System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving instructions entered by a user in Japanese; means for driving a generative model that automatically generates HTML code based on the received instructions in Japanese; and means for providing the generated HTML code to the user.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] Nowadays, many individuals and businesses need a website, but it requires specialized knowledge of programming languages ​​such as HTML, making it extremely difficult for beginners. Furthermore, using templates can lack originality, and hiring a paid engineer can be expensive. This makes it difficult to ensure the efficiency and quality of website creation. [Means for solving the problem]

[0005] The present invention relates to a system that receives instructions entered in Japanese by a user, automatically generates HTML code based on the instructions, and provides the generated HTML code to the user. The system includes a means for receiving instructions entered in Japanese by a user, a means for driving a generative model that automatically generates HTML code based on the received Japanese instructions, and a means for providing the generated HTML code to the user, allowing users to easily create high-quality homepages without any programming knowledge. The generative model also uses natural language processing technology, and includes a means for the user to re-enter instructions and modify the HTML code, a terminal means for entering instructions via a web interface and displaying the HTML code, and a means for providing the HTML code in JSON format. This invention can solve the problem of ensuring the efficiency and quality of homepage creation.

[0006] "User" refers to an individual or corporation who intends to create a homepage using the system.

[0007] "Instructions in Japanese" refers to the user's wishes and requirements regarding the structure and design of the homepage entered in natural language.

[0008] "Means for receiving" refers to a device or software that has the function of obtaining instructions input by a user through an interface.

[0009] "HTML code" refers to code written in a markup language for constructing web pages.

[0010] "Generative model" refers to artificial intelligence or an algorithm that automatically generates HTML code based on the user's Japanese instructions.

[0011] "Driver" refers to the device or software that initiates and controls the process of executing the Generative Model and generating HTML code based on user input.

[0012] "Means for providing to the user" refers to a device or software that has the function of outputting the generated HTML code in a form that can be viewed by the user.

[0013] "Natural language processing technology" refers to computer technology that enables understanding, interpreting, and responding to natural human language.

[0014] "Terminal means" refers to a device (e.g., a PC or smartphone) that a user uses to input instructions and check the generated HTML code.

[0015] "JSON format" refers to a lightweight data format widely used for exchanging and storing data. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] This invention relates to a system that receives instructions entered in Japanese by a user, automatically generates HTML code based on the instructions, and provides the generated HTML code to the user. This system allows users to easily create high-quality homepages even without programming knowledge.

[0038] First, the user uses a terminal to input specific instructions in Japanese regarding the structure and design of the homepage. For example, they can input instructions such as, "The homepage should have the company name at the top of the screen, with a menu bar below it, and the menu contents should be 'TOP, Company Overview, Product Introduction, IR Information'."

[0039] The device then sends the instructions to the server, which receives the request and initiates a process to drive the generative model, which uses natural language processing techniques to analyze the Japanese instructions and generate the corresponding HTML code.

[0040] Once the generative model generates HTML code based on the user's instructions, the server formats this code and sends it back to the device in JSON format. The device interprets the received HTML code and displays it to the user. The user can review the displayed code and enter corrections as needed. For example, they can change the color or adjust the layout.

[0041] For example, if a user inputs the following instruction, "The homepage should have the company name at the top of the screen, with a menu bar below it, and the menu contents should be 'TOP, Company Overview, Product Introduction, IR Information'," the generative model will generate the following HTML code (simplified):

[0042] <!DOCTYPE html>

[0043]

[0044]

[0045] <meta charset="UTF-8">

[0046] <title> Company Name< / title>

[0047]

[0048]

[0049] <header>

[0050] <h1> Company Name< / h1>

[0051] <nav>

[0052]

[0053] TOP

[0054] Company Profile

[0055] Product introduction

[0056] IR Information

[0057]

[0058] < / nav>

[0059] < / header>

[0060] <main>

[0061] <!-- コンテンツ -->

[0062] < / main>

[0063]

[0064]

[0065] In this way, users can quickly create a customized homepage based on their needs by simply entering simple instructions in Japanese. If further instructions are required, users can easily modify or update the generated HTML code by entering additional instructions in Japanese.

[0066] This system is particularly useful for small businesses and sole proprietors who want to strengthen their online presence with limited technical resources, as it allows for more efficient and cost-effective website creation.

[0067] The processing flow will be explained below.

[0068] Step 1:

[0069] A user opens a web application using a device. In a text area on the web page, the user enters instructions in Japanese regarding the structure and design of the homepage. For example, the user might enter, "The homepage should have the company name at the top of the screen, with a menu bar below it, and the menu contents should be 'TOP, Company Overview, Product Introduction, IR Information'."

[0070] Step 2:

[0071] When the user clicks the "Generate" button, the device sends the entered Japanese instructions to the server. Specifically, the device includes the instructions as JSON format data in the body of a POST request and sends it to the server's endpoint.

[0072] Step 3:

[0073] The server receives the request. The endpoint / generate_html specified in the Flask application processes this request. The server retrieves the user's Japanese instructions from the request body using request.json.get('user_input').

[0074] Step 4:

[0075] The server prepares to drive the generative model based on the Japanese instructions. Specifically, it generates a prompt in a format appropriate for the generative model. For example, the prompt is in the format "User instructions: {user_input}\nHTML code to generate:".

[0076] Step 5:

[0077] The server calls a generative model, such as the OpenAI API, and sends a prompt. The generative model uses natural language processing techniques to generate HTML code based on the prompt. The resulting HTML code is then returned to the server.

[0078] Step 6:

[0079] The server receives the HTML code returned by the generated model and formats it, specifically removing unnecessary whitespace and line breaks to make it neat.

[0080] Step 7:

[0081] The server wraps the generated HTML code in JSON format and returns it to the device as an HTTP response. The response includes the generated HTML code.

[0082] Step 8:

[0083] The device receives the response from the server, uses the JavaScript fetch API to parse the response into JSON format, and extracts the HTML code.

[0084] Step 9:

[0085] The device will then extract the HTML code from the web page at the specified location (e.g. <pre>The generated HTML code is displayed in the tag. The user can check the generated HTML code immediately.

[0086] Step 10:

[0087] If the user wishes to make further modifications to the generated HTML code, they can start again from step 1. For example, by changing the color or adjusting the layout, the generated HTML code can be regenerated and updated.

[0088] By following these steps, users can quickly create and edit high-quality homepages using simple instructions in Japanese.

[0089] Example 1

[0090] 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."

[0091] In today's world, creating a website is an important part of having an online presence, but it can be a time-consuming and labor-intensive task for users without programming knowledge. Furthermore, existing website creation tools are often complicated to use, placing a burden on small businesses and sole proprietors in particular. There is a need for a system that can solve this problem and enable anyone to easily create a high-quality website.

[0092] 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.

[0093] In this invention, the server includes a means for receiving instructions entered by a user in natural language, a means for driving a generative model that automatically generates HTML code based on the received natural language instructions, and a means for formatting the generated HTML code and returning it to the terminal. This allows users to quickly create high-quality homepages without programming knowledge, simply by entering simple instructions in Japanese. It also makes it easy for users to re-enter instructions and make corrections, strengthening online presence and reducing costs.

[0094] "User" refers to the person who operates the system and inputs instructions.

[0095] "Natural language" refers to a language that humans use on a daily basis, and in this system this includes Japanese.

[0096] "Instructions" refer to requests regarding the homepage configuration and design that are input by the user in natural language.

[0097] "Terminal" refers to a computer or mobile device used by a user to enter and receive instructions.

[0098] "Server" refers to a computer that analyzes instructions received from a user, generates HTML code, and returns it.

[0099] A "generative model" refers to an algorithm or program that automatically generates HTML code based on instructions entered in natural language.

[0100] "Natural language processing technology" refers to technology for understanding and analyzing human natural language, and in this system it is applied to a generative model.

[0101] "HTML code" refers to the standardized markup language used to construct homepages.

[0102] "Formatting" refers to the process of converting generated HTML code into a format suitable for presentation to the user.

[0103] "Return" refers to sending the generated HTML code back from the server to the terminal.

[0104] "Interpretation" refers to the process by which the terminal understands the content of the HTML code received and processes it to display it to the user.

[0105] "Display" refers to the terminal outputting the generated HTML code to the screen in a form that can be viewed by the user.

[0106] "Modification" refers to the user inputting new instructions into the generated HTML code and making changes.

[0107] This invention relates to a system that receives instructions entered by a user in natural language, automatically generates HTML code based on the instructions, and provides the generated HTML code to the user. This system allows users to easily create high-quality homepages even without programming knowledge.

[0108] First, the user uses a terminal to input specific instructions in natural language regarding the structure and design of the homepage. For example, the user might input specific instructions such as, "The homepage should have the company name at the top of the screen, with a menu bar below it, and the menu contents should be 'TOP, Company Overview, Product Introduction, IR Information'."

[0109] The device then sends this instruction to a server. The server receives the request and initiates a process to drive a generative AI model. This generative AI model uses natural language processing technology to analyze the natural language instructions entered by the user and generate the corresponding HTML code based on that. Specifically, the server uses a generative AI model such as GPT-4.

[0110] Once the generative model generates HTML code based on the user's instructions, the server formats this code and returns it to the device in JSON format. The device then parses the received JSON data, extracts the generated HTML code, and displays it to the user. This allows the user to review the generated HTML code and modify it as needed. For example, it is possible to change the color or adjust the layout.

[0111] For example, if a user inputs the following instruction, "The homepage should have the company name at the top of the screen, with a menu bar below it, and the menu contents should be 'TOP, Company Overview, Product Introduction, IR Information'," the generative AI model will generate the following HTML code:

[0112] <!DOCTYPE html>

[0113]

[0114]

[0115] <meta charset="UTF-8">

[0116] <title> Company Name< / title>

[0117]

[0118]

[0119] <header>

[0120] <h1> Company Name< / h1>

[0121] <nav>

[0122]

[0123] TOP

[0124] Company Profile

[0125] Product introduction

[0126] IR Information

[0127]

[0128] < / nav>

[0129] < / header>

[0130] <main>

[0131] <!-- コンテンツ -->

[0132] < / main>

[0133]

[0134]

[0135] In this way, users can quickly create a customized homepage based on their needs by simply entering simple natural language instructions. If further instructions are needed, users can easily modify or update the generated HTML code by entering additional natural language instructions.

[0136] This system is particularly useful for small businesses and sole proprietors who want to strengthen their online presence with limited technical resources, as it allows for more efficient and cost-effective website creation.

[0137] Prompt Sentence Examples

[0138] "Display the company name on the top page, and place a menu bar below it, with the items on the menu bar as 'TOP, Company Overview, Product Introduction, IR Information'"

[0139] This system is designed to enable users to easily create high-quality homepages by utilizing a generative AI model that uses natural language processing technology, with the terminal and server working in tandem.

[0140] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0141] Step 1:

[0142] The user uses a terminal to input specific instructions in natural language regarding the structure and design of the homepage.

[0143] As input, the user provides text information such as "The top page will have the company name at the top of the screen, with a menu bar below it, and the menu contents will be 'TOP, company overview, product introduction, IR information'."

[0144] Specifically, the user enters instructions into a text box displayed in the device's browser or app and clicks the "Send" button.

[0145] Step 2:

[0146] The terminal transmits the instructions input by the user to the server.

[0147] As input, the terminal sends the user's instruction text as an HTTP POST request to the server.

[0148] As an output, the server receives an HTTP request.

[0149] Specifically, the device compiles the input text as JSON-formatted data and sends an HTTP POST request to the specified URL endpoint.

[0150] Step 3:

[0151] The server receives the request and initiates the process to drive the generative AI model.

[0152] As input, the server parses the received JSON-formatted data and passes it to the generative AI model.

[0153] As an output, the generative AI model generates the corresponding HTML code.

[0154] Specifically, the server calls a text analysis API and passes a prompt sentence to the generative AI model (e.g., GPT-4) to request processing.

[0155] Step 4:

[0156] The generative AI model generates HTML code based on user instructions.

[0157] As input, the generative AI model takes parsed natural language instructions and generates corresponding HTML code.

[0158] As output, the generated HTML code is returned to the server.

[0159] Specifically, the generative AI model uses an internal natural language processing algorithm to analyze the input instruction text and generate HTML code.

[0160] Step 5:

[0161] The server formats the generated HTML code and returns it to the terminal in JSON format.

[0162] As input, it takes the HTML code returned by the generative AI model.

[0163] As output, it sends formatted HTML code to the terminal.

[0164] Specifically, the server assigns the generated HTML code to the appropriate JSON key and returns it to the terminal as an HTTP response.

[0165] Step 6:

[0166] The device parses the received JSON data, extracts the generated HTML code, and displays it to the user.

[0167] As input, the terminal receives the JSON data returned by the server.

[0168] The output is generated as HTML which is visually displayed to the user.

[0169] Specifically, the device's browser or app extracts HTML code from the received JSON data and renders it on the screen.

[0170] Step 7:

[0171] The user reviews the displayed HTML code and re-enters correction instructions as necessary.

[0172] As input, the user checks the displayed HTML content and inputs instructions in natural language regarding the parts to be corrected.

[0173] As an output, the modification instructions are sent to the server again.

[0174] Specifically, the user enters the correction instructions in the text box again and clicks the "Submit" button. This action repeats the flow from step 2 again, and the corrections are reflected.

[0175] This series of processes allows users to easily create high-quality homepages using only natural language instructions, and to modify and update them as needed.

[0176] (Application example 1)

[0177] 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."

[0178] Many users today are interested in creating web pages, but lack programming knowledge, making it difficult for them to easily build web pages themselves. Furthermore, users who want to build online shopping sites often lack the expertise to quickly and efficiently expand their businesses online. To solve this problem, a system is needed that allows users to create high-quality web pages, especially online shopping sites, in an easy and intuitive way.

[0179] 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.

[0180] In this invention, the server includes means for receiving instructions input in a language by a user, means for driving a generative model that automatically generates HTML code based on the received instructions in the language, means for providing the generated HTML code to a user, means for displaying a preview of the generated HTML code in real time, and means for receiving additional instructions for modifying the generated HTML code, thereby enabling users without programming knowledge to quickly and easily create and modify high-quality online shopping sites based on instructions in Japanese.

[0181] "User" refers to a general user who intends to create or modify a web page using this system.

[0182] "Instructions entered in language" refers to requests or instructions given to the system by the user using natural language, particularly Japanese.

[0183] "Means for receiving" refers to a method or device by which the system receives linguistic instructions entered by a user.

[0184] A "generative model" is an artificial intelligence model that uses natural language processing technology to analyze input linguistic instructions and automatically generate HTML code accordingly.

[0185] "HTML code" is a markup language used to describe the structure and content of web pages.

[0186] "Means for providing" refers to a method or device for displaying or transmitting the generated HTML code to a user.

[0187] "Means for displaying a preview in real time" refers to a method or device for instantly displaying the generated HTML code on the user's terminal, allowing the user to check the results immediately.

[0188] The "means for receiving additional instructions" refers to a method or device by which the system receives instructions when the user re-enters instructions to correct or modify the generated HTML code.

[0189] The present invention is a system that automatically generates HTML code based on instructions entered in a user's language. This system is particularly useful for users who want to easily create and edit web pages, especially for users who want to build online shopping sites. Specific embodiments for implementing the present invention are described below.

[0190] Server Configuration

[0191] The server is responsible for the main process of receiving the user's input in a language, parsing it, and generating the HTML code. The server's functions include:

[0192] 1. Receiving means:

[0193] The server receives instructions in the language entered by the user. For example, the user might enter, through a smartphone application, "Display a banner for sale items on the top page, and place a category list below it."

[0194] 2. Generation means:

[0195] The server inputs the received instructions into a generative model (specifically, a generative AI model using natural language processing technology) to automatically generate HTML code. This generative model can use, for example, the Hugging Face transformers library and apply the GPT-3 model.

[0196] 3. Means of provision:

[0197] The server formats the generated HTML code and provides it to the user. It also returns the code in JSON format, which is displayed as a real-time preview on the user's device.

[0198] 4. Remedies:

[0199] The user can preview the generated HTML code and make corrections by entering additional instructions as necessary. These additional instructions are also received, analyzed in the same way, and the HTML code is generated and provided again.

[0200] Device configuration

[0201] The terminal operated by the user has the following main functions:

[0202] 1. Instruction input interface:

[0203] Users input language instructions through the terminal. An intuitive and easy-to-use user interface (UI) is provided, and operations can be completed simply by entering instructions in a text box.

[0204] 2. Instruction sending function:

[0205] Instructions are sent to the server in real time, and communication takes place over the internet to minimize communication delays.

[0206] 3. Preview display:

[0207] The HTML code provided by the server is previewed in real time on the device, allowing the user to instantly check the results and input corrections as needed.

[0208] Software and hardware used

[0209] Server-side software:

[0210] Flask (for building the API server), Hugging Face's transformers library, and the GPT-3 model.

[0211] Hardware:

[0212] The server should preferably be a computer with high-performance computing capabilities. It is also possible to use cloud services (e.g., AWS, Google Cloud).

[0213] Client-side software:

[0214] Standard web browsers, smartphone applications (e.g. iOS app, Android app).

[0215] Specific examples

[0216] The user uses an application on their smartphone to enter the following instructions:

[0217] "The homepage will display a sale item banner, with a category list below it."

[0218] Example prompt sentence:

[0219] User Instructions: Display a sale item banner on the homepage, with a category list below it. Based on this, generate the following HTML code:

[0220] With the above system, users can easily build and modify online shopping websites without any programming knowledge. The generated HTML code is displayed to the user immediately, and further modifications can be made by entering additional instructions.

[0221] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0222] Step 1:

[0223] The user launches an application on the smartphone and inputs instructions in the language.

[0224] Input: Specific language instructions entered by the user (e.g., "Display a sale item banner on the homepage, with a category list below it")

[0225] Output: Input instruction data

[0226] Step 2:

[0227] The terminal transmits the input instruction data to the server.

[0228] Input: User-entered instruction data

[0229] Output: Request data to the server

[0230] Step 3:

[0231] The server analyzes the received instruction data and generates an appropriate prompt.

[0232] Input: The request data sent to the server

[0233] Output: A prompt given to the generative model (e.g., "User instructions: Display a sale item banner on the top page, with a category list below it. Based on this, generate the following HTML code.")

[0234] Step 4:

[0235] The server runs a generative model (such as GPT-3) to generate HTML code based on the prompt.

[0236] Input: prompt statement

[0237] Output: Generated HTML code

[0238] Step 5:

[0239] The server formats the generated HTML code into JSON format and returns it to the terminal.

[0240] Input: Generated HTML code

[0241] Output: Pretty HTML code in JSON format

[0242] Step 6:

[0243] The device analyzes the received JSON format HTML code and displays a preview on the screen in real time.

[0244] Input: JSON data received from the server

[0245] Output: Visual preview of the web page

[0246] Step 7:

[0247] The user checks the preview and, if necessary, enters additional instructions to make corrections.

