized. Additionally, keywords like "berry smoothie," "new menu," and "cafe" are applied as SEO keywords.

[1176] 6. The server saves the generated web page to a public directory and notifies the cafe owner of the new URL.

[1177] In this way, user sentiment is analyzed from Instagram posts, and based on the results, an SEO-friendly homepage reflecting the latest information is automatically generated, improving the user experience.

[1178] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1179] Step 1:

[1180] A user makes a new post on a social media platform (e.g., Instagram). The user uploads and publishes content such as images, text, and videos. The input is the image, text, and video created by the user. The output is the published post on the social media platform.

[1181] Step 2:

[1182] The device uses the API of the social media platform to obtain new post data. By making an API call, data such as the image URL, text, hashtags, and posting date and time is obtained. The input is the user's post information via the API call. The output is the obtained post data.

[1183] Step 3:

[1184] The data acquired by the terminal is converted into an appropriate format, such as JSON format, and sent to the server using a secure protocol (e.g., HTTPS). The input is the acquired post data. The output is the data converted into JSON format and the data sent securely.

[1185] Step 4:

[1186] The server receives the data sent from the terminal and stores it in a database. The stored data is used to efficiently manage user posted data. The input is securely transmitted JSON format data. The output is the posted data saved in the database.

[1187] Step 5:

[1188] The server parses the data stored in the database and converts it into a specific format. During the parsing process, data such as image URLs, text, and hashtags are segmented and cleansed. The input is the post data stored in the database. The output is the segmented and cleansed data.

[1189] Step 6:

[1190] The server passes the converted data to the emotion engine, which analyzes the text data and image data to recognize the user's emotions. For example, it uses text emotion analysis algorithms and image analysis techniques. The input is the segmented and cleansed data. The output is the user's emotional information (happiness, sadness, surprise, etc.).

[1191] Step 7:

[1192] The generative AI model automatically generates each section of a webpage, taking into account the emotion recognition results. The generated webpage includes images, text, and video. It also automatically generates appropriate keywords, meta tags, and alt text for SEO purposes. The input is the emotion-recognized user's emotion information. The output is the automatically generated webpage code.

[1193] Step 8:

[1194] The server receives the generated web page code and saves it in a public directory. The public directory is set up to be accessible on the Internet. It also notifies the user of the new web page URL. The input is the automatically generated web page code. The output is the web page saved in the public directory and the notified URL.

[1195] Step 9:

[1196] The user checks the notified URL and checks whether the new homepage has been generated as intended. The contents of the homepage can be modified as necessary. The input is the notified web page URL. The output is the confirmed and modified homepage.

[1197] (Application example 2)

[1198] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1199] There is a need for an effective method to provide real-time traffic information on the dashboard of an autonomous vehicle and display content tailored to the driver's emotions. Conventional systems do not provide information that takes the driver's emotions into consideration, and there is a lack of measures to reduce the driver's stress and anxiety. Against this background, the challenge is to develop a system that recognizes the driver's emotions and provides real-time information accordingly.

[1200] 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.

[1201] In this invention, the server includes an information transmission means including image and text data, an acquisition means for acquiring information from the information transmission means, a storage means for saving the acquired data, a conversion means for analyzing the saved data and converting it into a specified format, a generation means for generating a web page based on the analyzed and converted data, a publication means for publishing the web page generated by the generation means, an emotion engine for recognizing emotions and judging the driver's state based on the published web page, and a dashboard display means for displaying information customized based on the driver's emotions. This makes it possible to provide information according to the driver's emotions and a relaxing driving environment.

[1202] "Information dissemination means including images and text data" refers to means such as social media platforms that allow drivers or users to publish images and text data.

[1203] "Means of acquisition" refers to means such as APIs and communication protocols for collecting data made public from information dissemination means.

[1204] "Storage means" refers to a database or storage system that stores acquired data for a long period of time so that it can be analyzed or used later.

[1205] A "conversion means" is a program or algorithm that analyzes the stored data and converts it into a specified format.

[1206] "Generation means" refers to an AI model or automatic generation tool for automatically generating web pages based on the analyzed and converted data.

[1207] The "publication means" refers to a server or hosting service that makes the generated web page publicly available on the Internet and accessible to users.

[1208] An "emotion engine" is a program or algorithm that analyzes and recognizes the emotions of drivers or users from posted images and text data.

[1209] "Dashboard display means" refers to a display or interface system for displaying information on the dashboard of an autonomous vehicle according to the driver's emotional state.

[1210] An embodiment of the present invention will now be described. The present system is designed to provide real-time information according to the driver's emotions and to provide a relaxing driving environment.

[1211] The system mainly consists of the following components:

[1212] 1. Means of information dissemination

[1213] Drivers use social media platforms (e.g., Twitter and Instagram) to publish images and text data, which then becomes input into the system.

[1214] 2. Acquisition method

[1215] The device uses the API of the social media platform to retrieve new post data. The retrieved data includes image URLs, text, hashtags, and posting dates and times, allowing users to always obtain the latest information.

[1216] 3. Preservation means

[1217] The server stores the acquired data in a database, which is used to efficiently manage data over a long period of time.

[1218] 4. Conversion Methods

[1219] The server parses the stored data and converts it into the specified format. The parsing process involves data cleansing and reformatting.

[1220] 5. Generation means

[1221] Using a generative AI model, web pages are automatically generated based on the analyzed and converted data, incorporating the results of the emotion engine to arrange the design and content according to the user's emotions.

[1222] 6. Disclosure Methods

[1223] The generated web page is saved in a public directory for publishing on the Internet, making it accessible to users, and notifying users of the new URL.

[1224] 7. Emotion Engine

[1225] The server includes an emotion engine that analyzes text data and image data and recognizes the user's emotion, using, for example, a text emotion analysis algorithm or image analysis technology.

[1226] 8. Dashboard display method

[1227] The dashboard of an autonomous vehicle will display customized information based on the driver's emotions, reducing stress and anxiety for the driver and providing a more relaxed driving environment.

[1228] Operation example

[1229] For example, when a driver shares a post containing traffic congestion information or accident information on social media, the post is acquired by the acquisition means. The data stored in the storage means is analyzed and formatted by the conversion means, and the emotion engine identifies the driver's emotion.

