System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting content, a format, and a style of content desired to be generated by a user in text; means for analyzing the input information and specifying a type of content to be generated; means for selecting and initializing an optimal generative artificial intelligence model based on the specified type of content; means for generating content using the initialized generative artificial intelligence model; and means for providing the generated content 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] Modern content generation requires advanced technology and costs, and many individuals and businesses are seeking services that are easily accessible. However, it is difficult for users to find generative artificial intelligence (AI) that suits their purposes, and they often lack the technical knowledge and funds. There are also issues with the quality and safety of the generated content. As a result, a system that allows users to easily generate high-quality content is needed. [Means for solving the problem]

[0005] The present invention provides a system including: a means for a user to input, in text, the content, format, and style of content they wish to generate; a means for analyzing the input information and identifying the type of content to be generated; a means for selecting and initializing an optimal generative AI model based on the identified type of content; a means for generating content using the initialized generative AI model; and a means for providing the generated content to the user. The system may further include a means for collecting user feedback and improving the performance of the generative AI model; a means for the user to embed the generated content in a web page or social networking service; a means for the content generated based on the user input to include text, images, audio, and video; and a means for providing a simple interface that does not require the user to have specific programming knowledge. In this way, users can easily generate high-quality content.

[0006] "User" means any person or entity that uses the System to generate Content.

[0007] "Content to be generated" refers to media such as text, images, audio, and video that have the content, format, and style desired by the user.

[0008] The "text input means" is an interface for inputting details of the content the user wants to generate as text information.

[0009] "Means for analyzing input information" refers to technology for understanding the user's input and determining what content to generate.

[0010] "Type of content generated" refers to the specific media format (e.g., text, images, audio, video) generated in response to a user request.

[0011] "Means for selecting the optimal generative artificial intelligence model" refers to a technology that selects the AI ​​model that best meets the user's needs based on input information.

[0012] The "means for initializing" is an operation for setting the selected generative artificial intelligence model into a functional state and making it available for use.

[0013] The "means for generating content" is a technology for creating content desired by a user using an initialized generative artificial intelligence model.

[0014] The "means for providing generated content" is an interface for displaying the generated content to the user and making it available for use.

[0015] "Means for collecting feedback" refers to methods for obtaining opinions and ratings from users and using them to improve the system.

[0016] "Means for embedding in web pages and social networking services" refers to tools and methods for publishing and sharing the generated content on the Internet.

[0017] A "simple interface" is a system operation screen that is easy for users to operate and does not require complex technical knowledge. [Brief explanation of the drawings]

[0018] [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

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] The present invention provides a system that includes a means for a user to input, in text form, the content, format, and style of content they wish to generate, a means for analyzing the input information and identifying the type of content to be generated, a means for selecting and initializing an optimal generative AI model based on the identified type of content, a means for generating content using the initialized generative AI model, and a means for providing the generated content to the user. This system can provide the user with high-quality content generation.

[0040] Program processing and explanation

[0041] 1. Displaying the user interface

[0042] Terminal

[0043] The device displays a web page and provides a form for the user to enter information required to generate the content.

[0044] A text field appears where the user can enter the content, format, and style of the content they want to generate.

[0045] 2. Getting and Sending User Input

[0046] User

[0047] The user enters the required information into the input form and presses the "Generate" button to submit.

[0048] The device sends the user's input data to the server.

[0049] 3. Parsing User Input

[0050] server

[0051] The server analyzes the input data received from the user and identifies the type of content to be generated (text, images, audio, video, etc.).

[0052] It also identifies the content style based on the user's input.

[0053] 4. Generative AI model selection and initialization

[0054] server

[0055] Selecting the best generative artificial intelligence model based on the identified content type and style.

[0056] The selected generative artificial intelligence model is initialized and set to an executable state.

[0057] 5. Content Generation

[0058] server

[0059] The initialized generative artificial intelligence model is used to generate content desired by the user.

[0060] The generation process ensures high-quality output that adheres to the specified style.

[0061] 6. Providing Generated Content

[0062] server

[0063] The generated content is sent back to the device for provision to the user.

[0064] The provided content is displayed on the user's screen.

[0065] Specific examples

[0066] Here is an example where the user wants to generate a sentence:

[0067] 1. The user enters "I would like to generate a story-style text" into the input form on the web page.

[0068] 2. The device sends this input data to the server.

[0069] 3. The server analyzes the input data and determines that the content type is "text" and the style is "story."

[0070] 4. The server selects and initializes the generative artificial intelligence model that is best suited to generating story-style text.

[0071] 5. The server uses the initialized model to generate a story-style sentence.

[0072] 6. The server sends the generated text back to the terminal, which displays it on the user's screen.

[0073] The present invention aims to enable high-quality content generation without requiring users to have specific technical knowledge. It also makes it possible to utilize user feedback to continuously improve the performance of generative AI models. Generated content can be easily embedded into web pages and social networking services, improving the user experience.

[0074] The processing flow will be explained below.

[0075] Step 1:

[0076] A user accesses a web page on a terminal, which displays a form for inputting the content, format, and style of content the user wants to generate.

[0077] Step 2:

[0078] The user enters information about the content they want to generate into the form and clicks the "Generate" button, at which point detailed information such as content, format, and style is entered.

[0079] Step 3:

[0080] The terminal sends the entered user information to the server. The input data is sent to the server using an HTTP request.

[0081] Step 4:

[0082] The server analyzes the user input data it receives and determines the type of content to be generated (text, images, audio, video, etc.) based on the input information.

[0083] Step 5:

[0084] The server selects the most appropriate generative AI model based on the type and style of the identified content, for example, a text generation model for sentence generation.

[0085] Step 6:

[0086] The server initializes the selected generative AI model and loads the necessary parameters to set the model in a usable state.

[0087] Step 7:

[0088] The server generates content based on input data using the initialized generative artificial intelligence model. For example, in response to an input such as "I would like to generate a story-style sentence," the server generates a story-style sentence.

[0089] Step 8:

[0090] The server returns the generated content to the terminal to provide it to the user. The generated content is included in the HTTP response.

[0091] Step 9:

[0092] The device displays the received content to the user. The generated text, images, audio, video, etc. are displayed on the user's screen.

[0093] Step 10:

[0094] Users review the generated content and provide feedback as needed, which helps improve the system.

[0095] Example 1

[0096] 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."

[0097] Conventional content generation systems have had the problem that they require advanced technical knowledge to generate high-quality output in response to the user's requests for content they want to generate. In addition, when generating different types of content (e.g., text, images), the process of selecting and optimizing the appropriate generation model is complicated, which takes time and effort for the user.

[0098] 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.

[0099] In this invention, the server includes: means for a user to input the content, format, and style of content they wish to generate in text; means for analyzing the input information and identifying the type of content to be generated; means for selecting and initializing an optimal generative AI model based on the identified type of content; means for generating content using the initialized generative AI model; means for displaying a user interface and acquiring input from the user; means for transmitting user input data to the server and for the server to analyze the data; means for generating content according to the user's request via text input using an AI model; and means for returning the generated content to the user's terminal and displaying it. This enables users to generate high-quality content without requiring specific expert knowledge.

[0100] A "user interface" is an operation screen through which a user inputs information for content generation.

[0101] A "text entry method" is a feature that provides an input field for users to enter content, format, and style.

[0102] "Means for analyzing input information" refers to a function that analyzes information received from a user and identifies the type and style of content to be generated.

[0103] The "means for identifying the type of content to be generated" is a function that determines the format of the content to be generated (e.g., text, image, audio, video, etc.) based on input information.

[0104] "Generative Artificial Intelligence Model" means a machine learning algorithm used to generate particular Content.

[0105] The "means for selecting and initializing a generative artificial intelligence model" is the process of selecting the AI ​​model that best suits the identified content and setting that model into an operational state.

[0106] "Means for generating content" refers to a function that uses an initialized AI model to create new content based on the content and style specified by the user.

[0107] The "means for providing generated content" is a function for returning and displaying generated content to the user.

[0108] A "means for obtaining user input" is a process for collecting data entered by a user.

[0109] "Means for transmitting user input data to a server" refers to a function that sends data input by a user on a terminal to a server via a network.

[0110] "Means for generating content according to user requests through text input" refers to a function that creates content according to the specified content and style based on the text information entered by the user.

[0111] "Means for returning and displaying the generated content to the user's terminal" refers to the process by which the server sends the generated content to the user's terminal and displays it on that terminal.

[0112] The present invention is a system that includes a means for a user to input the content, format, and style of content they want to generate in text form, a means for analyzing the input information and identifying the type of content to be generated, a means for selecting and initializing an optimal generative artificial intelligence model based on the identified type of content, a means for generating content using the initialized generative artificial intelligence model, and a means for providing the generated content to the user.

[0113] User Interface Display

[0114] The device displays a web page and provides a form for the user to enter the information necessary to generate content. The web page displays fields for the user to enter text for the content, format, and style of the content they wish to generate. The form includes required input fields such as "content type," "style," and "content."

[0115] Getting and sending user input

[0116] The user enters the necessary information into the displayed input form. After entering the information, the user presses the "Generate" button to send the data to the device. The device uses JavaScript or other tools to collect the user's input data and sends it to the server in JSON format.

[0117] Parsing user input

[0118] The server parses the received JSON data and identifies the "content type" (e.g., text, image) and "style" (e.g., story, news article) from the data. To identify the content and style, it uses an NLP (Natural Language Processing) model, for example, using libraries such as spaCy or NLTK.

[0119] Generative AI model selection and initialization

[0120] The server selects the optimal generative AI model based on the type and style of the identified content. For example, it selects GPT-4 for text generation and DALL-E for image generation. After the selection, the server initializes the AI ​​model. Initialization involves loading the model and setting it to inference mode.

[0121] Content generation

[0122] The server uses the initialized generative AI model to generate the content desired by the user. During the generation process, the server performs appropriate filtering and text completion according to the style specified by the user. For example, in sentence generation using GPT-4, a prompt sentence is input and high-quality text is generated as a follow-up.

[0123] Providing generated content

[0124] The server returns the generated content in JSON format to the device, which then parses the data and displays it in a user-friendly format, for example, displaying the generated text as an HTML element.

[0125] Specific examples

[0126] Here is a specific example of a case where a user wishes to generate text. The user enters "I would like to generate a story-style text" into an input form on a webpage. The device sends this input data to the server, which analyzes the input data and determines that the content type is "text" and the style is "story." The server selects and initializes a generative artificial intelligence model that is optimal for generating story-style text, and generates a story-style text using the initialized model. The server sends the generated text back to the device, which displays it on the user's screen. An example of a specific prompt sentence is "One day, a mysterious incident occurred in the forest. It was..."

[0127] This system allows users to quickly generate high-quality content in various formats without requiring specific expertise. Furthermore, it is possible to utilize user feedback to continuously improve the performance of the generative AI model. Generated content can be easily embedded into web pages and social networking services, improving the user experience.

[0128] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0129] Step 1: Displaying the User Interface

[0130] The device downloads an HTML page from the web server and displays it to the user. This web page contains a form for the user to enter the content, format, and style of the content they wish to generate. To display the user interface, the device uses a web browser to render user input fields using a combination of HTML, CSS, and JavaScript. The input fields display items such as "content type," "style," and "content."

[0131] Input: None (initial display)

[0132] Output: A form that accepts user input

[0133] Step 2: Getting User Input

[0134] The user enters the required information into the displayed input form (e.g., content type = "Text", style = "Story", content = "One day, in the forest...") After entering the information, the user presses the "Generate" button to send the data.

[0135] Input: Entering information into form fields

[0136] Output: Information entered into form fields

[0137] Step 3: Sending User Input

[0138] The device uses JavaScript or other tools to collect data entered by the user, convert it into JSON format, and send the converted JSON data to the server.

[0139] Input: Information entered by the user

[0140] Output: JSON format data and send it to the server

[0141] Step 4: Parsing User Input

[0142] The server parses the received JSON data to identify the content type and style. Specifically, the server uses an NLP (Natural Language Processing) library (e.g., spaCy, NLTK) to tokenize the text data and extract the content type and style according to the requirements.

[0143] Input: JSON format data

[0144] Output: Identifying the type and style of content (e.g., "text" or "story")

[0145] Step 5: Selecting and initializing a generative artificial intelligence model

[0146] The server selects the most appropriate generative AI model based on the identified content type and style. For example, the GPT-4 model is selected for sentence generation. The server then initializes the selected AI model and sets it to inference mode. Initialization includes loading the model and adjusting its configuration parameters.

[0147] Input: Content type and style information

[0148] Output: An initialized generative AI model

[0149] Step 6: Generate content

[0150] The server uses the initialized generative AI model to generate the content desired by the user. For example, when using GPT-4, a story-style sentence is generated based on the prompt sentence, "One day, in the forest..." During the generation process, the AI ​​model complements the text based on the prompt sentence and generates high-quality sentences according to the specified style.

[0151] Input: A prompt and an initialized AI model

[0152] Output: Generated content (e.g., story-style text)

[0153] Step 7: Providing generated content

[0154] The server returns the generated content to the device in JSON format, which includes the generated text and metadata.

[0155] Input: Generated content

[0156] Output: Content data in JSON format and send it to the device

[0157] Step 8: Viewing Generated Content

[0158] The device parses the received JSON data and displays the generated content in the user's browser. For example, it renders the generated text as HTML elements and displays it in a format that is easy for the user to read. This allows the user to visually check the generated content.

[0159] Input: Content data in JSON format

[0160] Output: The generated content displayed in the browser

[0161] (Application example 1)

[0162] 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."

[0163] Conventional content generation systems have limited the ability for users to efficiently generate high-quality content, leaving them dissatisfied with the generation process and results. Furthermore, they lack sufficient means to properly display or embed the generated content, resulting in a poor user experience. Furthermore, they lack sufficient feedback mechanisms to continuously improve the performance of generative AI models.

[0164] 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.

[0165] In this invention, the server includes means for inputting the content, format, and style of content a user wants to generate in text, means for analyzing the input information and identifying the type of content to be generated, means for selecting and initializing an optimal generative AI model based on the identified type of content, means for generating content using the initialized generative AI model, means for providing the generated content to the user, and means for displaying the generated content in a smartphone application, thereby enabling users to efficiently generate high-quality content and smoothly view and use it on their smartphones.

[0166] "User" refers to the entity that uses the system to generate content.

[0167] "Content" refers to information expressed in various forms, such as text, images, audio, and video.

[0168] "Content" is information about the specific theme or topic of the content to be generated.

[0169] The "format" indicates the type of form in which the generated content is expressed.

[0170] "Style" refers to the characteristics of the expression, tone, writing style, etc. of the content produced.

[0171] "Input means" means a device or function that allows a user to input the content, format, and style of the content in text form.

[0172] "Analysis means" is a function for analyzing input information and understanding its purpose and content.

[0173] The "identification means" is a function that determines the type of content to be generated based on the information interpreted by the analysis means.

[0174] A "generative artificial intelligence model" is a machine learning model for generating content based on user input.

[0175] The "selection means" is a function for selecting the optimal generative artificial intelligence model based on the type of content identified.

[0176] The "initialization means" is a function for setting the selected generative artificial intelligence model into a usable state.

[0177] The "generation means" is a function for generating content using an initialized generative artificial intelligence model.

[0178] "Means of delivery" refers to the function for appropriately delivering the generated content to users.

[0179] The "display means" is a function for displaying the generated content on a smartphone application.

[0180] "Feedback" refers to the evaluations and opinions provided by users after use.

[0181] "Performance improvement means" is a function for improving the performance of generative artificial intelligence models based on collected feedback.

[0182] A "web page" is a collection of information displayed on the Internet and accessible through a browser.

[0183] A "social networking service" is an online platform that allows users to interact with each other over the Internet.

[0184] The system of the present invention begins with a user entering text describing the content, format, and style of the content they wish to generate. The system analyzes the entered information and identifies the type of content to be generated. Next, the system selects and initializes an optimal generative AI model based on the identified type of content. The system generates content using the initialized generative AI model and provides the generated content to the user.

[0185] This system is realized using the following components and technologies:

[0186] User Interface

[0187] A user launches a smartphone application. The application presents the user with a form to enter information needed to generate content. The form contains fields for inputting the content, format, and style of the content the user wants to generate.

[0188] Sending data

[0189] When the user enters the required information into the input form and presses the "Generate" button, the smartphone application sends this data to the server.

[0190] Data analysis

[0191] The server analyzes the input data received from the user and identifies the type of content to be generated (text, images, audio, video, etc.) using a natural language processing toolkit (e.g., Python's NLTK or spaCy).

[0192] Model Selection and Initialization

[0193] The server selects and initializes the optimal generative artificial intelligence model (e.g., OpenAI GPT-4, DALL-E 2) based on the type and style of the identified content. Model initialization uses a machine learning framework such as TensorFlow or PyTorch.

[0194] Content generation

[0195] The server uses the initialized generative artificial intelligence model to generate content in the format and style specified by the user, where certain calculations and data processing are performed.

[0196] Content provision

[0197] The server sends the generated content back to the smartphone application, which displays it on the user's screen.

[0198] Feedback and Improvements

[0199] Users provide feedback on the generated content, and the server collects this feedback to improve the performance of the AI ​​model, using data analysis tools (e.g., Scikit-learn).

[0200] Specific examples

[0201] Let's say a user wants to create a blog post and enters the following:

[0202] Content: Latest trends in technology

[0203] Format: Blog post

[0204] Style: A clear, friendly tone

[0205] This information is sent to the server, which analyzes it, selects a generative AI model (e.g., GPT-4), and initializes it. The selected model then generates a blog post, which is then displayed on the smartphone application. An example prompt is as follows:

[0206] Prompt Sentence Examples

[0207] Content: Latest trends in technology

[0208] Format: Blog post

[0209] Style: A clear, friendly tone

[0210] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0211] Step 1:

[0212] The device launches the smartphone application and displays a content creation form to the user. The user enters the content, format, and style of the content they want to create into this form. The entered data is temporarily stored on the device.

[0213] Input: User input of content, format, and style

[0214] Output: Input data saved on the device

[0215] Step 2:

[0216] The terminal detects that the "Generate" button has been pressed and sends the input data to the server using an HTTP POST request, where the input data is converted to JSON format.

[0217] Input: User-entered data, temporarily stored data, HTTP POST requests

[0218] Output: JSON formatted data sent to the server

[0219] Step 3:

[0220] The server parses the received JSON data to determine the type and style of content to generate, using a natural language processing toolkit (e.g., Python's NLTK or spaCy) to extract keywords and analyze intent.