[0248] Input: User correction instructions

[0249] Output: New instruction data

[0250] Step 8:

[0251] The terminal again transmits new instruction data to the server, and repeats the same process to correct the HTML code.

[0252] Input: New instruction data

[0253] Output: Modified HTML code

[0254] This allows users to easily create and edit web pages.

[0255] 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.

[0256] The present invention relates to a system that receives instructions entered by a user in Japanese, automatically generates HTML code based on the instructions, and provides the generated HTML code to the user. Furthermore, the present invention provides a function to adjust the design and color scheme of the generated HTML code by combining it with an emotion engine that recognizes the emotions contained in the user's instructions.

[0257] First, the user opens the web application on their device and enters instructions in Japanese about the homepage's structure and design into the text area. For example, they might enter, "The homepage will have the company name at the top of the screen, with a menu bar below it, and the menu contents will be 'TOP, Company Overview, Product Introduction, IR Information.' I want the design to have a bright feel."

[0258] Next, the device sends the input Japanese instructions to the server. The server receives the request and acquires the user's Japanese instructions. The emotion engine then analyzes the emotions contained in the instructions and detects positive emotions, such as "cheerful feeling." The emotion engine then adjusts the design and color scheme of the HTML code according to this emotion.

[0259] The server generates prompts to drive the generative model, which then generates HTML code based on the user's instructions and the results of sentiment analysis. For example, based on the sentiment "feeling cheerful," the model generates HTML code with a bright background color.

[0260] Once the generative model generates HTML code, the server formats it and sends it back to the device in JSON format. The device interprets the received HTML code and displays it to the user. The user can review the displayed code and re-enter correction instructions as needed. For example, additional instructions such as "make it more colorful" are analyzed again by the emotion engine and reflected in the generative model.

[0261] For example, if a user inputs the following instruction, "The homepage should have the company name at the top of the screen, with a menu bar below it, and the menu contents should be 'TOP, Company Overview, Product Introduction, IR Information'. I want this design to have a bright feel," the generative model will generate the following HTML code (simplified):

[0262] <!DOCTYPE html>

[0263]

[0264]

[0265] <meta charset="UTF-8">

[0266] <title> Company Name< / title>

[0267] <style>

[0268] body { background-color: f0f8ff;}

[0269] h1 { color: ff6347;}

[0270] < / style>

[0271]

[0272]

[0273] <header>

[0274] <h1> Company Name< / h1>

[0275] <nav>

[0276]

[0277] TOP

[0278] Company Profile

[0279] Product introduction

[0280] IR Information

[0281]

[0282] < / nav>

[0283] < / header>

[0284] <main>

[0285] <!-- コンテンツ -->

[0286] < / main>

[0287]

[0288]

[0289] In this way, users can quickly create a customized homepage that meets their needs by entering simple Japanese instructions and words that express their emotions. This system not only further improves the efficiency and quality of homepage creation, but also enables the automatic generation of designs that take users' emotions into consideration.

[0290] The processing flow will be explained below.

[0291] Step 1:

[0292] A user opens a web application using a device. In a text area on the web page, the user enters instructions in Japanese regarding the structure and design of the homepage. For example, the user might enter, "The homepage will have the company name at the top of the screen, with a menu bar below it, and the menu contents will be 'TOP, Company Overview, Product Introduction, IR Information.' I want the design to have a bright feel."

[0293] Step 2:

[0294] When the user clicks the "Generate" button, the device sends the entered Japanese instructions to the server. Specifically, the device includes the instructions as JSON format data in the body of a POST request and sends it to the server's endpoint.

[0295] Step 3:

[0296] The server receives the request. The endpoint / generate_html specified in the Flask application processes this request. The server retrieves the user's Japanese instructions from the request body using request.json.get('user_input').

[0297] Step 4:

[0298] The server runs an emotion engine to recognize emotions contained in user input. Specifically, the emotion engine analyzes the input Japanese text and detects emotional expressions such as "cheerful feeling."

[0299] Step 5:

[0300] The server prepares to drive the generative model based on Japanese instructions. Specifically, it generates prompts in a format suitable for the generative model. The prompts also include emotional information recognized by the emotion engine.

[0301] Step 6:

[0302] The server calls a generative model, such as the OpenAI API, to send the prompt. The generative model uses natural language processing techniques to generate HTML code based on the prompt. The emotion engine then adjusts the design and color scheme based on the emotion recognized.

[0303] Step 7:

[0304] Once the generative model has generated the HTML code, the server formats it, removing unnecessary whitespace and line breaks to make it neat.

[0305] Step 8:

[0306] The server wraps the generated HTML code in JSON format and returns it to the device as an HTTP response. The response includes the generated HTML code.

[0307] Step 9:

[0308] The device receives the response from the server, uses the JavaScript fetch API to parse the response into JSON format, and extracts the HTML code.

[0309] Step 10:

[0310] The device will then extract the HTML code from the web page at the specified location (e.g. <pre>The generated HTML code is displayed in the tag. The user can check the generated HTML code immediately.

[0311] Step 11:

[0312] If the user wishes to make further modifications to the generated HTML code, they can start again from step 1. For example, by changing the color or adjusting the layout, the generated HTML code can be regenerated and updated. The emotion engine is also activated again, recognizing the user's new emotional expression and reflecting it in the design.

[0313] By following the above steps, users can quickly create a customized homepage that meets their needs by entering simple Japanese instructions as well as words that express their emotions. This invention not only further improves the efficiency and quality of homepage creation, but also enables the automatic generation of designs that take user emotions into consideration.

[0314] Example 2

[0315] 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."

[0316] Conventional HTML code generation systems have difficulty reflecting the user's desired emotions and designs, and often produce results that do not meet the user's intentions. Furthermore, there are insufficient means for users to easily modify HTML code. This has resulted in a decrease in the efficiency of homepage creation and user satisfaction.

[0317] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0318] In this invention, the server includes means for receiving instructions entered by a user in natural language, means for driving an emotion engine that analyzes emotions based on the received instructions, means for generating prompt sentences based on the results of the emotion analysis and the instructions and driving a generative AI model, and means for formatting the generated HTML code and providing it to the user, thereby enabling automatic generation and easy modification of HTML code that reflects the user's intentions and emotions.

[0319] A "user" is a person who uses the system to create or edit a homepage.

[0320] A "natural language" is a language that humans use on a daily basis, such as Japanese.

[0321] "Instructions" are requests regarding the structure and design of the homepage that are input by the user into the terminal using natural language.

[0322] "Receiving" refers to the terminal transferring an instruction input by a user to a server, and the server acquiring it.

[0323] An "emotion engine" is software that uses natural language processing technology to analyze the emotions contained in a user's instructions.

[0324] "Sentiment analysis" is the process by which the emotion engine detects emotions, such as positive and negative, from user instructions.

[0325] A "prompt sentence" is an input sentence given to a generative AI model based on the results of sentiment analysis and user instructions.

[0326] A "generative AI model" is an artificial intelligence that automatically generates HTML code based on a prompt text.

[0327] "HTML code" refers to code written in a markup language used to construct a web page.

[0328] "Formatting" is the process of formatting the generated HTML code to make it look better and more functional.

[0329] "Serving" refers to formatting the generated HTML code and then sending it to the terminal for display to the user.

[0330] The present invention relates to a system that receives instructions entered by a user in natural language, automatically generates HTML code based on the instructions, and provides the generated HTML code to the user. Furthermore, the present invention provides a function to adjust the design and color scheme of the generated HTML code by combining it with an emotion engine that recognizes the emotions contained in the user's instructions.

[0331] First, the user opens the web application on their device and enters natural language instructions about the homepage's structure and design into the text area. For example, they might enter, "The homepage will have the company name at the top of the screen, with a menu bar below it, and the menu contents will be 'TOP, Company Overview, Product Introduction, IR Information.' I want the design to have a bright feel."

[0332] The device then sends the input instructions in natural language to the server, using the HTTPS protocol to ensure secure communication.

[0333] The server receives the HTTPS request and obtains the user's natural language instruction. It then uses an emotion engine to analyze the emotion contained in the instruction. For example, it detects a positive emotion such as "cheerful." The emotion engine uses a natural language processing library to calculate the emotion vector.

[0334] The server generates prompts to drive the generative AI model based on the results of sentiment analysis and user instructions. The prompts are generated using templates, such as "Please generate HTML code with a light background color and a menu bar at the top."

[0335] The generative AI model generates HTML code based on the input prompt. The model automatically generates code based on a pre-trained dataset. For example, based on the instruction "bright feel," it generates HTML code with a bright background color.

[0336] The server formats the generated HTML code and converts it to JSON format using a dedicated library. The formatted data is then sent back to the device.

[0337] The device interprets the JSON-formatted HTML code received from the server and displays it on a web page. The display is done using the browser's rendering engine. The user can review the displayed code and make corrections or add additional instructions as needed. For example, they can enter additional instructions such as "make it more colorful." These new instructions are sent back to the server, where the emotion engine and generative model analyze and generate the new code again.

[0338] For example, if a user inputs the following instruction, "The homepage should have the company name at the top of the screen, with a menu bar below it, and the menu contents should be 'TOP, Company Overview, Product Introduction, IR Information.' I would like the design to have a bright feel," the generative AI model will generate the following HTML code:

[0339] html

[0340] <!DOCTYPE html>

[0341]

[0342]

[0343] <meta charset="UTF-8">

[0344] <title> Company Name< / title>

[0345] <style>

[0346] body { background-color: f0f8ff;}

[0347] h1 { color: ff6347;}

[0348] < / style>

[0349]

[0350]

[0351] <header>

[0352] <h1> Company Name< / h1>

[0353] <nav>

[0354]

[0355] TOP

[0356] Company Profile

[0357] Product introduction

[0358] IR Information

[0359]

[0360] < / nav>

[0361] < / header>

[0362] <main>

[0363] <!-- コンテンツ -->

[0364] < / main>

[0365]

[0366]

[0367] In this way, users can quickly create a customized homepage that meets their needs by inputting simple natural language instructions and words that express their emotions. This system not only further improves the efficiency and quality of homepage creation, but also enables the automatic generation of designs that take users' emotions into consideration.

[0368] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0369] Step 1:

[0370] The user opens a web application on their device and enters natural language instructions into the text area regarding the homepage's structure and design. For example, they might enter, "The homepage will have the company name at the top of the screen, with a menu bar below it, and the menu contents will be 'TOP, Company Overview, Product Introduction, IR Information.' I want the design to have a bright feel." These instructions become the input data.

[0371] Step 2:

[0372] The terminal sends instructions entered by the user in natural language to the server. The transmission uses the HTTPS protocol to ensure secure communication. The input data is the user's instructions, and the output data is the request sent to the server.

[0373] Step 3:

[0374] The server receives the HTTPS request and retrieves the user's natural language instructions, which are the input data and are ready for the next stage of sentiment analysis.

[0375] Step 4:

[0376] The server's emotion engine analyzes the received natural language instructions and detects emotions. For example, it can detect positive emotions such as "cheerful feeling." The input for this emotion analysis is the natural language instructions, and the output is an emotion vector. The emotion engine uses natural language processing technology to quantify the emotions contained in the instructions.

[0377] Step 5:

[0378] The server generates a prompt based on the results of the sentiment analysis and the user's instructions. The input data are the sentiment vector and the user's instructions, and the output data is the generated prompt. A template is used to generate the prompt, and it can be formatted, for example, as "Please generate HTML code with a light background color and a menu bar at the top."

[0379] Step 6:

[0380] The generative AI model generates HTML code based on prompts input from the server. The input data is the prompts, and the output data is the generated HTML code. The model automatically generates HTML code using a pre-trained dataset.

[0381] Step 7:

[0382] The server formats the generated HTML code and converts it to JSON format. The input data is the generated HTML code, and the output data is formatted code in JSON format. A dedicated library is used for formatting.

[0383] Step 8:

[0384] The terminal interprets the JSON-formatted HTML code received from the server and displays it in the browser. The input data is the JSON-formatted code, and the output is a web page displayed in the browser. The user checks the displayed code.

[0385] Step 9:

[0386] The user checks the displayed HTML code and makes corrections or additions as necessary. For example, they input a new instruction such as "Make it more colorful." This new instruction returns to step 2, and the same process is repeated. The input data are correction instructions, and the output data is new HTML code.

[0387] (Application example 2)

[0388] 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."

[0389] Conventional web page generation systems have difficulty creating designs that reflect the user's emotions and intentions if the user does not have detailed knowledge of design and color schemes. Furthermore, they lack the functionality to automatically generate designs that take emotions into consideration, making it difficult to provide designs that intuitively satisfy the user. Especially in operating online virtual stores, it is important to quickly provide designs that respond to the user's emotions and expectations.

[0390] 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.

[0391] In this invention, the server includes means for receiving instructions entered in Japanese by a user, means for driving a generative model that automatically generates HTML code based on the received Japanese instructions, means for analyzing the emotions contained in the Japanese instructions and adjusting the design and color scheme of the HTML code, and means for providing the generated HTML code to the user. This makes it possible to automatically generate designs based on the user's emotions and intentions, and to easily build an intuitive and attractive virtual store.

[0392] "User" refers to any person or entity that uses the System to generate HTML code.

[0393] "Instructions entered in Japanese" refers to text written in Japanese by a user expressing requests regarding the structure and design of a web page.

[0394] "Means for receiving" refers to a component or program that has the function of obtaining Japanese language instructions entered by a user and storing them for processing.

[0395] "Generative model" refers to an algorithm or artificial intelligence system that automatically generates HTML code based on received Japanese instructions.

[0396] "Driven" refers to hardware or software that executes a generative model and accomplishes a specified task.

[0397] "Means for analyzing emotions" refers to algorithms or artificial intelligence systems that analyze the emotions and intentions contained in the instructions entered by the user and adjust the design and color scheme based on the results.

[0398] "Means for adjusting design and color scheme" refers to components or programs for changing the appearance and color scheme of the HTML code generated based on the results of sentiment analysis.

[0399] "Means of providing" refers to the functions and services for displaying or transmitting the generated HTML code to the user.

[0400] "Natural language processing technology" refers to the general algorithms and methods that allow computers to understand and process human language.

[0401] "Means for making modifications" refers to a component or program that has the function of receiving instructions re-entered by the user and modifying or correcting existing HTML code based on the instructions.

[0402] "Design elements such as background and font color" refers to the visual components of a web page's appearance and user experience, such as background color, font color, size, and placement.

[0403] This invention is a system that receives instructions entered by a user in Japanese and automatically generates HTML code based on those instructions. Furthermore, this system also provides a function to adjust the design and color scheme of the generated HTML code by combining it with an emotion engine that recognizes the emotions contained in the user's instructions.

[0404] System configuration

[0405] The system includes the following components:

[0406] 1. Input receiving means: A web application for receiving instructions entered in Japanese by the user. Users access this web application using devices such as smartphones, tablets, and PCs.

[0407] 2. Generative model driving means: A server that drives a generative model that automatically generates HTML code based on Japanese instructions. The generative model uses natural language processing technology.

[0408] 3. Sentiment analysis: An emotion engine that analyzes the emotions contained in the Japanese instructions entered by the user. For example, the pipeline from the Hugging Face transformers library is used.

[0409] 4. Design Adjustment: A software component for adjusting design elements such as background and font color of the generated HTML code based on the results of sentiment analysis.

[0410] 5. Code Delivery Method: A function for providing the generated HTML code to the user. The generated code is returned in JSON format and interpreted and displayed on the user's device.

[0411] Processing flow explanation

[0412] 1. Receiving user input:

[0413] The user opens the web application and enters instructions in Japanese about the homepage structure and design in a text area. For example, they can enter specific instructions such as, "The homepage should have the company name at the top of the screen, with a menu bar below it, and the menu contents should be 'TOP, Company Overview, Product Introduction, IR Information.' I would like the design to have a bright feel."

[0414] 2. Sending instructions:

[0415] Instructions entered by the user are sent from the terminal to the server.

[0416] 3. Sentiment analysis:

[0417] The server analyzes the received instructions using an emotion engine to recognize the emotion contained in the input text. For example, if a positive emotion such as "cheerful" is detected, the background color will be set to a bright color.

[0418] 4. Driving the generative model:

[0419] The server runs a generative AI model that uses natural language processing techniques to generate HTML code based on user instructions and sentiment analysis.

[0420] 5. Code Submission:

[0421] The generated HTML code is formatted and sent back to the user's device in JSON format. The device interprets the received HTML code and displays it to the user. The user can check the displayed code and enter instructions again if necessary.

[0422] Specific examples

[0423] For example, the user enters the following prompt:

[0424] "The homepage will have the company name at the top of the screen, with a menu bar below it, with the menu contents being 'TOP, Company Overview, Product Introduction, IR Information.' I want this design to have a bright feel."

[0425] Based on this example, the system performs sentiment analysis and drives a generative model to generate HTML code with a light background color. This prompt allows for the rapid creation of a customized homepage tailored to the user's needs.

[0426] As described above, the present invention improves the efficiency and quality of homepage creation, and also makes it possible to automatically generate designs that take into consideration the user's feelings.

[0427] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0428] Step 1:

[0429] The user enters instructions in Japanese.

[0430] Users access the system using a smartphone, tablet, or PC and input instructions in Japanese regarding the homepage structure and design. For example, they might input, "The homepage will have the company name at the top of the screen, with a menu bar below it, and the menu contents will be 'TOP, Company Overview, Product Introduction, IR Information.' I want this design to have a bright feel." The input data is saved in text format on the device.

[0431] Step 2:

[0432] The terminal sends the input instructions to the server

[0433] The terminal transmits the instructions in Japanese entered by the user to the server. The transmitted data is the text data entered by the user.

[0434] Step 3:

[0435] The server receives and parses the Japanese instructions.

[0436] The server parses the received Japanese instructions. It receives the input data (Japanese instructions) and analyzes the grammar and syntax using a text analysis library. The main processing here is to split the received data and extract elements from the instructions.

[0437] Step 4:

[0438] Analyze emotions with the emotion engine

[0439] The server analyzes the emotions from the parsed instructions. For emotion analysis, it uses the pipeline of Hugging Face's transformers library. In this step, the emotion (positive, negative, etc.) contained in the input data (instructions) is identified and the result is stored internally on the server. For example, the instruction "feeling cheerful" is recognized as a positive emotion.

[0440] Step 5:

[0441] Generate HTML code by driving the generative model

[0442] The server drives a generative AI model based on the results of sentiment analysis and Japanese instructions. It uses natural language processing technology to generate HTML code that includes designs and color schemes that match the emotions. In this step, the input data (Japanese instructions and sentiment analysis results) is sent as prompts to the generative AI model, and output data (generated HTML code) is obtained.

[0443] Step 6:

[0444] Format and serve the generated HTML code

[0445] The generated HTML code is formatted and converted to JSON format. The server returns this formatted HTML code to the user. The output data is formatted HTML code.

[0446] Step 7:

[0447] Display the HTML code received by the device

[0448] The terminal interprets the HTML code received from the server and displays it to the user using a browser or similar. The user can check the displayed code and enter instructions again if necessary. The output data is an HTML display that the user can view.