[1230] Example prompts for generative AI models

[1231] "Design a driver dashboard application that collects the latest traffic information from social media and customizes it based on emotions. In stressful situations, provide relaxation suggestions, and for enjoyable driving, provide recommended spots."

[1232] As described above, the present invention is a system that provides real-time information according to the emotional state of the driver and optimizes the driving environment.

[1233] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1234] Step 1:

[1235] The device uses the API of a social media platform to obtain new post data. The input is the image URL, text, hashtag, and posting date and time data obtained by the API call, and the output is JSON format data. This data includes information such as the image and text posted by the user on social media.

[1236] Step 2:

[1237] The JSON format data acquired by the terminal is sent to the storage means, and the data is stored in the server's database. The input is the JSON format data to be stored, and the output is the data stored in the database. This database is used to efficiently manage user posted data.

[1238] Step 3:

[1239] The server parses the stored data and converts it into the specified format. The input is JSON format data retrieved from the database, and the parsed and converted format (e.g., segmented text and image URLs assigned to appropriate fields) is obtained as output. Specific operations include cleansing the text data and organizing image URLs.

[1240] Step 4:

[1241] The server passes the parsed and converted data to the emotion engine for emotion analysis. The input is the converted data, and the output is the user's emotional state (e.g., happy or sad). The emotion engine uses text analysis algorithms and image analysis techniques to determine the emotion.

[1242] Step 5:

[1243] The generation means generates a web page based on the results of the emotion engine. The input is emotion information and analyzed / converted data, and the output is an SEO-optimized web page. Specific operations include different designs and content placement depending on the emotion.

[1244] Step 6:

[1245] The server saves the generated web page in a public directory and makes it available on the net. The input is the generated web page code, and the output is a new URL that can be accessed on the internet. The server notifies the user of this URL.

[1246] Step 7:

[1247] The terminal displays information on the dashboard display means based on the published web page and emotional information. The input is the URL of the generated web page and emotional information, and the output is customized content that the driver can view on the dashboard. Specific operations include displaying information according to the driver's emotions and making suggestions to help them relax.

[1248] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1249] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1250] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1251] [Fourth embodiment]

[1252] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1253] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1254] 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).

[1255] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1256] 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.

[1257] 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).

[1258] 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.

[1259] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1260] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1261] 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.

[1262] 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.

[1263] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1264] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1265] The present invention provides a system that automatically generates web pages based on data acquired from social media platforms and publishes homepages that incorporate SEO (search engine optimization) measures. The following describes an embodiment of this system.

[1266] The system includes the following main components:

[1267] 1. Means of information dissemination

[1268] A social media platform (e.g., Instagram) is used as a means of disseminating information, through which users post content such as images, text, and videos.

[1269] 2. Acquisition method

[1270] The device automatically retrieves user post data using the API of the social media platform, including image URLs, text, hashtags, and posting dates and times.

[1271] 3. Preservation means

[1272] The server stores the acquired data in a database, which is used to efficiently manage the acquired data and use it in the analysis and generation process described below.

[1273] 4. Conversion Methods

[1274] The server then analyzes the stored data and converts it into a format that can be easily consumed by the generative AI model, which involves cleansing and reformatting the data, for example, by assigning image URLs to the appropriate fields and segmenting text data.

[1275] 5. Generation means

[1276] The generative AI model automatically generates web pages based on the converted data. Specifically, it analyzes social media posts and builds each section (new product introduction, gallery, etc.). It also generates keywords, meta tags, alt text, etc. for SEO purposes.

[1277] 6. Disclosure Methods

[1278] The server saves the generated web page in a public directory, making it available on the Internet, and notifies the user of the new URL.

[1279] Program processing explanation

[1280] 1. Users

[1281] A user posts a new post on a social media platform (e.g., Instagram), which can contain content such as images, text, and videos.

[1282] 2. Terminal

[1283] The device automatically calls the social media API to retrieve the user's latest posted data, which is then sent to the server in JSON format.

[1284] 3. Server

[1285] The server receives the data sent from the terminal and stores it in a database.

[1286] The data stored in the database is analyzed and converted into a format that is easy for the generative AI model to use using a conversion method.

[1287] 4. Generative AI Models

[1288] Based on the analyzed and converted data, each section of the web page is automatically generated, including content such as images, text, and videos.

[1289] Automatically generates appropriate keywords, meta tags, Alt text, etc. as SEO measures.

[1290] 5. Server

[1291] Receive the generated web page code and save it in a public directory.

[1292] Notify users of the new homepage URL via email or other notification services.

[1293] Specific examples

[1294] A cafe owner posts an Instagram post introducing a new product, a "berry smoothie." The following process takes place:

[1295] 1. Users

[1296] The owner publishes a post on Instagram with an image and text of "Berry Smoothie."

[1297] 2. Terminal

[1298] The device retrieves this new post data through Instagram's API and sends it to the server.

[1299] 3. Server

[1300] The data acquired by the server is stored in a database and analyzed and converted using a conversion means.

[1301] The converted data is passed to a generative AI model to instruct it to generate a web page.

[1302] 4. Generative AI Models

[1303] The model automatically generates a web page containing an introductory section for "berry smoothies."

[1304] Generate appropriate keywords and meta tags as part of your SEO strategy.

[1305] 5. Server

[1306] The server saves the generated web page in a public directory and notifies the cafe owner of the new URL.

[1307] In this way, an SEO-friendly homepage that reflects the latest information is automatically generated based on Instagram posts.

[1308] The processing flow will be explained below.

[1309] Step 1:

[1310] A user posts a new post on a social media platform (e.g., Instagram). The user uploads and publishes content such as images, text, and videos.

[1311] Step 2:

[1312] The device retrieves new post data using the social media platform's API. The API call retrieves data such as the image URL, text, hashtags, and post date and time.

[1313] Step 3:

[1314] The device converts the acquired data into an appropriate format, such as JSON, and sends the data to the server. Data transfer is performed using a secure protocol (e.g., HTTPS).

[1315] Step 4:

[1316] The server receives the data sent from the terminal and stores it in a database, which is used to efficiently manage and store user-submitted data.

[1317] Step 5:

[1318] The server parses the data stored in the database and converts it into a specific format. During the parsing process, data such as image URLs, text, and hashtags are segmented and cleansed.