[0221] Input: JSON format data sent, natural language processing toolkit

[0222] Output: Identified content type and style (e.g. blog post, clear, friendly tone)

[0223] Step 4:

[0224] The server selects the optimal generative AI model based on the type and style of the identified content. To select a model, it searches a database of various AI models for a model that matches the criteria and selects it. After selection, it initializes the model and sets it to an executable state.

[0225] Input: Identified content type and style, AI model database

[0226] Output: Initialized optimal generative AI model

[0227] Step 5:

[0228] The server generates content by providing prompts based on the specified content and style to an initialized generative AI model, where the generative AI model (e.g., OpenAI GPT-4) outputs sentences in response to the prompts.

[0229] Input: Initialized generative AI model, prompt

[0230] Output: Generated content (text, images, audio, video)

[0231] Step 6:

[0232] The server returns the generated content to the smartphone application using an HTTP response, with the generated content being sent in JSON format.

[0233] Input: Generated content, HTTP response

[0234] Output: Content sent to the smartphone application

[0235] Step 7:

[0236] The device receives the content sent back from the server and displays it to the user. The display uses the smartphone application UI and displays the content on the screen according to its format (text, image, audio, video).

[0237] Input: Received content, smartphone UI

[0238] Output: The generated content displayed to the user

[0239] Step 8:

[0240] Users provide feedback on the generated content, which is sent via their device to the server and used to improve the performance of the AI ​​model. Feedback is sent again using an HTTP POST request.

[0241] Input: User feedback, HTTP POST request

[0242] Output: Feedback data sent to the server

[0243] Step 9:

[0244] The server analyzes the received feedback data and adjusts the generative AI model, using data analysis tools (e.g., Scikit-learn) to optimize the model parameters.

[0245] Input: Feedback data, data analysis tools

[0246] Output: A generative AI model with improved performance

[0247] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0248] The present invention provides a system including: means for a user to input, in text, the content, format, and style of content they wish to generate; means for analyzing the input information and identifying the type of content to be generated; means for selecting and initializing an optimal generative artificial intelligence model based on the identified type of content; means for generating content using the initialized generative artificial intelligence model; means for providing the generated content to the user; means for collecting user feedback and improving performance of the generative artificial intelligence model; means for embedding the generated content in a web page or social networking service; means for including text, images, audio, and video in the content generated based on the user input; and means for providing a simple interface that does not require the user to have specific programming knowledge. The system further includes means for including an emotion engine that recognizes the user's emotional state from the user's text input, means for adjusting the tone and style of the generated content based on the recognized user's emotional state, and means for optimizing the performance of the generative artificial intelligence model based on the user's emotional state and feedback, thereby achieving more personalized content generation.

[0249] Program processing and explanation

[0250] 1. Displaying the user interface

[0251] Terminal

[0252] The device displays a web page and provides a form for the user to enter information required to generate the content.

[0253] A text field appears where the user can enter the content, format, and style of the content they want to generate.

[0254] 2. Getting and Sending User Input

[0255] User

[0256] The user enters information about the content they want to generate into the form and clicks the "Generate" button.

[0257] The device sends the user's input data to the server.

[0258] 3. Analyzing user input and recognizing emotional states

[0259] server

[0260] The server parses the received user input data.

[0261] Use an emotion engine to recognize the emotional state of a user from their input text.

[0262] The type of content to generate is determined based on the analyzed information and the perceived emotional state.

[0263] 4. Generative AI model selection and initialization

[0264] server

[0265] Select the most appropriate generative artificial intelligence model based on the identified content type and the user's emotional state.

[0266] The selected generative artificial intelligence model is initialized and set to an executable state.

[0267] 5. Content Generation

[0268] server

[0269] The initialized generative artificial intelligence model is used to generate content based on the user's input data and emotional state.

[0270] The generation process applies a tone and style that reflects the perceived emotional state, resulting in personalized, high-quality content.

[0271] 6. Providing Generated Content

[0272] server

[0273] The generated content is sent back to the device for provision to the user.

[0274] The device displays the generated content to the user.

[0275] Specific examples

[0276] Here is an example of story-style text generation when the user has a sad emotion:

[0277] 1. A user fills out a form on a webpage with the request, "I would like a story-style text generated that will help me feel better about my sadness."

[0278] 2. The device sends this input data to the server.

[0279] 3. The server analyzes the input data and uses an emotion engine to recognize that the user's emotional state is "sad."

[0280] 4. The server identifies the content type as "text" and the style as "healing story," and selects and initializes the corresponding generative AI model.

[0281] 5. The server generates a healing story-style sentence that reflects the recognized emotion, "sadness."

[0282] 6. The server sends the generated text back to the terminal, which displays it on the user's screen.

[0283] In this way, recognizing the user's emotional state and generating personalized content that reflects it can improve the user experience. Furthermore, collecting user feedback and improving the model's performance can improve the overall quality of the system.

[0284] The processing flow will be explained below.

[0285] Step 1:

[0286] A user accesses a web page on a terminal, which displays a form for inputting the content, format, and style of content the user wants to generate.

[0287] Step 2:

[0288] The user enters information about the content they want to generate in the form (e.g., "I want you to generate a healing story-style text for those feeling sad") and clicks the "Generate" button.

[0289] Step 3:

[0290] The terminal sends the entered user information to the server. The input data is sent to the server using an HTTP request.

[0291] Step 4:

[0292] The server analyzes the received user input data and determines the type (e.g., text) and style (e.g., story style) of content to be generated from the input.

[0293] Step 5:

[0294] The server uses an emotion engine to recognize an emotional state from the user's text input, for example, recognizing the emotion "sad" from the input.

[0295] Step 6:

[0296] The server selects the optimal generative AI model based on the identified content type and the recognized emotional state. For example, it selects a model suitable for generating story-style sentences to soothe "sadness."

[0297] Step 7:

[0298] The server initializes the selected generative AI model, sets the model to a usable state, and loads the parameters required for generation.

[0299] Step 8:

[0300] The server uses the initialized generative artificial intelligence model to generate content based on the user's input data and emotional state, for example, generating comforting story-style text that reflects "sadness."

[0301] Step 9:

[0302] The server returns the generated content to the terminal to provide it to the user. The generated content is included in the HTTP response.

[0303] Step 10:

[0304] The terminal displays the received content to the user. The generated content, such as text, images, audio, and video, is displayed on the user's screen.

[0305] Step 11:

[0306] The user reviews the generated content and provides feedback as needed. The server collects the user's feedback and uses it to improve the performance of the generative AI model.

[0307] Example 2

[0308] 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."

[0309] Conventional content generation systems require a lot of effort to adjust the content, format, and style of generated content based on user input, and suffer from insufficient personalization based on the user's emotional state. Furthermore, feedback that would lead to improved quality of generated content and user experience is not effectively utilized, resulting in a uniformity of generated content.

[0310] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for inputting the content, format, and style of the content the user wants to generate in text; means for analyzing the input information and identifying the type of content to be generated; means for selecting and initializing an optimal generative AI model based on the identified type of content; means for generating content using the initialized generative AI model; means for providing the generated content to the user; means for recognizing the user's emotional state from the input text; and means for adjusting the tone and style of the generated content based on the recognized emotional state. This enables the generation of high-quality, personalized content based on user input.

[0311] "User" refers to a person who uses the system to generate content.

[0312] "Content" refers to any informational output, such as text, images, audio, or video, that is generated based on user input.

[0313] "Text input" refers to the act of a user providing a string of characters to a system using a keyboard or other input device.

[0314] "Entered Information" refers to data provided by a User to the System through text input.

[0315] "Analysis" refers to the process of analyzing information input by a user and converting it into useful information for internal use.

[0316] The "type of content to be generated" indicates the specific content format to be generated based on the user's request, and includes, for example, text, images, audio, video, and the like.

[0317] "Generative AI model" refers to an artificial intelligence algorithm or model used to generate content based on the type of content being generated.

[0318] "Initialization" refers to a series of preparatory steps that put a generative artificial intelligence model into a usable state.

[0319] "Emotional state" refers to the type of emotion recognized from the user's input text, including, for example, joy, sadness, anger, surprise, etc.

[0320] "Tone and style" refers to the way the content is expressed and the style or tone that reflects a particular emotion.

[0321] "Serving" refers to the act of delivering the generated content to a user, typically including displaying it on a screen or making it available for download.

[0322] This invention provides a system that includes a means for a user to input the content, format, and style of content they wish to generate in text form, and a means for analyzing the input information and identifying the type of content to be generated. The system also includes a means for selecting and initializing an optimal generative AI model based on the identified type of content. The system also includes a means for generating content using the initialized generative AI model and providing the generated content to a user.

[0323] The user accesses an input form on a web page through the device and inputs the content, format, and style of the content they want to generate. For example, if the user inputs "Please generate a healing story," the device sends this input data to the server.

[0324] The server analyzes the received input data, analyzes the text using a natural language processing engine, and uses an emotion engine to recognize the user's emotional state from the input text. Based on this recognized emotional state, the server determines the type of content to generate.

[0325] Based on the identified content type and the user's emotional state, the server selects the most appropriate generative AI model. For example, if the user is looking for a comforting story, an appropriate natural language generation model (e.g., GPT-3) is selected and initialized.

[0326] Using the initialized generative AI model, the server generates content based on the user's input data and emotional state, applying a tone and style that reflects the perceived emotional state to generate personalized content for each user.

[0327] The generated content is sent back to the device from the server, where it is analyzed and displayed on the user's screen. For example, a soothing story text may be displayed based on the user's request. It also has a feedback function that allows users to collect feedback and improve the performance of the generative AI model.

[0328] As a concrete example, consider the case where the user enters the prompt sentence as follows:

[0329] "I want to generate soothing story-style text for sad people."

[0330] Based on this prompt, the system recognizes the user's emotional state and uses an appropriate generative AI model to generate and display a personalized story, thereby providing high-quality content that responds to each user's emotions.

[0331] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0332] Step 1:

[0333] Terminal

[0334] The device displays a web page and provides a form for the user to enter information needed to generate content, including fields for specifying the content, format, and style of the content they want to generate.

[0335] Input: None

[0336] Output: A form to input the content, format, and style of the content you want to generate.

[0337] Step 2:

[0338] User

[0339] The user enters the content, format, and style of the content they want to generate into the input form and clicks the "Generate" button. For example, they might enter "Generate a healing story."

[0340] Input: Text describing the contract, format, and style

[0341] Output: The "Generate" button was clicked and the text data entered.

[0342] Step 3:

[0343] Terminal

[0344] The terminal generates and sends an HTTP request to send the user's input data to the server.

[0345] Input: User input data (text format)

[0346] Output: Data sent to the server as an HTTP request

[0347] Step 4:

[0348] server

[0349] The server parses the received user input, using a natural language processing engine to analyze the text and identify the intent and topic of the input.

[0350] Input: User input data sent from the terminal

[0351] Output: Structured information (intent, topic, etc.) from the parsed input data

[0352] Step 5:

[0353] server

[0354] The server uses an emotion engine to recognize the emotional state from the user's input text.

[0355] Input: Parsed input data

[0356] Output: User's emotional state (e.g. sadness, joy, etc.)

[0357] Step 6:

[0358] server

[0359] The server determines the type of content to generate based on the analyzed information and the perceived emotional state.

[0360] Input: Parsed input data and the user's emotional state

[0361] Output: The type of content to generate (e.g., a healing story)

[0362] Step 7:

[0363] server

[0364] Based on the identified content type and the user's emotional state, select the most appropriate generative artificial intelligence model, for example, a natural language generation model (e.g., GPT-3).

[0365] Input: The type of content to generate and the user's emotional state

[0366] Output: The selected generative artificial intelligence model

[0367] Step 8:

[0368] server

[0369] The selected generative artificial intelligence model is initialized and set to an executable state.

[0370] Input: A selected generative artificial intelligence model

[0371] Output: An initialized generative artificial intelligence model

[0372] Step 9:

[0373] server

[0374] The initialized generative artificial intelligence model is used to generate content based on the user's input data and emotional state. Specifically, the generative AI model is given input data and executed to generate personalized content.

[0375] Input: An initialized generative AI model, user input data, and the user's emotional state.

[0376] Output: Generated content (e.g., healing story text)

[0377] Step 10:

[0378] server

[0379] The generated content is returned to the terminal to be provided to the user. The generated content data is sent as an HTTP response.

[0380] Input: Generated content

[0381] Output: The generated content data as an HTTP response.

[0382] Step 11:

[0383] Terminal

[0384] The device receives the HTTP response, parses the generated content, and displays it to the user, for example, displaying the generated story text on a web page.

[0385] Input: Generated content data as an HTTP response

[0386] Output: Generated content that is displayed on the user's screen (e.g., story text)

[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] Many modern virtual stores lack personalized suggestions tailored to individual emotions and circumstances when users search for specific products. This poses a risk of lowering user engagement and satisfaction. Furthermore, conventional content generation systems struggle to generate content that takes into account the user's emotional state, preventing them from providing product recommendations and suggestions that are in tune with the user's emotions. Furthermore, many of these systems require specific programming knowledge, making them difficult for everyday users to use.

[0390] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting the content, format, and style of the content the user wants to generate in text; means for analyzing the input information and identifying the type of content to be generated; means for selecting and initializing an optimal generative AI model based on the identified type of content; means for generating content using the initialized generative AI model; means for providing the generated content to the user; means including an emotion engine that recognizes the user's emotional state from the input information; means for adjusting the tone and style of the generated content based on the recognized emotional state; and means for providing a simple interface that does not require the user to have any specific programming knowledge. This enables personalized product introductions based on the user's emotional state.

[0391] "Content that the user wishes to generate" refers to information such as text, images, audio, and video that the user wishes to create.

[0392] "Format" refers to the structural information about the media format in which the content should be presented.

[0393] "Style" refers to the look, tone, approach, and other aspects of your content.

[0394] "Text input means" means a method by which a user enters information into a system using a keyboard or other text input device.

[0395] "Means for analyzing input information" refers to the technology that allows the system to understand the data received from the user and extract the necessary information.

[0396] "Means for determining the type of content to generate" refers to a method for determining what type of content to generate based on the analyzed information.

[0397] "Generative AI model" refers to an AI algorithm for generating new content from input data.

[0398] "Means for initializing" refers to a method for setting up a selected artificial intelligence model in an operational state.

[0399] "Generated Content" refers to data such as text, images, audio, and video created by a generative artificial intelligence model.

[0400] "Means of providing to users" refers to the methods by which generated content is displayed or transmitted to users.

[0401] "Emotion Engine" refers to technology that identifies the emotional state of a user from their input text.

[0402] "Tone and style adjustments" refers to techniques that change the way generated content is presented depending on a perceived emotional state.

[0403] "Simple interface" refers to a system interface design that is easy for users to understand and operate.

[0404] "Means for collecting feedback" refers to methods for collecting user ratings and opinions and using them to improve the system.

[0405] The present invention provides a system for automatically generating personalized content based on a user's emotional state. This system can be used to generate emotion-based product introduction pages in a virtual store. A specific embodiment of this system is described below.

[0406] System configuration and processing

[0407] The server includes the following means:

[0408] 1. A means of textually inputting the content, format, and style of content that users want to generate.

[0409] Through the user interface, users input the necessary information using a keyboard, etc. The interface is provided on the screen of a smartphone or PC and is implemented using web technologies such as HTML and JavaScript.

[0410] 2. A means of analyzing input information and identifying the type of content to generate

[0411] The server receives the data sent by the user and analyzes it using a natural language processing (NLP) engine, such as spaCy or NLTK.

[0412] 3. Means including an emotional engine

[0413] The server uses Hugging Face's Transformers library to recognize the emotional state from the user's text data, which allows it to identify the emotions the user is feeling (e.g., stress, sadness, joy, etc.).

[0414] 4. A means for selecting and initializing the optimal generative artificial intelligence model based on the type of content identified.

[0415] Based on the analysis and emotion recognition results, the server selects and initializes a suitable generative AI model (e.g., OpenAI GPT-4). The selected model is then immediately available for content generation.

[0416] 5. Means for generating content using an initialized generative artificial intelligence model

[0417] The server inputs the prompt text into the selected generative AI model and generates a product page in a tone and style based on the user's emotional state. The prompt text looks like this example:

[0418] Typed text: I've been feeling stressed lately and am looking for items to help me relax at home. Type of content to generate: Product listing page. Tone and style: Soothing, relaxing. Examples of items to generate include: Aroma diffuser, massage chair, soothing music playlist.

[0419] 6. Means of providing generated content to users

[0420] The server then sends the generated content data back to the front end for display on the user's device, using web pages written in HTML and CSS.

[0421] Specific examples

[0422] The user inputs, "I've been feeling stressed lately, so I'm looking for a product that will help me relax." The device sends this data to the server. The server analyzes the input data and recognizes "stress" using its emotion engine. The server then selects and initializes a generative AI model related to "relaxation." After that, it generates content introducing relaxation products (e.g., aroma diffusers, massage chairs, soothing music playlists) that reflect the user's emotional state. Finally, the server sends the generated content back to the user, and the product introduction page is displayed on the user's device.

[0423] This emotionally driven product recommendation creates a highly engaging and personalized shopping experience for users.

[0424] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0425] Step 1:

[0426] The user enters the content, format, and style of the content they want to generate as text into an input form provided on the screen of their smartphone or PC. A user interface implemented using HTML and JavaScript is used for input. An example of input data here would be "I've been feeling stressed lately, so I'm looking for a product that will help me relax." The entered information is sent from the device to the server as JSON format data.

[0427] Step 2:

[0428] The server receives input data in JSON format sent from the device. It then analyzes the input data using natural language processing (NLP) tools (e.g., spaCy or NLTK). Specifically, it divides the input text into sentences and words, and performs a tokenization process to understand the content of each. The analysis results in information that the user is looking for relaxation items.

[0429] Step 3:

[0430] The server uses an emotion engine (e.g., Hugging Face's Transformers library) to recognize the user's emotional state from the parsed data. The input text is fed into the emotion engine, which identifies the emotional state "stressed." Here, the input is the user text, and the output is the emotional state.

[0431] Step 4:

[0432] The server selects and initializes the optimal generative AI model (e.g., OpenAI GPT-4) based on the identified emotional state "stress." The selected model is then ready to generate the required content format by inputting a specific prompt. The input here is the emotional state and the type of content to be generated, and the output is the initialized generative AI model.