[0449] By following the above steps, users can quickly create a homepage that meets their needs using simple Japanese instructions and emotional expressions.

[0450] 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.

[0451] 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.

[0452] 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.

[0453] [Second embodiment]

[0454] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0455] 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.

[0456] 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).

[0457] 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.

[0458] 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.

[0459] 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).

[0460] 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.

[0461] 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.

[0462] 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.

[0463] 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.

[0464] 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.

[0465] 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."

[0466] This invention relates to a system that receives instructions entered in Japanese by a user, automatically generates HTML code based on the instructions, and provides the generated HTML code to the user. This system allows users to easily create high-quality homepages even without programming knowledge.

[0467] First, the user uses a terminal to input specific instructions in Japanese regarding the structure and design of the homepage. For example, they can input instructions such as, "The homepage should have the company name at the top of the screen, with a menu bar below it, and the menu contents should be 'TOP, Company Overview, Product Introduction, IR Information'."

[0468] The device then sends the instructions to the server, which receives the request and initiates a process to drive the generative model, which uses natural language processing techniques to analyze the Japanese instructions and generate the corresponding HTML code.

[0469] Once the generative model generates HTML code based on the user's instructions, the server formats this code and sends it back to the device in JSON format. The device interprets the received HTML code and displays it to the user. The user can review the displayed code and enter corrections as needed. For example, they can change the color or adjust the layout.

[0470] For example, if a user inputs the following instruction, "The homepage should have the company name at the top of the screen, with a menu bar below it, and the menu contents should be 'TOP, Company Overview, Product Introduction, IR Information'," the generative model will generate the following HTML code (simplified):

[0471] <!DOCTYPE html>

[0472]

[0473]

[0474] <meta charset="UTF-8">

[0475] <title> Company Name< / title>

[0476]

[0477]

[0478] <header>

[0479] <h1> Company Name< / h1>

[0480] <nav>

[0481]

[0482] TOP

[0483] Company Profile

[0484] Product introduction

[0485] IR Information

[0486]

[0487] < / nav>

[0488] < / header>

[0489] <main>

[0490] <!-- コンテンツ -->

[0491] < / main>

[0492]

[0493]

[0494] In this way, users can quickly create a customized homepage based on their needs by simply entering simple instructions in Japanese. If further instructions are required, users can easily modify or update the generated HTML code by entering additional instructions in Japanese.

[0495] This system is particularly useful for small businesses and sole proprietors who want to strengthen their online presence with limited technical resources, as it allows for more efficient and cost-effective website creation.

[0496] The processing flow will be explained below.

[0497] Step 1:

[0498] A user opens a web application using a device. In a text area on the web page, the user enters instructions in Japanese regarding the structure and design of the homepage. For example, the user might enter, "The homepage should have the company name at the top of the screen, with a menu bar below it, and the menu contents should be 'TOP, Company Overview, Product Introduction, IR Information'."

[0499] Step 2:

[0500] When the user clicks the "Generate" button, the device sends the entered Japanese instructions to the server. Specifically, the device includes the instructions as JSON format data in the body of a POST request and sends it to the server's endpoint.

[0501] Step 3:

[0502] The server receives the request. The endpoint / generate_html specified in the Flask application processes this request. The server retrieves the user's Japanese instructions from the request body using request.json.get('user_input').

[0503] Step 4:

[0504] The server prepares to drive the generative model based on the Japanese instructions. Specifically, it generates a prompt in a format appropriate for the generative model. For example, the prompt is in the format "User instructions: {user_input}\nHTML code to generate:".

[0505] Step 5:

[0506] The server calls a generative model, such as the OpenAI API, and sends a prompt. The generative model uses natural language processing techniques to generate HTML code based on the prompt. The resulting HTML code is then returned to the server.

[0507] Step 6:

[0508] The server receives the HTML code returned by the generated model and formats it, specifically removing unnecessary whitespace and line breaks to make it neat.

[0509] Step 7:

[0510] The server wraps the generated HTML code in JSON format and returns it to the device as an HTTP response. The response includes the generated HTML code.

[0511] Step 8:

[0512] The device receives the response from the server, uses the JavaScript fetch API to parse the response into JSON format, and extracts the HTML code.

[0513] Step 9:

[0514] The device will then extract the HTML code from the web page at the specified location (e.g. <pre>The generated HTML code is displayed in the tag. The user can check the generated HTML code immediately.

[0515] Step 10:

[0516] If the user wishes to make further modifications to the generated HTML code, they can start again from step 1. For example, by changing the color or adjusting the layout, the generated HTML code can be regenerated and updated.

[0517] By following these steps, users can quickly create and edit high-quality homepages using simple instructions in Japanese.

[0518] Example 1

[0519] 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."

[0520] In today's world, creating a website is an important part of having an online presence, but it can be a time-consuming and labor-intensive task for users without programming knowledge. Furthermore, existing website creation tools are often complicated to use, placing a burden on small businesses and sole proprietors in particular. There is a need for a system that can solve this problem and enable anyone to easily create a high-quality website.

[0521] 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.

[0522] In this invention, the server includes a means for receiving instructions entered by a user in natural language, a means for driving a generative model that automatically generates HTML code based on the received natural language instructions, and a means for formatting the generated HTML code and returning it to the terminal. This allows users to quickly create high-quality homepages without programming knowledge, simply by entering simple instructions in Japanese. It also makes it easy for users to re-enter instructions and make corrections, strengthening online presence and reducing costs.

[0523] "User" refers to the person who operates the system and inputs instructions.

[0524] "Natural language" refers to a language that humans use on a daily basis, and in this system this includes Japanese.

[0525] "Instructions" refer to requests regarding the homepage configuration and design that are input by the user in natural language.

[0526] "Terminal" refers to a computer or mobile device used by a user to enter and receive instructions.

[0527] "Server" refers to a computer that analyzes instructions received from a user, generates HTML code, and returns it.

[0528] A "generative model" refers to an algorithm or program that automatically generates HTML code based on instructions entered in natural language.

[0529] "Natural language processing technology" refers to technology for understanding and analyzing human natural language, and in this system it is applied to a generative model.

[0530] "HTML code" refers to the standardized markup language used to construct homepages.

[0531] "Formatting" refers to the process of converting generated HTML code into a format suitable for presentation to the user.

[0532] "Return" refers to sending the generated HTML code back from the server to the terminal.

[0533] "Interpretation" refers to the process by which the terminal understands the content of the HTML code received and processes it to display it to the user.

[0534] "Display" refers to the terminal outputting the generated HTML code to the screen in a form that can be viewed by the user.

[0535] "Modification" refers to the user inputting new instructions into the generated HTML code and making changes.

[0536] This invention relates to a system that receives instructions entered by a user in natural language, automatically generates HTML code based on the instructions, and provides the generated HTML code to the user. This system allows users to easily create high-quality homepages even without programming knowledge.

[0537] First, the user uses a terminal to input specific instructions in natural language regarding the structure and design of the homepage. For example, the user might input specific instructions such as, "The homepage should have the company name at the top of the screen, with a menu bar below it, and the menu contents should be 'TOP, Company Overview, Product Introduction, IR Information'."

[0538] The device then sends this instruction to a server. The server receives the request and initiates a process to drive a generative AI model. This generative AI model uses natural language processing technology to analyze the natural language instructions entered by the user and generate the corresponding HTML code based on that. Specifically, the server uses a generative AI model such as GPT-4.

[0539] Once the generative model generates HTML code based on the user's instructions, the server formats this code and returns it to the device in JSON format. The device then parses the received JSON data, extracts the generated HTML code, and displays it to the user. This allows the user to review the generated HTML code and modify it as needed. For example, it is possible to change the color or adjust the layout.

[0540] For example, if a user inputs the following instruction, "The homepage should have the company name at the top of the screen, with a menu bar below it, and the menu contents should be 'TOP, Company Overview, Product Introduction, IR Information'," the generative AI model will generate the following HTML code:

[0541] <!DOCTYPE html>

[0542]

[0543]

[0544] <meta charset="UTF-8">

[0545] <title> Company Name< / title>

[0546]

[0547]

[0548] <header>

[0549] <h1> Company Name< / h1>

[0550] <nav>

[0551]

[0552] TOP

[0553] Company Profile

[0554] Product introduction

[0555] IR Information

[0556]

[0557] < / nav>

[0558] < / header>

[0559] <main>

[0560] <!-- コンテンツ -->

[0561] < / main>

[0562]

[0563]

[0564] In this way, users can quickly create a customized homepage based on their needs by simply entering simple natural language instructions. If further instructions are needed, users can easily modify or update the generated HTML code by entering additional natural language instructions.

[0565] This system is particularly useful for small businesses and sole proprietors who want to strengthen their online presence with limited technical resources, as it allows for more efficient and cost-effective website creation.

[0566] Prompt Sentence Examples

[0567] "Display the company name on the top page, and place a menu bar below it, with the items on the menu bar as 'TOP, Company Overview, Product Introduction, IR Information'"

[0568] This system is designed to enable users to easily create high-quality homepages by utilizing a generative AI model that uses natural language processing technology, with the terminal and server working in tandem.

[0569] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0570] Step 1:

[0571] The user uses a terminal to input specific instructions in natural language regarding the structure and design of the homepage.

[0572] As input, the user provides text information such as "The top page will have the company name at the top of the screen, with a menu bar below it, and the menu contents will be 'TOP, company overview, product introduction, IR information'."

[0573] Specifically, the user enters instructions into a text box displayed in the device's browser or app and clicks the "Send" button.

[0574] Step 2:

[0575] The terminal transmits the instructions input by the user to the server.

[0576] As input, the terminal sends the user's instruction text as an HTTP POST request to the server.

[0577] As an output, the server receives an HTTP request.

[0578] Specifically, the device compiles the input text as JSON-formatted data and sends an HTTP POST request to the specified URL endpoint.

[0579] Step 3:

[0580] The server receives the request and initiates the process to drive the generative AI model.

[0581] As input, the server parses the received JSON-formatted data and passes it to the generative AI model.

[0582] As an output, the generative AI model generates the corresponding HTML code.

[0583] Specifically, the server calls a text analysis API and passes a prompt sentence to the generative AI model (e.g., GPT-4) to request processing.

[0584] Step 4:

[0585] The generative AI model generates HTML code based on user instructions.

[0586] As input, the generative AI model takes parsed natural language instructions and generates corresponding HTML code.

[0587] As output, the generated HTML code is returned to the server.

[0588] Specifically, the generative AI model uses an internal natural language processing algorithm to analyze the input instruction text and generate HTML code.

[0589] Step 5:

[0590] The server formats the generated HTML code and returns it to the terminal in JSON format.

[0591] As input, it takes the HTML code returned by the generative AI model.

[0592] As output, it sends formatted HTML code to the terminal.

[0593] Specifically, the server assigns the generated HTML code to the appropriate JSON key and returns it to the terminal as an HTTP response.

[0594] Step 6:

[0595] The device parses the received JSON data, extracts the generated HTML code, and displays it to the user.

[0596] As input, the terminal receives the JSON data returned by the server.

[0597] The output is generated as HTML which is visually displayed to the user.

[0598] Specifically, the device's browser or app extracts HTML code from the received JSON data and renders it on the screen.

[0599] Step 7:

[0600] The user reviews the displayed HTML code and re-enters correction instructions as necessary.

[0601] As input, the user checks the displayed HTML content and inputs instructions in natural language regarding the parts to be corrected.

[0602] As an output, the modification instructions are sent to the server again.

[0603] Specifically, the user enters the correction instructions in the text box again and clicks the "Submit" button. This action repeats the flow from step 2 again, and the corrections are reflected.

[0604] This series of processes allows users to easily create high-quality homepages using only natural language instructions, and to modify and update them as needed.

[0605] (Application example 1)

[0606] 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."

[0607] Many users today are interested in creating web pages, but lack programming knowledge, making it difficult for them to easily build web pages themselves. Furthermore, users who want to build online shopping sites often lack the expertise to quickly and efficiently expand their businesses online. To solve this problem, a system is needed that allows users to create high-quality web pages, especially online shopping sites, in an easy and intuitive way.

[0608] 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.

[0609] In this invention, the server includes means for receiving instructions input in a language by a user, means for driving a generative model that automatically generates HTML code based on the received instructions in the language, means for providing the generated HTML code to a user, means for displaying a preview of the generated HTML code in real time, and means for receiving additional instructions for modifying the generated HTML code, thereby enabling users without programming knowledge to quickly and easily create and modify high-quality online shopping sites based on instructions in Japanese.

[0610] "User" refers to a general user who intends to create or modify a web page using this system.

[0611] "Instructions entered in language" refers to requests or instructions given to the system by the user using natural language, particularly Japanese.

[0612] "Means for receiving" refers to a method or device by which the system receives linguistic instructions entered by a user.

[0613] A "generative model" is an artificial intelligence model that uses natural language processing technology to analyze input linguistic instructions and automatically generate HTML code accordingly.

[0614] "HTML code" is a markup language used to describe the structure and content of web pages.

[0615] "Means for providing" refers to a method or device for displaying or transmitting the generated HTML code to a user.

[0616] "Means for displaying a preview in real time" refers to a method or device for instantly displaying the generated HTML code on the user's terminal, allowing the user to check the results immediately.

[0617] The "means for receiving additional instructions" refers to a method or device by which the system receives instructions when the user re-enters instructions to correct or modify the generated HTML code.

[0618] The present invention is a system that automatically generates HTML code based on instructions entered in a user's language. This system is particularly useful for users who want to easily create and edit web pages, especially for users who want to build online shopping sites. Specific embodiments for implementing the present invention are described below.

[0619] Server Configuration

[0620] The server is responsible for the main process of receiving the user's input in a language, parsing it, and generating the HTML code. The server's functions include:

[0621] 1. Receiving means:

[0622] The server receives instructions in the language entered by the user. For example, the user might enter, through a smartphone application, "Display a banner for sale items on the top page, and place a category list below it."

[0623] 2. Generation means:

[0624] The server inputs the received instructions into a generative model (specifically, a generative AI model using natural language processing technology) to automatically generate HTML code. This generative model can use, for example, the Hugging Face transformers library and apply the GPT-3 model.

[0625] 3. Means of provision:

[0626] The server formats the generated HTML code and provides it to the user. It also returns the code in JSON format, which is displayed as a real-time preview on the user's device.

[0627] 4. Remedies:

[0628] The user can preview the generated HTML code and make corrections by entering additional instructions as necessary. These additional instructions are also received, analyzed in the same way, and the HTML code is generated and provided again.

[0629] Device configuration

[0630] The terminal operated by the user has the following main functions:

[0631] 1. Instruction input interface:

[0632] Users input language instructions through the terminal. An intuitive and easy-to-use user interface (UI) is provided, and operations can be completed simply by entering instructions in a text box.

[0633] 2. Instruction sending function:

[0634] Instructions are sent to the server in real time, and communication takes place over the internet to minimize communication delays.

[0635] 3. Preview display:

[0636] The HTML code provided by the server is previewed in real time on the device, allowing the user to instantly check the results and input corrections as needed.

[0637] Software and hardware used

[0638] Server-side software:

[0639] Flask (for building the API server), Hugging Face's transformers library, and the GPT-3 model.

[0640] Hardware:

[0641] The server should preferably be a computer with high-performance computing capabilities. It is also possible to use cloud services (e.g., AWS, Google Cloud).

[0642] Client-side software:

[0643] Standard web browsers, smartphone applications (e.g. iOS app, Android app).

[0644] Specific examples

[0645] The user uses an application on their smartphone to enter the following instructions:

[0646] "The homepage will display a sale item banner, with a category list below it."

[0647] Example prompt sentence:

[0648] User Instructions: Display a sale item banner on the homepage, with a category list below it. Based on this, generate the following HTML code:

[0649] With the above system, users can easily build and modify online shopping websites without any programming knowledge. The generated HTML code is displayed to the user immediately, and further modifications can be made by entering additional instructions.

[0650] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0651] Step 1:

[0652] The user launches an application on the smartphone and inputs instructions in the language.

[0653] Input: Specific language instructions entered by the user (e.g., "Display a sale item banner on the homepage, with a category list below it")

[0654] Output: Input instruction data

[0655] Step 2:

[0656] The terminal transmits the input instruction data to the server.

[0657] Input: User-entered instruction data

[0658] Output: Request data to the server

[0659] Step 3:

[0660] The server analyzes the received instruction data and generates an appropriate prompt.

[0661] Input: The request data sent to the server

[0662] Output: A prompt given to the generative model (e.g., "User instructions: Display a sale item banner on the top page, with a category list below it. Based on this, generate the following HTML code.")

[0663] Step 4:

[0664] The server runs a generative model (such as GPT-3) to generate HTML code based on the prompt.

[0665] Input: prompt statement

[0666] Output: Generated HTML code

[0667] Step 5:

[0668] The server formats the generated HTML code into JSON format and returns it to the terminal.

[0669] Input: Generated HTML code

[0670] Output: Pretty HTML code in JSON format

[0671] Step 6:

[0672] The device analyzes the received JSON format HTML code and displays a preview on the screen in real time.

[0673] Input: JSON data received from the server

[0674] Output: Visual preview of the web page

[0675] Step 7:

[0676] The user checks the preview and, if necessary, enters additional instructions to make corrections.

[0677] Input: User correction instructions

[0678] Output: New instruction data

[0679] Step 8:

[0680] The terminal again transmits new instruction data to the server, and repeats the same process to correct the HTML code.

[0681] Input: New instruction data

[0682] Output: Modified HTML code

[0683] This allows users to easily create and edit web pages.

[0684] 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.

[0685] The present invention relates to a system that receives instructions entered by a user in Japanese, automatically generates HTML code based on the instructions, and provides the generated HTML code to the user. Furthermore, the present invention provides a function to adjust the design and color scheme of the generated HTML code by combining it with an emotion engine that recognizes the emotions contained in the user's instructions.

[0686] First, the user opens the web application on their device and enters instructions in Japanese about the homepage's structure and design into the text area. For example, they might enter, "The homepage will have the company name at the top of the screen, with a menu bar below it, and the menu contents will be 'TOP, Company Overview, Product Introduction, IR Information.' I want the design to have a bright feel."

[0687] Next, the device sends the input Japanese instructions to the server. The server receives the request and acquires the user's Japanese instructions. The emotion engine then analyzes the emotions contained in the instructions and detects positive emotions, such as "cheerful feeling." The emotion engine then adjusts the design and color scheme of the HTML code according to this emotion.

[0688] The server generates prompts to drive the generative model, which then generates HTML code based on the user's instructions and the results of sentiment analysis. For example, based on the sentiment "feeling cheerful," the model generates HTML code with a bright background color.

[0689] Once the generative model generates HTML code, the server formats it and sends it back to the device in JSON format. The device interprets the received HTML code and displays it to the user. The user can review the displayed code and re-enter correction instructions as needed. For example, additional instructions such as "make it more colorful" are analyzed again by the emotion engine and reflected in the generative model.