[1319] Step 6:

[1320] The server then inputs the converted data into a generative AI model, which instructs it to generate a webpage. This input includes the analyzed image and text data, as well as meta information and keywords.

[1321] Step 7:

[1322] Based on the input data, the generative AI model automatically generates sections of a webpage, such as a new product introduction section, gallery section, etc. It also automatically generates keywords, meta tags, alt text, etc. for SEO purposes.

[1323] Step 8:

[1324] The generative AI model sends the generated web page code (HTML, CSS, JavaScript, etc.) back to the server.

[1325] Step 9:

[1326] The server receives the generated web page code and stores it in a public directory that is accessible on the Internet.

[1327] Step 10:

[1328] The server will notify the user of the new web page URL via email or a notification service.

[1329] Step 11:

[1330] The user checks the notified URL and checks whether the new homepage has been generated as intended. The user can then modify the homepage content as necessary.

[1331] Through the above processing steps, social media posts are automatically retrieved and an SEO-friendly homepage is generated and published.

[1332] Example 1

[1333] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1334] To automatically generate and publish web pages with SEO measures by effectively utilizing a huge amount of posted data on social media, and to provide web pages that always contain the latest information while reducing the user's workload.

[1335] 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.

[1336] In this invention, the server includes a terminal that includes an information acquisition process based on user posts, a server that places generated web pages in a storage directory, and a generation means that automatically builds web page sections using a generative AI model. This makes it possible to efficiently acquire and analyze social media post data and automatically generate and publish SEO-optimized web pages that include the latest information.

[1337] "Information transmission means" refers to a medium through which a user publishes information including images and text data.

[1338] "Acquisition means" refers to a device or program that has the function of automatically acquiring data from information transmission means.

[1339] "Storage means" refers to a mechanism for storing acquired data in a storage device such as a database.

[1340] "Conversion means" refers to a process or device that analyzes and converts stored data into a specified format.

[1341] "Generation means" refers to a program or system that automatically generates a web page based on the analyzed and converted data.

[1342] The "publication means" refers to a mechanism for publishing the generated web page on the Internet so that users can access it.

[1343] "Terminal" refers to a device or system that acquires information based on posts from users and transmits data to a server.

[1344] "Server" refers to a device or system that places generated web pages in a storage directory and makes them available on the Internet.

[1345] "Generative AI model" refers to an artificial intelligence model that performs a process of data analysis and automatic generation to build each section of a web page.

[1346] A "prompt sentence" refers to an input sentence that triggers a generative AI model.

[1347] This invention provides a system that automatically generates web pages based on data acquired from social media platforms and publishes homepages that incorporate SEO measures. The following describes in detail an embodiment of this system.

[1348] Key Components of the System

[1349] The system includes the following main components:

[1350] 1. Means of information dissemination

[1351] A social media platform is used as a means of disseminating information. Users can post content such as images, text, and videos through this platform. This information becomes the source data for the system.

[1352] 2. Acquisition method

[1353] The device automatically retrieves user post data using the API of the social media platform, including image URLs, text, hashtags, and posting dates and times.

[1354] 3. Preservation means

[1355] The server stores the acquired data in a database, which is used to efficiently manage the acquired data and utilize it in the analysis and production processes.

[1356] 4. Conversion Methods

[1357] The server then analyzes the stored data and converts it into a format that can be easily consumed by the generative AI model, which involves cleansing and reformatting the data, for example, by assigning image URLs to the appropriate fields and segmenting text data.

[1358] 5. Generation means

[1359] The generative AI model automatically generates web pages based on the converted data. Specifically, it analyzes social media posts and builds each section (new product introduction, gallery, etc.). It also generates keywords, meta tags, alt text, etc. for SEO purposes.

[1360] 6. Disclosure Methods

[1361] The server saves the generated web page in a public directory, making it available on the Internet, and notifies the user of the new URL via email or other notification service.

[1362] Specific examples

[1363] For example, when a cafe owner posts on social media to introduce a new product, a "berry smoothie," the system executes the following process:

[1364] 1. Users

[1365] The owner publishes a post on social media with an image of a "berry smoothie" and text, such as "Today's new menu item: Berry Smoothie! Loaded with fresh berries. New item at the cafe."

[1366] 2. Terminal

[1367] The device obtains this new post data through the social media API and sends it to the server.

[1368] 3. Server

[1369] The server stores the acquired data in a database, analyzes and converts it using a conversion method, and passes the converted data to a generative AI model to instruct it to generate a web page.

[1370] 4. Generative AI Models

[1371] The model automatically generates a web page with an introductory section for "Berry Smoothie," including images, text, and related videos, as well as appropriate keywords and meta tags for SEO.

[1372] 5. Server

[1373] The server saves the generated web page in a public directory and notifies the cafe owner of the new URL.

[1374] Prompt Sentence Examples

[1375] Examples of input prompts for generative AI models include:

[1376] "Create a webpage with a new product section based on your latest Instagram posts. Use the following data: image URL, text, and hashtags. For SEO purposes, please also generate appropriate keywords, meta tags, and alt text."

[1377] Using this prompt, the generative AI model can automatically generate a web page based on the request, allowing users to effectively transform their social media posts into up-to-date, SEO-optimized homepages.

[1378] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1379] Step 1: User Submission

[1380] Input: A user makes a post on a social media platform that includes an image, text, hashtags, video, etc.

[1381] Specific operation: The user enters the content of the post and presses the publish button. At that time, the necessary data (image URL, text, hashtags, posting date and time) is included in the post.

[1382] Output: Posts published on social media platforms.

[1383] Step 2: Data acquisition by device

[1384] Input: A new post on a social media platform.

[1385] Specific operation: The device periodically calls the API of the social media platform to obtain new post data, and processes the obtained data in JSON format, etc.

[1386] Data processing: Format the acquired RAW data and extract only the necessary information.

[1387] Output: Formatted post data (image URL, text, hashtags, post date and time).

[1388] Step 3: Save data to the server

[1389] Input: Formatted post data sent from the device.

[1390] Specific operation: The server receives the data sent from the device and saves it in the database. When saving, it checks the integrity of the data and assigns it to the appropriate fields.

[1391] Data processing: Checking the integrity of the received data and assigning it to fields.

[1392] Output: Post data saved in the database.

[1393] Step 4: Analyze and transform the data

[1394] Input: Post data stored in the database.