[0433] Step 5:

[0434] The server generates content by inputting a prompt sentence into the initialized generative AI model. The specific prompt sentence is as follows:

[0435] Typed text: I've been feeling stressed lately and am looking for items to help me relax at home. Type of content to generate: Product listing page. Tone and style: Soothing, relaxing. Examples of items to generate include: Aroma diffuser, massage chair, soothing music playlist.

[0436] This prompt sentence is input into a generative AI model to generate a product introduction page related to relaxation. Here, the input is the prompt sentence, and the output is the generated content.

[0437] Step 6:

[0438] The server sends the generated content back to the device. The HTTP protocol is often used for communication. The device then appropriately analyzes the received data and displays the generated content (for example, a relaxation product introduction page) on the user's screen. The output product introduction page includes relaxation items such as aroma diffusers, massage chairs, and soothing music playlists. The input here is the generated content data, and the output is the display on the user interface.

[0439] 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.

[0440] 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.

[0441] 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.

[0442] [Second embodiment]

[0443] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0444] 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.

[0445] 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).

[0446] 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.

[0447] 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.

[0448] 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).

[0449] 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.

[0450] 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.

[0451] 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.

[0452] 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.

[0453] 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.

[0454] 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."

[0455] The present invention provides a system that includes a means for a user to input, in text form, the content, format, and style of content they wish to generate, a means for analyzing the input information and identifying the type of content to be generated, a means for selecting and initializing an optimal generative AI model based on the identified type of content, a means for generating content using the initialized generative AI model, and a means for providing the generated content to the user. This system can provide the user with high-quality content generation.

[0456] Program processing and explanation

[0457] 1. Displaying the user interface

[0458] Terminal

[0459] The device displays a web page and provides a form for the user to enter information required to generate the content.

[0460] A text field appears where the user can enter the content, format, and style of the content they want to generate.

[0461] 2. Getting and Sending User Input

[0462] User

[0463] The user enters the required information into the input form and presses the "Generate" button to submit.

[0464] The device sends the user's input data to the server.

[0465] 3. Parsing User Input

[0466] server

[0467] The server analyzes the input data received from the user and identifies the type of content to be generated (text, images, audio, video, etc.).

[0468] It also identifies the content style based on the user's input.

[0469] 4. Generative AI model selection and initialization

[0470] server

[0471] Selecting the best generative artificial intelligence model based on the identified content type and style.

[0472] The selected generative artificial intelligence model is initialized and set to an executable state.

[0473] 5. Content Generation

[0474] server

[0475] The initialized generative artificial intelligence model is used to generate content desired by the user.

[0476] The generation process ensures high-quality output that adheres to the specified style.

[0477] 6. Providing Generated Content

[0478] server

[0479] The generated content is sent back to the device for provision to the user.

[0480] The provided content is displayed on the user's screen.

[0481] Specific examples

[0482] Here is an example where the user wants to generate a sentence:

[0483] 1. The user enters "I would like to generate a story-style text" into the input form on the web page.

[0484] 2. The device sends this input data to the server.

[0485] 3. The server analyzes the input data and determines that the content type is "text" and the style is "story."

[0486] 4. The server selects and initializes the generative artificial intelligence model that is best suited to generating story-style text.

[0487] 5. The server uses the initialized model to generate a story-style sentence.

[0488] 6. The server sends the generated text back to the terminal, which displays it on the user's screen.

[0489] The present invention aims to enable high-quality content generation without requiring users to have specific technical knowledge. It also makes it possible to utilize user feedback to continuously improve the performance of generative AI models. Generated content can be easily embedded into web pages and social networking services, improving the user experience.

[0490] The processing flow will be explained below.

[0491] Step 1:

[0492] A user accesses a web page on a terminal, which displays a form for inputting the content, format, and style of content the user wants to generate.

[0493] Step 2:

[0494] The user enters information about the content they want to generate into the form and clicks the "Generate" button, at which point detailed information such as content, format, and style is entered.

[0495] Step 3:

[0496] The terminal sends the entered user information to the server. The input data is sent to the server using an HTTP request.

[0497] Step 4:

[0498] The server analyzes the user input data it receives and determines the type of content to be generated (text, images, audio, video, etc.) based on the input information.

[0499] Step 5:

[0500] The server selects the most appropriate generative AI model based on the type and style of the identified content, for example, a text generation model for sentence generation.

[0501] Step 6:

[0502] The server initializes the selected generative AI model and loads the necessary parameters to set the model in a usable state.

[0503] Step 7:

[0504] The server generates content based on input data using the initialized generative artificial intelligence model. For example, in response to an input such as "I would like to generate a story-style sentence," the server generates a story-style sentence.

[0505] Step 8:

[0506] The server returns the generated content to the terminal to provide it to the user. The generated content is included in the HTTP response.

[0507] Step 9:

[0508] The device displays the received content to the user. The generated text, images, audio, video, etc. are displayed on the user's screen.

[0509] Step 10:

[0510] Users review the generated content and provide feedback as needed, which helps improve the system.

[0511] Example 1

[0512] 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."

[0513] Conventional content generation systems have had the problem that they require advanced technical knowledge to generate high-quality output in response to the user's requests for content they want to generate. In addition, when generating different types of content (e.g., text, images), the process of selecting and optimizing the appropriate generation model is complicated, which takes time and effort for the user.

[0514] 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.

[0515] In this invention, the server includes: means for a user to input the content, format, and style of content they wish to generate in text; means for analyzing the input information and identifying the type of content to be generated; means for selecting and initializing an optimal generative AI model based on the identified type of content; means for generating content using the initialized generative AI model; means for displaying a user interface and acquiring input from the user; means for transmitting user input data to the server and for the server to analyze the data; means for generating content according to the user's request via text input using an AI model; and means for returning the generated content to the user's terminal and displaying it. This enables users to generate high-quality content without requiring specific expert knowledge.

[0516] A "user interface" is an operation screen through which a user inputs information for content generation.

[0517] A "text entry method" is a feature that provides an input field for users to enter content, format, and style.

[0518] "Means for analyzing input information" refers to a function that analyzes information received from a user and identifies the type and style of content to be generated.

[0519] The "means for identifying the type of content to be generated" is a function that determines the format of the content to be generated (e.g., text, image, audio, video, etc.) based on input information.

[0520] "Generative Artificial Intelligence Model" means a machine learning algorithm used to generate particular Content.

[0521] The "means for selecting and initializing a generative artificial intelligence model" is the process of selecting the AI ​​model that best suits the identified content and setting that model into an operational state.

[0522] "Means for generating content" refers to a function that uses an initialized AI model to create new content based on the content and style specified by the user.

[0523] The "means for providing generated content" is a function for returning and displaying generated content to the user.

[0524] A "means for obtaining user input" is a process for collecting data entered by a user.

[0525] "Means for transmitting user input data to a server" refers to a function that sends data input by a user on a terminal to a server via a network.

[0526] "Means for generating content according to user requests through text input" refers to a function that creates content according to the specified content and style based on the text information entered by the user.

[0527] "Means for returning and displaying the generated content to the user's terminal" refers to the process by which the server sends the generated content to the user's terminal and displays it on that terminal.

[0528] The present invention is a system that includes a means for a user to input the content, format, and style of content they want to generate in text form, a means for analyzing the input information and identifying the type of content to be generated, a means for selecting and initializing an optimal generative artificial intelligence model based on the identified type of content, a means for generating content using the initialized generative artificial intelligence model, and a means for providing the generated content to the user.

[0529] User Interface Display

[0530] The device displays a web page and provides a form for the user to enter the information necessary to generate content. The web page displays fields for the user to enter text for the content, format, and style of the content they wish to generate. The form includes required input fields such as "content type," "style," and "content."

[0531] Getting and sending user input

[0532] The user enters the necessary information into the displayed input form. After entering the information, the user presses the "Generate" button to send the data to the device. The device uses JavaScript or other tools to collect the user's input data and sends it to the server in JSON format.

[0533] Parsing user input

[0534] The server parses the received JSON data and identifies the "content type" (e.g., text, image) and "style" (e.g., story, news article) from the data. To identify the content and style, it uses an NLP (Natural Language Processing) model, for example, using libraries such as spaCy or NLTK.

[0535] Generative AI model selection and initialization

[0536] The server selects the optimal generative AI model based on the type and style of the identified content. For example, it selects GPT-4 for text generation and DALL-E for image generation. After the selection, the server initializes the AI ​​model. Initialization involves loading the model and setting it to inference mode.

[0537] Content generation

[0538] The server uses the initialized generative AI model to generate the content desired by the user. During the generation process, the server performs appropriate filtering and text completion according to the style specified by the user. For example, in sentence generation using GPT-4, a prompt sentence is input and high-quality text is generated as a follow-up.

[0539] Providing generated content

[0540] The server returns the generated content in JSON format to the device, which then parses the data and displays it in a user-friendly format, for example, displaying the generated text as an HTML element.

[0541] Specific examples

[0542] Here is a specific example of a case where a user wishes to generate text. The user enters "I would like to generate a story-style text" into an input form on a webpage. The device sends this input data to the server, which analyzes the input data and determines that the content type is "text" and the style is "story." The server selects and initializes a generative artificial intelligence model that is optimal for generating story-style text, and generates a story-style text using the initialized model. The server sends the generated text back to the device, which displays it on the user's screen. An example of a specific prompt sentence is "One day, a mysterious incident occurred in the forest. It was..."

[0543] This system allows users to quickly generate high-quality content in various formats without requiring specific expertise. Furthermore, it is possible to utilize user feedback to continuously improve the performance of the generative AI model. Generated content can be easily embedded into web pages and social networking services, improving the user experience.

[0544] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0545] Step 1: Displaying the User Interface

[0546] The device downloads an HTML page from the web server and displays it to the user. This web page contains a form for the user to enter the content, format, and style of the content they wish to generate. To display the user interface, the device uses a web browser to render user input fields using a combination of HTML, CSS, and JavaScript. The input fields display items such as "content type," "style," and "content."

[0547] Input: None (initial display)

[0548] Output: A form that accepts user input

[0549] Step 2: Getting User Input

[0550] The user enters the required information into the displayed input form (e.g., content type = "Text", style = "Story", content = "One day, in the forest...") After entering the information, the user presses the "Generate" button to send the data.

[0551] Input: Entering information into form fields

[0552] Output: Information entered into form fields

[0553] Step 3: Sending User Input

[0554] The device uses JavaScript or other tools to collect data entered by the user, convert it into JSON format, and send the converted JSON data to the server.

[0555] Input: Information entered by the user

[0556] Output: JSON format data and send it to the server

[0557] Step 4: Parsing User Input

[0558] The server parses the received JSON data to identify the content type and style. Specifically, the server uses an NLP (Natural Language Processing) library (e.g., spaCy, NLTK) to tokenize the text data and extract the content type and style according to the requirements.

[0559] Input: JSON format data

[0560] Output: Identifying the type and style of content (e.g., "text" or "story")

[0561] Step 5: Selecting and initializing a generative artificial intelligence model

[0562] The server selects the most appropriate generative AI model based on the identified content type and style. For example, the GPT-4 model is selected for sentence generation. The server then initializes the selected AI model and sets it to inference mode. Initialization includes loading the model and adjusting its configuration parameters.

[0563] Input: Content type and style information

[0564] Output: An initialized generative AI model

[0565] Step 6: Generate content

[0566] The server uses the initialized generative AI model to generate the content desired by the user. For example, when using GPT-4, a story-style sentence is generated based on the prompt sentence, "One day, in the forest..." During the generation process, the AI ​​model complements the text based on the prompt sentence and generates high-quality sentences according to the specified style.

[0567] Input: A prompt and an initialized AI model

[0568] Output: Generated content (e.g., story-style text)

[0569] Step 7: Providing generated content

[0570] The server returns the generated content to the device in JSON format, which includes the generated text and metadata.

[0571] Input: Generated content

[0572] Output: Content data in JSON format and send it to the device

[0573] Step 8: Viewing Generated Content

[0574] The device parses the received JSON data and displays the generated content in the user's browser. For example, it renders the generated text as HTML elements and displays it in a format that is easy for the user to read. This allows the user to visually check the generated content.

[0575] Input: Content data in JSON format

[0576] Output: The generated content displayed in the browser

[0577] (Application example 1)

[0578] 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."

[0579] Conventional content generation systems have limited the ability for users to efficiently generate high-quality content, leaving them dissatisfied with the generation process and results. Furthermore, they lack sufficient means to properly display or embed the generated content, resulting in a poor user experience. Furthermore, they lack sufficient feedback mechanisms to continuously improve the performance of generative AI models.

[0580] 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.

[0581] In this invention, the server includes means for inputting the content, format, and style of content a user wants to generate in text, means for analyzing the input information and identifying the type of content to be generated, means for selecting and initializing an optimal generative AI model based on the identified type of content, means for generating content using the initialized generative AI model, means for providing the generated content to the user, and means for displaying the generated content in a smartphone application, thereby enabling users to efficiently generate high-quality content and smoothly view and use it on their smartphones.

[0582] "User" refers to the entity that uses the system to generate content.

[0583] "Content" refers to information expressed in various forms, such as text, images, audio, and video.

[0584] "Content" is information about the specific theme or topic of the content to be generated.

[0585] The "format" indicates the type of form in which the generated content is expressed.

[0586] "Style" refers to the characteristics of the expression, tone, writing style, etc. of the content produced.

[0587] "Input means" means a device or function that allows a user to input the content, format, and style of the content in text form.

[0588] "Analysis means" is a function for analyzing input information and understanding its purpose and content.

[0589] The "identification means" is a function that determines the type of content to be generated based on the information interpreted by the analysis means.

[0590] A "generative artificial intelligence model" is a machine learning model for generating content based on user input.

[0591] The "selection means" is a function for selecting the optimal generative artificial intelligence model based on the type of content identified.

[0592] The "initialization means" is a function for setting the selected generative artificial intelligence model into a usable state.

[0593] The "generation means" is a function for generating content using an initialized generative artificial intelligence model.

[0594] "Means of delivery" refers to the function for appropriately delivering the generated content to users.

[0595] The "display means" is a function for displaying the generated content on a smartphone application.

[0596] "Feedback" refers to the evaluations and opinions provided by users after use.

[0597] "Performance improvement means" is a function for improving the performance of generative artificial intelligence models based on collected feedback.

[0598] A "web page" is a collection of information displayed on the Internet and accessible through a browser.

[0599] A "social networking service" is an online platform that allows users to interact with each other over the Internet.

[0600] The system of the present invention begins with a user entering text describing the content, format, and style of the content they wish to generate. The system analyzes the entered information and identifies the type of content to be generated. Next, the system selects and initializes an optimal generative AI model based on the identified type of content. The system generates content using the initialized generative AI model and provides the generated content to the user.

[0601] This system is realized using the following components and technologies:

[0602] User Interface

[0603] A user launches a smartphone application. The application presents the user with a form to enter information needed to generate content. The form contains fields for inputting the content, format, and style of the content the user wants to generate.

[0604] Sending data

[0605] When the user enters the required information into the input form and presses the "Generate" button, the smartphone application sends this data to the server.

[0606] Data analysis

[0607] The server analyzes the input data received from the user and identifies the type of content to be generated (text, images, audio, video, etc.) using a natural language processing toolkit (e.g., Python's NLTK or spaCy).

[0608] Model Selection and Initialization

[0609] The server selects and initializes the optimal generative artificial intelligence model (e.g., OpenAI GPT-4, DALL-E 2) based on the type and style of the identified content. Model initialization uses a machine learning framework such as TensorFlow or PyTorch.

[0610] Content generation

[0611] The server uses the initialized generative artificial intelligence model to generate content in the format and style specified by the user, where certain calculations and data processing are performed.

[0612] Content provision

[0613] The server sends the generated content back to the smartphone application, which displays it on the user's screen.

[0614] Feedback and Improvements

[0615] Users provide feedback on the generated content, and the server collects this feedback to improve the performance of the AI ​​model, using data analysis tools (e.g., Scikit-learn).

[0616] Specific examples

[0617] Let's say a user wants to create a blog post and enters the following:

[0618] Content: Latest trends in technology

[0619] Format: Blog post

[0620] Style: A clear, friendly tone

[0621] This information is sent to the server, which analyzes it, selects a generative AI model (e.g., GPT-4), and initializes it. The selected model then generates a blog post, which is then displayed on the smartphone application. An example prompt is as follows:

[0622] Prompt Sentence Examples

[0623] Content: Latest trends in technology

[0624] Format: Blog post

[0625] Style: A clear, friendly tone

[0626] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0627] Step 1:

[0628] The device launches the smartphone application and displays a content creation form to the user. The user enters the content, format, and style of the content they want to create into this form. The entered data is temporarily stored on the device.

[0629] Input: User input of content, format, and style

[0630] Output: Input data saved on the device

[0631] Step 2:

[0632] The terminal detects that the "Generate" button has been pressed and sends the input data to the server using an HTTP POST request, where the input data is converted to JSON format.

[0633] Input: User-entered data, temporarily stored data, HTTP POST requests

[0634] Output: JSON formatted data sent to the server

[0635] Step 3:

[0636] The server parses the received JSON data to determine the type and style of content to generate, using a natural language processing toolkit (e.g., Python's NLTK or spaCy) to extract keywords and analyze intent.

[0637] Input: JSON format data sent, natural language processing toolkit

[0638] Output: Identified content type and style (e.g. blog post, clear, friendly tone)

[0639] Step 4:

[0640] The server selects the optimal generative AI model based on the type and style of the identified content. To select a model, it searches a database of various AI models for a model that matches the criteria and selects it. After selection, it initializes the model and sets it to an executable state.

[0641] Input: Identified content type and style, AI model database

[0642] Output: Initialized optimal generative AI model

[0643] Step 5:

[0644] The server generates content by providing prompts based on the specified content and style to an initialized generative AI model, where the generative AI model (e.g., OpenAI GPT-4) outputs sentences in response to the prompts.

[0645] Input: Initialized generative AI model, prompt

[0646] Output: Generated content (text, images, audio, video)

[0647] Step 6:

[0648] The server returns the generated content to the smartphone application using an HTTP response, with the generated content being sent in JSON format.