[0690] For example, if a user inputs the following instruction, "The homepage should have the company name at the top of the screen, with a menu bar below it, and the menu contents should be 'TOP, Company Overview, Product Introduction, IR Information'. I want this design to have a bright feel," the generative model will generate the following HTML code (simplified):

[0691] <!DOCTYPE html>

[0692]

[0693]

[0694] <meta charset="UTF-8">

[0695] <title> Company Name< / title>

[0696] <style>

[0697] body { background-color: f0f8ff;}

[0698] h1 { color: ff6347;}

[0699] < / style>

[0700]

[0701]

[0702] <header>

[0703] <h1> Company Name< / h1>

[0704] <nav>

[0705]

[0706] TOP

[0707] Company Profile

[0708] Product introduction

[0709] IR Information

[0710]

[0711] < / nav>

[0712] < / header>

[0713] <main>

[0714] <!-- コンテンツ -->

[0715] < / main>

[0716]

[0717]

[0718] In this way, users can quickly create a customized homepage that meets their needs by entering simple Japanese instructions and words that express their emotions. This system not only further improves the efficiency and quality of homepage creation, but also enables the automatic generation of designs that take users' emotions into consideration.

[0719] The processing flow will be explained below.

[0720] Step 1:

[0721] A user opens a web application using a device. In a text area on the web page, the user enters instructions in Japanese regarding the structure and design of the homepage. For example, the user might enter, "The homepage will have the company name at the top of the screen, with a menu bar below it, and the menu contents will be 'TOP, Company Overview, Product Introduction, IR Information.' I want the design to have a bright feel."

[0722] Step 2:

[0723] When the user clicks the "Generate" button, the device sends the entered Japanese instructions to the server. Specifically, the device includes the instructions as JSON format data in the body of a POST request and sends it to the server's endpoint.

[0724] Step 3:

[0725] The server receives the request. The endpoint / generate_html specified in the Flask application processes this request. The server retrieves the user's Japanese instructions from the request body using request.json.get('user_input').

[0726] Step 4:

[0727] The server runs an emotion engine to recognize emotions contained in user input. Specifically, the emotion engine analyzes the input Japanese text and detects emotional expressions such as "cheerful feeling."

[0728] Step 5:

[0729] The server prepares to drive the generative model based on Japanese instructions. Specifically, it generates prompts in a format suitable for the generative model. The prompts also include emotional information recognized by the emotion engine.

[0730] Step 6:

[0731] The server calls a generative model, such as the OpenAI API, to send the prompt. The generative model uses natural language processing techniques to generate HTML code based on the prompt. The emotion engine then adjusts the design and color scheme based on the emotion recognized.

[0732] Step 7:

[0733] Once the generative model has generated the HTML code, the server formats it, removing unnecessary whitespace and line breaks to make it neat.

[0734] Step 8:

[0735] The server wraps the generated HTML code in JSON format and returns it to the device as an HTTP response. The response includes the generated HTML code.

[0736] Step 9:

[0737] The device receives the response from the server, uses the JavaScript fetch API to parse the response into JSON format, and extracts the HTML code.

[0738] Step 10:

[0739] The device will then extract the HTML code from the web page at the specified location (e.g. <pre>The generated HTML code is displayed in the tag. The user can check the generated HTML code immediately.

[0740] Step 11:

[0741] If the user wishes to make further modifications to the generated HTML code, they can start again from step 1. For example, by changing the color or adjusting the layout, the generated HTML code can be regenerated and updated. The emotion engine is also activated again, recognizing the user's new emotional expression and reflecting it in the design.

[0742] By following the above steps, users can quickly create a customized homepage that meets their needs by entering simple Japanese instructions as well as words that express their emotions. This invention not only further improves the efficiency and quality of homepage creation, but also enables the automatic generation of designs that take user emotions into consideration.

[0743] Example 2

[0744] 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."

[0745] Conventional HTML code generation systems have difficulty reflecting the user's desired emotions and designs, and often produce results that do not meet the user's intentions. Furthermore, there are insufficient means for users to easily modify HTML code. This has resulted in a decrease in the efficiency of homepage creation and user satisfaction.

[0746] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0747] In this invention, the server includes means for receiving instructions entered by a user in natural language, means for driving an emotion engine that analyzes emotions based on the received instructions, means for generating prompt sentences based on the results of the emotion analysis and the instructions and driving a generative AI model, and means for formatting the generated HTML code and providing it to the user, thereby enabling automatic generation and easy modification of HTML code that reflects the user's intentions and emotions.

[0748] A "user" is a person who uses the system to create or edit a homepage.

[0749] A "natural language" is a language that humans use on a daily basis, such as Japanese.

[0750] "Instructions" are requests regarding the structure and design of the homepage that are input by the user into the terminal using natural language.

[0751] "Receiving" refers to the terminal transferring an instruction input by a user to a server, and the server acquiring it.

[0752] An "emotion engine" is software that uses natural language processing technology to analyze the emotions contained in a user's instructions.

[0753] "Sentiment analysis" is the process by which the emotion engine detects emotions, such as positive and negative, from user instructions.

[0754] A "prompt sentence" is an input sentence given to a generative AI model based on the results of sentiment analysis and user instructions.

[0755] A "generative AI model" is an artificial intelligence that automatically generates HTML code based on a prompt text.

[0756] "HTML code" refers to code written in a markup language used to construct a web page.

[0757] "Formatting" is the process of formatting the generated HTML code to make it look better and more functional.

[0758] "Serving" refers to formatting the generated HTML code and then sending it to the terminal for display to the user.

[0759] The present invention relates to a system that receives instructions entered by a user in natural language, automatically generates HTML code based on the instructions, and provides the generated HTML code to the user. Furthermore, the present invention provides a function to adjust the design and color scheme of the generated HTML code by combining it with an emotion engine that recognizes the emotions contained in the user's instructions.

[0760] First, the user opens the web application on their device and enters natural language instructions about the homepage's structure and design into the text area. For example, they might enter, "The homepage will have the company name at the top of the screen, with a menu bar below it, and the menu contents will be 'TOP, Company Overview, Product Introduction, IR Information.' I want the design to have a bright feel."

[0761] The device then sends the input instructions in natural language to the server, using the HTTPS protocol to ensure secure communication.

[0762] The server receives the HTTPS request and obtains the user's natural language instruction. It then uses an emotion engine to analyze the emotion contained in the instruction. For example, it detects a positive emotion such as "cheerful." The emotion engine uses a natural language processing library to calculate the emotion vector.

[0763] The server generates prompts to drive the generative AI model based on the results of sentiment analysis and user instructions. The prompts are generated using templates, such as "Please generate HTML code with a light background color and a menu bar at the top."

[0764] The generative AI model generates HTML code based on the input prompt. The model automatically generates code based on a pre-trained dataset. For example, based on the instruction "bright feel," it generates HTML code with a bright background color.

[0765] The server formats the generated HTML code and converts it to JSON format using a dedicated library. The formatted data is then sent back to the device.

[0766] The device interprets the JSON-formatted HTML code received from the server and displays it on a web page. The display is done using the browser's rendering engine. The user can review the displayed code and make corrections or add additional instructions as needed. For example, they can enter additional instructions such as "make it more colorful." These new instructions are sent back to the server, where the emotion engine and generative model analyze and generate the new code again.

[0767] For example, if a user inputs the following instruction, "The homepage should have the company name at the top of the screen, with a menu bar below it, and the menu contents should be 'TOP, Company Overview, Product Introduction, IR Information.' I would like the design to have a bright feel," the generative AI model will generate the following HTML code:

[0768] html

[0769] <!DOCTYPE html>

[0770]

[0771]

[0772] <meta charset="UTF-8">

[0773] <title> Company Name< / title>

[0774] <style>

[0775] body { background-color: f0f8ff;}

[0776] h1 { color: ff6347;}

[0777] < / style>

[0778]

[0779]

[0780] <header>

[0781] <h1> Company Name< / h1>

[0782] <nav>

[0783]

[0784] TOP

[0785] Company Profile

[0786] Product introduction

[0787] IR Information

[0788]

[0789] < / nav>

[0790] < / header>

[0791] <main>

[0792] <!-- コンテンツ -->

[0793] < / main>

[0794]

[0795]

[0796] In this way, users can quickly create a customized homepage that meets their needs by inputting simple natural language instructions and words that express their emotions. This system not only further improves the efficiency and quality of homepage creation, but also enables the automatic generation of designs that take users' emotions into consideration.

[0797] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0798] Step 1:

[0799] The user opens a web application on their device and enters natural language instructions into the text area regarding the homepage's structure and design. For example, they might enter, "The homepage will have the company name at the top of the screen, with a menu bar below it, and the menu contents will be 'TOP, Company Overview, Product Introduction, IR Information.' I want the design to have a bright feel." These instructions become the input data.

[0800] Step 2:

[0801] The terminal sends instructions entered by the user in natural language to the server. The transmission uses the HTTPS protocol to ensure secure communication. The input data is the user's instructions, and the output data is the request sent to the server.

[0802] Step 3:

[0803] The server receives the HTTPS request and retrieves the user's natural language instructions, which are the input data and are ready for the next stage of sentiment analysis.

[0804] Step 4:

[0805] The server's emotion engine analyzes the received natural language instructions and detects emotions. For example, it can detect positive emotions such as "cheerful feeling." The input for this emotion analysis is the natural language instructions, and the output is an emotion vector. The emotion engine uses natural language processing technology to quantify the emotions contained in the instructions.

[0806] Step 5:

[0807] The server generates a prompt based on the results of the sentiment analysis and the user's instructions. The input data are the sentiment vector and the user's instructions, and the output data is the generated prompt. A template is used to generate the prompt, and it can be formatted, for example, as "Please generate HTML code with a light background color and a menu bar at the top."

[0808] Step 6:

[0809] The generative AI model generates HTML code based on prompts input from the server. The input data is the prompts, and the output data is the generated HTML code. The model automatically generates HTML code using a pre-trained dataset.

[0810] Step 7:

[0811] The server formats the generated HTML code and converts it to JSON format. The input data is the generated HTML code, and the output data is formatted code in JSON format. A dedicated library is used for formatting.

[0812] Step 8:

[0813] The terminal interprets the JSON-formatted HTML code received from the server and displays it in the browser. The input data is the JSON-formatted code, and the output is a web page displayed in the browser. The user checks the displayed code.

[0814] Step 9:

[0815] The user checks the displayed HTML code and makes corrections or additions as necessary. For example, they input a new instruction such as "Make it more colorful." This new instruction returns to step 2, and the same process is repeated. The input data are correction instructions, and the output data is new HTML code.

[0816] (Application example 2)

[0817] 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."

[0818] Conventional web page generation systems have difficulty creating designs that reflect the user's emotions and intentions if the user does not have detailed knowledge of design and color schemes. Furthermore, they lack the functionality to automatically generate designs that take emotions into consideration, making it difficult to provide designs that intuitively satisfy the user. Especially in operating online virtual stores, it is important to quickly provide designs that respond to the user's emotions and expectations.

[0819] 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.

[0820] In this invention, the server includes means for receiving instructions entered in Japanese by a user, means for driving a generative model that automatically generates HTML code based on the received Japanese instructions, means for analyzing the emotions contained in the Japanese instructions and adjusting the design and color scheme of the HTML code, and means for providing the generated HTML code to the user. This makes it possible to automatically generate designs based on the user's emotions and intentions, and to easily build an intuitive and attractive virtual store.

[0821] "User" refers to any person or entity that uses the System to generate HTML code.

[0822] "Instructions entered in Japanese" refers to text written in Japanese by a user expressing requests regarding the structure and design of a web page.

[0823] "Means for receiving" refers to a component or program that has the function of obtaining Japanese language instructions entered by a user and storing them for processing.

[0824] "Generative model" refers to an algorithm or artificial intelligence system that automatically generates HTML code based on received Japanese instructions.

[0825] "Driven" refers to hardware or software that executes a generative model and accomplishes a specified task.

[0826] "Means for analyzing emotions" refers to algorithms or artificial intelligence systems that analyze the emotions and intentions contained in the instructions entered by the user and adjust the design and color scheme based on the results.

[0827] "Means for adjusting design and color scheme" refers to components or programs for changing the appearance and color scheme of the HTML code generated based on the results of sentiment analysis.

[0828] "Means of providing" refers to the functions and services for displaying or transmitting the generated HTML code to the user.

[0829] "Natural language processing technology" refers to the general algorithms and methods that allow computers to understand and process human language.

[0830] "Means for making modifications" refers to a component or program that has the function of receiving instructions re-entered by the user and modifying or correcting existing HTML code based on the instructions.

[0831] "Design elements such as background and font color" refers to the visual components of a web page's appearance and user experience, such as background color, font color, size, and placement.

[0832] This invention is a system that receives instructions entered by a user in Japanese and automatically generates HTML code based on those instructions. Furthermore, this system also provides a function to adjust the design and color scheme of the generated HTML code by combining it with an emotion engine that recognizes the emotions contained in the user's instructions.

[0833] System configuration

[0834] The system includes the following components:

[0835] 1. Input receiving means: A web application for receiving instructions entered in Japanese by the user. Users access this web application using devices such as smartphones, tablets, and PCs.

[0836] 2. Generative model driving means: A server that drives a generative model that automatically generates HTML code based on Japanese instructions. The generative model uses natural language processing technology.

[0837] 3. Sentiment analysis: An emotion engine that analyzes the emotions contained in the Japanese instructions entered by the user. For example, the pipeline from the Hugging Face transformers library is used.

[0838] 4. Design Adjustment: A software component for adjusting design elements such as background and font color of the generated HTML code based on the results of sentiment analysis.

[0839] 5. Code Delivery Method: A function for providing the generated HTML code to the user. The generated code is returned in JSON format and interpreted and displayed on the user's device.

[0840] Processing flow explanation

[0841] 1. Receiving user input:

[0842] The user opens the web application and enters instructions in Japanese about the homepage structure and design in a text area. For example, they can enter specific instructions such as, "The homepage should have the company name at the top of the screen, with a menu bar below it, and the menu contents should be 'TOP, Company Overview, Product Introduction, IR Information.' I would like the design to have a bright feel."

[0843] 2. Sending instructions:

[0844] Instructions entered by the user are sent from the terminal to the server.

[0845] 3. Sentiment analysis:

[0846] The server analyzes the received instructions using an emotion engine to recognize the emotion contained in the input text. For example, if a positive emotion such as "cheerful" is detected, the background color will be set to a bright color.

[0847] 4. Driving the generative model:

[0848] The server runs a generative AI model that uses natural language processing techniques to generate HTML code based on user instructions and sentiment analysis.

[0849] 5. Code Submission:

[0850] The generated HTML code is formatted and sent back to the user's device in JSON format. The device interprets the received HTML code and displays it to the user. The user can check the displayed code and enter instructions again if necessary.

[0851] Specific examples

[0852] For example, the user enters the following prompt:

[0853] "The homepage will have the company name at the top of the screen, with a menu bar below it, with the menu contents being 'TOP, Company Overview, Product Introduction, IR Information.' I want this design to have a bright feel."

[0854] Based on this example, the system performs sentiment analysis and drives a generative model to generate HTML code with a light background color. This prompt allows for the rapid creation of a customized homepage tailored to the user's needs.

[0855] As described above, the present invention improves the efficiency and quality of homepage creation, and also makes it possible to automatically generate designs that take into consideration the user's feelings.

[0856] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0857] Step 1:

[0858] The user enters instructions in Japanese.

[0859] Users access the system using a smartphone, tablet, or PC and input instructions in Japanese regarding the homepage structure and design. For example, they might input, "The homepage will have the company name at the top of the screen, with a menu bar below it, and the menu contents will be 'TOP, Company Overview, Product Introduction, IR Information.' I want this design to have a bright feel." The input data is saved in text format on the device.

[0860] Step 2:

[0861] The terminal sends the input instructions to the server

[0862] The terminal transmits the instructions in Japanese entered by the user to the server. The transmitted data is the text data entered by the user.

[0863] Step 3:

[0864] The server receives and parses the Japanese instructions.

[0865] The server parses the received Japanese instructions. It receives the input data (Japanese instructions) and analyzes the grammar and syntax using a text analysis library. The main processing here is to split the received data and extract elements from the instructions.

[0866] Step 4:

[0867] Analyze emotions with the emotion engine

[0868] The server analyzes the emotions from the parsed instructions. For emotion analysis, it uses the pipeline of Hugging Face's transformers library. In this step, the emotion (positive, negative, etc.) contained in the input data (instructions) is identified and the result is stored internally on the server. For example, the instruction "feeling cheerful" is recognized as a positive emotion.

[0869] Step 5:

[0870] Generate HTML code by driving the generative model

[0871] The server drives a generative AI model based on the results of sentiment analysis and Japanese instructions. It uses natural language processing technology to generate HTML code that includes designs and color schemes that match the emotions. In this step, the input data (Japanese instructions and sentiment analysis results) is sent as prompts to the generative AI model, and output data (generated HTML code) is obtained.

[0872] Step 6:

[0873] Format and serve the generated HTML code

[0874] The generated HTML code is formatted and converted to JSON format. The server returns this formatted HTML code to the user. The output data is formatted HTML code.

[0875] Step 7:

[0876] Display the HTML code received by the device

[0877] The terminal interprets the HTML code received from the server and displays it to the user using a browser or similar. The user can check the displayed code and enter instructions again if necessary. The output data is an HTML display that the user can view.

[0878] By following the above steps, users can quickly create a homepage that meets their needs using simple Japanese instructions and emotional expressions.

[0879] 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.

[0880] 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.

[0881] 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.

[0882] [Third embodiment]

[0883] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0884] 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.

[0885] 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).

[0886] 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.

[0887] 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.

[0888] 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).

[0889] 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.

[0890] 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.

[0891] 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.

[0892] 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.

[0893] 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.

[0894] 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."

[0895] This invention relates to a system that receives instructions entered in Japanese by a user, automatically generates HTML code based on the instructions, and provides the generated HTML code to the user. This system allows users to easily create high-quality homepages even without programming knowledge.

[0896] First, the user uses a terminal to input specific instructions in Japanese regarding the structure and design of the homepage. For example, they can input instructions such as, "The homepage should have the company name at the top of the screen, with a menu bar below it, and the menu contents should be 'TOP, Company Overview, Product Introduction, IR Information'."

[0897] The device then sends the instructions to the server, which receives the request and initiates a process to drive the generative model, which uses natural language processing techniques to analyze the Japanese instructions and generate the corresponding HTML code.

[0898] Once the generative model generates HTML code based on the user's instructions, the server formats this code and sends it back to the device in JSON format. The device interprets the received HTML code and displays it to the user. The user can review the displayed code and enter corrections as needed. For example, they can change the color or adjust the layout.