[1395] What it does: The server analyzes the data and performs data cleansing to remove unnecessary data and noise. After analysis, the data is converted into a format that is easy for the generative AI model to process. For example, it segments text and assigns image URLs to the appropriate fields.

[1396] Data processing: data cleansing, reformatting, field assignment.

[1397] Output: Parsed and transformed data.

[1398] Step 5: Generative AI model generates webpage

[1399] Input: Parsed and transformed data.

[1400] What it does: The server generates a prompt and inputs the converted data into the generative AI model. The prompt might look something like this: "Create a webpage with a new product introduction section based on the latest Instagram post data. Please use the following data: image URL, text, and hashtags. For SEO purposes, please also generate appropriate keywords, meta tags, and alt text."

[1401] Data processing: Building web pages using generative AI models, generating keywords, meta tags, and alt text.

[1402] Output: Auto-generated web page code.

[1403] Step 6: Publish and advertise your webpage

[1404] Input: The code for the generated web page.

[1405] What it does: The server receives the generated web page code and saves it in a public directory, then generates a new URL and notifies the user via email or other notification services.

[1406] Data processing: web page storage, URL generation, notification process.

[1407] Output: Web page saved to public directory, new URL, notified users.

[1408] (Application example 1)

[1409] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1410] Food delivery services are rapidly becoming more popular these days, requiring businesses to maintain their online presence and update it quickly with the latest information. However, doing this manually requires a great deal of effort and time, making it inefficient. Creating web pages with SEO optimization also requires specialized knowledge, placing a significant burden on many webmasters. Therefore, there is a need for a system that can automatically and efficiently generate web pages that reflect the latest information and publish them while incorporating SEO optimization.

[1411] 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.

[1412] In this invention, the server includes an information transmission means including image and text data, an acquisition means for acquiring information from the information transmission means, a storage means for saving the acquired data, a conversion means for analyzing the saved data and converting it into a specified format, a generation means for generating a web page based on the analyzed and converted data, a publishing means for updating the generated web page in an application and website of the electronic device, and a notification means for notifying a new URL. This enables operators of food delivery services to efficiently update their latest information and automatically generate and publish SEO-friendly web pages.

[1413] "Information dissemination means" refers to an online communication platform that allows users to provide content such as images and text data over the Internet.

[1414] "Acquisition means" refers to the means for automatically acquiring content provided by a user via the API of the online communication platform.

[1415] "Storage means" refers to a database or storage system that stores acquired data and keeps it in a state that allows it to be used for later processing.

[1416] "Conversion means" refers to the means for analyzing stored data and converting it into a format that is easy for the generative AI model to handle.

[1417] "Generating means" means a means including a generative AI model for automatically generating a web page based on the transformed data.

[1418] The "publication means" refers to a means for publishing the generated web page on the Internet and reflecting it in the application and website of the electronic device.

[1419] The "notification means" refers to a means for notifying the user of the URL of the newly generated web page.

[1420] The present invention provides a system that automatically generates web pages based on data posted on an online communication platform by operators of food delivery services, implements SEO measures, and publishes them. Specific embodiments of the system are described below.

[1421] System Configuration

[1422] The system includes the following main components:

[1423] 1. Means of information dissemination

[1424] Online communication platforms (e.g., Instagram) are used as a means of disseminating this information, and food delivery service operators post information about new menu items and promotions on these platforms.

[1425] 2. Acquisition method

[1426] The device automatically retrieves post data from food delivery service operators using the API of the online communication platform, including image URLs, text, hashtags, and posting dates and times.

[1427] 3. Preservation means

[1428] The server stores the acquired data in a database, which is used to efficiently manage the acquired data and use it in the analysis and generation process described below.

[1429] 4. Conversion Methods

[1430] The server then analyzes the stored data and converts it into a format that can be easily consumed by the generative AI model, which involves cleaning and reformatting the data, for example, by assigning image URLs to the appropriate fields and segmenting text data.

[1431] 5. Generation means

[1432] The generative AI model automatically generates web pages based on the converted data. Specifically, it analyzes posts on online communication platforms and builds each section (new product introduction, gallery, etc.). It also generates keywords, meta tags, alt text, etc. for SEO purposes.

[1433] 6. Disclosure Methods

[1434] The server saves the generated web page in a public directory, making it available on the Internet, and notifies the user of the new URL via email or other notification service.

[1435] System operation and concrete examples

[1436] The operation of the system will be explained below using a specific example.

[1437] example

[1438] A food delivery service operator posts an image and description of a new "specialty pizza" on an online communication platform.

[1439] 1. User Operation

[1440] The operator posts an image and description of the "special pizza" on an online communication platform, including hashtags and other text information.

[1441] 2. Data Acquisition

[1442] The device automatically calls the API of the online communication platform, obtains this new posting data, and sends it to the server.

[1443] 3. Data storage and analysis

[1444] The server stores the acquired data in a database and uses a conversion tool to analyze and convert it, for example, assigning image URLs and text to the appropriate fields.

[1445] 4. Web Page Generation

[1446] The generative AI model automatically generates a webpage containing an introductory section for the "Specialty Pizza" based on the converted data, as well as generating appropriate keywords and meta tags for SEO purposes.

[1447] 5. Webpage Publication and Notification

[1448] The server saves the generated web page in a public directory and notifies the operator of the new URL, possibly via email or other notification service.

[1449] Prompt Sentence Examples

[1450] Generate a page introducing the new menu item based on social media post data.

[1451] Post content:

[1452] Image URL: https: / / example.com / image.jpg

[1453] Text: Special pizza, only 999 yen now!

[1454] Hashtag: New Menu Pizza

[1455] Posting date: YYYY-MM-DD HH:MM

[1456] This allows food delivery service operators to efficiently reflect the latest information and automatically generate and publish web pages that are SEO-friendly.

[1457] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1458] Step 1:

[1459] A user posts new menu items or promotion information to an online communication platform. The input includes an image URL, text, hashtags, and posting date and time. This posting data is accepted by the online communication platform.

[1460] Step 2:

[1461] The terminal calls the API of the online communication platform and automatically obtains the user's posted data. The input is a posted data request, and the output includes the posted data in JSON format. This JSON format data is sent to the server.

[1462] Step 3:

[1463] The server stores the JSON-formatted post data received from the terminal in a database. The input is JSON-formatted post data, and the output is structured data stored in the database. This stored data is used in subsequent processes.