[0649] Input: Generated content, HTTP response

[0650] Output: Content sent to the smartphone application

[0651] Step 7:

[0652] The device receives the content sent back from the server and displays it to the user. The display uses the smartphone application UI and displays the content on the screen according to its format (text, image, audio, video).

[0653] Input: Received content, smartphone UI

[0654] Output: The generated content displayed to the user

[0655] Step 8:

[0656] Users provide feedback on the generated content, which is sent via their device to the server and used to improve the performance of the AI ​​model. Feedback is sent again using an HTTP POST request.

[0657] Input: User feedback, HTTP POST request

[0658] Output: Feedback data sent to the server

[0659] Step 9:

[0660] The server analyzes the received feedback data and adjusts the generative AI model, using data analysis tools (e.g., Scikit-learn) to optimize the model parameters.

[0661] Input: Feedback data, data analysis tools

[0662] Output: A generative AI model with improved performance

[0663] 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.

[0664] The present invention provides a system including: means for a user to input, in text, the content, format, and style of content they wish to generate; means for analyzing the input information and identifying the type of content to be generated; means for selecting and initializing an optimal generative artificial intelligence model based on the identified type of content; means for generating content using the initialized generative artificial intelligence model; means for providing the generated content to the user; means for collecting user feedback and improving performance of the generative artificial intelligence model; means for embedding the generated content in a web page or social networking service; means for including text, images, audio, and video in the content generated based on the user input; and means for providing a simple interface that does not require the user to have specific programming knowledge. The system further includes means for including an emotion engine that recognizes the user's emotional state from the user's text input, means for adjusting the tone and style of the generated content based on the recognized user's emotional state, and means for optimizing the performance of the generative artificial intelligence model based on the user's emotional state and feedback, thereby achieving more personalized content generation.

[0665] Program processing and explanation

[0666] 1. Displaying the user interface

[0667] Terminal

[0668] The device displays a web page and provides a form for the user to enter information required to generate the content.

[0669] A text field appears where the user can enter the content, format, and style of the content they want to generate.

[0670] 2. Getting and Sending User Input

[0671] User

[0672] The user enters information about the content they want to generate into the form and clicks the "Generate" button.

[0673] The device sends the user's input data to the server.

[0674] 3. Analyzing user input and recognizing emotional states

[0675] server

[0676] The server parses the received user input data.

[0677] Use an emotion engine to recognize the emotional state of a user from their input text.

[0678] The type of content to generate is determined based on the analyzed information and the perceived emotional state.

[0679] 4. Generative AI model selection and initialization

[0680] server

[0681] Select the most appropriate generative artificial intelligence model based on the identified content type and the user's emotional state.

[0682] The selected generative artificial intelligence model is initialized and set to an executable state.

[0683] 5. Content Generation

[0684] server

[0685] The initialized generative artificial intelligence model is used to generate content based on the user's input data and emotional state.

[0686] The generation process applies a tone and style that reflects the perceived emotional state, resulting in personalized, high-quality content.

[0687] 6. Providing Generated Content

[0688] server

[0689] The generated content is sent back to the device for provision to the user.

[0690] The device displays the generated content to the user.

[0691] Specific examples

[0692] Here is an example of story-style text generation when the user has a sad emotion:

[0693] 1. A user fills out a form on a webpage with the request, "I would like a story-style text generated that will help me feel better about my sadness."

[0694] 2. The device sends this input data to the server.

[0695] 3. The server analyzes the input data and uses an emotion engine to recognize that the user's emotional state is "sad."

[0696] 4. The server identifies the content type as "text" and the style as "healing story," and selects and initializes the corresponding generative AI model.

[0697] 5. The server generates a healing story-style sentence that reflects the recognized emotion, "sadness."

[0698] 6. The server sends the generated text back to the terminal, which displays it on the user's screen.

[0699] In this way, recognizing the user's emotional state and generating personalized content that reflects it can improve the user experience. Furthermore, collecting user feedback and improving the model's performance can improve the overall quality of the system.

[0700] The processing flow will be explained below.

[0701] Step 1:

[0702] A user accesses a web page on a terminal, which displays a form for inputting the content, format, and style of content the user wants to generate.

[0703] Step 2:

[0704] The user enters information about the content they want to generate in the form (e.g., "I want you to generate a healing story-style text for those feeling sad") and clicks the "Generate" button.

[0705] Step 3:

[0706] The terminal sends the entered user information to the server. The input data is sent to the server using an HTTP request.

[0707] Step 4:

[0708] The server analyzes the received user input data and determines the type (e.g., text) and style (e.g., story style) of content to be generated from the input.

[0709] Step 5:

[0710] The server uses an emotion engine to recognize an emotional state from the user's text input, for example, recognizing the emotion "sad" from the input.

[0711] Step 6:

[0712] The server selects the optimal generative AI model based on the identified content type and the recognized emotional state. For example, it selects a model suitable for generating story-style sentences to soothe "sadness."

[0713] Step 7:

[0714] The server initializes the selected generative AI model, sets the model to a usable state, and loads the parameters required for generation.

[0715] Step 8:

[0716] The server uses the initialized generative artificial intelligence model to generate content based on the user's input data and emotional state, for example, generating comforting story-style text that reflects "sadness."

[0717] Step 9:

[0718] The server returns the generated content to the terminal to provide it to the user. The generated content is included in the HTTP response.

[0719] Step 10:

[0720] The terminal displays the received content to the user. The generated content, such as text, images, audio, and video, is displayed on the user's screen.

[0721] Step 11:

[0722] The user reviews the generated content and provides feedback as needed. The server collects the user's feedback and uses it to improve the performance of the generative AI model.

[0723] Example 2

[0724] 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."

[0725] Conventional content generation systems require a lot of effort to adjust the content, format, and style of generated content based on user input, and suffer from insufficient personalization based on the user's emotional state. Furthermore, feedback that would lead to improved quality of generated content and user experience is not effectively utilized, resulting in a uniformity of generated content.

[0726] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for inputting the content, format, and style of the content the user wants to generate in text; means for analyzing the input information and identifying the type of content to be generated; means for selecting and initializing an optimal generative AI model based on the identified type of content; means for generating content using the initialized generative AI model; means for providing the generated content to the user; means for recognizing the user's emotional state from the input text; and means for adjusting the tone and style of the generated content based on the recognized emotional state. This enables the generation of high-quality, personalized content based on user input.

[0727] "User" refers to a person who uses the system to generate content.

[0728] "Content" refers to any informational output, such as text, images, audio, or video, that is generated based on user input.

[0729] "Text input" refers to the act of a user providing a string of characters to a system using a keyboard or other input device.

[0730] "Entered Information" refers to data provided by a User to the System through text input.

[0731] "Analysis" refers to the process of analyzing information input by a user and converting it into useful information for internal use.

[0732] The "type of content to be generated" indicates the specific content format to be generated based on the user's request, and includes, for example, text, images, audio, video, and the like.

[0733] "Generative AI model" refers to an artificial intelligence algorithm or model used to generate content based on the type of content being generated.

[0734] "Initialization" refers to a series of preparatory steps that put a generative artificial intelligence model into a usable state.

[0735] "Emotional state" refers to the type of emotion recognized from the user's input text, including, for example, joy, sadness, anger, surprise, etc.

[0736] "Tone and style" refers to the way the content is expressed and the style or tone that reflects a particular emotion.

[0737] "Serving" refers to the act of delivering the generated content to a user, typically including displaying it on a screen or making it available for download.

[0738] This invention provides a system that includes a means for a user to input the content, format, and style of content they wish to generate in text form, and a means for analyzing the input information and identifying the type of content to be generated. The system also includes a means for selecting and initializing an optimal generative AI model based on the identified type of content. The system also includes a means for generating content using the initialized generative AI model and providing the generated content to a user.

[0739] The user accesses an input form on a web page through the device and inputs the content, format, and style of the content they want to generate. For example, if the user inputs "Please generate a healing story," the device sends this input data to the server.

[0740] The server analyzes the received input data, analyzes the text using a natural language processing engine, and uses an emotion engine to recognize the user's emotional state from the input text. Based on this recognized emotional state, the server determines the type of content to generate.

[0741] Based on the identified content type and the user's emotional state, the server selects the most appropriate generative AI model. For example, if the user is looking for a comforting story, an appropriate natural language generation model (e.g., GPT-3) is selected and initialized.

[0742] Using the initialized generative AI model, the server generates content based on the user's input data and emotional state, applying a tone and style that reflects the perceived emotional state to generate personalized content for each user.

[0743] The generated content is sent back to the device from the server, where it is analyzed and displayed on the user's screen. For example, a soothing story text may be displayed based on the user's request. It also has a feedback function that allows users to collect feedback and improve the performance of the generative AI model.

[0744] As a concrete example, consider the case where the user enters the prompt sentence as follows:

[0745] "I want to generate soothing story-style text for sad people."

[0746] Based on this prompt, the system recognizes the user's emotional state and uses an appropriate generative AI model to generate and display a personalized story, thereby providing high-quality content that responds to each user's emotions.

[0747] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0748] Step 1:

[0749] Terminal

[0750] The device displays a web page and provides a form for the user to enter information needed to generate content, including fields for specifying the content, format, and style of the content they want to generate.

[0751] Input: None

[0752] Output: A form to input the content, format, and style of the content you want to generate.

[0753] Step 2:

[0754] User

[0755] The user enters the content, format, and style of the content they want to generate into the input form and clicks the "Generate" button. For example, they might enter "Generate a healing story."

[0756] Input: Text describing the contract, format, and style

[0757] Output: The "Generate" button was clicked and the text data entered.

[0758] Step 3:

[0759] Terminal

[0760] The terminal generates and sends an HTTP request to send the user's input data to the server.

[0761] Input: User input data (text format)

[0762] Output: Data sent to the server as an HTTP request

[0763] Step 4:

[0764] server

[0765] The server parses the received user input, using a natural language processing engine to analyze the text and identify the intent and topic of the input.

[0766] Input: User input data sent from the terminal

[0767] Output: Structured information (intent, topic, etc.) from the parsed input data

[0768] Step 5:

[0769] server

[0770] The server uses an emotion engine to recognize the emotional state from the user's input text.

[0771] Input: Parsed input data

[0772] Output: User's emotional state (e.g. sadness, joy, etc.)

[0773] Step 6:

[0774] server

[0775] The server determines the type of content to generate based on the analyzed information and the perceived emotional state.

[0776] Input: Parsed input data and the user's emotional state

[0777] Output: The type of content to generate (e.g., a healing story)

[0778] Step 7:

[0779] server

[0780] Based on the identified content type and the user's emotional state, select the most appropriate generative artificial intelligence model, for example, a natural language generation model (e.g., GPT-3).

[0781] Input: The type of content to generate and the user's emotional state

[0782] Output: The selected generative artificial intelligence model

[0783] Step 8:

[0784] server

[0785] The selected generative artificial intelligence model is initialized and set to an executable state.

[0786] Input: A selected generative artificial intelligence model

[0787] Output: An initialized generative artificial intelligence model

[0788] Step 9:

[0789] server

[0790] The initialized generative artificial intelligence model is used to generate content based on the user's input data and emotional state. Specifically, the generative AI model is given input data and executed to generate personalized content.

[0791] Input: An initialized generative AI model, user input data, and the user's emotional state.

[0792] Output: Generated content (e.g., healing story text)

[0793] Step 10:

[0794] server

[0795] The generated content is returned to the terminal to be provided to the user. The generated content data is sent as an HTTP response.

[0796] Input: Generated content

[0797] Output: The generated content data as an HTTP response.

[0798] Step 11:

[0799] Terminal

[0800] The device receives the HTTP response, parses the generated content, and displays it to the user, for example, displaying the generated story text on a web page.

[0801] Input: Generated content data as an HTTP response

[0802] Output: Generated content that is displayed on the user's screen (e.g., story text)

[0803] (Application example 2)

[0804] 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."

[0805] Many modern virtual stores lack personalized suggestions tailored to individual emotions and circumstances when users search for specific products. This poses a risk of lowering user engagement and satisfaction. Furthermore, conventional content generation systems struggle to generate content that takes into account the user's emotional state, preventing them from providing product recommendations and suggestions that are in tune with the user's emotions. Furthermore, many of these systems require specific programming knowledge, making them difficult for everyday users to use.

[0806] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting the content, format, and style of the content the user wants to generate in text; means for analyzing the input information and identifying the type of content to be generated; means for selecting and initializing an optimal generative AI model based on the identified type of content; means for generating content using the initialized generative AI model; means for providing the generated content to the user; means including an emotion engine that recognizes the user's emotional state from the input information; means for adjusting the tone and style of the generated content based on the recognized emotional state; and means for providing a simple interface that does not require the user to have any specific programming knowledge. This enables personalized product introductions based on the user's emotional state.

[0807] "Content that the user wishes to generate" refers to information such as text, images, audio, and video that the user wishes to create.

[0808] "Format" refers to the structural information about the media format in which the content should be presented.

[0809] "Style" refers to the look, tone, approach, and other aspects of your content.

[0810] "Text input means" means a method by which a user enters information into a system using a keyboard or other text input device.

[0811] "Means for analyzing input information" refers to the technology that allows the system to understand the data received from the user and extract the necessary information.

[0812] "Means for determining the type of content to generate" refers to a method for determining what type of content to generate based on the analyzed information.

[0813] "Generative AI model" refers to an AI algorithm for generating new content from input data.

[0814] "Means for initializing" refers to a method for setting up a selected artificial intelligence model in an operational state.

[0815] "Generated Content" refers to data such as text, images, audio, and video created by a generative artificial intelligence model.

[0816] "Means of providing to users" refers to the methods by which generated content is displayed or transmitted to users.

[0817] "Emotion Engine" refers to technology that identifies the emotional state of a user from their input text.

[0818] "Tone and style adjustments" refers to techniques that change the way generated content is presented depending on a perceived emotional state.

[0819] "Simple interface" refers to a system interface design that is easy for users to understand and operate.

[0820] "Means for collecting feedback" refers to methods for collecting user ratings and opinions and using them to improve the system.

[0821] The present invention provides a system for automatically generating personalized content based on a user's emotional state. This system can be used to generate emotion-based product introduction pages in a virtual store. A specific embodiment of this system is described below.

[0822] System configuration and processing

[0823] The server includes the following means:

[0824] 1. A means of textually inputting the content, format, and style of content that users want to generate.

[0825] Through the user interface, users input the necessary information using a keyboard, etc. The interface is provided on the screen of a smartphone or PC and is implemented using web technologies such as HTML and JavaScript.

[0826] 2. A means of analyzing input information and identifying the type of content to generate

[0827] The server receives the data sent by the user and analyzes it using a natural language processing (NLP) engine, such as spaCy or NLTK.

[0828] 3. Means including an emotional engine

[0829] The server uses Hugging Face's Transformers library to recognize the emotional state from the user's text data, which allows it to identify the emotions the user is feeling (e.g., stress, sadness, joy, etc.).

[0830] 4. A means for selecting and initializing the optimal generative artificial intelligence model based on the type of content identified.

[0831] Based on the analysis and emotion recognition results, the server selects and initializes a suitable generative AI model (e.g., OpenAI GPT-4). The selected model is then immediately available for content generation.

[0832] 5. Means for generating content using an initialized generative artificial intelligence model

[0833] The server inputs the prompt text into the selected generative AI model and generates a product page in a tone and style based on the user's emotional state. The prompt text looks like this example:

[0834] Typed text: I've been feeling stressed lately and am looking for items to help me relax at home. Type of content to generate: Product listing page. Tone and style: Soothing, relaxing. Examples of items to generate include: Aroma diffuser, massage chair, soothing music playlist.

[0835] 6. Means of providing generated content to users

[0836] The server then sends the generated content data back to the front end for display on the user's device, using web pages written in HTML and CSS.

[0837] Specific examples

[0838] The user inputs, "I've been feeling stressed lately, so I'm looking for a product that will help me relax." The device sends this data to the server. The server analyzes the input data and recognizes "stress" using its emotion engine. The server then selects and initializes a generative AI model related to "relaxation." After that, it generates content introducing relaxation products (e.g., aroma diffusers, massage chairs, soothing music playlists) that reflect the user's emotional state. Finally, the server sends the generated content back to the user, and the product introduction page is displayed on the user's device.

[0839] This emotionally driven product recommendation creates a highly engaging and personalized shopping experience for users.

[0840] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0841] Step 1:

[0842] The user enters the content, format, and style of the content they want to generate as text into an input form provided on the screen of their smartphone or PC. A user interface implemented using HTML and JavaScript is used for input. An example of input data here would be "I've been feeling stressed lately, so I'm looking for a product that will help me relax." The entered information is sent from the device to the server as JSON format data.

[0843] Step 2:

[0844] The server receives input data in JSON format sent from the device. It then analyzes the input data using natural language processing (NLP) tools (e.g., spaCy or NLTK). Specifically, it divides the input text into sentences and words, and performs a tokenization process to understand the content of each. The analysis results in information that the user is looking for relaxation items.

[0845] Step 3:

[0846] The server uses an emotion engine (e.g., Hugging Face's Transformers library) to recognize the user's emotional state from the parsed data. The input text is fed into the emotion engine, which identifies the emotional state "stressed." Here, the input is the user text, and the output is the emotional state.

[0847] Step 4:

[0848] The server selects and initializes the optimal generative AI model (e.g., OpenAI GPT-4) based on the identified emotional state "stress." The selected model is then ready to generate the required content format by inputting a specific prompt. The input here is the emotional state and the type of content to be generated, and the output is the initialized generative AI model.

[0849] Step 5:

[0850] The server generates content by inputting a prompt sentence into the initialized generative AI model. The specific prompt sentence is as follows:

[0851] Typed text: I've been feeling stressed lately and am looking for items to help me relax at home. Type of content to generate: Product listing page. Tone and style: Soothing, relaxing. Examples of items to generate include: Aroma diffuser, massage chair, soothing music playlist.

[0852] This prompt sentence is input into a generative AI model to generate a product introduction page related to relaxation. Here, the input is the prompt sentence, and the output is the generated content.

[0853] Step 6:

[0854] The server sends the generated content back to the device. The HTTP protocol is often used for communication. The device then appropriately analyzes the received data and displays the generated content (for example, a relaxation product introduction page) on the user's screen. The output product introduction page includes relaxation items such as aroma diffusers, massage chairs, and soothing music playlists. The input here is the generated content data, and the output is the display on the user interface.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] [Third embodiment]

[0859] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0860] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0861] 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).

[0862] 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.

[0863] 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.