[0899] For example, if a user inputs the following instruction, "The homepage should have the company name at the top of the screen, with a menu bar below it, and the menu contents should be 'TOP, Company Overview, Product Introduction, IR Information'," the generative model will generate the following HTML code (simplified):

[0900] <!DOCTYPE html>

[0901]

[0902]

[0903] <meta charset="UTF-8">

[0904] <title> Company Name< / title>

[0905]

[0906]

[0907] <header>

[0908] <h1> Company Name< / h1>

[0909] <nav>

[0910]

[0911] TOP

[0912] Company Profile

[0913] Product introduction

[0914] IR Information

[0915]

[0916] < / nav>

[0917] < / header>

[0918] <main>

[0919] <!-- コンテンツ -->

[0920] < / main>

[0921]

[0922]

[0923] In this way, users can quickly create a customized homepage based on their needs by simply entering simple instructions in Japanese. If further instructions are required, users can easily modify or update the generated HTML code by entering additional instructions in Japanese.

[0924] This system is particularly useful for small businesses and sole proprietors who want to strengthen their online presence with limited technical resources, as it allows for more efficient and cost-effective website creation.

[0925] The processing flow will be explained below.

[0926] Step 1:

[0927] A user opens a web application using a device. In a text area on the web page, the user enters instructions in Japanese regarding the structure and design of the homepage. For example, the user might enter, "The homepage should have the company name at the top of the screen, with a menu bar below it, and the menu contents should be 'TOP, Company Overview, Product Introduction, IR Information'."

[0928] Step 2:

[0929] When the user clicks the "Generate" button, the device sends the entered Japanese instructions to the server. Specifically, the device includes the instructions as JSON format data in the body of a POST request and sends it to the server's endpoint.

[0930] Step 3:

[0931] The server receives the request. The endpoint / generate_html specified in the Flask application processes this request. The server retrieves the user's Japanese instructions from the request body using request.json.get('user_input').

[0932] Step 4:

[0933] The server prepares to drive the generative model based on the Japanese instructions. Specifically, it generates a prompt in a format appropriate for the generative model. For example, the prompt is in the format "User instructions: {user_input}\nHTML code to generate:".

[0934] Step 5:

[0935] The server calls a generative model, such as the OpenAI API, and sends a prompt. The generative model uses natural language processing techniques to generate HTML code based on the prompt. The resulting HTML code is then returned to the server.

[0936] Step 6:

[0937] The server receives the HTML code returned by the generated model and formats it, specifically removing unnecessary whitespace and line breaks to make it neat.

[0938] Step 7:

[0939] The server wraps the generated HTML code in JSON format and returns it to the device as an HTTP response. The response includes the generated HTML code.

[0940] Step 8:

[0941] The device receives the response from the server, uses the JavaScript fetch API to parse the response into JSON format, and extracts the HTML code.

[0942] Step 9:

[0943] The device will then extract the HTML code from the web page at the specified location (e.g. <pre>The generated HTML code is displayed in the tag. The user can check the generated HTML code immediately.

[0944] Step 10:

[0945] If the user wishes to make further modifications to the generated HTML code, they can start again from step 1. For example, by changing the color or adjusting the layout, the generated HTML code can be regenerated and updated.

[0946] By following these steps, users can quickly create and edit high-quality homepages using simple instructions in Japanese.

[0947] Example 1

[0948] 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."

[0949] In today's world, creating a website is an important part of having an online presence, but it can be a time-consuming and labor-intensive task for users without programming knowledge. Furthermore, existing website creation tools are often complicated to use, placing a burden on small businesses and sole proprietors in particular. There is a need for a system that can solve this problem and enable anyone to easily create a high-quality website.

[0950] 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.

[0951] In this invention, the server includes a means for receiving instructions entered by a user in natural language, a means for driving a generative model that automatically generates HTML code based on the received natural language instructions, and a means for formatting the generated HTML code and returning it to the terminal. This allows users to quickly create high-quality homepages without programming knowledge, simply by entering simple instructions in Japanese. It also makes it easy for users to re-enter instructions and make corrections, strengthening online presence and reducing costs.

[0952] "User" refers to the person who operates the system and inputs instructions.

[0953] "Natural language" refers to a language that humans use on a daily basis, and in this system this includes Japanese.

[0954] "Instructions" refer to requests regarding the homepage configuration and design that are input by the user in natural language.

[0955] "Terminal" refers to a computer or mobile device used by a user to enter and receive instructions.

[0956] "Server" refers to a computer that analyzes instructions received from a user, generates HTML code, and returns it.

[0957] A "generative model" refers to an algorithm or program that automatically generates HTML code based on instructions entered in natural language.

[0958] "Natural language processing technology" refers to technology for understanding and analyzing human natural language, and in this system it is applied to a generative model.

[0959] "HTML code" refers to the standardized markup language used to construct homepages.

[0960] "Formatting" refers to the process of converting generated HTML code into a format suitable for presentation to the user.

[0961] "Return" refers to sending the generated HTML code back from the server to the terminal.

[0962] "Interpretation" refers to the process by which the terminal understands the content of the HTML code received and processes it to display it to the user.

[0963] "Display" refers to the terminal outputting the generated HTML code to the screen in a form that can be viewed by the user.

[0964] "Modification" refers to the user inputting new instructions into the generated HTML code and making changes.

[0965] This invention relates to a system that receives instructions entered by a user in natural language, automatically generates HTML code based on the instructions, and provides the generated HTML code to the user. This system allows users to easily create high-quality homepages even without programming knowledge.

[0966] First, the user uses a terminal to input specific instructions in natural language regarding the structure and design of the homepage. For example, the user might input specific instructions such as, "The homepage should have the company name at the top of the screen, with a menu bar below it, and the menu contents should be 'TOP, Company Overview, Product Introduction, IR Information'."

[0967] The device then sends this instruction to a server. The server receives the request and initiates a process to drive a generative AI model. This generative AI model uses natural language processing technology to analyze the natural language instructions entered by the user and generate the corresponding HTML code based on that. Specifically, the server uses a generative AI model such as GPT-4.

[0968] Once the generative model generates HTML code based on the user's instructions, the server formats this code and returns it to the device in JSON format. The device then parses the received JSON data, extracts the generated HTML code, and displays it to the user. This allows the user to review the generated HTML code and modify it as needed. For example, it is possible to change the color or adjust the layout.

[0969] For example, if a user inputs the following instruction, "The homepage should have the company name at the top of the screen, with a menu bar below it, and the menu contents should be 'TOP, Company Overview, Product Introduction, IR Information'," the generative AI model will generate the following HTML code:

[0970] <!DOCTYPE html>

[0971]

[0972]

[0973] <meta charset="UTF-8">

[0974] <title> Company Name< / title>

[0975]

[0976]

[0977] <header>

[0978] <h1> Company Name< / h1>

[0979] <nav>

[0980]

[0981] TOP

[0982] Company Profile

[0983] Product introduction

[0984] IR Information

[0985]

[0986] < / nav>

[0987] < / header>

[0988] <main>

[0989] <!-- コンテンツ -->

[0990] < / main>

[0991]

[0992]

[0993] In this way, users can quickly create a customized homepage based on their needs by simply entering simple natural language instructions. If further instructions are needed, users can easily modify or update the generated HTML code by entering additional natural language instructions.

[0994] This system is particularly useful for small businesses and sole proprietors who want to strengthen their online presence with limited technical resources, as it allows for more efficient and cost-effective website creation.

[0995] Prompt Sentence Examples

[0996] "Display the company name on the top page, and place a menu bar below it, with the items on the menu bar as 'TOP, Company Overview, Product Introduction, IR Information'"

[0997] This system is designed to enable users to easily create high-quality homepages by utilizing a generative AI model that uses natural language processing technology, with the terminal and server working in tandem.

[0998] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0999] Step 1:

[1000] The user uses a terminal to input specific instructions in natural language regarding the structure and design of the homepage.

[1001] As input, the user provides text information such as "The top page will have the company name at the top of the screen, with a menu bar below it, and the menu contents will be 'TOP, company overview, product introduction, IR information'."

[1002] Specifically, the user enters instructions into a text box displayed in the device's browser or app and clicks the "Send" button.

[1003] Step 2:

[1004] The terminal transmits the instructions input by the user to the server.

[1005] As input, the terminal sends the user's instruction text as an HTTP POST request to the server.

[1006] As an output, the server receives an HTTP request.

[1007] Specifically, the device compiles the input text as JSON-formatted data and sends an HTTP POST request to the specified URL endpoint.

[1008] Step 3:

[1009] The server receives the request and initiates the process to drive the generative AI model.

[1010] As input, the server parses the received JSON-formatted data and passes it to the generative AI model.

[1011] As an output, the generative AI model generates the corresponding HTML code.

[1012] Specifically, the server calls a text analysis API and passes a prompt sentence to the generative AI model (e.g., GPT-4) to request processing.

[1013] Step 4:

[1014] The generative AI model generates HTML code based on user instructions.

[1015] As input, the generative AI model takes parsed natural language instructions and generates corresponding HTML code.

[1016] As output, the generated HTML code is returned to the server.

[1017] Specifically, the generative AI model uses an internal natural language processing algorithm to analyze the input instruction text and generate HTML code.

[1018] Step 5:

[1019] The server formats the generated HTML code and returns it to the terminal in JSON format.

[1020] As input, it takes the HTML code returned by the generative AI model.

[1021] As output, it sends formatted HTML code to the terminal.

[1022] Specifically, the server assigns the generated HTML code to the appropriate JSON key and returns it to the terminal as an HTTP response.

[1023] Step 6:

[1024] The device parses the received JSON data, extracts the generated HTML code, and displays it to the user.

[1025] As input, the terminal receives the JSON data returned by the server.

[1026] The output is generated as HTML which is visually displayed to the user.

[1027] Specifically, the device's browser or app extracts HTML code from the received JSON data and renders it on the screen.

[1028] Step 7:

[1029] The user reviews the displayed HTML code and re-enters correction instructions as necessary.

[1030] As input, the user checks the displayed HTML content and inputs instructions in natural language regarding the parts to be corrected.

[1031] As an output, the modification instructions are sent to the server again.

[1032] Specifically, the user enters the correction instructions in the text box again and clicks the "Submit" button. This action repeats the flow from step 2 again, and the corrections are reflected.

[1033] This series of processes allows users to easily create high-quality homepages using only natural language instructions, and to modify and update them as needed.

[1034] (Application example 1)

[1035] 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."

[1036] Many users today are interested in creating web pages, but lack programming knowledge, making it difficult for them to easily build web pages themselves. Furthermore, users who want to build online shopping sites often lack the expertise to quickly and efficiently expand their businesses online. To solve this problem, a system is needed that allows users to create high-quality web pages, especially online shopping sites, in an easy and intuitive way.

[1037] 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.

[1038] In this invention, the server includes means for receiving instructions input in a language by a user, means for driving a generative model that automatically generates HTML code based on the received instructions in the language, means for providing the generated HTML code to a user, means for displaying a preview of the generated HTML code in real time, and means for receiving additional instructions for modifying the generated HTML code, thereby enabling users without programming knowledge to quickly and easily create and modify high-quality online shopping sites based on instructions in Japanese.

[1039] "User" refers to a general user who intends to create or modify a web page using this system.

[1040] "Instructions entered in language" refers to requests or instructions given to the system by the user using natural language, particularly Japanese.

[1041] "Means for receiving" refers to a method or device by which the system receives linguistic instructions entered by a user.

[1042] A "generative model" is an artificial intelligence model that uses natural language processing technology to analyze input linguistic instructions and automatically generate HTML code accordingly.

[1043] "HTML code" is a markup language used to describe the structure and content of web pages.

[1044] "Means for providing" refers to a method or device for displaying or transmitting the generated HTML code to a user.

[1045] "Means for displaying a preview in real time" refers to a method or device for instantly displaying the generated HTML code on the user's terminal, allowing the user to check the results immediately.

[1046] The "means for receiving additional instructions" refers to a method or device by which the system receives instructions when the user re-enters instructions to correct or modify the generated HTML code.

[1047] The present invention is a system that automatically generates HTML code based on instructions entered in a user's language. This system is particularly useful for users who want to easily create and edit web pages, especially for users who want to build online shopping sites. Specific embodiments for implementing the present invention are described below.

[1048] Server Configuration

[1049] The server is responsible for the main process of receiving the user's input in a language, parsing it, and generating the HTML code. The server's functions include:

[1050] 1. Receiving means:

[1051] The server receives instructions in the language entered by the user. For example, the user might enter, through a smartphone application, "Display a banner for sale items on the top page, and place a category list below it."

[1052] 2. Generation means:

[1053] The server inputs the received instructions into a generative model (specifically, a generative AI model using natural language processing technology) to automatically generate HTML code. This generative model can use, for example, the Hugging Face transformers library and apply the GPT-3 model.

[1054] 3. Means of provision:

[1055] The server formats the generated HTML code and provides it to the user. It also returns the code in JSON format, which is displayed as a real-time preview on the user's device.

[1056] 4. Remedies:

[1057] The user can preview the generated HTML code and make corrections by entering additional instructions as necessary. These additional instructions are also received, analyzed in the same way, and the HTML code is generated and provided again.

[1058] Device configuration

[1059] The terminal operated by the user has the following main functions:

[1060] 1. Instruction input interface:

[1061] Users input language instructions through the terminal. An intuitive and easy-to-use user interface (UI) is provided, and operations can be completed simply by entering instructions in a text box.

[1062] 2. Instruction sending function:

[1063] Instructions are sent to the server in real time, and communication takes place over the internet to minimize communication delays.

[1064] 3. Preview display:

[1065] The HTML code provided by the server is previewed in real time on the device, allowing the user to instantly check the results and input corrections as needed.

[1066] Software and hardware used

[1067] Server-side software:

[1068] Flask (for building the API server), Hugging Face's transformers library, and the GPT-3 model.

[1069] Hardware:

[1070] The server should preferably be a computer with high-performance computing capabilities. It is also possible to use cloud services (e.g., AWS, Google Cloud).

[1071] Client-side software:

[1072] Standard web browsers, smartphone applications (e.g. iOS app, Android app).

[1073] Specific examples

[1074] The user uses an application on their smartphone to enter the following instructions:

[1075] "The homepage will display a sale item banner, with a category list below it."

[1076] Example prompt sentence:

[1077] User Instructions: Display a sale item banner on the homepage, with a category list below it. Based on this, generate the following HTML code:

[1078] With the above system, users can easily build and modify online shopping websites without any programming knowledge. The generated HTML code is displayed to the user immediately, and further modifications can be made by entering additional instructions.

[1079] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1080] Step 1:

[1081] The user launches an application on the smartphone and inputs instructions in the language.

[1082] Input: Specific language instructions entered by the user (e.g., "Display a sale item banner on the homepage, with a category list below it")

[1083] Output: Input instruction data

[1084] Step 2:

[1085] The terminal transmits the input instruction data to the server.

[1086] Input: User-entered instruction data

[1087] Output: Request data to the server

[1088] Step 3:

[1089] The server analyzes the received instruction data and generates an appropriate prompt.

[1090] Input: The request data sent to the server

[1091] Output: A prompt given to the generative model (e.g., "User instructions: Display a sale item banner on the top page, with a category list below it. Based on this, generate the following HTML code.")

[1092] Step 4:

[1093] The server runs a generative model (such as GPT-3) to generate HTML code based on the prompt.

[1094] Input: prompt statement

[1095] Output: Generated HTML code

[1096] Step 5:

[1097] The server formats the generated HTML code into JSON format and returns it to the terminal.

[1098] Input: Generated HTML code

[1099] Output: Pretty HTML code in JSON format

[1100] Step 6:

[1101] The device analyzes the received JSON format HTML code and displays a preview on the screen in real time.

[1102] Input: JSON data received from the server

[1103] Output: Visual preview of the web page

[1104] Step 7:

[1105] The user checks the preview and, if necessary, enters additional instructions to make corrections.

[1106] Input: User correction instructions

[1107] Output: New instruction data

[1108] Step 8:

[1109] The terminal again transmits new instruction data to the server, and repeats the same process to correct the HTML code.

[1110] Input: New instruction data

[1111] Output: Modified HTML code

[1112] This allows users to easily create and edit web pages.

[1113] 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.

[1114] The present invention relates to a system that receives instructions entered by a user in Japanese, automatically generates HTML code based on the instructions, and provides the generated HTML code to the user. Furthermore, the present invention provides a function to adjust the design and color scheme of the generated HTML code by combining it with an emotion engine that recognizes the emotions contained in the user's instructions.

[1115] First, the user opens the web application on their device and enters instructions in Japanese about the homepage's structure and design into the text area. For example, they might enter, "The homepage will have the company name at the top of the screen, with a menu bar below it, and the menu contents will be 'TOP, Company Overview, Product Introduction, IR Information.' I want the design to have a bright feel."

[1116] Next, the device sends the input Japanese instructions to the server. The server receives the request and acquires the user's Japanese instructions. The emotion engine then analyzes the emotions contained in the instructions and detects positive emotions, such as "cheerful feeling." The emotion engine then adjusts the design and color scheme of the HTML code according to this emotion.

[1117] The server generates prompts to drive the generative model, which then generates HTML code based on the user's instructions and the results of sentiment analysis. For example, based on the sentiment "feeling cheerful," the model generates HTML code with a bright background color.

[1118] Once the generative model generates HTML code, the server formats it and sends it back to the device in JSON format. The device interprets the received HTML code and displays it to the user. The user can review the displayed code and re-enter correction instructions as needed. For example, additional instructions such as "make it more colorful" are analyzed again by the emotion engine and reflected in the generative model.

[1119] For example, if a user inputs the following instruction, "The homepage should have the company name at the top of the screen, with a menu bar below it, and the menu contents should be 'TOP, Company Overview, Product Introduction, IR Information'. I want this design to have a bright feel," the generative model will generate the following HTML code (simplified):

[1120] <!DOCTYPE html>

[1121]

[1122]

[1123] <meta charset="UTF-8">

[1124] <title> Company Name< / title>

[1125] <style>

[1126] body { background-color: f0f8ff;}

[1127] h1 { color: ff6347;}

[1128] < / style>

[1129]

[1130]

[1131] <header>

[1132] <h1> Company Name< / h1>

[1133] <nav>

[1134]

[1135] TOP

[1136] Company Profile

[1137] Product introduction

[1138] IR Information

[1139]

[1140] < / nav>

[1141] < / header>

[1142] <main>

[1143] <!-- コンテンツ -->

[1144] < / main>

[1145]

[1146]

[1147] In this way, users can quickly create a customized homepage that meets their needs by entering simple Japanese instructions and words that express their emotions. This system not only further improves the efficiency and quality of homepage creation, but also enables the automatic generation of designs that take users' emotions into consideration.

[1148] The processing flow will be explained below.

[1149] Step 1:

[1150] A user opens a web application using a device. In a text area on the web page, the user enters instructions in Japanese regarding the structure and design of the homepage. For example, the user might enter, "The homepage will have the company name at the top of the screen, with a menu bar below it, and the menu contents will be 'TOP, Company Overview, Product Introduction, IR Information.' I want the design to have a bright feel."