[1464] Step 4:

[1465] The server analyzes and preprocesses the data stored in the database. The input is the data stored in the database, and the output is data converted into a format that is easy for the generative AI model to use. Specific operations include cleaning the data, assigning fields, and segmenting the text data.

[1466] Step 5:

[1467] The server inputs the converted data into a generative AI model to generate a webpage. The input is preprocessed data, and the output is HTML code with the webpage structure. The generative AI model creates a new menu introduction page and applies SEO optimization.

[1468] Step 6:

[1469] The server saves the generated HTML code of the web page in a public directory. The input is the generated HTML code, and the output is a web page that can be accessed on the Internet. Specifically, an HTML file is placed in the destination directory.

[1470] Step 7:

[1471] The server notifies the user of the URL of the new web page. The input is the URL of the generated web page, and the output is a notification message to the user. Notification can occur via email or other notification service.

[1472] 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.

[1473] This invention is a system that automatically generates web pages based on data obtained from social media platforms and publishes homepages with SEO (search engine optimization) measures. This system incorporates an emotion engine that recognizes user emotions, and can display web page content more effectively based on emotional information.

[1474] The system includes the following main components:

[1475] 1. Means of information dissemination

[1476] A social media platform (e.g., Instagram) is used as a means of disseminating information, through which users post content such as images, text, and videos.

[1477] 2. Acquisition method

[1478] The device retrieves new post data using the social media platform's API. The API call retrieves data such as the image URL, text, hashtags, and post date and time.

[1479] 3. Preservation means

[1480] The server stores the acquired data in a database, which is used to efficiently manage user-submitted data and to use it in the analysis and generation process described below.

[1481] 4. Conversion Methods

[1482] The server analyzes the stored data and converts it into a format that can be easily consumed by the generative AI model. This process involves cleansing and reformatting the data, for example, by assigning image URLs to the appropriate fields and segmenting text data.

[1483] 5. Emotion Engine

[1484] The emotion engine built into the server recognizes the user's emotions from the analyzed data. The emotion engine uses an algorithm to analyze the posted text and images and determine the user's emotions (happiness, sadness, surprise, etc.).

[1485] 6. Generation means

[1486] A generative AI model automatically generates sections of a webpage based on the results of the emotion engine, for example by creating different designs and content placements depending on the emotion, as well as generating keywords, meta tags, and alt text for SEO purposes.

[1487] 7. Disclosure Methods

[1488] The server saves the generated web page in a public directory, makes it available on the Internet, and notifies the user of the new URL.

[1489] Program processing explanation

[1490] 1. Users

[1491] A user posts a new post on a social media platform (e.g., Instagram). The user uploads and publishes content such as images, text, and videos.

[1492] 2. Terminal

[1493] The device retrieves new post data using the social media platform's API. The API call retrieves data such as the image URL, text, hashtags, and post date and time.

[1494] 3. Terminal

[1495] It converts the acquired data into an appropriate format, such as JSON, and sends the data to the server using a secure protocol (e.g., HTTPS).

[1496] 4. Server

[1497] The server receives the data sent from the terminal and stores it in a database, which is used to efficiently manage and store user-submitted data.

[1498] 5. Server

[1499] The server parses the data stored in the database and converts it into a specific format. During the parsing process, the data is segmented into image URLs, text, hashtags, etc., and data cleansing is performed.

[1500] 6. Server

[1501] The converted data is passed to an emotion engine, which analyzes the text and image data to recognize the user's emotions. For example, it uses text emotion analysis algorithms and image analysis technology.

[1502] 7. Server

[1503] The emotion engine uses the emotional information it recognizes to direct the generation of web pages, for example, by highlighting upbeat design and positive content that matches the emotion of "joy."

[1504] 8. Generative AI Models

[1505] The generative AI model automatically generates sections of a webpage, including images, text, and video, taking into account emotion recognition results. It also automatically generates keywords, meta tags, and alt text for SEO optimization.

[1506] 9. Server

[1507] It takes the generated web page code and stores it in a public directory that is accessible on the Internet.

[1508] 10. Server

[1509] The new web page URL will be notified to the user via email or a notification service.

[1510] 11. Users

[1511] The user checks the notified URL and checks whether the new homepage has been generated as intended. The user can then modify the homepage content as necessary.

[1512] Specific examples

[1513] A cafe owner posts an Instagram post introducing a new product, a "berry smoothie." The following process takes place:

[1514] 1. Users

[1515] The owner publishes a post on Instagram with an image of a "berry smoothie" and text such as, "A new berry smoothie has arrived! Made with plenty of fresh berries. New berry smoothie menu item."

[1516] 2. Terminal

[1517] The device retrieves new post data through Instagram's API and sends it to the server.

[1518] 3. Server

[1519] The server stores the retrieved data in a database, where it performs analysis and data cleansing, properly formatting image URLs, text, hashtags, etc.

[1520] 4. Emotion Engine

[1521] The server uses an emotion engine to analyze the text and images and recognize the user's emotions. In this case, the emotion "joy" is extracted from the text "New berry smoothie has arrived!"

[1522] 5. Generative AI Models

[1523] The system generates web pages based on the results of the emotion engine. For example, because the user is expressing joy, it emphasizes content with a bright design and positive messages. It also applies SEO keywords like "berry smoothie," "new menu item," and "cafe."

[1524] 6. Server

[1525] Save the generated web page in a public directory and notify the cafe owner of the new URL.

[1526] In this way, user sentiment is analyzed from Instagram posts, and based on the results, an SEO-friendly homepage reflecting the latest information is automatically generated, improving the user experience.

[1527] The processing flow will be explained below.

[1528] Step 1:

[1529] A user posts a new post on a social media platform (e.g., Instagram). The user uploads and publishes content such as images, text, and videos.

[1530] Step 2:

[1531] The device retrieves new post data using the social media platform's API. The API call retrieves data such as the image URL, text, hashtags, and post date and time.

[1532] Step 3:

[1533] The device converts the acquired data into an appropriate format, such as JSON, and sends the data to the server. Data transfer is performed using a secure protocol (e.g., HTTPS).

[1534] Step 4:

[1535] The server receives the data sent from the terminal and stores it in a database, which is used to efficiently manage and store user-submitted data.