[0864] 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).

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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."

[0871] The present invention provides a system that includes a means for a user to input, in text form, the content, format, and style of content they wish to generate, a means for analyzing the input information and identifying the type of content to be generated, a means for selecting and initializing an optimal generative AI model based on the identified type of content, a means for generating content using the initialized generative AI model, and a means for providing the generated content to the user. This system can provide the user with high-quality content generation.

[0872] Program processing and explanation

[0873] 1. Displaying the user interface

[0874] Terminal

[0875] The device displays a web page and provides a form for the user to enter information required to generate the content.

[0876] A text field appears where the user can enter the content, format, and style of the content they want to generate.

[0877] 2. Getting and Sending User Input

[0878] User

[0879] The user enters the required information into the input form and presses the "Generate" button to submit.

[0880] The device sends the user's input data to the server.

[0881] 3. Parsing User Input

[0882] server

[0883] The server analyzes the input data received from the user and identifies the type of content to be generated (text, images, audio, video, etc.).

[0884] It also identifies the content style based on the user's input.

[0885] 4. Generative AI model selection and initialization

[0886] server

[0887] Selecting the best generative artificial intelligence model based on the identified content type and style.

[0888] The selected generative artificial intelligence model is initialized and set to an executable state.

[0889] 5. Content Generation

[0890] server

[0891] The initialized generative artificial intelligence model is used to generate content desired by the user.

[0892] The generation process ensures high-quality output that adheres to the specified style.

[0893] 6. Providing Generated Content

[0894] server

[0895] The generated content is sent back to the device for provision to the user.

[0896] The provided content is displayed on the user's screen.

[0897] Specific examples

[0898] Here is an example where the user wants to generate a sentence:

[0899] 1. The user enters "I would like to generate a story-style text" into the input form on the web page.

[0900] 2. The device sends this input data to the server.

[0901] 3. The server analyzes the input data and determines that the content type is "text" and the style is "story."

[0902] 4. The server selects and initializes the generative artificial intelligence model that is best suited to generating story-style text.

[0903] 5. The server uses the initialized model to generate a story-style sentence.

[0904] 6. The server sends the generated text back to the terminal, which displays it on the user's screen.

[0905] The present invention aims to enable high-quality content generation without requiring users to have specific technical knowledge. It also makes it possible to utilize user feedback to continuously improve the performance of generative AI models. Generated content can be easily embedded into web pages and social networking services, improving the user experience.

[0906] The processing flow will be explained below.

[0907] Step 1:

[0908] A user accesses a web page on a terminal, which displays a form for inputting the content, format, and style of content the user wants to generate.

[0909] Step 2:

[0910] The user enters information about the content they want to generate into the form and clicks the "Generate" button, at which point detailed information such as content, format, and style is entered.

[0911] Step 3:

[0912] The terminal sends the entered user information to the server. The input data is sent to the server using an HTTP request.

[0913] Step 4:

[0914] The server analyzes the user input data it receives and determines the type of content to be generated (text, images, audio, video, etc.) based on the input information.

[0915] Step 5:

[0916] The server selects the most appropriate generative AI model based on the type and style of the identified content, for example, a text generation model for sentence generation.

[0917] Step 6:

[0918] The server initializes the selected generative AI model and loads the necessary parameters to set the model in a usable state.

[0919] Step 7:

[0920] The server generates content based on input data using the initialized generative artificial intelligence model. For example, in response to an input such as "I would like to generate a story-style sentence," the server generates a story-style sentence.

[0921] Step 8:

[0922] The server returns the generated content to the terminal to provide it to the user. The generated content is included in the HTTP response.

[0923] Step 9:

[0924] The device displays the received content to the user. The generated text, images, audio, video, etc. are displayed on the user's screen.

[0925] Step 10:

[0926] Users review the generated content and provide feedback as needed, which helps improve the system.

[0927] Example 1

[0928] 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."

[0929] Conventional content generation systems have had the problem that they require advanced technical knowledge to generate high-quality output in response to the user's requests for content they want to generate. In addition, when generating different types of content (e.g., text, images), the process of selecting and optimizing the appropriate generation model is complicated, which takes time and effort for the user.

[0930] 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.

[0931] In this invention, the server includes: means for a user to input the content, format, and style of content they wish to generate in text; means for analyzing the input information and identifying the type of content to be generated; means for selecting and initializing an optimal generative AI model based on the identified type of content; means for generating content using the initialized generative AI model; means for displaying a user interface and acquiring input from the user; means for transmitting user input data to the server and for the server to analyze the data; means for generating content according to the user's request via text input using an AI model; and means for returning the generated content to the user's terminal and displaying it. This enables users to generate high-quality content without requiring specific expert knowledge.

[0932] A "user interface" is an operation screen through which a user inputs information for content generation.

[0933] A "text entry method" is a feature that provides an input field for users to enter content, format, and style.

[0934] "Means for analyzing input information" refers to a function that analyzes information received from a user and identifies the type and style of content to be generated.

[0935] The "means for identifying the type of content to be generated" is a function that determines the format of the content to be generated (e.g., text, image, audio, video, etc.) based on input information.

[0936] "Generative Artificial Intelligence Model" means a machine learning algorithm used to generate particular Content.

[0937] The "means for selecting and initializing a generative artificial intelligence model" is the process of selecting the AI ​​model that best suits the identified content and setting that model into an operational state.

[0938] "Means for generating content" refers to a function that uses an initialized AI model to create new content based on the content and style specified by the user.

[0939] The "means for providing generated content" is a function for returning and displaying generated content to the user.

[0940] A "means for obtaining user input" is a process for collecting data entered by a user.

[0941] "Means for transmitting user input data to a server" refers to a function that sends data input by a user on a terminal to a server via a network.

[0942] "Means for generating content according to user requests through text input" refers to a function that creates content according to the specified content and style based on the text information entered by the user.

[0943] "Means for returning and displaying the generated content to the user's terminal" refers to the process by which the server sends the generated content to the user's terminal and displays it on that terminal.

[0944] The present invention is a system that includes a means for a user to input the content, format, and style of content they want to generate in text form, a means for analyzing the input information and identifying the type of content to be generated, a means for selecting and initializing an optimal generative artificial intelligence model based on the identified type of content, a means for generating content using the initialized generative artificial intelligence model, and a means for providing the generated content to the user.

[0945] User Interface Display

[0946] The device displays a web page and provides a form for the user to enter the information necessary to generate content. The web page displays fields for the user to enter text for the content, format, and style of the content they wish to generate. The form includes required input fields such as "content type," "style," and "content."

[0947] Getting and sending user input

[0948] The user enters the necessary information into the displayed input form. After entering the information, the user presses the "Generate" button to send the data to the device. The device uses JavaScript or other tools to collect the user's input data and sends it to the server in JSON format.

[0949] Parsing user input

[0950] The server parses the received JSON data and identifies the "content type" (e.g., text, image) and "style" (e.g., story, news article) from the data. To identify the content and style, it uses an NLP (Natural Language Processing) model, for example, using libraries such as spaCy or NLTK.

[0951] Generative AI model selection and initialization

[0952] The server selects the optimal generative AI model based on the type and style of the identified content. For example, it selects GPT-4 for text generation and DALL-E for image generation. After the selection, the server initializes the AI ​​model. Initialization involves loading the model and setting it to inference mode.

[0953] Content generation

[0954] The server uses the initialized generative AI model to generate the content desired by the user. During the generation process, the server performs appropriate filtering and text completion according to the style specified by the user. For example, in sentence generation using GPT-4, a prompt sentence is input and high-quality text is generated as a follow-up.

[0955] Providing generated content

[0956] The server returns the generated content in JSON format to the device, which then parses the data and displays it in a user-friendly format, for example, displaying the generated text as an HTML element.

[0957] Specific examples

[0958] Here is a specific example of a case where a user wishes to generate text. The user enters "I would like to generate a story-style text" into an input form on a webpage. The device sends this input data to the server, which analyzes the input data and determines that the content type is "text" and the style is "story." The server selects and initializes a generative artificial intelligence model that is optimal for generating story-style text, and generates a story-style text using the initialized model. The server sends the generated text back to the device, which displays it on the user's screen. An example of a specific prompt sentence is "One day, a mysterious incident occurred in the forest. It was..."

[0959] This system allows users to quickly generate high-quality content in various formats without requiring specific expertise. Furthermore, it is possible to utilize user feedback to continuously improve the performance of the generative AI model. Generated content can be easily embedded into web pages and social networking services, improving the user experience.

[0960] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0961] Step 1: Displaying the User Interface

[0962] The device downloads an HTML page from the web server and displays it to the user. This web page contains a form for the user to enter the content, format, and style of the content they wish to generate. To display the user interface, the device uses a web browser to render user input fields using a combination of HTML, CSS, and JavaScript. The input fields display items such as "content type," "style," and "content."

[0963] Input: None (initial display)

[0964] Output: A form that accepts user input

[0965] Step 2: Getting User Input

[0966] The user enters the required information into the displayed input form (e.g., content type = "Text", style = "Story", content = "One day, in the forest...") After entering the information, the user presses the "Generate" button to send the data.

[0967] Input: Entering information into form fields

[0968] Output: Information entered into form fields

[0969] Step 3: Sending User Input

[0970] The device uses JavaScript or other tools to collect data entered by the user, convert it into JSON format, and send the converted JSON data to the server.

[0971] Input: Information entered by the user

[0972] Output: JSON format data and send it to the server

[0973] Step 4: Parsing User Input

[0974] The server parses the received JSON data to identify the content type and style. Specifically, the server uses an NLP (Natural Language Processing) library (e.g., spaCy, NLTK) to tokenize the text data and extract the content type and style according to the requirements.

[0975] Input: JSON format data

[0976] Output: Identifying the type and style of content (e.g., "text" or "story")

[0977] Step 5: Selecting and initializing a generative artificial intelligence model

[0978] The server selects the most appropriate generative AI model based on the identified content type and style. For example, the GPT-4 model is selected for sentence generation. The server then initializes the selected AI model and sets it to inference mode. Initialization includes loading the model and adjusting its configuration parameters.

[0979] Input: Content type and style information

[0980] Output: An initialized generative AI model

[0981] Step 6: Generate content

[0982] The server uses the initialized generative AI model to generate the content desired by the user. For example, when using GPT-4, a story-style sentence is generated based on the prompt sentence, "One day, in the forest..." During the generation process, the AI ​​model complements the text based on the prompt sentence and generates high-quality sentences according to the specified style.

[0983] Input: A prompt and an initialized AI model

[0984] Output: Generated content (e.g., story-style text)

[0985] Step 7: Providing generated content

[0986] The server returns the generated content to the device in JSON format, which includes the generated text and metadata.

[0987] Input: Generated content

[0988] Output: Content data in JSON format and send it to the device

[0989] Step 8: Viewing Generated Content

[0990] The device parses the received JSON data and displays the generated content in the user's browser. For example, it renders the generated text as HTML elements and displays it in a format that is easy for the user to read. This allows the user to visually check the generated content.

[0991] Input: Content data in JSON format

[0992] Output: The generated content displayed in the browser

[0993] (Application example 1)

[0994] 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."

[0995] Conventional content generation systems have limited the ability for users to efficiently generate high-quality content, leaving them dissatisfied with the generation process and results. Furthermore, they lack sufficient means to properly display or embed the generated content, resulting in a poor user experience. Furthermore, they lack sufficient feedback mechanisms to continuously improve the performance of generative AI models.

[0996] 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.

[0997] In this invention, the server includes means for inputting the content, format, and style of content a user wants to generate in text, means for analyzing the input information and identifying the type of content to be generated, means for selecting and initializing an optimal generative AI model based on the identified type of content, means for generating content using the initialized generative AI model, means for providing the generated content to the user, and means for displaying the generated content in a smartphone application, thereby enabling users to efficiently generate high-quality content and smoothly view and use it on their smartphones.

[0998] "User" refers to the entity that uses the system to generate content.

[0999] "Content" refers to information expressed in various forms, such as text, images, audio, and video.

[1000] "Content" is information about the specific theme or topic of the content to be generated.

[1001] The "format" indicates the type of form in which the generated content is expressed.

[1002] "Style" refers to the characteristics of the expression, tone, writing style, etc. of the content produced.

[1003] "Input means" means a device or function that allows a user to input the content, format, and style of the content in text form.

[1004] "Analysis means" is a function for analyzing input information and understanding its purpose and content.

[1005] The "identification means" is a function that determines the type of content to be generated based on the information interpreted by the analysis means.

[1006] A "generative artificial intelligence model" is a machine learning model for generating content based on user input.

[1007] The "selection means" is a function for selecting the optimal generative artificial intelligence model based on the type of content identified.

[1008] The "initialization means" is a function for setting the selected generative artificial intelligence model into a usable state.

[1009] The "generation means" is a function for generating content using an initialized generative artificial intelligence model.

[1010] "Means of delivery" refers to the function for appropriately delivering the generated content to users.

[1011] The "display means" is a function for displaying the generated content on a smartphone application.

[1012] "Feedback" refers to the evaluations and opinions provided by users after use.

[1013] "Performance improvement means" is a function for improving the performance of generative artificial intelligence models based on collected feedback.

[1014] A "web page" is a collection of information displayed on the Internet and accessible through a browser.

[1015] A "social networking service" is an online platform that allows users to interact with each other over the Internet.

[1016] The system of the present invention begins with a user entering text describing the content, format, and style of the content they wish to generate. The system analyzes the entered information and identifies the type of content to be generated. Next, the system selects and initializes an optimal generative AI model based on the identified type of content. The system generates content using the initialized generative AI model and provides the generated content to the user.

[1017] This system is realized using the following components and technologies:

[1018] User Interface

[1019] A user launches a smartphone application. The application presents the user with a form to enter information needed to generate content. The form contains fields for inputting the content, format, and style of the content the user wants to generate.

[1020] Sending data

[1021] When the user enters the required information into the input form and presses the "Generate" button, the smartphone application sends this data to the server.

[1022] Data analysis

[1023] The server analyzes the input data received from the user and identifies the type of content to be generated (text, images, audio, video, etc.) using a natural language processing toolkit (e.g., Python's NLTK or spaCy).

[1024] Model Selection and Initialization

[1025] The server selects and initializes the optimal generative artificial intelligence model (e.g., OpenAI GPT-4, DALL-E 2) based on the type and style of the identified content. Model initialization uses a machine learning framework such as TensorFlow or PyTorch.

[1026] Content generation

[1027] The server uses the initialized generative artificial intelligence model to generate content in the format and style specified by the user, where certain calculations and data processing are performed.

[1028] Content provision

[1029] The server sends the generated content back to the smartphone application, which displays it on the user's screen.

[1030] Feedback and Improvements

[1031] Users provide feedback on the generated content, and the server collects this feedback to improve the performance of the AI ​​model, using data analysis tools (e.g., Scikit-learn).

[1032] Specific examples

[1033] Let's say a user wants to create a blog post and enters the following:

[1034] Content: Latest trends in technology

[1035] Format: Blog post

[1036] Style: A clear, friendly tone

[1037] This information is sent to the server, which analyzes it, selects a generative AI model (e.g., GPT-4), and initializes it. The selected model then generates a blog post, which is then displayed on the smartphone application. An example prompt is as follows:

[1038] Prompt Sentence Examples

[1039] Content: Latest trends in technology

[1040] Format: Blog post

[1041] Style: A clear, friendly tone

[1042] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1043] Step 1:

[1044] The device launches the smartphone application and displays a content creation form to the user. The user enters the content, format, and style of the content they want to create into this form. The entered data is temporarily stored on the device.

[1045] Input: User input of content, format, and style

[1046] Output: Input data saved on the device

[1047] Step 2:

[1048] The terminal detects that the "Generate" button has been pressed and sends the input data to the server using an HTTP POST request, where the input data is converted to JSON format.

[1049] Input: User-entered data, temporarily stored data, HTTP POST requests

[1050] Output: JSON formatted data sent to the server

[1051] Step 3:

[1052] The server parses the received JSON data to determine the type and style of content to generate, using a natural language processing toolkit (e.g., Python's NLTK or spaCy) to extract keywords and analyze intent.

[1053] Input: JSON format data sent, natural language processing toolkit

[1054] Output: Identified content type and style (e.g. blog post, clear, friendly tone)

[1055] Step 4:

[1056] The server selects the optimal generative AI model based on the type and style of the identified content. To select a model, it searches a database of various AI models for a model that matches the criteria and selects it. After selection, it initializes the model and sets it to an executable state.

[1057] Input: Identified content type and style, AI model database

[1058] Output: Initialized optimal generative AI model

[1059] Step 5:

[1060] The server generates content by providing prompts based on the specified content and style to an initialized generative AI model, where the generative AI model (e.g., OpenAI GPT-4) outputs sentences in response to the prompts.

[1061] Input: Initialized generative AI model, prompt

[1062] Output: Generated content (text, images, audio, video)

[1063] Step 6:

[1064] The server returns the generated content to the smartphone application using an HTTP response, with the generated content being sent in JSON format.

[1065] Input: Generated content, HTTP response

[1066] Output: Content sent to the smartphone application

[1067] Step 7:

[1068] The device receives the content sent back from the server and displays it to the user. The display uses the smartphone application UI and displays the content on the screen according to its format (text, image, audio, video).

[1069] Input: Received content, smartphone UI

[1070] Output: The generated content displayed to the user

[1071] Step 8:

[1072] Users provide feedback on the generated content, which is sent via their device to the server and used to improve the performance of the AI ​​model. Feedback is sent again using an HTTP POST request.

[1073] Input: User feedback, HTTP POST request

[1074] Output: Feedback data sent to the server

[1075] Step 9:

[1076] The server analyzes the received feedback data and adjusts the generative AI model, using data analysis tools (e.g., Scikit-learn) to optimize the model parameters.

[1077] Input: Feedback data, data analysis tools

[1078] Output: A generative AI model with improved performance

[1079] 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.

[1080] The present invention provides a system including: means for a user to input, in text, the content, format, and style of content they wish to generate; means for analyzing the input information and identifying the type of content to be generated; means for selecting and initializing an optimal generative artificial intelligence model based on the identified type of content; means for generating content using the initialized generative artificial intelligence model; means for providing the generated content to the user; means for collecting user feedback and improving performance of the generative artificial intelligence model; means for embedding the generated content in a web page or social networking service; means for including text, images, audio, and video in the content generated based on the user input; and means for providing a simple interface that does not require the user to have specific programming knowledge. The system further includes means for including an emotion engine that recognizes the user's emotional state from the user's text input, means for adjusting the tone and style of the generated content based on the recognized user's emotional state, and means for optimizing the performance of the generative artificial intelligence model based on the user's emotional state and feedback, thereby achieving more personalized content generation.