[1151] Step 2:

[1152] When the user clicks the "Generate" button, the device sends the entered Japanese instructions to the server. Specifically, the device includes the instructions as JSON format data in the body of a POST request and sends it to the server's endpoint.

[1153] Step 3:

[1154] The server receives the request. The endpoint / generate_html specified in the Flask application processes this request. The server retrieves the user's Japanese instructions from the request body using request.json.get('user_input').

[1155] Step 4:

[1156] The server runs an emotion engine to recognize emotions contained in user input. Specifically, the emotion engine analyzes the input Japanese text and detects emotional expressions such as "cheerful feeling."

[1157] Step 5:

[1158] The server prepares to drive the generative model based on Japanese instructions. Specifically, it generates prompts in a format suitable for the generative model. The prompts also include emotional information recognized by the emotion engine.

[1159] Step 6:

[1160] The server calls a generative model, such as the OpenAI API, to send the prompt. The generative model uses natural language processing techniques to generate HTML code based on the prompt. The emotion engine then adjusts the design and color scheme based on the emotion recognized.

[1161] Step 7:

[1162] Once the generative model has generated the HTML code, the server formats it, removing unnecessary whitespace and line breaks to make it neat.

[1163] Step 8:

[1164] The server wraps the generated HTML code in JSON format and returns it to the device as an HTTP response. The response includes the generated HTML code.

[1165] Step 9:

[1166] The device receives the response from the server, uses the JavaScript fetch API to parse the response into JSON format, and extracts the HTML code.

[1167] Step 10:

[1168] The device will then extract the HTML code from the web page at the specified location (e.g. <pre>The generated HTML code is displayed in the tag. The user can check the generated HTML code immediately.

[1169] Step 11:

[1170] If the user wishes to make further modifications to the generated HTML code, they can start again from step 1. For example, by changing the color or adjusting the layout, the generated HTML code can be regenerated and updated. The emotion engine is also activated again, recognizing the user's new emotional expression and reflecting it in the design.

[1171] By following the above steps, users can quickly create a customized homepage that meets their needs by entering simple Japanese instructions as well as words that express their emotions. This invention not only further improves the efficiency and quality of homepage creation, but also enables the automatic generation of designs that take user emotions into consideration.

[1172] Example 2

[1173] 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."

[1174] Conventional HTML code generation systems have difficulty reflecting the user's desired emotions and designs, and often produce results that do not meet the user's intentions. Furthermore, there are insufficient means for users to easily modify HTML code. This has resulted in a decrease in the efficiency of homepage creation and user satisfaction.

[1175] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1176] In this invention, the server includes means for receiving instructions entered by a user in natural language, means for driving an emotion engine that analyzes emotions based on the received instructions, means for generating prompt sentences based on the results of the emotion analysis and the instructions and driving a generative AI model, and means for formatting the generated HTML code and providing it to the user, thereby enabling automatic generation and easy modification of HTML code that reflects the user's intentions and emotions.

[1177] A "user" is a person who uses the system to create or edit a homepage.

[1178] A "natural language" is a language that humans use on a daily basis, such as Japanese.

[1179] "Instructions" are requests regarding the structure and design of the homepage that are input by the user into the terminal using natural language.

[1180] "Receiving" refers to the terminal transferring an instruction input by a user to a server, and the server acquiring it.

[1181] An "emotion engine" is software that uses natural language processing technology to analyze the emotions contained in a user's instructions.

[1182] "Sentiment analysis" is the process by which the emotion engine detects emotions, such as positive and negative, from user instructions.

[1183] A "prompt sentence" is an input sentence given to a generative AI model based on the results of sentiment analysis and user instructions.

[1184] A "generative AI model" is an artificial intelligence that automatically generates HTML code based on a prompt text.

[1185] "HTML code" refers to code written in a markup language used to construct a web page.

[1186] "Formatting" is the process of formatting the generated HTML code to make it look better and more functional.

[1187] "Serving" refers to formatting the generated HTML code and then sending it to the terminal for display to the user.

[1188] The present invention relates to a system that receives instructions entered by a user in natural language, automatically generates HTML code based on the instructions, and provides the generated HTML code to the user. Furthermore, the present invention provides a function to adjust the design and color scheme of the generated HTML code by combining it with an emotion engine that recognizes the emotions contained in the user's instructions.

[1189] First, the user opens the web application on their device and enters natural language instructions about the homepage's structure and design into the text area. For example, they might enter, "The homepage will have the company name at the top of the screen, with a menu bar below it, and the menu contents will be 'TOP, Company Overview, Product Introduction, IR Information.' I want the design to have a bright feel."

[1190] The device then sends the input instructions in natural language to the server, using the HTTPS protocol to ensure secure communication.

[1191] The server receives the HTTPS request and obtains the user's natural language instruction. It then uses an emotion engine to analyze the emotion contained in the instruction. For example, it detects a positive emotion such as "cheerful." The emotion engine uses a natural language processing library to calculate the emotion vector.

[1192] The server generates prompts to drive the generative AI model based on the results of sentiment analysis and user instructions. The prompts are generated using templates, such as "Please generate HTML code with a light background color and a menu bar at the top."

[1193] The generative AI model generates HTML code based on the input prompt. The model automatically generates code based on a pre-trained dataset. For example, based on the instruction "bright feel," it generates HTML code with a bright background color.

[1194] The server formats the generated HTML code and converts it to JSON format using a dedicated library. The formatted data is then sent back to the device.

[1195] The device interprets the JSON-formatted HTML code received from the server and displays it on a web page. The display is done using the browser's rendering engine. The user can review the displayed code and make corrections or add additional instructions as needed. For example, they can enter additional instructions such as "make it more colorful." These new instructions are sent back to the server, where the emotion engine and generative model analyze and generate the new code again.

[1196] For example, if a user inputs the following instruction, "The homepage should have the company name at the top of the screen, with a menu bar below it, and the menu contents should be 'TOP, Company Overview, Product Introduction, IR Information.' I would like the design to have a bright feel," the generative AI model will generate the following HTML code:

[1197] html

[1198] <!DOCTYPE html>

[1199]

[1200]

[1201] <meta charset="UTF-8">

[1202] <title> Company Name< / title>

[1203] <style>

[1204] body { background-color: f0f8ff;}

[1205] h1 { color: ff6347;}

[1206] < / style>

[1207]

[1208]

[1209] <header>

[1210] <h1> Company Name< / h1>

[1211] <nav>

[1212]

[1213] TOP

[1214] Company Profile

[1215] Product introduction

[1216] IR Information

[1217]

[1218] < / nav>

[1219] < / header>

[1220] <main>

[1221] <!-- コンテンツ -->

[1222] < / main>

[1223]

[1224]

[1225] In this way, users can quickly create a customized homepage that meets their needs by inputting simple natural language instructions and words that express their emotions. This system not only further improves the efficiency and quality of homepage creation, but also enables the automatic generation of designs that take users' emotions into consideration.

[1226] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1227] Step 1:

[1228] The user opens a web application on their device and enters natural language instructions into the text area regarding the homepage's structure and design. For example, they might enter, "The homepage will have the company name at the top of the screen, with a menu bar below it, and the menu contents will be 'TOP, Company Overview, Product Introduction, IR Information.' I want the design to have a bright feel." These instructions become the input data.

[1229] Step 2:

[1230] The terminal sends instructions entered by the user in natural language to the server. The transmission uses the HTTPS protocol to ensure secure communication. The input data is the user's instructions, and the output data is the request sent to the server.

[1231] Step 3:

[1232] The server receives the HTTPS request and retrieves the user's natural language instructions, which are the input data and are ready for the next stage of sentiment analysis.

[1233] Step 4:

[1234] The server's emotion engine analyzes the received natural language instructions and detects emotions. For example, it can detect positive emotions such as "cheerful feeling." The input for this emotion analysis is the natural language instructions, and the output is an emotion vector. The emotion engine uses natural language processing technology to quantify the emotions contained in the instructions.

[1235] Step 5:

[1236] The server generates a prompt based on the results of the sentiment analysis and the user's instructions. The input data are the sentiment vector and the user's instructions, and the output data is the generated prompt. A template is used to generate the prompt, and it can be formatted, for example, as "Please generate HTML code with a light background color and a menu bar at the top."

[1237] Step 6:

[1238] The generative AI model generates HTML code based on prompts input from the server. The input data is the prompts, and the output data is the generated HTML code. The model automatically generates HTML code using a pre-trained dataset.

[1239] Step 7:

[1240] The server formats the generated HTML code and converts it to JSON format. The input data is the generated HTML code, and the output data is formatted code in JSON format. A dedicated library is used for formatting.

[1241] Step 8:

[1242] The terminal interprets the JSON-formatted HTML code received from the server and displays it in the browser. The input data is the JSON-formatted code, and the output is a web page displayed in the browser. The user checks the displayed code.

[1243] Step 9:

[1244] The user checks the displayed HTML code and makes corrections or additions as necessary. For example, they input a new instruction such as "Make it more colorful." This new instruction returns to step 2, and the same process is repeated. The input data are correction instructions, and the output data is new HTML code.

[1245] (Application example 2)

[1246] 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."

[1247] Conventional web page generation systems have difficulty creating designs that reflect the user's emotions and intentions if the user does not have detailed knowledge of design and color schemes. Furthermore, they lack the functionality to automatically generate designs that take emotions into consideration, making it difficult to provide designs that intuitively satisfy the user. Especially in operating online virtual stores, it is important to quickly provide designs that respond to the user's emotions and expectations.

[1248] 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.

[1249] In this invention, the server includes means for receiving instructions entered in Japanese by a user, means for driving a generative model that automatically generates HTML code based on the received Japanese instructions, means for analyzing the emotions contained in the Japanese instructions and adjusting the design and color scheme of the HTML code, and means for providing the generated HTML code to the user. This makes it possible to automatically generate designs based on the user's emotions and intentions, and to easily build an intuitive and attractive virtual store.

[1250] "User" refers to any person or entity that uses the System to generate HTML code.

[1251] "Instructions entered in Japanese" refers to text written in Japanese by a user expressing requests regarding the structure and design of a web page.

[1252] "Means for receiving" refers to a component or program that has the function of obtaining Japanese language instructions entered by a user and storing them for processing.

[1253] "Generative model" refers to an algorithm or artificial intelligence system that automatically generates HTML code based on received Japanese instructions.

[1254] "Driven" refers to hardware or software that executes a generative model and accomplishes a specified task.

[1255] "Means for analyzing emotions" refers to algorithms or artificial intelligence systems that analyze the emotions and intentions contained in the instructions entered by the user and adjust the design and color scheme based on the results.

[1256] "Means for adjusting design and color scheme" refers to components or programs for changing the appearance and color scheme of the HTML code generated based on the results of sentiment analysis.

[1257] "Means of providing" refers to the functions and services for displaying or transmitting the generated HTML code to the user.

[1258] "Natural language processing technology" refers to the general algorithms and methods that allow computers to understand and process human language.

[1259] "Means for making modifications" refers to a component or program that has the function of receiving instructions re-entered by the user and modifying or correcting existing HTML code based on the instructions.

[1260] "Design elements such as background and font color" refers to the visual components of a web page's appearance and user experience, such as background color, font color, size, and placement.

[1261] This invention is a system that receives instructions entered by a user in Japanese and automatically generates HTML code based on those instructions. Furthermore, this system also provides a function to adjust the design and color scheme of the generated HTML code by combining it with an emotion engine that recognizes the emotions contained in the user's instructions.

[1262] System configuration

[1263] The system includes the following components:

[1264] 1. Input receiving means: A web application for receiving instructions entered in Japanese by the user. Users access this web application using devices such as smartphones, tablets, and PCs.

[1265] 2. Generative model driving means: A server that drives a generative model that automatically generates HTML code based on Japanese instructions. The generative model uses natural language processing technology.

[1266] 3. Sentiment analysis: An emotion engine that analyzes the emotions contained in the Japanese instructions entered by the user. For example, the pipeline from the Hugging Face transformers library is used.

[1267] 4. Design Adjustment: A software component for adjusting design elements such as background and font color of the generated HTML code based on the results of sentiment analysis.

[1268] 5. Code Delivery Method: A function for providing the generated HTML code to the user. The generated code is returned in JSON format and interpreted and displayed on the user's device.

[1269] Processing flow explanation

[1270] 1. Receiving user input:

[1271] The user opens the web application and enters instructions in Japanese about the homepage structure and design in a text area. For example, they can enter specific instructions such as, "The homepage should have the company name at the top of the screen, with a menu bar below it, and the menu contents should be 'TOP, Company Overview, Product Introduction, IR Information.' I would like the design to have a bright feel."

[1272] 2. Sending instructions:

[1273] Instructions entered by the user are sent from the terminal to the server.

[1274] 3. Sentiment analysis:

[1275] The server analyzes the received instructions using an emotion engine to recognize the emotion contained in the input text. For example, if a positive emotion such as "cheerful" is detected, the background color will be set to a bright color.

[1276] 4. Driving the generative model:

[1277] The server runs a generative AI model that uses natural language processing techniques to generate HTML code based on user instructions and sentiment analysis.

[1278] 5. Code Submission:

[1279] The generated HTML code is formatted and sent back to the user's device in JSON format. The device interprets the received HTML code and displays it to the user. The user can check the displayed code and enter instructions again if necessary.

[1280] Specific examples

[1281] For example, the user enters the following prompt:

[1282] "The homepage will have the company name at the top of the screen, with a menu bar below it, with the menu contents being 'TOP, Company Overview, Product Introduction, IR Information.' I want this design to have a bright feel."

[1283] Based on this example, the system performs sentiment analysis and drives a generative model to generate HTML code with a light background color. This prompt allows for the rapid creation of a customized homepage tailored to the user's needs.

[1284] As described above, the present invention improves the efficiency and quality of homepage creation, and also makes it possible to automatically generate designs that take into consideration the user's feelings.

[1285] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1286] Step 1:

[1287] The user enters instructions in Japanese.

[1288] Users access the system using a smartphone, tablet, or PC and input instructions in Japanese regarding the homepage structure and design. For example, they might input, "The homepage will have the company name at the top of the screen, with a menu bar below it, and the menu contents will be 'TOP, Company Overview, Product Introduction, IR Information.' I want this design to have a bright feel." The input data is saved in text format on the device.

[1289] Step 2:

[1290] The terminal sends the input instructions to the server

[1291] The terminal transmits the instructions in Japanese entered by the user to the server. The transmitted data is the text data entered by the user.

[1292] Step 3:

[1293] The server receives and parses the Japanese instructions.

[1294] The server parses the received Japanese instructions. It receives the input data (Japanese instructions) and analyzes the grammar and syntax using a text analysis library. The main processing here is to split the received data and extract elements from the instructions.

[1295] Step 4:

[1296] Analyze emotions with the emotion engine

[1297] The server analyzes the emotions from the parsed instructions. For emotion analysis, it uses the pipeline of Hugging Face's transformers library. In this step, the emotion (positive, negative, etc.) contained in the input data (instructions) is identified and the result is stored internally on the server. For example, the instruction "feeling cheerful" is recognized as a positive emotion.

[1298] Step 5:

[1299] Generate HTML code by driving the generative model

[1300] The server drives a generative AI model based on the results of sentiment analysis and Japanese instructions. It uses natural language processing technology to generate HTML code that includes designs and color schemes that match the emotions. In this step, the input data (Japanese instructions and sentiment analysis results) is sent as prompts to the generative AI model, and output data (generated HTML code) is obtained.

[1301] Step 6:

[1302] Format and serve the generated HTML code

[1303] The generated HTML code is formatted and converted to JSON format. The server returns this formatted HTML code to the user. The output data is formatted HTML code.

[1304] Step 7:

[1305] Display the HTML code received by the device

[1306] The terminal interprets the HTML code received from the server and displays it to the user using a browser or similar. The user can check the displayed code and enter instructions again if necessary. The output data is an HTML display that the user can view.

[1307] By following the above steps, users can quickly create a homepage that meets their needs using simple Japanese instructions and emotional expressions.

[1308] 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.

[1309] 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.

[1310] 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.

[1311] [Fourth embodiment]

[1312] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1313] 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.

[1314] 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).

[1315] 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.

[1316] 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.

[1317] 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).

[1318] 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.

[1319] 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.

[1320] 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.

[1321] 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.

[1322] 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.

[1323] 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.

[1324] 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."

[1325] This invention relates to a system that receives instructions entered in Japanese by a user, automatically generates HTML code based on the instructions, and provides the generated HTML code to the user. This system allows users to easily create high-quality homepages even without programming knowledge.

[1326] First, the user uses a terminal to input specific instructions in Japanese regarding the structure and design of the homepage. For example, they can input instructions such as, "The homepage should have the company name at the top of the screen, with a menu bar below it, and the menu contents should be 'TOP, Company Overview, Product Introduction, IR Information'."

[1327] The device then sends the instructions to the server, which receives the request and initiates a process to drive the generative model, which uses natural language processing techniques to analyze the Japanese instructions and generate the corresponding HTML code.

[1328] Once the generative model generates HTML code based on the user's instructions, the server formats this code and sends it back to the device in JSON format. The device interprets the received HTML code and displays it to the user. The user can review the displayed code and enter corrections as needed. For example, they can change the color or adjust the layout.

[1329] For example, if a user inputs the following instruction, "The homepage should have the company name at the top of the screen, with a menu bar below it, and the menu contents should be 'TOP, Company Overview, Product Introduction, IR Information'," the generative model will generate the following HTML code (simplified):

[1330] <!DOCTYPE html>

[1331]

[1332]

[1333] <meta charset="UTF-8">

[1334] <title> Company Name< / title>

[1335]

[1336]

[1337] <header>

[1338] <h1> Company Name< / h1>

[1339] <nav>

[1340]

[1341] TOP

[1342] Company Profile

[1343] Product introduction

[1344] IR Information

[1345]

[1346] < / nav>

[1347] < / header>

[1348] <main>

[1349] <!-- コンテンツ -->

[1350] < / main>

[1351]

[1352]

[1353] In this way, users can quickly create a customized homepage based on their needs by simply entering simple instructions in Japanese. If further instructions are required, users can easily modify or update the generated HTML code by entering additional instructions in Japanese.

[1354] This system is particularly useful for small businesses and sole proprietors who want to strengthen their online presence with limited technical resources, as it allows for more efficient and cost-effective website creation.

[1355] The processing flow will be explained below.

[1356] Step 1:

[1357] A user opens a web application using a device. In a text area on the web page, the user enters instructions in Japanese regarding the structure and design of the homepage. For example, the user might enter, "The homepage should have the company name at the top of the screen, with a menu bar below it, and the menu contents should be 'TOP, Company Overview, Product Introduction, IR Information'."

[1358] Step 2:

[1359] When the user clicks the "Generate" button, the device sends the entered Japanese instructions to the server. Specifically, the device includes the instructions as JSON format data in the body of a POST request and sends it to the server's endpoint.

[1360] Step 3:

[1361] The server receives the request. The endpoint / generate_html specified in the Flask application processes this request. The server retrieves the user's Japanese instructions from the request body using request.json.get('user_input').