[1536] Step 5:

[1537] The server parses the data stored in the database and converts it into a specific format. During the parsing process, the data is segmented into image URLs, text, hashtags, etc., and data cleansing is performed.

[1538] Step 6:

[1539] The server passes the converted data to the emotion engine, which analyzes the text and image data to recognize the user's emotions. For example, it uses text emotion analysis algorithms and image analysis technology.

[1540] Step 7:

[1541] The emotion engine recognizes emotions such as "joy," "sadness," and "surprise" from user posts and passes the results to the generative AI model.

[1542] Step 8:

[1543] The server uses the results of the emotion engine to direct the generation of web pages, for example, if the user indicates the emotion "joy," it will emphasize upbeat design and positive content that matches that emotion.

[1544] Step 9:

[1545] The generative AI model automatically generates sections of a webpage, including images, text, and video, taking into account emotion recognition results. It also automatically generates keywords, meta tags, and alt text for SEO optimization.

[1546] Step 10:

[1547] The server receives the generated web page code and stores it in a public directory that is accessible on the Internet.

[1548] Step 11:

[1549] The server will notify the user of the new web page URL via email or a notification service.

[1550] Step 12:

[1551] The user checks the notified URL and checks whether the new homepage has been generated as intended. The user can then modify the homepage content as necessary.

[1552] Example 2

[1553] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1554] Conventional web page generation systems do not optimize content based on user sentiment, making it difficult to maximize user experience. In addition, SEO measures are often not implemented properly, making it difficult to attract new customers and rank highly in search engines.

[1555] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an information transmission means including image and text data, an acquisition means for acquiring information from the information transmission means, a storage means for saving the acquired data, a conversion means for analyzing the saved data and converting it into a specified format, a generation means for generating a web page based on the analyzed and converted data, a publishing means for publishing the web page generated by the generation means, an emotion recognition means for recognizing a user's emotion using an emotion engine, and an optimization means for optimizing the design and content of the web page based on the recognized emotion information. This makes it possible to automatically generate content optimized based on the user's emotion and provide a web page with SEO measures.

[1556] "Information dissemination means" refers to a means by which a user publishes information, including images and text data, and includes social media platforms.

[1557] "Means of acquisition" refers to the means for acquiring information made public from information dissemination means, and involves acquiring data via the API of the social media platform.

[1558] The "storage means" is a means for temporarily or continuously storing and managing the data acquired by the acquisition means.

[1559] "Conversion Means" means means for analyzing data stored by the Storage Means and converting it into a particular format, including data cleansing and reformatting.

[1560] The "generation means" is a means for automatically generating a new web page based on the analyzed and converted data.

[1561] The "publication means" is a means for publicizing the web page generated by the generation means on the Internet.

[1562] The "emotion recognition means" is a means for recognizing the user's emotions using an emotion engine, and analyzes text data and image data to determine emotions.

[1563] "Optimization measures" are measures for optimizing the design and content of web pages based on the recognized emotional information.

[1564] This invention is a system that automatically generates web pages based on data obtained from social media platforms and publishes homepages with SEO measures. This system incorporates an emotion engine that recognizes user emotions, and can display web page content more effectively based on emotional information.

[1565] Hardware and software used

[1566] 1. Server: The core of the system. The server is responsible for storing, analyzing, transforming, generating, and publishing data.

[1567] 2. Terminal: It is responsible for obtaining data using the API of the social media platform and sending it to the server.

[1568] 3. User: The entity that posts on a social media platform.

[1569] Data processing and calculation

[1570] 1. Means of information dissemination: A user posts a new content on a social media platform (e.g., Instagram). The user uploads and publishes content such as images, text, and videos.

[1571] 2. Acquisition method: The device retrieves new post data using the social media platform's API. The API call retrieves data such as image URL, text, hashtags, and post date and time. The retrieved data is converted into an appropriate format, such as JSON, and sent to the server using a secure protocol (e.g., HTTPS).

[1572] 3. Storage: The server receives the data sent from the terminal and stores it in a database. This database is used to efficiently manage and store the data posted by users.

[1573] 4. Transformation: The server parses the data stored in the database and transforms it into a specific format. During the parsing process, data such as image URLs, text, and hashtags are segmented and data cleansed.

[1574] 5. Emotion recognition means: The server passes the converted data to the emotion engine, which analyzes the text data and image data to recognize the user's emotions. For example, it uses text emotion analysis algorithms or image analysis technology.

[1575] 6. Generation: The generative AI model automatically generates each section of a webpage, taking into account the emotion recognition results. The generated webpage includes images, text, and video. It also automatically generates keywords, meta tags, and alt text appropriate for SEO.

[1576] 7. Optimization: Optimize the design and content of a web page based on the recognized emotion. For example, if a user expresses the emotion "happy," emphasize a bright design and positive content that matches that emotion.

[1577] 8. Publishing: The server receives the generated web page code and saves it in a public directory. This directory is set up to be accessible on the Internet. The new web page URL is notified to the user. Notification can be done via email or a notification service.

[1578] Specific examples

[1579] For example, if a cafe owner posts an Instagram post introducing a new product, a "berry smoothie," the following process takes place:

[1580] 1. A user publishes a post on Instagram that includes an image of a "berry smoothie" and text such as "A new berry smoothie has arrived! Made with plenty of fresh berries. New berry smoothie menu item."

[1581] 2. The device retrieves new post data through Instagram's API and sends it to the server.

[1582] 3. The server stores the retrieved data in a database, where it performs analysis and data cleansing, properly formatting image URLs, text, hashtags, etc.

[1583] 4. The emotion recognition unit analyzes the text and image to recognize the user’s emotion. In this case, the emotion “joy” is extracted from the text “New berry smoothie is here!”

[1584] 5. The generator generates a web page based on the results of the emotion engine. Because the user is expressing joy, content with a bright design and positive messages is emphasized. Additionally, keywords like "berry smoothie," "new menu," and "cafe" are applied as SEO keywords.

[1585] 6. The server saves the generated web page to a public directory and notifies the cafe owner of the new URL.

[1586] In this way, user sentiment is analyzed from Instagram posts, and based on the results, an SEO-friendly homepage reflecting the latest information is automatically generated, improving the user experience.