[1081] Program processing and explanation

[1082] 1. Displaying the user interface

[1083] Terminal

[1084] The device displays a web page and provides a form for the user to enter information required to generate the content.

[1085] A text field appears where the user can enter the content, format, and style of the content they want to generate.

[1086] 2. Getting and Sending User Input

[1087] User

[1088] The user enters information about the content they want to generate into the form and clicks the "Generate" button.

[1089] The device sends the user's input data to the server.

[1090] 3. Analyzing user input and recognizing emotional states

[1091] server

[1092] The server parses the received user input data.

[1093] Use an emotion engine to recognize the emotional state of a user from their input text.

[1094] The type of content to generate is determined based on the analyzed information and the perceived emotional state.

[1095] 4. Generative AI model selection and initialization

[1096] server

[1097] Select the most appropriate generative artificial intelligence model based on the identified content type and the user's emotional state.

[1098] The selected generative artificial intelligence model is initialized and set to an executable state.

[1099] 5. Content Generation

[1100] server

[1101] The initialized generative artificial intelligence model is used to generate content based on the user's input data and emotional state.

[1102] The generation process applies a tone and style that reflects the perceived emotional state, resulting in personalized, high-quality content.

[1103] 6. Providing Generated Content

[1104] server

[1105] The generated content is sent back to the device for provision to the user.

[1106] The device displays the generated content to the user.

[1107] Specific examples

[1108] Here is an example of story-style text generation when the user has a sad emotion:

[1109] 1. A user fills out a form on a webpage with the request, "I would like a story-style text generated that will help me feel better about my sadness."

[1110] 2. The device sends this input data to the server.

[1111] 3. The server analyzes the input data and uses an emotion engine to recognize that the user's emotional state is "sad."

[1112] 4. The server identifies the content type as "text" and the style as "healing story," and selects and initializes the corresponding generative AI model.

[1113] 5. The server generates a healing story-style sentence that reflects the recognized emotion, "sadness."

[1114] 6. The server sends the generated text back to the terminal, which displays it on the user's screen.

[1115] In this way, recognizing the user's emotional state and generating personalized content that reflects it can improve the user experience. Furthermore, collecting user feedback and improving the model's performance can improve the overall quality of the system.

[1116] The processing flow will be explained below.

[1117] Step 1:

[1118] A user accesses a web page on a terminal, which displays a form for inputting the content, format, and style of content the user wants to generate.

[1119] Step 2:

[1120] The user enters information about the content they want to generate in the form (e.g., "I want you to generate a healing story-style text for those feeling sad") and clicks the "Generate" button.

[1121] Step 3:

[1122] The terminal sends the entered user information to the server. The input data is sent to the server using an HTTP request.

[1123] Step 4:

[1124] The server analyzes the received user input data and determines the type (e.g., text) and style (e.g., story style) of content to be generated from the input.

[1125] Step 5:

[1126] The server uses an emotion engine to recognize an emotional state from the user's text input, for example, recognizing the emotion "sad" from the input.

[1127] Step 6:

[1128] The server selects the optimal generative AI model based on the identified content type and the recognized emotional state. For example, it selects a model suitable for generating story-style sentences to soothe "sadness."

[1129] Step 7:

[1130] The server initializes the selected generative AI model, sets the model to a usable state, and loads the parameters required for generation.

[1131] Step 8:

[1132] The server uses the initialized generative artificial intelligence model to generate content based on the user's input data and emotional state, for example, generating comforting story-style text that reflects "sadness."

[1133] Step 9:

[1134] The server returns the generated content to the terminal to provide it to the user. The generated content is included in the HTTP response.

[1135] Step 10:

[1136] The terminal displays the received content to the user. The generated content, such as text, images, audio, and video, is displayed on the user's screen.

[1137] Step 11:

[1138] The user reviews the generated content and provides feedback as needed. The server collects the user's feedback and uses it to improve the performance of the generative AI model.

[1139] Example 2

[1140] 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."

[1141] Conventional content generation systems require a lot of effort to adjust the content, format, and style of generated content based on user input, and suffer from insufficient personalization based on the user's emotional state. Furthermore, feedback that would lead to improved quality of generated content and user experience is not effectively utilized, resulting in a uniformity of generated content.

[1142] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for inputting the content, format, and style of the content the user wants to generate in text; means for analyzing the input information and identifying the type of content to be generated; means for selecting and initializing an optimal generative AI model based on the identified type of content; means for generating content using the initialized generative AI model; means for providing the generated content to the user; means for recognizing the user's emotional state from the input text; and means for adjusting the tone and style of the generated content based on the recognized emotional state. This enables the generation of high-quality, personalized content based on user input.

[1143] "User" refers to a person who uses the system to generate content.

[1144] "Content" refers to any informational output, such as text, images, audio, or video, that is generated based on user input.

[1145] "Text input" refers to the act of a user providing a string of characters to a system using a keyboard or other input device.

[1146] "Entered Information" refers to data provided by a User to the System through text input.

[1147] "Analysis" refers to the process of analyzing information input by a user and converting it into useful information for internal use.

[1148] The "type of content to be generated" indicates the specific content format to be generated based on the user's request, and includes, for example, text, images, audio, video, and the like.

[1149] "Generative AI model" refers to an artificial intelligence algorithm or model used to generate content based on the type of content being generated.

[1150] "Initialization" refers to a series of preparatory steps that put a generative artificial intelligence model into a usable state.

[1151] "Emotional state" refers to the type of emotion recognized from the user's input text, including, for example, joy, sadness, anger, surprise, etc.

[1152] "Tone and style" refers to the way the content is expressed and the style or tone that reflects a particular emotion.

[1153] "Serving" refers to the act of delivering the generated content to a user, typically including displaying it on a screen or making it available for download.

[1154] This invention provides a system that includes a means for a user to input the content, format, and style of content they wish to generate in text form, and a means for analyzing the input information and identifying the type of content to be generated. The system also includes a means for selecting and initializing an optimal generative AI model based on the identified type of content. The system also includes a means for generating content using the initialized generative AI model and providing the generated content to a user.

[1155] The user accesses an input form on a web page through the device and inputs the content, format, and style of the content they want to generate. For example, if the user inputs "Please generate a healing story," the device sends this input data to the server.

[1156] The server analyzes the received input data, analyzes the text using a natural language processing engine, and uses an emotion engine to recognize the user's emotional state from the input text. Based on this recognized emotional state, the server determines the type of content to generate.

[1157] Based on the identified content type and the user's emotional state, the server selects the most appropriate generative AI model. For example, if the user is looking for a comforting story, an appropriate natural language generation model (e.g., GPT-3) is selected and initialized.

[1158] Using the initialized generative AI model, the server generates content based on the user's input data and emotional state, applying a tone and style that reflects the perceived emotional state to generate personalized content for each user.

[1159] The generated content is sent back to the device from the server, where it is analyzed and displayed on the user's screen. For example, a soothing story text may be displayed based on the user's request. It also has a feedback function that allows users to collect feedback and improve the performance of the generative AI model.

[1160] As a concrete example, consider the case where the user enters the prompt sentence as follows:

[1161] "I want to generate soothing story-style text for sad people."

[1162] Based on this prompt, the system recognizes the user's emotional state and uses an appropriate generative AI model to generate and display a personalized story, thereby providing high-quality content that responds to each user's emotions.

[1163] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1164] Step 1:

[1165] Terminal

[1166] The device displays a web page and provides a form for the user to enter information needed to generate content, including fields for specifying the content, format, and style of the content they want to generate.

[1167] Input: None

[1168] Output: A form to input the content, format, and style of the content you want to generate.

[1169] Step 2:

[1170] User

[1171] The user enters the content, format, and style of the content they want to generate into the input form and clicks the "Generate" button. For example, they might enter "Generate a healing story."

[1172] Input: Text describing the contract, format, and style

[1173] Output: The "Generate" button was clicked and the text data entered.

[1174] Step 3:

[1175] Terminal

[1176] The terminal generates and sends an HTTP request to send the user's input data to the server.

[1177] Input: User input data (text format)

[1178] Output: Data sent to the server as an HTTP request

[1179] Step 4:

[1180] server

[1181] The server parses the received user input, using a natural language processing engine to analyze the text and identify the intent and topic of the input.

[1182] Input: User input data sent from the terminal

[1183] Output: Structured information (intent, topic, etc.) from the parsed input data

[1184] Step 5:

[1185] server

[1186] The server uses an emotion engine to recognize the emotional state from the user's input text.

[1187] Input: Parsed input data

[1188] Output: User's emotional state (e.g. sadness, joy, etc.)

[1189] Step 6:

[1190] server

[1191] The server determines the type of content to generate based on the analyzed information and the perceived emotional state.

[1192] Input: Parsed input data and the user's emotional state

[1193] Output: The type of content to generate (e.g., a healing story)

[1194] Step 7:

[1195] server

[1196] Based on the identified content type and the user's emotional state, select the most appropriate generative artificial intelligence model, for example, a natural language generation model (e.g., GPT-3).

[1197] Input: The type of content to generate and the user's emotional state

[1198] Output: The selected generative artificial intelligence model

[1199] Step 8:

[1200] server

[1201] The selected generative artificial intelligence model is initialized and set to an executable state.

[1202] Input: A selected generative artificial intelligence model

[1203] Output: An initialized generative artificial intelligence model

[1204] Step 9:

[1205] server

[1206] The initialized generative artificial intelligence model is used to generate content based on the user's input data and emotional state. Specifically, the generative AI model is given input data and executed to generate personalized content.

[1207] Input: An initialized generative AI model, user input data, and the user's emotional state.

[1208] Output: Generated content (e.g., healing story text)

[1209] Step 10:

[1210] server

[1211] The generated content is returned to the terminal to be provided to the user. The generated content data is sent as an HTTP response.

[1212] Input: Generated content

[1213] Output: The generated content data as an HTTP response.

[1214] Step 11:

[1215] Terminal

[1216] The device receives the HTTP response, parses the generated content, and displays it to the user, for example, displaying the generated story text on a web page.

[1217] Input: Generated content data as an HTTP response

[1218] Output: Generated content that is displayed on the user's screen (e.g., story text)

[1219] (Application example 2)

[1220] 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."

[1221] Many modern virtual stores lack personalized suggestions tailored to individual emotions and circumstances when users search for specific products. This poses a risk of lowering user engagement and satisfaction. Furthermore, conventional content generation systems struggle to generate content that takes into account the user's emotional state, preventing them from providing product recommendations and suggestions that are in tune with the user's emotions. Furthermore, many of these systems require specific programming knowledge, making them difficult for everyday users to use.

[1222] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting the content, format, and style of the content the user wants to generate in text; means for analyzing the input information and identifying the type of content to be generated; means for selecting and initializing an optimal generative AI model based on the identified type of content; means for generating content using the initialized generative AI model; means for providing the generated content to the user; means including an emotion engine that recognizes the user's emotional state from the input information; means for adjusting the tone and style of the generated content based on the recognized emotional state; and means for providing a simple interface that does not require the user to have any specific programming knowledge. This enables personalized product introductions based on the user's emotional state.

[1223] "Content that the user wishes to generate" refers to information such as text, images, audio, and video that the user wishes to create.

[1224] "Format" refers to the structural information about the media format in which the content should be presented.

[1225] "Style" refers to the look, tone, approach, and other aspects of your content.

[1226] "Text input means" means a method by which a user enters information into a system using a keyboard or other text input device.

[1227] "Means for analyzing input information" refers to the technology that allows the system to understand the data received from the user and extract the necessary information.

[1228] "Means for determining the type of content to generate" refers to a method for determining what type of content to generate based on the analyzed information.

[1229] "Generative AI model" refers to an AI algorithm for generating new content from input data.

[1230] "Means for initializing" refers to a method for setting up a selected artificial intelligence model in an operational state.

[1231] "Generated Content" refers to data such as text, images, audio, and video created by a generative artificial intelligence model.

[1232] "Means of providing to users" refers to the methods by which generated content is displayed or transmitted to users.

[1233] "Emotion Engine" refers to technology that identifies the emotional state of a user from their input text.

[1234] "Tone and style adjustments" refers to techniques that change the way generated content is presented depending on a perceived emotional state.

[1235] "Simple interface" refers to a system interface design that is easy for users to understand and operate.

[1236] "Means for collecting feedback" refers to methods for collecting user ratings and opinions and using them to improve the system.

[1237] The present invention provides a system for automatically generating personalized content based on a user's emotional state. This system can be used to generate emotion-based product introduction pages in a virtual store. A specific embodiment of this system is described below.

[1238] System configuration and processing

[1239] The server includes the following means:

[1240] 1. A means of textually inputting the content, format, and style of content that users want to generate.

[1241] Through the user interface, users input the necessary information using a keyboard, etc. The interface is provided on the screen of a smartphone or PC and is implemented using web technologies such as HTML and JavaScript.

[1242] 2. A means of analyzing input information and identifying the type of content to generate

[1243] The server receives the data sent by the user and analyzes it using a natural language processing (NLP) engine, such as spaCy or NLTK.

[1244] 3. Means including an emotional engine

[1245] The server uses Hugging Face's Transformers library to recognize the emotional state from the user's text data, which allows it to identify the emotions the user is feeling (e.g., stress, sadness, joy, etc.).

[1246] 4. A means for selecting and initializing the optimal generative artificial intelligence model based on the type of content identified.

[1247] Based on the analysis and emotion recognition results, the server selects and initializes a suitable generative AI model (e.g., OpenAI GPT-4). The selected model is then immediately available for content generation.

[1248] 5. Means for generating content using an initialized generative artificial intelligence model

[1249] The server inputs the prompt text into the selected generative AI model and generates a product page in a tone and style based on the user's emotional state. The prompt text looks like this example:

[1250] Typed text: I've been feeling stressed lately and am looking for items to help me relax at home. Type of content to generate: Product listing page. Tone and style: Soothing, relaxing. Examples of items to generate include: Aroma diffuser, massage chair, soothing music playlist.

[1251] 6. Means of providing generated content to users

[1252] The server then sends the generated content data back to the front end for display on the user's device, using web pages written in HTML and CSS.

[1253] Specific examples

[1254] The user inputs, "I've been feeling stressed lately, so I'm looking for a product that will help me relax." The device sends this data to the server. The server analyzes the input data and recognizes "stress" using its emotion engine. The server then selects and initializes a generative AI model related to "relaxation." After that, it generates content introducing relaxation products (e.g., aroma diffusers, massage chairs, soothing music playlists) that reflect the user's emotional state. Finally, the server sends the generated content back to the user, and the product introduction page is displayed on the user's device.

[1255] This emotionally driven product recommendation creates a highly engaging and personalized shopping experience for users.

[1256] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1257] Step 1:

[1258] The user enters the content, format, and style of the content they want to generate as text into an input form provided on the screen of their smartphone or PC. A user interface implemented using HTML and JavaScript is used for input. An example of input data here would be "I've been feeling stressed lately, so I'm looking for a product that will help me relax." The entered information is sent from the device to the server as JSON format data.

[1259] Step 2:

[1260] The server receives input data in JSON format sent from the device. It then analyzes the input data using natural language processing (NLP) tools (e.g., spaCy or NLTK). Specifically, it divides the input text into sentences and words, and performs a tokenization process to understand the content of each. The analysis results in information that the user is looking for relaxation items.

[1261] Step 3:

[1262] The server uses an emotion engine (e.g., Hugging Face's Transformers library) to recognize the user's emotional state from the parsed data. The input text is fed into the emotion engine, which identifies the emotional state "stressed." Here, the input is the user text, and the output is the emotional state.

[1263] Step 4:

[1264] The server selects and initializes the optimal generative AI model (e.g., OpenAI GPT-4) based on the identified emotional state "stress." The selected model is then ready to generate the required content format by inputting a specific prompt. The input here is the emotional state and the type of content to be generated, and the output is the initialized generative AI model.

[1265] Step 5:

[1266] The server generates content by inputting a prompt sentence into the initialized generative AI model. The specific prompt sentence is as follows:

[1267] Typed text: I've been feeling stressed lately and am looking for items to help me relax at home. Type of content to generate: Product listing page. Tone and style: Soothing, relaxing. Examples of items to generate include: Aroma diffuser, massage chair, soothing music playlist.

[1268] This prompt sentence is input into a generative AI model to generate a product introduction page related to relaxation. Here, the input is the prompt sentence, and the output is the generated content.

[1269] Step 6:

[1270] The server sends the generated content back to the device. The HTTP protocol is often used for communication. The device then appropriately analyzes the received data and displays the generated content (for example, a relaxation product introduction page) on the user's screen. The output product introduction page includes relaxation items such as aroma diffusers, massage chairs, and soothing music playlists. The input here is the generated content data, and the output is the display on the user interface.

[1271] 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.

[1272] 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.

[1273] 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.

[1274] [Fourth embodiment]

[1275] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1276] 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.

[1277] 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).

[1278] 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.

[1279] 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.

[1280] 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).

[1281] 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.

[1282] 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.

[1283] 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.

[1284] 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.

[1285] 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.

[1286] 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.

[1287] 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."

[1288] The present invention provides a system that includes a means for a user to input, in text form, the content, format, and style of content they wish to generate, a means for analyzing the input information and identifying the type of content to be generated, a means for selecting and initializing an optimal generative AI model based on the identified type of content, a means for generating content using the initialized generative AI model, and a means for providing the generated content to the user. This system can provide the user with high-quality content generation.

[1289] Program processing and explanation

[1290] 1. Displaying the user interface

[1291] Terminal

[1292] The device displays a web page and provides a form for the user to enter information required to generate the content.

[1293] A text field appears where the user can enter the content, format, and style of the content they want to generate.

[1294] 2. Getting and Sending User Input

[1295] User

[1296] The user enters the required information into the input form and presses the "Generate" button to submit.

[1297] The device sends the user's input data to the server.

[1298] 3. Parsing User Input

[1299] server

[1300] The server analyzes the input data received from the user and identifies the type of content to be generated (text, images, audio, video, etc.).

[1301] It also identifies the content style based on the user's input.