[1362] Step 4:

[1363] The server prepares to drive the generative model based on the Japanese instructions. Specifically, it generates a prompt in a format appropriate for the generative model. For example, the prompt is in the format "User instructions: {user_input}\nHTML code to generate:".

[1364] Step 5:

[1365] The server calls a generative model, such as the OpenAI API, and sends a prompt. The generative model uses natural language processing techniques to generate HTML code based on the prompt. The resulting HTML code is then returned to the server.

[1366] Step 6:

[1367] The server receives the HTML code returned by the generated model and formats it, specifically removing unnecessary whitespace and line breaks to make it neat.

[1368] Step 7:

[1369] The server wraps the generated HTML code in JSON format and returns it to the device as an HTTP response. The response includes the generated HTML code.

[1370] Step 8:

[1371] The device receives the response from the server, uses the JavaScript fetch API to parse the response into JSON format, and extracts the HTML code.

[1372] Step 9:

[1373] The device will then extract the HTML code from the web page at the specified location (e.g. <pre>The generated HTML code is displayed in the tag. The user can check the generated HTML code immediately.

[1374] Step 10:

[1375] If the user wishes to make further modifications to the generated HTML code, they can start again from step 1. For example, by changing the color or adjusting the layout, the generated HTML code can be regenerated and updated.

[1376] By following these steps, users can quickly create and edit high-quality homepages using simple instructions in Japanese.

[1377] Example 1

[1378] 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."

[1379] In today's world, creating a website is an important part of having an online presence, but it can be a time-consuming and labor-intensive task for users without programming knowledge. Furthermore, existing website creation tools are often complicated to use, placing a burden on small businesses and sole proprietors in particular. There is a need for a system that can solve this problem and enable anyone to easily create a high-quality website.

[1380] 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.

[1381] In this invention, the server includes a means for receiving instructions entered by a user in natural language, a means for driving a generative model that automatically generates HTML code based on the received natural language instructions, and a means for formatting the generated HTML code and returning it to the terminal. This allows users to quickly create high-quality homepages without programming knowledge, simply by entering simple instructions in Japanese. It also makes it easy for users to re-enter instructions and make corrections, strengthening online presence and reducing costs.

[1382] "User" refers to the person who operates the system and inputs instructions.

[1383] "Natural language" refers to a language that humans use on a daily basis, and in this system this includes Japanese.

[1384] "Instructions" refer to requests regarding the homepage configuration and design that are input by the user in natural language.

[1385] "Terminal" refers to a computer or mobile device used by a user to enter and receive instructions.

[1386] "Server" refers to a computer that analyzes instructions received from a user, generates HTML code, and returns it.

[1387] A "generative model" refers to an algorithm or program that automatically generates HTML code based on instructions entered in natural language.

[1388] "Natural language processing technology" refers to technology for understanding and analyzing human natural language, and in this system it is applied to a generative model.

[1389] "HTML code" refers to the standardized markup language used to construct homepages.

[1390] "Formatting" refers to the process of converting generated HTML code into a format suitable for presentation to the user.

[1391] "Return" refers to sending the generated HTML code back from the server to the terminal.

[1392] "Interpretation" refers to the process by which the terminal understands the content of the HTML code received and processes it to display it to the user.

[1393] "Display" refers to the terminal outputting the generated HTML code to the screen in a form that can be viewed by the user.

[1394] "Modification" refers to the user inputting new instructions into the generated HTML code and making changes.

[1395] This invention relates to a system that receives instructions entered by a user in natural language, automatically generates HTML code based on the instructions, and provides the generated HTML code to the user. This system allows users to easily create high-quality homepages even without programming knowledge.

[1396] First, the user uses a terminal to input specific instructions in natural language regarding the structure and design of the homepage. For example, the user might input specific instructions such as, "The homepage should have the company name at the top of the screen, with a menu bar below it, and the menu contents should be 'TOP, Company Overview, Product Introduction, IR Information'."

[1397] The device then sends this instruction to a server. The server receives the request and initiates a process to drive a generative AI model. This generative AI model uses natural language processing technology to analyze the natural language instructions entered by the user and generate the corresponding HTML code based on that. Specifically, the server uses a generative AI model such as GPT-4.

[1398] Once the generative model generates HTML code based on the user's instructions, the server formats this code and returns it to the device in JSON format. The device then parses the received JSON data, extracts the generated HTML code, and displays it to the user. This allows the user to review the generated HTML code and modify it as needed. For example, it is possible to change the color or adjust the layout.

[1399] For example, if a user inputs the following instruction, "The homepage should have the company name at the top of the screen, with a menu bar below it, and the menu contents should be 'TOP, Company Overview, Product Introduction, IR Information'," the generative AI model will generate the following HTML code:

[1400] <!DOCTYPE html>

[1401]

[1402]

[1403] <meta charset="UTF-8">

[1404] <title> Company Name< / title>

[1405]

[1406]

[1407] <header>

[1408] <h1> Company Name< / h1>

[1409] <nav>

[1410]

[1411] TOP

[1412] Company Profile

[1413] Product introduction

[1414] IR Information

[1415]

[1416] < / nav>

[1417] < / header>

[1418] <main>

[1419] <!-- コンテンツ -->

[1420] < / main>

[1421]

[1422]

[1423] In this way, users can quickly create a customized homepage based on their needs by simply entering simple natural language instructions. If further instructions are needed, users can easily modify or update the generated HTML code by entering additional natural language instructions.

[1424] This system is particularly useful for small businesses and sole proprietors who want to strengthen their online presence with limited technical resources, as it allows for more efficient and cost-effective website creation.

[1425] Prompt Sentence Examples

[1426] "Display the company name on the top page, and place a menu bar below it, with the items on the menu bar as 'TOP, Company Overview, Product Introduction, IR Information'"

[1427] This system is designed to enable users to easily create high-quality homepages by utilizing a generative AI model that uses natural language processing technology, with the terminal and server working in tandem.

[1428] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1429] Step 1:

[1430] The user uses a terminal to input specific instructions in natural language regarding the structure and design of the homepage.

[1431] As input, the user provides text information such as "The top page will have the company name at the top of the screen, with a menu bar below it, and the menu contents will be 'TOP, company overview, product introduction, IR information'."

[1432] Specifically, the user enters instructions into a text box displayed in the device's browser or app and clicks the "Send" button.

[1433] Step 2:

[1434] The terminal transmits the instructions input by the user to the server.

[1435] As input, the terminal sends the user's instruction text as an HTTP POST request to the server.

[1436] As an output, the server receives an HTTP request.

[1437] Specifically, the device compiles the input text as JSON-formatted data and sends an HTTP POST request to the specified URL endpoint.

[1438] Step 3:

[1439] The server receives the request and initiates the process to drive the generative AI model.

[1440] As input, the server parses the received JSON-formatted data and passes it to the generative AI model.

[1441] As an output, the generative AI model generates the corresponding HTML code.

[1442] Specifically, the server calls a text analysis API and passes a prompt sentence to the generative AI model (e.g., GPT-4) to request processing.

[1443] Step 4:

[1444] The generative AI model generates HTML code based on user instructions.

[1445] As input, the generative AI model takes parsed natural language instructions and generates corresponding HTML code.

[1446] As output, the generated HTML code is returned to the server.

[1447] Specifically, the generative AI model uses an internal natural language processing algorithm to analyze the input instruction text and generate HTML code.

[1448] Step 5:

[1449] The server formats the generated HTML code and returns it to the terminal in JSON format.

[1450] As input, it takes the HTML code returned by the generative AI model.

[1451] As output, it sends formatted HTML code to the terminal.

[1452] Specifically, the server assigns the generated HTML code to the appropriate JSON key and returns it to the terminal as an HTTP response.

[1453] Step 6:

[1454] The device parses the received JSON data, extracts the generated HTML code, and displays it to the user.

[1455] As input, the terminal receives the JSON data returned by the server.

[1456] The output is generated as HTML which is visually displayed to the user.

[1457] Specifically, the device's browser or app extracts HTML code from the received JSON data and renders it on the screen.

[1458] Step 7:

[1459] The user reviews the displayed HTML code and re-enters correction instructions as necessary.

[1460] As input, the user checks the displayed HTML content and inputs instructions in natural language regarding the parts to be corrected.

[1461] As an output, the modification instructions are sent to the server again.

[1462] Specifically, the user enters the correction instructions in the text box again and clicks the "Submit" button. This action repeats the flow from step 2 again, and the corrections are reflected.

[1463] This series of processes allows users to easily create high-quality homepages using only natural language instructions, and to modify and update them as needed.

[1464] (Application example 1)

[1465] 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."

[1466] Many users today are interested in creating web pages, but lack programming knowledge, making it difficult for them to easily build web pages themselves. Furthermore, users who want to build online shopping sites often lack the expertise to quickly and efficiently expand their businesses online. To solve this problem, a system is needed that allows users to create high-quality web pages, especially online shopping sites, in an easy and intuitive way.

[1467] 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.

[1468] In this invention, the server includes means for receiving instructions input in a language by a user, means for driving a generative model that automatically generates HTML code based on the received instructions in the language, means for providing the generated HTML code to a user, means for displaying a preview of the generated HTML code in real time, and means for receiving additional instructions for modifying the generated HTML code, thereby enabling users without programming knowledge to quickly and easily create and modify high-quality online shopping sites based on instructions in Japanese.

[1469] "User" refers to a general user who intends to create or modify a web page using this system.

[1470] "Instructions entered in language" refers to requests or instructions given to the system by the user using natural language, particularly Japanese.

[1471] "Means for receiving" refers to a method or device by which the system receives linguistic instructions entered by a user.

[1472] A "generative model" is an artificial intelligence model that uses natural language processing technology to analyze input linguistic instructions and automatically generate HTML code accordingly.

[1473] "HTML code" is a markup language used to describe the structure and content of web pages.

[1474] "Means for providing" refers to a method or device for displaying or transmitting the generated HTML code to a user.

[1475] "Means for displaying a preview in real time" refers to a method or device for instantly displaying the generated HTML code on the user's terminal, allowing the user to check the results immediately.

[1476] The "means for receiving additional instructions" refers to a method or device by which the system receives instructions when the user re-enters instructions to correct or modify the generated HTML code.

[1477] The present invention is a system that automatically generates HTML code based on instructions entered in a user's language. This system is particularly useful for users who want to easily create and edit web pages, especially for users who want to build online shopping sites. Specific embodiments for implementing the present invention are described below.

[1478] Server Configuration

[1479] The server is responsible for the main process of receiving the user's input in a language, parsing it, and generating the HTML code. The server's functions include:

[1480] 1. Receiving means:

[1481] The server receives instructions in the language entered by the user. For example, the user might enter, through a smartphone application, "Display a banner for sale items on the top page, and place a category list below it."

[1482] 2. Generation means:

[1483] The server inputs the received instructions into a generative model (specifically, a generative AI model using natural language processing technology) to automatically generate HTML code. This generative model can use, for example, the Hugging Face transformers library and apply the GPT-3 model.

[1484] 3. Means of provision:

[1485] The server formats the generated HTML code and provides it to the user. It also returns the code in JSON format, which is displayed as a real-time preview on the user's device.

[1486] 4. Remedies:

[1487] The user can preview the generated HTML code and make corrections by entering additional instructions as necessary. These additional instructions are also received, analyzed in the same way, and the HTML code is generated and provided again.

[1488] Device configuration

[1489] The terminal operated by the user has the following main functions:

[1490] 1. Instruction input interface:

[1491] Users input language instructions through the terminal. An intuitive and easy-to-use user interface (UI) is provided, and operations can be completed simply by entering instructions in a text box.

[1492] 2. Instruction sending function:

[1493] Instructions are sent to the server in real time, and communication takes place over the internet to minimize communication delays.

[1494] 3. Preview display:

[1495] The HTML code provided by the server is previewed in real time on the device, allowing the user to instantly check the results and input corrections as needed.

[1496] Software and hardware used

[1497] Server-side software:

[1498] Flask (for building the API server), Hugging Face's transformers library, and the GPT-3 model.

[1499] Hardware:

[1500] The server should preferably be a computer with high-performance computing capabilities. It is also possible to use cloud services (e.g., AWS, Google Cloud).

[1501] Client-side software:

[1502] Standard web browsers, smartphone applications (e.g. iOS app, Android app).

[1503] Specific examples

[1504] The user uses an application on their smartphone to enter the following instructions:

[1505] "The homepage will display a sale item banner, with a category list below it."

[1506] Example prompt sentence:

[1507] User Instructions: Display a sale item banner on the homepage, with a category list below it. Based on this, generate the following HTML code:

[1508] With the above system, users can easily build and modify online shopping websites without any programming knowledge. The generated HTML code is displayed to the user immediately, and further modifications can be made by entering additional instructions.

[1509] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1510] Step 1:

[1511] The user launches an application on the smartphone and inputs instructions in the language.

[1512] Input: Specific language instructions entered by the user (e.g., "Display a sale item banner on the homepage, with a category list below it")

[1513] Output: Input instruction data

[1514] Step 2:

[1515] The terminal transmits the input instruction data to the server.

[1516] Input: User-entered instruction data

[1517] Output: Request data to the server

[1518] Step 3:

[1519] The server analyzes the received instruction data and generates an appropriate prompt.

[1520] Input: The request data sent to the server

[1521] Output: A prompt given to the generative model (e.g., "User instructions: Display a sale item banner on the top page, with a category list below it. Based on this, generate the following HTML code.")

[1522] Step 4:

[1523] The server runs a generative model (such as GPT-3) to generate HTML code based on the prompt.

[1524] Input: prompt statement

[1525] Output: Generated HTML code

[1526] Step 5:

[1527] The server formats the generated HTML code into JSON format and returns it to the terminal.

[1528] Input: Generated HTML code

[1529] Output: Pretty HTML code in JSON format

[1530] Step 6:

[1531] The device analyzes the received JSON format HTML code and displays a preview on the screen in real time.

[1532] Input: JSON data received from the server

[1533] Output: Visual preview of the web page

[1534] Step 7:

[1535] The user checks the preview and, if necessary, enters additional instructions to make corrections.

[1536] Input: User correction instructions

[1537] Output: New instruction data

[1538] Step 8:

[1539] The terminal again transmits new instruction data to the server, and repeats the same process to correct the HTML code.

[1540] Input: New instruction data

[1541] Output: Modified HTML code

[1542] This allows users to easily create and edit web pages.

[1543] 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.

[1544] The present invention relates to a system that receives instructions entered by a user in Japanese, automatically generates HTML code based on the instructions, and provides the generated HTML code to the user. Furthermore, the present invention provides a function to adjust the design and color scheme of the generated HTML code by combining it with an emotion engine that recognizes the emotions contained in the user's instructions.

[1545] First, the user opens the web application on their device and enters instructions in Japanese about the homepage's structure and design into the text area. For example, they might enter, "The homepage will have the company name at the top of the screen, with a menu bar below it, and the menu contents will be 'TOP, Company Overview, Product Introduction, IR Information.' I want the design to have a bright feel."

[1546] Next, the device sends the input Japanese instructions to the server. The server receives the request and acquires the user's Japanese instructions. The emotion engine then analyzes the emotions contained in the instructions and detects positive emotions, such as "cheerful feeling." The emotion engine then adjusts the design and color scheme of the HTML code according to this emotion.

[1547] The server generates prompts to drive the generative model, which then generates HTML code based on the user's instructions and the results of sentiment analysis. For example, based on the sentiment "feeling cheerful," the model generates HTML code with a bright background color.

[1548] Once the generative model generates HTML code, the server formats it and sends it back to the device in JSON format. The device interprets the received HTML code and displays it to the user. The user can review the displayed code and re-enter correction instructions as needed. For example, additional instructions such as "make it more colorful" are analyzed again by the emotion engine and reflected in the generative model.

[1549] For example, if a user inputs the following instruction, "The homepage should have the company name at the top of the screen, with a menu bar below it, and the menu contents should be 'TOP, Company Overview, Product Introduction, IR Information'. I want this design to have a bright feel," the generative model will generate the following HTML code (simplified):

[1550] <!DOCTYPE html>

[1551]

[1552]

[1553] <meta charset="UTF-8">

[1554] <title> Company Name< / title>

[1555] <style>

[1556] body { background-color: f0f8ff;}

[1557] h1 { color: ff6347;}

[1558] < / style>

[1559]

[1560]

[1561] <header>

[1562] <h1> Company Name< / h1>

[1563] <nav>

[1564]

[1565] TOP

[1566] Company Profile

[1567] Product introduction

[1568] IR Information

[1569]

[1570] < / nav>

[1571] < / header>

[1572] <main>

[1573] <!-- コンテンツ -->

[1574] < / main>

[1575]

[1576]

[1577] In this way, users can quickly create a customized homepage that meets their needs by entering simple Japanese instructions and words that express their emotions. This system not only further improves the efficiency and quality of homepage creation, but also enables the automatic generation of designs that take users' emotions into consideration.

[1578] The processing flow will be explained below.

[1579] Step 1:

[1580] A user opens a web application using a device. In a text area on the web page, the user enters instructions in Japanese regarding the structure and design of the homepage. For example, the user might enter, "The homepage will have the company name at the top of the screen, with a menu bar below it, and the menu contents will be 'TOP, Company Overview, Product Introduction, IR Information.' I want the design to have a bright feel."

[1581] Step 2:

[1582] When the user clicks the "Generate" button, the device sends the entered Japanese instructions to the server. Specifically, the device includes the instructions as JSON format data in the body of a POST request and sends it to the server's endpoint.

[1583] Step 3:

[1584] The server receives the request. The endpoint / generate_html specified in the Flask application processes this request. The server retrieves the user's Japanese instructions from the request body using request.json.get('user_input').

[1585] Step 4:

[1586] The server runs an emotion engine to recognize emotions contained in user input. Specifically, the emotion engine analyzes the input Japanese text and detects emotional expressions such as "cheerful feeling."

[1587] Step 5:

[1588] The server prepares to drive the generative model based on Japanese instructions. Specifically, it generates prompts in a format suitable for the generative model. The prompts also include emotional information recognized by the emotion engine.

[1589] Step 6:

[1590] The server calls a generative model, such as the OpenAI API, to send the prompt. The generative model uses natural language processing techniques to generate HTML code based on the prompt. The emotion engine then adjusts the design and color scheme based on the emotion recognized.

[1591] Step 7:

[1592] Once the generative model has generated the HTML code, the server formats it, removing unnecessary whitespace and line breaks to make it neat.

[1593] Step 8:

[1594] The server wraps the generated HTML code in JSON format and returns it to the device as an HTTP response. The response includes the generated HTML code.

[1595] Step 9:

[1596] The device receives the response from the server, uses the JavaScript fetch API to parse the response into JSON format, and extracts the HTML code.

[1597] Step 10:

[1598] The device will then extract the HTML code from the web page at the specified location (e.g. <pre>The generated HTML code is displayed in the tag. The user can check the generated HTML code immediately.