[1587] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1588] Step 1:

[1589] A user makes a new post on a social media platform (e.g., Instagram). The user uploads and publishes content such as images, text, and videos. The input is the image, text, and video created by the user. The output is the published post on the social media platform.

[1590] Step 2:

[1591] The device uses the API of the social media platform to obtain new post data. By making an API call, data such as the image URL, text, hashtags, and posting date and time is obtained. The input is the user's post information via the API call. The output is the obtained post data.

[1592] Step 3:

[1593] The data acquired by the terminal is converted into an appropriate format, such as JSON format, and sent to the server using a secure protocol (e.g., HTTPS). The input is the acquired post data. The output is the data converted into JSON format and the data sent securely.

[1594] Step 4:

[1595] The server receives the data sent from the terminal and stores it in a database. The stored data is used to efficiently manage user posted data. The input is securely transmitted JSON format data. The output is the posted data saved in the database.

[1596] Step 5:

[1597] The server parses the data stored in the database and converts it into a specific format. During the parsing process, data such as image URLs, text, and hashtags are segmented and cleansed. The input is the post data stored in the database. The output is the segmented and cleansed data.

[1598] Step 6:

[1599] The server passes the converted data to the emotion engine, which analyzes the text data and image data to recognize the user's emotions. For example, it uses text emotion analysis algorithms and image analysis techniques. The input is the segmented and cleansed data. The output is the user's emotional information (happiness, sadness, surprise, etc.).

[1600] Step 7:

[1601] The generative AI model automatically generates each section of a webpage, taking into account the emotion recognition results. The generated webpage includes images, text, and video. It also automatically generates appropriate keywords, meta tags, and alt text for SEO purposes. The input is the emotion-recognized user's emotion information. The output is the automatically generated webpage code.

[1602] Step 8:

[1603] The server receives the generated web page code and saves it in a public directory. The public directory is set up to be accessible on the Internet. It also notifies the user of the new web page URL. The input is the automatically generated web page code. The output is the web page saved in the public directory and the notified URL.

[1604] Step 9:

[1605] The user checks the notified URL and checks whether the new homepage has been generated as intended. The contents of the homepage can be modified as necessary. The input is the notified web page URL. The output is the confirmed and modified homepage.

[1606] (Application example 2)

[1607] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1608] There is a need for an effective method to provide real-time traffic information on the dashboard of an autonomous vehicle and display content tailored to the driver's emotions. Conventional systems do not provide information that takes the driver's emotions into consideration, and there is a lack of measures to reduce the driver's stress and anxiety. Against this background, the challenge is to develop a system that recognizes the driver's emotions and provides real-time information accordingly.

[1609] 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.

[1610] In this invention, the server includes an information transmission means including image and text data, an acquisition means for acquiring information from the information transmission means, a storage means for saving the acquired data, a conversion means for analyzing the saved data and converting it into a specified format, a generation means for generating a web page based on the analyzed and converted data, a publication means for publishing the web page generated by the generation means, an emotion engine for recognizing emotions and judging the driver's state based on the published web page, and a dashboard display means for displaying information customized based on the driver's emotions. This makes it possible to provide information according to the driver's emotions and a relaxing driving environment.

[1611] "Information dissemination means including images and text data" refers to means such as social media platforms that allow drivers or users to publish images and text data.

[1612] "Means of acquisition" refers to means such as APIs and communication protocols for collecting data made public from information dissemination means.

[1613] "Storage means" refers to a database or storage system that stores acquired data for a long period of time so that it can be analyzed or used later.

[1614] A "conversion means" is a program or algorithm that analyzes the stored data and converts it into a specified format.

[1615] "Generation means" refers to an AI model or automatic generation tool for automatically generating web pages based on the analyzed and converted data.

[1616] The "publication means" refers to a server or hosting service that makes the generated web page publicly available on the Internet and accessible to users.

[1617] An "emotion engine" is a program or algorithm that analyzes and recognizes the emotions of drivers or users from posted images and text data.

[1618] "Dashboard display means" refers to a display or interface system for displaying information on the dashboard of an autonomous vehicle according to the driver's emotional state.

[1619] An embodiment of the present invention will now be described. The present system is designed to provide real-time information according to the driver's emotions and to provide a relaxing driving environment.

[1620] The system mainly consists of the following components:

[1621] 1. Means of information dissemination

[1622] Drivers use social media platforms (e.g., Twitter and Instagram) to publish images and text data, which then becomes input into the system.

[1623] 2. Acquisition method

[1624] The device uses the API of the social media platform to retrieve new post data. The retrieved data includes image URLs, text, hashtags, and posting dates and times, allowing users to always obtain the latest information.

[1625] 3. Preservation means

[1626] The server stores the acquired data in a database, which is used to efficiently manage data over a long period of time.

[1627] 4. Conversion Methods

[1628] The server parses the stored data and converts it into the specified format. The parsing process involves data cleansing and reformatting.

[1629] 5. Generation means

[1630] Using a generative AI model, web pages are automatically generated based on the analyzed and converted data, incorporating the results of the emotion engine to arrange the design and content according to the user's emotions.

[1631] 6. Disclosure Methods

[1632] The generated web page is saved in a public directory for publishing on the Internet, making it accessible to users, and notifying users of the new URL.

[1633] 7. Emotion Engine

[1634] The server includes an emotion engine that analyzes text data and image data and recognizes the user's emotion, using, for example, a text emotion analysis algorithm or image analysis technology.

[1635] 8. Dashboard display method

[1636] The dashboard of an autonomous vehicle will display customized information based on the driver's emotions, reducing stress and anxiety for the driver and providing a more relaxed driving environment.

[1637] Operation example

[1638] For example, when a driver shares a post containing traffic congestion information or accident information on social media, the post is acquired by the acquisition means. The data stored in the storage means is analyzed and formatted by the conversion means, and the emotion engine identifies the driver's emotion.

[1639] Example prompts for generative AI models

[1640] "Design a driver dashboard application that collects the latest traffic information from social media and customizes it based on emotions. In stressful situations, provide relaxation suggestions, and for enjoyable driving, provide recommended spots."

[1641] As described above, the present invention is a system that provides real-time information according to the emotional state of the driver and optimizes the driving environment.

[1642] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1643] Step 1:

[1644] The device uses the API of a social media platform to obtain new post data. The input is the image URL, text, hashtag, and posting date and time data obtained by the API call, and the output is JSON format data. This data includes information such as the image and text posted by the user on social media.