[1302] 4. Generative AI model selection and initialization

[1303] server

[1304] Selecting the best generative artificial intelligence model based on the identified content type and style.

[1305] The selected generative artificial intelligence model is initialized and set to an executable state.

[1306] 5. Content Generation

[1307] server

[1308] The initialized generative artificial intelligence model is used to generate content desired by the user.

[1309] The generation process ensures high-quality output that adheres to the specified style.

[1310] 6. Providing Generated Content

[1311] server

[1312] The generated content is sent back to the device for provision to the user.

[1313] The provided content is displayed on the user's screen.

[1314] Specific examples

[1315] Here is an example where the user wants to generate a sentence:

[1316] 1. The user enters "I would like to generate a story-style text" into the input form on the web page.

[1317] 2. The device sends this input data to the server.

[1318] 3. The server analyzes the input data and determines that the content type is "text" and the style is "story."

[1319] 4. The server selects and initializes the generative artificial intelligence model that is best suited to generating story-style text.

[1320] 5. The server uses the initialized model to generate a story-style sentence.

[1321] 6. The server sends the generated text back to the terminal, which displays it on the user's screen.

[1322] The present invention aims to enable high-quality content generation without requiring users to have specific technical knowledge. It also makes it possible to utilize user feedback to continuously improve the performance of generative AI models. Generated content can be easily embedded into web pages and social networking services, improving the user experience.

[1323] The processing flow will be explained below.

[1324] Step 1:

[1325] A user accesses a web page on a terminal, which displays a form for inputting the content, format, and style of content the user wants to generate.

[1326] Step 2:

[1327] The user enters information about the content they want to generate into the form and clicks the "Generate" button, at which point detailed information such as content, format, and style is entered.

[1328] Step 3:

[1329] The terminal sends the entered user information to the server. The input data is sent to the server using an HTTP request.

[1330] Step 4:

[1331] The server analyzes the user input data it receives and determines the type of content to be generated (text, images, audio, video, etc.) based on the input information.

[1332] Step 5:

[1333] The server selects the most appropriate generative AI model based on the type and style of the identified content, for example, a text generation model for sentence generation.

[1334] Step 6:

[1335] The server initializes the selected generative AI model and loads the necessary parameters to set the model in a usable state.

[1336] Step 7:

[1337] The server generates content based on input data using the initialized generative artificial intelligence model. For example, in response to an input such as "I would like to generate a story-style sentence," the server generates a story-style sentence.

[1338] Step 8:

[1339] The server returns the generated content to the terminal to provide it to the user. The generated content is included in the HTTP response.

[1340] Step 9:

[1341] The device displays the received content to the user. The generated text, images, audio, video, etc. are displayed on the user's screen.

[1342] Step 10:

[1343] Users review the generated content and provide feedback as needed, which helps improve the system.

[1344] Example 1

[1345] 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."

[1346] Conventional content generation systems have had the problem that they require advanced technical knowledge to generate high-quality output in response to the user's requests for content they want to generate. In addition, when generating different types of content (e.g., text, images), the process of selecting and optimizing the appropriate generation model is complicated, which takes time and effort for the user.

[1347] 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.

[1348] In this invention, the server includes: means for a user to input the content, format, and style of content they wish to generate in text; means for analyzing the input information and identifying the type of content to be generated; means for selecting and initializing an optimal generative AI model based on the identified type of content; means for generating content using the initialized generative AI model; means for displaying a user interface and acquiring input from the user; means for transmitting user input data to the server and for the server to analyze the data; means for generating content according to the user's request via text input using an AI model; and means for returning the generated content to the user's terminal and displaying it. This enables users to generate high-quality content without requiring specific expert knowledge.

[1349] A "user interface" is an operation screen through which a user inputs information for content generation.

[1350] A "text entry method" is a feature that provides an input field for users to enter content, format, and style.

[1351] "Means for analyzing input information" refers to a function that analyzes information received from a user and identifies the type and style of content to be generated.

[1352] The "means for identifying the type of content to be generated" is a function that determines the format of the content to be generated (e.g., text, image, audio, video, etc.) based on input information.

[1353] "Generative Artificial Intelligence Model" means a machine learning algorithm used to generate particular Content.

[1354] The "means for selecting and initializing a generative artificial intelligence model" is the process of selecting the AI ​​model that best suits the identified content and setting that model into an operational state.

[1355] "Means for generating content" refers to a function that uses an initialized AI model to create new content based on the content and style specified by the user.

[1356] The "means for providing generated content" is a function for returning and displaying generated content to the user.

[1357] A "means for obtaining user input" is a process for collecting data entered by a user.

[1358] "Means for transmitting user input data to a server" refers to a function that sends data input by a user on a terminal to a server via a network.

[1359] "Means for generating content according to user requests through text input" refers to a function that creates content according to the specified content and style based on the text information entered by the user.

[1360] "Means for returning and displaying the generated content to the user's terminal" refers to the process by which the server sends the generated content to the user's terminal and displays it on that terminal.

[1361] The present invention is a system that includes a means for a user to input the content, format, and style of content they want to generate in text form, a means for analyzing the input information and identifying the type of content to be generated, a means for selecting and initializing an optimal generative artificial intelligence model based on the identified type of content, a means for generating content using the initialized generative artificial intelligence model, and a means for providing the generated content to the user.

[1362] User Interface Display

[1363] The device displays a web page and provides a form for the user to enter the information necessary to generate content. The web page displays fields for the user to enter text for the content, format, and style of the content they wish to generate. The form includes required input fields such as "content type," "style," and "content."

[1364] Getting and sending user input

[1365] The user enters the necessary information into the displayed input form. After entering the information, the user presses the "Generate" button to send the data to the device. The device uses JavaScript or other tools to collect the user's input data and sends it to the server in JSON format.

[1366] Parsing user input

[1367] The server parses the received JSON data and identifies the "content type" (e.g., text, image) and "style" (e.g., story, news article) from the data. To identify the content and style, it uses an NLP (Natural Language Processing) model, for example, using libraries such as spaCy or NLTK.

[1368] Generative AI model selection and initialization

[1369] The server selects the optimal generative AI model based on the type and style of the identified content. For example, it selects GPT-4 for text generation and DALL-E for image generation. After the selection, the server initializes the AI ​​model. Initialization involves loading the model and setting it to inference mode.

[1370] Content generation

[1371] The server uses the initialized generative AI model to generate the content desired by the user. During the generation process, the server performs appropriate filtering and text completion according to the style specified by the user. For example, in sentence generation using GPT-4, a prompt sentence is input and high-quality text is generated as a follow-up.

[1372] Providing generated content

[1373] The server returns the generated content in JSON format to the device, which then parses the data and displays it in a user-friendly format, for example, displaying the generated text as an HTML element.

[1374] Specific examples

[1375] Here is a specific example of a case where a user wishes to generate text. The user enters "I would like to generate a story-style text" into an input form on a webpage. The device sends this input data to the server, which analyzes the input data and determines that the content type is "text" and the style is "story." The server selects and initializes a generative artificial intelligence model that is optimal for generating story-style text, and generates a story-style text using the initialized model. The server sends the generated text back to the device, which displays it on the user's screen. An example of a specific prompt sentence is "One day, a mysterious incident occurred in the forest. It was..."

[1376] This system allows users to quickly generate high-quality content in various formats without requiring specific expertise. Furthermore, it is possible to utilize user feedback to continuously improve the performance of the generative AI model. Generated content can be easily embedded into web pages and social networking services, improving the user experience.

[1377] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1378] Step 1: Displaying the User Interface

[1379] The device downloads an HTML page from the web server and displays it to the user. This web page contains a form for the user to enter the content, format, and style of the content they wish to generate. To display the user interface, the device uses a web browser to render user input fields using a combination of HTML, CSS, and JavaScript. The input fields display items such as "content type," "style," and "content."

[1380] Input: None (initial display)

[1381] Output: A form that accepts user input

[1382] Step 2: Getting User Input

[1383] The user enters the required information into the displayed input form (e.g., content type = "Text", style = "Story", content = "One day, in the forest...") After entering the information, the user presses the "Generate" button to send the data.

[1384] Input: Entering information into form fields

[1385] Output: Information entered into form fields

[1386] Step 3: Sending User Input

[1387] The device uses JavaScript or other tools to collect data entered by the user, convert it into JSON format, and send the converted JSON data to the server.

[1388] Input: Information entered by the user

[1389] Output: JSON format data and send it to the server

[1390] Step 4: Parsing User Input

[1391] The server parses the received JSON data to identify the content type and style. Specifically, the server uses an NLP (Natural Language Processing) library (e.g., spaCy, NLTK) to tokenize the text data and extract the content type and style according to the requirements.

[1392] Input: JSON format data

[1393] Output: Identifying the type and style of content (e.g., "text" or "story")

[1394] Step 5: Selecting and initializing a generative artificial intelligence model

[1395] The server selects the most appropriate generative AI model based on the identified content type and style. For example, the GPT-4 model is selected for sentence generation. The server then initializes the selected AI model and sets it to inference mode. Initialization includes loading the model and adjusting its configuration parameters.

[1396] Input: Content type and style information

[1397] Output: An initialized generative AI model

[1398] Step 6: Generate content

[1399] The server uses the initialized generative AI model to generate the content desired by the user. For example, when using GPT-4, a story-style sentence is generated based on the prompt sentence, "One day, in the forest..." During the generation process, the AI ​​model complements the text based on the prompt sentence and generates high-quality sentences according to the specified style.

[1400] Input: A prompt and an initialized AI model

[1401] Output: Generated content (e.g., story-style text)

[1402] Step 7: Providing generated content

[1403] The server returns the generated content to the device in JSON format, which includes the generated text and metadata.

[1404] Input: Generated content

[1405] Output: Content data in JSON format and send it to the device

[1406] Step 8: Viewing Generated Content

[1407] The device parses the received JSON data and displays the generated content in the user's browser. For example, it renders the generated text as HTML elements and displays it in a format that is easy for the user to read. This allows the user to visually check the generated content.

[1408] Input: Content data in JSON format

[1409] Output: The generated content displayed in the browser

[1410] (Application example 1)

[1411] 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."

[1412] Conventional content generation systems have limited the ability for users to efficiently generate high-quality content, leaving them dissatisfied with the generation process and results. Furthermore, they lack sufficient means to properly display or embed the generated content, resulting in a poor user experience. Furthermore, they lack sufficient feedback mechanisms to continuously improve the performance of generative AI models.

[1413] 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.

[1414] In this invention, the server includes means for inputting the content, format, and style of content a user wants to generate in text, means for analyzing the input information and identifying the type of content to be generated, means for selecting and initializing an optimal generative AI model based on the identified type of content, means for generating content using the initialized generative AI model, means for providing the generated content to the user, and means for displaying the generated content in a smartphone application, thereby enabling users to efficiently generate high-quality content and smoothly view and use it on their smartphones.

[1415] "User" refers to the entity that uses the system to generate content.

[1416] "Content" refers to information expressed in various forms, such as text, images, audio, and video.

[1417] "Content" is information about the specific theme or topic of the content to be generated.

[1418] The "format" indicates the type of form in which the generated content is expressed.

[1419] "Style" refers to the characteristics of the expression, tone, writing style, etc. of the content produced.

[1420] "Input means" means a device or function that allows a user to input the content, format, and style of the content in text form.

[1421] "Analysis means" is a function for analyzing input information and understanding its purpose and content.

[1422] The "identification means" is a function that determines the type of content to be generated based on the information interpreted by the analysis means.

[1423] A "generative artificial intelligence model" is a machine learning model for generating content based on user input.

[1424] The "selection means" is a function for selecting the optimal generative artificial intelligence model based on the type of content identified.

[1425] The "initialization means" is a function for setting the selected generative artificial intelligence model into a usable state.

[1426] The "generation means" is a function for generating content using an initialized generative artificial intelligence model.

[1427] "Means of delivery" refers to the function for appropriately delivering the generated content to users.

[1428] The "display means" is a function for displaying the generated content on a smartphone application.

[1429] "Feedback" refers to the evaluations and opinions provided by users after use.

[1430] "Performance improvement means" is a function for improving the performance of generative artificial intelligence models based on collected feedback.

[1431] A "web page" is a collection of information displayed on the Internet and accessible through a browser.

[1432] A "social networking service" is an online platform that allows users to interact with each other over the Internet.

[1433] The system of the present invention begins with a user entering text describing the content, format, and style of the content they wish to generate. The system analyzes the entered information and identifies the type of content to be generated. Next, the system selects and initializes an optimal generative AI model based on the identified type of content. The system generates content using the initialized generative AI model and provides the generated content to the user.

[1434] This system is realized using the following components and technologies:

[1435] User Interface

[1436] A user launches a smartphone application. The application presents the user with a form to enter information needed to generate content. The form contains fields for inputting the content, format, and style of the content the user wants to generate.

[1437] Sending data

[1438] When the user enters the required information into the input form and presses the "Generate" button, the smartphone application sends this data to the server.

[1439] Data analysis

[1440] The server analyzes the input data received from the user and identifies the type of content to be generated (text, images, audio, video, etc.) using a natural language processing toolkit (e.g., Python's NLTK or spaCy).

[1441] Model Selection and Initialization

[1442] The server selects and initializes the optimal generative artificial intelligence model (e.g., OpenAI GPT-4, DALL-E 2) based on the type and style of the identified content. Model initialization uses a machine learning framework such as TensorFlow or PyTorch.

[1443] Content generation

[1444] The server uses the initialized generative artificial intelligence model to generate content in the format and style specified by the user, where certain calculations and data processing are performed.

[1445] Content provision

[1446] The server sends the generated content back to the smartphone application, which displays it on the user's screen.

[1447] Feedback and Improvements

[1448] Users provide feedback on the generated content, and the server collects this feedback to improve the performance of the AI ​​model, using data analysis tools (e.g., Scikit-learn).

[1449] Specific examples

[1450] Let's say a user wants to create a blog post and enters the following:

[1451] Content: Latest trends in technology

[1452] Format: Blog post

[1453] Style: A clear, friendly tone

[1454] This information is sent to the server, which analyzes it, selects a generative AI model (e.g., GPT-4), and initializes it. The selected model then generates a blog post, which is then displayed on the smartphone application. An example prompt is as follows:

[1455] Prompt Sentence Examples

[1456] Content: Latest trends in technology

[1457] Format: Blog post

[1458] Style: A clear, friendly tone

[1459] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1460] Step 1:

[1461] The device launches the smartphone application and displays a content creation form to the user. The user enters the content, format, and style of the content they want to create into this form. The entered data is temporarily stored on the device.

[1462] Input: User input of content, format, and style

[1463] Output: Input data saved on the device

[1464] Step 2:

[1465] The terminal detects that the "Generate" button has been pressed and sends the input data to the server using an HTTP POST request, where the input data is converted to JSON format.

[1466] Input: User-entered data, temporarily stored data, HTTP POST requests

[1467] Output: JSON formatted data sent to the server

[1468] Step 3:

[1469] The server parses the received JSON data to determine the type and style of content to generate, using a natural language processing toolkit (e.g., Python's NLTK or spaCy) to extract keywords and analyze intent.

[1470] Input: JSON format data sent, natural language processing toolkit

[1471] Output: Identified content type and style (e.g. blog post, clear, friendly tone)

[1472] Step 4:

[1473] The server selects the optimal generative AI model based on the type and style of the identified content. To select a model, it searches a database of various AI models for a model that matches the criteria and selects it. After selection, it initializes the model and sets it to an executable state.

[1474] Input: Identified content type and style, AI model database

[1475] Output: Initialized optimal generative AI model

[1476] Step 5:

[1477] The server generates content by providing prompts based on the specified content and style to an initialized generative AI model, where the generative AI model (e.g., OpenAI GPT-4) outputs sentences in response to the prompts.

[1478] Input: Initialized generative AI model, prompt

[1479] Output: Generated content (text, images, audio, video)

[1480] Step 6:

[1481] The server returns the generated content to the smartphone application using an HTTP response, with the generated content being sent in JSON format.

[1482] Input: Generated content, HTTP response

[1483] Output: Content sent to the smartphone application

[1484] Step 7:

[1485] The device receives the content sent back from the server and displays it to the user. The display uses the smartphone application UI and displays the content on the screen according to its format (text, image, audio, video).

[1486] Input: Received content, smartphone UI

[1487] Output: The generated content displayed to the user

[1488] Step 8:

[1489] Users provide feedback on the generated content, which is sent via their device to the server and used to improve the performance of the AI ​​model. Feedback is sent again using an HTTP POST request.

[1490] Input: User feedback, HTTP POST request

[1491] Output: Feedback data sent to the server

[1492] Step 9:

[1493] The server analyzes the received feedback data and adjusts the generative AI model, using data analysis tools (e.g., Scikit-learn) to optimize the model parameters.

[1494] Input: Feedback data, data analysis tools

[1495] Output: A generative AI model with improved performance

[1496] 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.

[1497] The present invention provides a system including: means for a user to input, in text, the content, format, and style of content they wish to generate; means for analyzing the input information and identifying the type of content to be generated; means for selecting and initializing an optimal generative artificial intelligence model based on the identified type of content; means for generating content using the initialized generative artificial intelligence model; means for providing the generated content to the user; means for collecting user feedback and improving performance of the generative artificial intelligence model; means for embedding the generated content in a web page or social networking service; means for including text, images, audio, and video in the content generated based on the user input; and means for providing a simple interface that does not require the user to have specific programming knowledge. The system further includes means for including an emotion engine that recognizes the user's emotional state from the user's text input, means for adjusting the tone and style of the generated content based on the recognized user's emotional state, and means for optimizing the performance of the generative artificial intelligence model based on the user's emotional state and feedback, thereby achieving more personalized content generation.

[1498] Program processing and explanation

[1499] 1. Displaying the user interface

[1500] Terminal

[1501] The device displays a web page and provides a form for the user to enter information required to generate the content.

[1502] A text field appears where the user can enter the content, format, and style of the content they want to generate.

[1503] 2. Getting and Sending User Input

[1504] User

[1505] The user enters information about the content they want to generate into the form and clicks the "Generate" button.

[1506] The device sends the user's input data to the server.

[1507] 3. Analyzing user input and recognizing emotional states

[1508] server

[1509] The server parses the received user input data.

[1510] Use an emotion engine to recognize the emotional state of a user from their input text.

[1511] The type of content to generate is determined based on the analyzed information and the perceived emotional state.