[1599] Step 11:

[1600] If the user wishes to make further modifications to the generated HTML code, they can start again from step 1. For example, by changing the color or adjusting the layout, the generated HTML code can be regenerated and updated. The emotion engine is also activated again, recognizing the user's new emotional expression and reflecting it in the design.

[1601] By following the above steps, users can quickly create a customized homepage that meets their needs by entering simple Japanese instructions as well as words that express their emotions. This invention not only further improves the efficiency and quality of homepage creation, but also enables the automatic generation of designs that take user emotions into consideration.

[1602] Example 2

[1603] 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."

[1604] Conventional HTML code generation systems have difficulty reflecting the user's desired emotions and designs, and often produce results that do not meet the user's intentions. Furthermore, there are insufficient means for users to easily modify HTML code. This has resulted in a decrease in the efficiency of homepage creation and user satisfaction.

[1605] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1606] In this invention, the server includes means for receiving instructions entered by a user in natural language, means for driving an emotion engine that analyzes emotions based on the received instructions, means for generating prompt sentences based on the results of the emotion analysis and the instructions and driving a generative AI model, and means for formatting the generated HTML code and providing it to the user, thereby enabling automatic generation and easy modification of HTML code that reflects the user's intentions and emotions.

[1607] A "user" is a person who uses the system to create or edit a homepage.

[1608] A "natural language" is a language that humans use on a daily basis, such as Japanese.

[1609] "Instructions" are requests regarding the structure and design of the homepage that are input by the user into the terminal using natural language.

[1610] "Receiving" refers to the terminal transferring an instruction input by a user to a server, and the server acquiring it.

[1611] An "emotion engine" is software that uses natural language processing technology to analyze the emotions contained in a user's instructions.

[1612] "Sentiment analysis" is the process by which the emotion engine detects emotions, such as positive and negative, from user instructions.

[1613] A "prompt sentence" is an input sentence given to a generative AI model based on the results of sentiment analysis and user instructions.

[1614] A "generative AI model" is an artificial intelligence that automatically generates HTML code based on a prompt text.

[1615] "HTML code" refers to code written in a markup language used to construct a web page.

[1616] "Formatting" is the process of formatting the generated HTML code to make it look better and more functional.

[1617] "Serving" refers to formatting the generated HTML code and then sending it to the terminal for display to the user.

[1618] The present invention relates to a system that receives instructions entered by a user in natural language, automatically generates HTML code based on the instructions, and provides the generated HTML code to the user. Furthermore, the present invention provides a function to adjust the design and color scheme of the generated HTML code by combining it with an emotion engine that recognizes the emotions contained in the user's instructions.

[1619] First, the user opens the web application on their device and enters natural language instructions about the homepage's structure and design into the text area. For example, they might enter, "The homepage will have the company name at the top of the screen, with a menu bar below it, and the menu contents will be 'TOP, Company Overview, Product Introduction, IR Information.' I want the design to have a bright feel."

[1620] The device then sends the input instructions in natural language to the server, using the HTTPS protocol to ensure secure communication.

[1621] The server receives the HTTPS request and obtains the user's natural language instruction. It then uses an emotion engine to analyze the emotion contained in the instruction. For example, it detects a positive emotion such as "cheerful." The emotion engine uses a natural language processing library to calculate the emotion vector.

[1622] The server generates prompts to drive the generative AI model based on the results of sentiment analysis and user instructions. The prompts are generated using templates, such as "Please generate HTML code with a light background color and a menu bar at the top."

[1623] The generative AI model generates HTML code based on the input prompt. The model automatically generates code based on a pre-trained dataset. For example, based on the instruction "bright feel," it generates HTML code with a bright background color.

[1624] The server formats the generated HTML code and converts it to JSON format using a dedicated library. The formatted data is then sent back to the device.

[1625] The device interprets the JSON-formatted HTML code received from the server and displays it on a web page. The display is done using the browser's rendering engine. The user can review the displayed code and make corrections or add additional instructions as needed. For example, they can enter additional instructions such as "make it more colorful." These new instructions are sent back to the server, where the emotion engine and generative model analyze and generate the new code again.

[1626] For example, if a user inputs the following instruction, "The homepage should have the company name at the top of the screen, with a menu bar below it, and the menu contents should be 'TOP, Company Overview, Product Introduction, IR Information.' I would like the design to have a bright feel," the generative AI model will generate the following HTML code:

[1627] html

[1628] <!DOCTYPE html>

[1629]

[1630]

[1631] <meta charset="UTF-8">

[1632] <title> Company Name< / title>

[1633] <style>

[1634] body { background-color: f0f8ff;}

[1635] h1 { color: ff6347;}

[1636] < / style>

[1637]

[1638]

[1639] <header>

[1640] <h1> Company Name< / h1>

[1641] <nav>

[1642]

[1643] TOP

[1644] Company Profile

[1645] Product introduction

[1646] IR Information

[1647]

[1648] < / nav>

[1649] < / header>

[1650] <main>

[1651] <!-- コンテンツ -->

[1652] < / main>

[1653]

[1654]

[1655] In this way, users can quickly create a customized homepage that meets their needs by inputting simple natural language instructions and words that express their emotions. This system not only further improves the efficiency and quality of homepage creation, but also enables the automatic generation of designs that take users' emotions into consideration.

[1656] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1657] Step 1:

[1658] The user opens a web application on their device and enters natural language instructions into the text area regarding the homepage's structure and design. For example, they might enter, "The homepage will have the company name at the top of the screen, with a menu bar below it, and the menu contents will be 'TOP, Company Overview, Product Introduction, IR Information.' I want the design to have a bright feel." These instructions become the input data.

[1659] Step 2:

[1660] The terminal sends instructions entered by the user in natural language to the server. The transmission uses the HTTPS protocol to ensure secure communication. The input data is the user's instructions, and the output data is the request sent to the server.

[1661] Step 3:

[1662] The server receives the HTTPS request and retrieves the user's natural language instructions, which are the input data and are ready for the next stage of sentiment analysis.

[1663] Step 4:

[1664] The server's emotion engine analyzes the received natural language instructions and detects emotions. For example, it can detect positive emotions such as "cheerful feeling." The input for this emotion analysis is the natural language instructions, and the output is an emotion vector. The emotion engine uses natural language processing technology to quantify the emotions contained in the instructions.

[1665] Step 5:

[1666] The server generates a prompt based on the results of the sentiment analysis and the user's instructions. The input data are the sentiment vector and the user's instructions, and the output data is the generated prompt. A template is used to generate the prompt, and it can be formatted, for example, as "Please generate HTML code with a light background color and a menu bar at the top."

[1667] Step 6:

[1668] The generative AI model generates HTML code based on prompts input from the server. The input data is the prompts, and the output data is the generated HTML code. The model automatically generates HTML code using a pre-trained dataset.

[1669] Step 7:

[1670] The server formats the generated HTML code and converts it to JSON format. The input data is the generated HTML code, and the output data is formatted code in JSON format. A dedicated library is used for formatting.

[1671] Step 8:

[1672] The terminal interprets the JSON-formatted HTML code received from the server and displays it in the browser. The input data is the JSON-formatted code, and the output is a web page displayed in the browser. The user checks the displayed code.

[1673] Step 9:

[1674] The user checks the displayed HTML code and makes corrections or additions as necessary. For example, they input a new instruction such as "Make it more colorful." This new instruction returns to step 2, and the same process is repeated. The input data are correction instructions, and the output data is new HTML code.

[1675] (Application example 2)

[1676] 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."

[1677] Conventional web page generation systems have difficulty creating designs that reflect the user's emotions and intentions if the user does not have detailed knowledge of design and color schemes. Furthermore, they lack the functionality to automatically generate designs that take emotions into consideration, making it difficult to provide designs that intuitively satisfy the user. Especially in operating online virtual stores, it is important to quickly provide designs that respond to the user's emotions and expectations.

[1678] 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.

[1679] In this invention, the server includes means for receiving instructions entered in Japanese by a user, means for driving a generative model that automatically generates HTML code based on the received Japanese instructions, means for analyzing the emotions contained in the Japanese instructions and adjusting the design and color scheme of the HTML code, and means for providing the generated HTML code to the user. This makes it possible to automatically generate designs based on the user's emotions and intentions, and to easily build an intuitive and attractive virtual store.

[1680] "User" refers to any person or entity that uses the System to generate HTML code.

[1681] "Instructions entered in Japanese" refers to text written in Japanese by a user expressing requests regarding the structure and design of a web page.

[1682] "Means for receiving" refers to a component or program that has the function of obtaining Japanese language instructions entered by a user and storing them for processing.

[1683] "Generative model" refers to an algorithm or artificial intelligence system that automatically generates HTML code based on received Japanese instructions.

[1684] "Driven" refers to hardware or software that executes a generative model and accomplishes a specified task.

[1685] "Means for analyzing emotions" refers to algorithms or artificial intelligence systems that analyze the emotions and intentions contained in the instructions entered by the user and adjust the design and color scheme based on the results.

[1686] "Means for adjusting design and color scheme" refers to components or programs for changing the appearance and color scheme of the HTML code generated based on the results of sentiment analysis.

[1687] "Means of providing" refers to the functions and services for displaying or transmitting the generated HTML code to the user.

[1688] "Natural language processing technology" refers to the general algorithms and methods that allow computers to understand and process human language.

[1689] "Means for making modifications" refers to a component or program that has the function of receiving instructions re-entered by the user and modifying or correcting existing HTML code based on the instructions.

[1690] "Design elements such as background and font color" refers to the visual components of a web page's appearance and user experience, such as background color, font color, size, and placement.

[1691] This invention is a system that receives instructions entered by a user in Japanese and automatically generates HTML code based on those instructions. Furthermore, this system also provides a function to adjust the design and color scheme of the generated HTML code by combining it with an emotion engine that recognizes the emotions contained in the user's instructions.

[1692] System configuration

[1693] The system includes the following components:

[1694] 1. Input receiving means: A web application for receiving instructions entered in Japanese by the user. Users access this web application using devices such as smartphones, tablets, and PCs.

[1695] 2. Generative model driving means: A server that drives a generative model that automatically generates HTML code based on Japanese instructions. The generative model uses natural language processing technology.

[1696] 3. Sentiment analysis: An emotion engine that analyzes the emotions contained in the Japanese instructions entered by the user. For example, the pipeline from the Hugging Face transformers library is used.

[1697] 4. Design Adjustment: A software component for adjusting design elements such as background and font color of the generated HTML code based on the results of sentiment analysis.

[1698] 5. Code Delivery Method: A function for providing the generated HTML code to the user. The generated code is returned in JSON format and interpreted and displayed on the user's device.

[1699] Processing flow explanation

[1700] 1. Receiving user input:

[1701] The user opens the web application and enters instructions in Japanese about the homepage structure and design in a text area. For example, they can enter specific instructions such as, "The homepage should have the company name at the top of the screen, with a menu bar below it, and the menu contents should be 'TOP, Company Overview, Product Introduction, IR Information.' I would like the design to have a bright feel."

[1702] 2. Sending instructions:

[1703] Instructions entered by the user are sent from the terminal to the server.

[1704] 3. Sentiment analysis:

[1705] The server analyzes the received instructions using an emotion engine to recognize the emotion contained in the input text. For example, if a positive emotion such as "cheerful" is detected, the background color will be set to a bright color.

[1706] 4. Driving the generative model:

[1707] The server runs a generative AI model that uses natural language processing techniques to generate HTML code based on user instructions and sentiment analysis.

[1708] 5. Code Submission:

[1709] The generated HTML code is formatted and sent back to the user's device in JSON format. The device interprets the received HTML code and displays it to the user. The user can check the displayed code and enter instructions again if necessary.

[1710] Specific examples

[1711] For example, the user enters the following prompt:

[1712] "The homepage will have the company name at the top of the screen, with a menu bar below it, with the menu contents being 'TOP, Company Overview, Product Introduction, IR Information.' I want this design to have a bright feel."

[1713] Based on this example, the system performs sentiment analysis and drives a generative model to generate HTML code with a light background color. This prompt allows for the rapid creation of a customized homepage tailored to the user's needs.

[1714] As described above, the present invention improves the efficiency and quality of homepage creation, and also makes it possible to automatically generate designs that take into consideration the user's feelings.

[1715] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1716] Step 1:

[1717] The user enters instructions in Japanese.

[1718] Users access the system using a smartphone, tablet, or PC and input instructions in Japanese regarding the homepage structure and design. For example, they might input, "The homepage will have the company name at the top of the screen, with a menu bar below it, and the menu contents will be 'TOP, Company Overview, Product Introduction, IR Information.' I want this design to have a bright feel." The input data is saved in text format on the device.

[1719] Step 2:

[1720] The terminal sends the input instructions to the server

[1721] The terminal transmits the instructions in Japanese entered by the user to the server. The transmitted data is the text data entered by the user.

[1722] Step 3:

[1723] The server receives and parses the Japanese instructions.

[1724] The server parses the received Japanese instructions. It receives the input data (Japanese instructions) and analyzes the grammar and syntax using a text analysis library. The main processing here is to split the received data and extract elements from the instructions.

[1725] Step 4:

[1726] Analyze emotions with the emotion engine

[1727] The server analyzes the emotions from the parsed instructions. For emotion analysis, it uses the pipeline of Hugging Face's transformers library. In this step, the emotion (positive, negative, etc.) contained in the input data (instructions) is identified and the result is stored internally on the server. For example, the instruction "feeling cheerful" is recognized as a positive emotion.

[1728] Step 5:

[1729] Generate HTML code by driving the generative model

[1730] The server drives a generative AI model based on the results of sentiment analysis and Japanese instructions. It uses natural language processing technology to generate HTML code that includes designs and color schemes that match the emotions. In this step, the input data (Japanese instructions and sentiment analysis results) is sent as prompts to the generative AI model, and output data (generated HTML code) is obtained.

[1731] Step 6:

[1732] Format and serve the generated HTML code

[1733] The generated HTML code is formatted and converted to JSON format. The server returns this formatted HTML code to the user. The output data is formatted HTML code.

[1734] Step 7:

[1735] Display the HTML code received by the device

[1736] The terminal interprets the HTML code received from the server and displays it to the user using a browser or similar. The user can check the displayed code and enter instructions again if necessary. The output data is an HTML display that the user can view.

[1737] By following the above steps, users can quickly create a homepage that meets their needs using simple Japanese instructions and emotional expressions.

[1738] 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.

[1739] 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.

[1740] 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.

[1741] 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.

[1742] 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.

[1743] 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.

[1744] 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).

[1745] 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.

[1746] 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."

[1747] 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.

[1748] 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).

[1749] 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.

[1750] 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.

[1751] 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.

[1752] 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.

[1753] 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.

[1754] 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.

[1755] 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.

[1756] 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.

[1757] 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.

[1758] 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.

[1759] The following is further disclosed regarding the above embodiment.

[1760] (Claim 1)

[1761] means for receiving instructions input by a user in Japanese;

[1762] means for driving a generative model that automatically generates HTML code based on the received Japanese instructions;

[1763] means for providing the generated HTML code to a user;

[1764] A system including:

[1765] (Claim 2)

[1766] The system of claim 1 , wherein the generative model uses natural language processing techniques.

[1767] (Claim 3)

[1768] 2. The system according to claim 1, further comprising means for the user to input instructions again and correct the HTML code.

[1769] (Claim 4)

[1770] 2. The system according to claim 1, further comprising terminal means for the user to input the instructions via a Web interface and for displaying the HTML code.

[1771] (Claim 5)

[1772] The system of claim 1 further comprising: means for providing the generated HTML code to a user in JSON format.

[1773] "Example 1"

[1774] (Claim 1)

[1775] means for receiving instructions inputted by a user in natural language;

[1776] means for driving a generative model that automatically generates HTML code based on the received natural language instructions;

[1777] means for formatting the generated HTML code and returning it to a terminal;

[1778] means for interpreting the HTML code received by the terminal and displaying it to the user;

[1779] a means for the user to input instructions again and correct the HTML code;

[1780] A system including:

[1781] (Claim 2)

[1782] The system of claim 1 , wherein the generative model uses natural language processing techniques.

[1783] (Claim 3)

[1784] 2. The system according to claim 1, further comprising means for the user to input instructions using a terminal, and for the terminal to transmit the instructions to a server.

[1785] "Application Example 1"

[1786] (Claim 1)

[1787] means for receiving instructions inputted in language by a user;

[1788] means for driving a generative model that automatically generates HTML code based on the received language instructions;

[1789] means for providing the generated HTML code to a user;

[1790] means for previewing the generated HTML code in real time;

[1791] means for receiving further instructions for modifying the generated HTML code;

[1792] A system including:

[1793] (Claim 2)

[1794] The system of claim 1 , wherein the generative model uses natural language processing techniques.

[1795] (Claim 3)

[1796] 2. The system according to claim 1, further comprising means for previewing the generated HTML code in real time and allowing a user to input additional instructions to modify the HTML code.

[1797] "Example 2: Combining Emotion Engines"

[1798] (Claim 1)

[1799] means for receiving instructions inputted by a user in natural language;

[1800] means for driving an emotion engine that analyzes emotions based on the received instructions;

[1801] A means for generating a prompt sentence based on the result of the sentiment analysis and the instruction, and driving a generative AI model;

[1802] means for formatting the generated HTML code and providing it to a user;

[1803] A system including:

[1804] (Claim 2)

[1805] The system of claim 1 , wherein the generative AI model uses natural language processing techniques.

[1806] (Claim 3)

[1807] 2. The system according to claim 1, further comprising means for the user to input instructions again and correct the HTML code.

[1808] "Application example 2 when combining emotion engines"

[1809] (Claim 1)

[1810] means for receiving instructions input by a user in Japanese;

[1811] means for driving a generative model that automatically generates HTML code based on the received Japanese instructions;

[1812] A means for analyzing the emotions contained in the Japanese instructions and adjusting the design and color scheme of the HTML code;

[1813] means for providing the generated HTML code to a user;

[1814] A system including:

[1815] (Claim 2)

[1816] The system of claim 1 , wherein the generative model uses natural language processing techniques.

[1817] (Claim 3)

[1818] 2. The system according to claim 1, further comprising means for the user to input instructions again and correct the HTML code.

[1819] (Claim 4)

[1820] The system of claim 1, further comprising means for adjusting design elements such as background and font color of HTML code based on the sentiment analysis. [Explanation of symbols]

[1821] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / pre> < / pre> < / url:> < / pre> < / pre> < / url:> < / pre> < / pre> < / url:> < / pre> < / pre>

Claims

1. means for receiving instructions input by a user in Japanese; means for driving a generative model that automatically generates HTML code based on the received Japanese instructions; means for providing the generated HTML code to a user; A system including:

2. The system of claim 1 , wherein the generative model uses natural language processing techniques.

3. 2. The system according to claim 1, further comprising means for the user to input instructions again and correct the HTML code.

4. 2. The system according to claim 1, further comprising terminal means for the user to input the instructions via a Web interface and for displaying the HTML code.

5. The system of claim 1 , further comprising: means for providing the generated HTML code to a user in JSON format.

Images