[1645] Step 2:

[1646] The JSON format data acquired by the terminal is sent to the storage means, and the data is stored in the server's database. The input is the JSON format data to be stored, and the output is the data stored in the database. This database is used to efficiently manage user posted data.

[1647] Step 3:

[1648] The server parses the stored data and converts it into the specified format. The input is JSON format data retrieved from the database, and the parsed and converted format (e.g., segmented text and image URLs assigned to appropriate fields) is obtained as output. Specific operations include cleansing the text data and organizing image URLs.

[1649] Step 4:

[1650] The server passes the parsed and converted data to the emotion engine for emotion analysis. The input is the converted data, and the output is the user's emotional state (e.g., happy or sad). The emotion engine uses text analysis algorithms and image analysis techniques to determine the emotion.

[1651] Step 5:

[1652] The generation means generates a web page based on the results of the emotion engine. The input is emotion information and analyzed / converted data, and the output is an SEO-optimized web page. Specific operations include different designs and content placement depending on the emotion.

[1653] Step 6:

[1654] The server saves the generated web page in a public directory and makes it available on the net. The input is the generated web page code, and the output is a new URL that can be accessed on the internet. The server notifies the user of this URL.

[1655] Step 7:

[1656] The terminal displays information on the dashboard display means based on the published web page and emotional information. The input is the URL of the generated web page and emotional information, and the output is customized content that the driver can view on the dashboard. Specific operations include displaying information according to the driver's emotions and making suggestions to help them relax.

[1657] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1658] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1659] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1660] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1661] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1662] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1663] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1664] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1665] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1666] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1667] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1668] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1669] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1670] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1671] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1672] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1673] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1674] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1675] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1676] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1677] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1678] The following is further disclosed regarding the above embodiment.

[1679] (Claim 1)

[1680] an information transmission means including image and text data;

[1681] an acquisition means for acquiring information from the information transmission means;

[1682] a storage means for storing the acquired data;

[1683] a conversion means for analyzing the stored data and converting it into a specified format;

[1684] a generating means for generating a web page based on the analyzed and converted data;

[1685] publishing means for publishing the web page generated by the generating means;

[1686] A system including:

[1687] (Claim 2)

[1688] 2. The system according to claim 1, wherein the information dissemination means is a social media platform.

[1689] (Claim 3)

[1690] 2. The system according to claim 1, wherein the acquiring means acquires information via an API of a social media platform.

[1691] "Example 1"

[1692] (Claim 1)

[1693] an information transmission means including image and text data;

[1694] an acquisition means for acquiring information from the information transmission means;

[1695] a storage means for storing the acquired data;

[1696] a conversion means for analyzing the stored data and converting it into a specified format;

[1697] a generating means for generating a web page based on the analyzed and converted data;

[1698] publishing means for publishing the web page generated by the generating means;

[1699] a terminal including a step of acquiring information based on posts from users;

[1700] a server that places the generated web page in a storage directory;

[1701] a generation means for automatically constructing sections of a web page using a generative AI model;

[1702] A system including:

[1703] (Claim 2)

[1704] 2. The system according to claim 1, wherein the information dissemination means is a social media platform.

[1705] (Claim 3)

[1706] 2. The system according to claim 1, wherein the acquiring means acquires information via an API of a social media platform.

[1707] "Application Example 1"

[1708] (Claim 1)

[1709] an information transmission means including image and text data;

[1710] an acquisition means for acquiring information from the information transmission means;

[1711] a storage means for storing the acquired data;

[1712] a conversion means for analyzing the stored data and converting it into a specified format;

[1713] a generating means for generating a web page based on the analyzed and converted data;

[1714] publishing means for reflecting the generated web page in an application and a website of the electronic device;

[1715] A notification method to notify the new URL,

[1716] A system including:

[1717] (Claim 2)

[1718] 2. The system according to claim 1, wherein the information dissemination means is an online communication platform.

[1719] (Claim 3)

[1720] 2. The system according to claim 1, wherein the acquiring means acquires information via an API of an online communication platform.

[1721] "Example 2: Combining Emotion Engines"

[1722] (Claim 1)

[1723] an information transmission means including image and text data;

[1724] an acquisition means for acquiring information from the information transmission means;

[1725] a storage means for storing the acquired data;

[1726] a conversion means for analyzing the stored data and converting it into a specified format;

[1727] a generating means for generating a web page based on the analyzed and converted data;

[1728] publishing means for publishing the web page generated by the generating means;

[1729] emotion recognition means for recognizing an emotion of a user using an emotion engine;

[1730] an optimization method for optimizing the design and content of a web page based on the recognized emotion information;

[1731] A system including:

[1732] (Claim 2)

[1733] 2. The system according to claim 1, wherein the information dissemination means is a social media platform.

[1734] (Claim 3)

[1735] 2. The system according to claim 1, wherein the acquiring means acquires information via an API of a social media platform.

[1736] "Application example 2 when combining emotion engines"

[1737] (Claim 1)

[1738] an information transmission means including image and text data;

[1739] an acquisition means for acquiring information from the information transmission means;

[1740] a storage means for storing the acquired data;

[1741] a conversion means for analyzing the stored data and converting it into a specified format;

[1742] a generating means for generating a web page based on the analyzed and converted data;

[1743] publishing means for publishing the web page generated by the generating means;

[1744] An emotion engine that recognizes emotions and judges the driver's state based on published web pages;

[1745] a dashboard display means for displaying customized information based on the driver's emotions;

[1746] A system including:

[1747] (Claim 2)

[1748] 2. The system according to claim 1, wherein the information dissemination means is a social media platform.

[1749] (Claim 3)

[1750] 2. The system according to claim 1, wherein the acquiring means acquires information via an API of a social media platform. [Explanation of symbols]

[1751] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. an information transmission means including image and text data; an acquisition means for acquiring information from the information transmission means; a storage means for storing the acquired data; a conversion means for analyzing the stored data and converting it into a specified format; a generating means for generating a web page based on the analyzed and converted data; publishing means for publishing the web page generated by the generating means; A system including:

2. 2. The system according to claim 1, wherein the information dissemination means is a social media platform.

3. 2. The system according to claim 1, wherein the acquiring means acquires information via an API of a social media platform.

Images