[1512] 4. Generative AI model selection and initialization

[1513] server

[1514] Select the most appropriate generative artificial intelligence model based on the identified content type and the user's emotional state.

[1515] The selected generative artificial intelligence model is initialized and set to an executable state.

[1516] 5. Content Generation

[1517] server

[1518] The initialized generative artificial intelligence model is used to generate content based on the user's input data and emotional state.

[1519] The generation process applies a tone and style that reflects the perceived emotional state, resulting in personalized, high-quality content.

[1520] 6. Providing Generated Content

[1521] server

[1522] The generated content is sent back to the device for provision to the user.

[1523] The device displays the generated content to the user.

[1524] Specific examples

[1525] Here is an example of story-style text generation when the user has a sad emotion:

[1526] 1. A user fills out a form on a webpage with the request, "I would like a story-style text generated that will help me feel better about my sadness."

[1527] 2. The device sends this input data to the server.

[1528] 3. The server analyzes the input data and uses an emotion engine to recognize that the user's emotional state is "sad."

[1529] 4. The server identifies the content type as "text" and the style as "healing story," and selects and initializes the corresponding generative AI model.

[1530] 5. The server generates a healing story-style sentence that reflects the recognized emotion, "sadness."

[1531] 6. The server sends the generated text back to the terminal, which displays it on the user's screen.

[1532] In this way, recognizing the user's emotional state and generating personalized content that reflects it can improve the user experience. Furthermore, collecting user feedback and improving the model's performance can improve the overall quality of the system.

[1533] The processing flow will be explained below.

[1534] Step 1:

[1535] A user accesses a web page on a terminal, which displays a form for inputting the content, format, and style of content the user wants to generate.

[1536] Step 2:

[1537] The user enters information about the content they want to generate in the form (e.g., "I want you to generate a healing story-style text for those feeling sad") and clicks the "Generate" button.

[1538] Step 3:

[1539] The terminal sends the entered user information to the server. The input data is sent to the server using an HTTP request.

[1540] Step 4:

[1541] The server analyzes the received user input data and determines the type (e.g., text) and style (e.g., story style) of content to be generated from the input.

[1542] Step 5:

[1543] The server uses an emotion engine to recognize an emotional state from the user's text input, for example, recognizing the emotion "sad" from the input.

[1544] Step 6:

[1545] The server selects the optimal generative AI model based on the identified content type and the recognized emotional state. For example, it selects a model suitable for generating story-style sentences to soothe "sadness."

[1546] Step 7:

[1547] The server initializes the selected generative AI model, sets the model to a usable state, and loads the parameters required for generation.

[1548] Step 8:

[1549] The server uses the initialized generative artificial intelligence model to generate content based on the user's input data and emotional state, for example, generating comforting story-style text that reflects "sadness."

[1550] Step 9:

[1551] The server returns the generated content to the terminal to provide it to the user. The generated content is included in the HTTP response.

[1552] Step 10:

[1553] The terminal displays the received content to the user. The generated content, such as text, images, audio, and video, is displayed on the user's screen.

[1554] Step 11:

[1555] The user reviews the generated content and provides feedback as needed. The server collects the user's feedback and uses it to improve the performance of the generative AI model.

[1556] Example 2

[1557] 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."

[1558] Conventional content generation systems require a lot of effort to adjust the content, format, and style of generated content based on user input, and suffer from insufficient personalization based on the user's emotional state. Furthermore, feedback that would lead to improved quality of generated content and user experience is not effectively utilized, resulting in a uniformity of generated content.

[1559] The specification process by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for inputting the content, format, and style of the content the user wants to generate in text; means for analyzing the input information and identifying the type of content to be generated; means for selecting and initializing an optimal generative AI model based on the identified type of content; means for generating content using the initialized generative AI model; means for providing the generated content to the user; means for recognizing the user's emotional state from the input text; and means for adjusting the tone and style of the generated content based on the recognized emotional state. This enables the generation of high-quality, personalized content based on user input.

[1560] "User" refers to a person who uses the system to generate content.

[1561] "Content" refers to any informational output, such as text, images, audio, or video, that is generated based on user input.

[1562] "Text input" refers to the act of a user providing a string of characters to a system using a keyboard or other input device.

[1563] "Entered Information" refers to data provided by a User to the System through text input.

[1564] "Analysis" refers to the process of analyzing information input by a user and converting it into useful information for internal use.

[1565] The "type of content to be generated" indicates the specific content format to be generated based on the user's request, and includes, for example, text, images, audio, video, and the like.

[1566] "Generative AI model" refers to an artificial intelligence algorithm or model used to generate content based on the type of content being generated.

[1567] "Initialization" refers to a series of preparatory steps that put a generative artificial intelligence model into a usable state.

[1568] "Emotional state" refers to the type of emotion recognized from the user's input text, including, for example, joy, sadness, anger, surprise, etc.

[1569] "Tone and style" refers to the way the content is expressed and the style or tone that reflects a particular emotion.

[1570] "Serving" refers to the act of delivering the generated content to a user, typically including displaying it on a screen or making it available for download.

[1571] This invention provides a system that includes a means for a user to input the content, format, and style of content they wish to generate in text form, and a means for analyzing the input information and identifying the type of content to be generated. The system also includes a means for selecting and initializing an optimal generative AI model based on the identified type of content. The system also includes a means for generating content using the initialized generative AI model and providing the generated content to a user.

[1572] The user accesses an input form on a web page through the device and inputs the content, format, and style of the content they want to generate. For example, if the user inputs "Please generate a healing story," the device sends this input data to the server.

[1573] The server analyzes the received input data, analyzes the text using a natural language processing engine, and uses an emotion engine to recognize the user's emotional state from the input text. Based on this recognized emotional state, the server determines the type of content to generate.

[1574] Based on the identified content type and the user's emotional state, the server selects the most appropriate generative AI model. For example, if the user is looking for a comforting story, an appropriate natural language generation model (e.g., GPT-3) is selected and initialized.

[1575] Using the initialized generative AI model, the server generates content based on the user's input data and emotional state, applying a tone and style that reflects the perceived emotional state to generate personalized content for each user.

[1576] The generated content is sent back to the device from the server, where it is analyzed and displayed on the user's screen. For example, a soothing story text may be displayed based on the user's request. It also has a feedback function that allows users to collect feedback and improve the performance of the generative AI model.

[1577] As a concrete example, consider the case where the user enters the prompt sentence as follows:

[1578] "I want to generate soothing story-style text for sad people."

[1579] Based on this prompt, the system recognizes the user's emotional state and uses an appropriate generative AI model to generate and display a personalized story, thereby providing high-quality content that responds to each user's emotions.

[1580] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1581] Step 1:

[1582] Terminal

[1583] The device displays a web page and provides a form for the user to enter information needed to generate content, including fields for specifying the content, format, and style of the content they want to generate.

[1584] Input: None

[1585] Output: A form to input the content, format, and style of the content you want to generate.

[1586] Step 2:

[1587] User

[1588] The user enters the content, format, and style of the content they want to generate into the input form and clicks the "Generate" button. For example, they might enter "Generate a healing story."

[1589] Input: Text describing the contract, format, and style

[1590] Output: The "Generate" button was clicked and the text data entered.

[1591] Step 3:

[1592] Terminal

[1593] The terminal generates and sends an HTTP request to send the user's input data to the server.

[1594] Input: User input data (text format)

[1595] Output: Data sent to the server as an HTTP request

[1596] Step 4:

[1597] server

[1598] The server parses the received user input, using a natural language processing engine to analyze the text and identify the intent and topic of the input.

[1599] Input: User input data sent from the terminal

[1600] Output: Structured information (intent, topic, etc.) from the parsed input data

[1601] Step 5:

[1602] server

[1603] The server uses an emotion engine to recognize the emotional state from the user's input text.

[1604] Input: Parsed input data

[1605] Output: User's emotional state (e.g. sadness, joy, etc.)

[1606] Step 6:

[1607] server

[1608] The server determines the type of content to generate based on the analyzed information and the perceived emotional state.

[1609] Input: Parsed input data and the user's emotional state

[1610] Output: The type of content to generate (e.g., a healing story)

[1611] Step 7:

[1612] server

[1613] Based on the identified content type and the user's emotional state, select the most appropriate generative artificial intelligence model, for example, a natural language generation model (e.g., GPT-3).

[1614] Input: The type of content to generate and the user's emotional state

[1615] Output: The selected generative artificial intelligence model

[1616] Step 8:

[1617] server

[1618] The selected generative artificial intelligence model is initialized and set to an executable state.

[1619] Input: A selected generative artificial intelligence model

[1620] Output: An initialized generative artificial intelligence model

[1621] Step 9:

[1622] server

[1623] The initialized generative artificial intelligence model is used to generate content based on the user's input data and emotional state. Specifically, the generative AI model is given input data and executed to generate personalized content.

[1624] Input: An initialized generative AI model, user input data, and the user's emotional state.

[1625] Output: Generated content (e.g., healing story text)

[1626] Step 10:

[1627] server

[1628] The generated content is returned to the terminal to be provided to the user. The generated content data is sent as an HTTP response.

[1629] Input: Generated content

[1630] Output: The generated content data as an HTTP response.

[1631] Step 11:

[1632] Terminal

[1633] The device receives the HTTP response, parses the generated content, and displays it to the user, for example, displaying the generated story text on a web page.

[1634] Input: Generated content data as an HTTP response

[1635] Output: Generated content that is displayed on the user's screen (e.g., story text)

[1636] (Application example 2)

[1637] 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."

[1638] Many modern virtual stores lack personalized suggestions tailored to individual emotions and circumstances when users search for specific products. This poses a risk of lowering user engagement and satisfaction. Furthermore, conventional content generation systems struggle to generate content that takes into account the user's emotional state, preventing them from providing product recommendations and suggestions that are in tune with the user's emotions. Furthermore, many of these systems require specific programming knowledge, making them difficult for everyday users to use.

[1639] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting the content, format, and style of the content the user wants to generate in text; means for analyzing the input information and identifying the type of content to be generated; means for selecting and initializing an optimal generative AI model based on the identified type of content; means for generating content using the initialized generative AI model; means for providing the generated content to the user; means including an emotion engine that recognizes the user's emotional state from the input information; means for adjusting the tone and style of the generated content based on the recognized emotional state; and means for providing a simple interface that does not require the user to have any specific programming knowledge. This enables personalized product introductions based on the user's emotional state.

[1640] "Content that the user wishes to generate" refers to information such as text, images, audio, and video that the user wishes to create.

[1641] "Format" refers to the structural information about the media format in which the content should be presented.

[1642] "Style" refers to the look, tone, approach, and other aspects of your content.

[1643] "Text input means" means a method by which a user enters information into a system using a keyboard or other text input device.

[1644] "Means for analyzing input information" refers to the technology that allows the system to understand the data received from the user and extract the necessary information.

[1645] "Means for determining the type of content to generate" refers to a method for determining what type of content to generate based on the analyzed information.

[1646] "Generative AI model" refers to an AI algorithm for generating new content from input data.

[1647] "Means for initializing" refers to a method for setting up a selected artificial intelligence model in an operational state.

[1648] "Generated Content" refers to data such as text, images, audio, and video created by a generative artificial intelligence model.

[1649] "Means of providing to users" refers to the methods by which generated content is displayed or transmitted to users.

[1650] "Emotion Engine" refers to technology that identifies the emotional state of a user from their input text.

[1651] "Tone and style adjustments" refers to techniques that change the way generated content is presented depending on a perceived emotional state.

[1652] "Simple interface" refers to a system interface design that is easy for users to understand and operate.

[1653] "Means for collecting feedback" refers to methods for collecting user ratings and opinions and using them to improve the system.

[1654] The present invention provides a system for automatically generating personalized content based on a user's emotional state. This system can be used to generate emotion-based product introduction pages in a virtual store. A specific embodiment of this system is described below.

[1655] System configuration and processing

[1656] The server includes the following means:

[1657] 1. A means of textually inputting the content, format, and style of content that users want to generate.

[1658] Through the user interface, users input the necessary information using a keyboard, etc. The interface is provided on the screen of a smartphone or PC and is implemented using web technologies such as HTML and JavaScript.

[1659] 2. A means of analyzing input information and identifying the type of content to generate

[1660] The server receives the data sent by the user and analyzes it using a natural language processing (NLP) engine, such as spaCy or NLTK.

[1661] 3. Means including an emotional engine

[1662] The server uses Hugging Face's Transformers library to recognize the emotional state from the user's text data, which allows it to identify the emotions the user is feeling (e.g., stress, sadness, joy, etc.).

[1663] 4. A means for selecting and initializing the optimal generative artificial intelligence model based on the type of content identified.

[1664] Based on the analysis and emotion recognition results, the server selects and initializes a suitable generative AI model (e.g., OpenAI GPT-4). The selected model is then immediately available for content generation.

[1665] 5. Means for generating content using an initialized generative artificial intelligence model

[1666] The server inputs the prompt text into the selected generative AI model and generates a product page in a tone and style based on the user's emotional state. The prompt text looks like this example:

[1667] Typed text: I've been feeling stressed lately and am looking for items to help me relax at home. Type of content to generate: Product listing page. Tone and style: Soothing, relaxing. Examples of items to generate include: Aroma diffuser, massage chair, soothing music playlist.

[1668] 6. Means of providing generated content to users

[1669] The server then sends the generated content data back to the front end for display on the user's device, using web pages written in HTML and CSS.

[1670] Specific examples

[1671] The user inputs, "I've been feeling stressed lately, so I'm looking for a product that will help me relax." The device sends this data to the server. The server analyzes the input data and recognizes "stress" using its emotion engine. The server then selects and initializes a generative AI model related to "relaxation." After that, it generates content introducing relaxation products (e.g., aroma diffusers, massage chairs, soothing music playlists) that reflect the user's emotional state. Finally, the server sends the generated content back to the user, and the product introduction page is displayed on the user's device.

[1672] This emotionally driven product recommendation creates a highly engaging and personalized shopping experience for users.

[1673] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1674] Step 1:

[1675] The user enters the content, format, and style of the content they want to generate as text into an input form provided on the screen of their smartphone or PC. A user interface implemented using HTML and JavaScript is used for input. An example of input data here would be "I've been feeling stressed lately, so I'm looking for a product that will help me relax." The entered information is sent from the device to the server as JSON format data.

[1676] Step 2:

[1677] The server receives input data in JSON format sent from the device. It then analyzes the input data using natural language processing (NLP) tools (e.g., spaCy or NLTK). Specifically, it divides the input text into sentences and words, and performs a tokenization process to understand the content of each. The analysis results in information that the user is looking for relaxation items.

[1678] Step 3:

[1679] The server uses an emotion engine (e.g., Hugging Face's Transformers library) to recognize the user's emotional state from the parsed data. The input text is fed into the emotion engine, which identifies the emotional state "stressed." Here, the input is the user text, and the output is the emotional state.

[1680] Step 4:

[1681] The server selects and initializes the optimal generative AI model (e.g., OpenAI GPT-4) based on the identified emotional state "stress." The selected model is then ready to generate the required content format by inputting a specific prompt. The input here is the emotional state and the type of content to be generated, and the output is the initialized generative AI model.

[1682] Step 5:

[1683] The server generates content by inputting a prompt sentence into the initialized generative AI model. The specific prompt sentence is as follows:

[1684] Typed text: I've been feeling stressed lately and am looking for items to help me relax at home. Type of content to generate: Product listing page. Tone and style: Soothing, relaxing. Examples of items to generate include: Aroma diffuser, massage chair, soothing music playlist.

[1685] This prompt sentence is input into a generative AI model to generate a product introduction page related to relaxation. Here, the input is the prompt sentence, and the output is the generated content.

[1686] Step 6:

[1687] The server sends the generated content back to the device. The HTTP protocol is often used for communication. The device then appropriately analyzes the received data and displays the generated content (for example, a relaxation product introduction page) on the user's screen. The output product introduction page includes relaxation items such as aroma diffusers, massage chairs, and soothing music playlists. The input here is the generated content data, and the output is the display on the user interface.

[1688] 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.

[1689] 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.

[1690] 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.

[1691] 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.

[1692] 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.

[1693] 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.

[1694] 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).

[1695] 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.

[1696] 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."

[1697] 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.

[1698] 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).

[1699] 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.

[1700] 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.

[1701] 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.

[1702] 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.

[1703] 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.

[1704] 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.

[1705] 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.

[1706] 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.

[1707] 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.

[1708] 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.

[1709] The following is further disclosed regarding the above embodiment.

[1710] (Claim 1)

[1711] A means of textually inputting the content, format, and style of content the user wishes to generate;

[1712] A means for analyzing input information and identifying the type of content to be generated;

[1713] means for selecting and initializing an optimal generative artificial intelligence model based on the identified content type;

[1714] means for generating content using the initialized generative artificial intelligence model;

[1715] a means for providing the generated content to a user;

[1716] A system including:

[1717] (Claim 2)

[1718] 10. The system of claim 1, further comprising means for collecting user feedback and improving performance of the generative artificial intelligence model.

[1719] (Claim 3)

[1720] 10. The system of claim 1, further comprising means for a user to embed the generated content into a web page or a social networking service.

[1721] (Claim 4)

[1722] 2. The system of claim 1, wherein the content generated based on user input includes text, images, audio, and video.

[1723] (Claim 5)

[1724] 10. The system of claim 1, further comprising means for providing a simple interface that does not require a user to have specific programming knowledge.

[1725] "Example 1"

[1726] (Claim 1)

[1727] A means of textually inputting the content, format, and style of content the user wishes to generate;

[1728] A means for analyzing input information and identifying the type of content to be generated;

[1729] means for selecting and initializing an optimal generative artificial intelligence model based on the identified content type;

[1730] means for generating content using the initialized generative artificial intelligence model;

[1731] a means for providing the generated content to a user;

[1732] means for displaying a user interface and obtaining input from a user;

[1733] a means for transmitting user input data to a server and for the server to parse the data;

[1734] A means for generating content based on user requests via text input using an AI model;

[1735] means for returning and displaying the generated content to the user's terminal;

[1736] A system including:

[1737] (Claim 2)

[1738] 10. The system of claim 1, further comprising means for collecting user feedback and improving performance of the generative artificial intelligence model.

[1739] (Claim 3)

[1740] 10. The system of claim 1, further comprising means for a user to embed the generated content into a web page or a social networking service.

[1741] "Application Example 1"

[1742] (Claim 1)

[1743] A means of textually inputting the content, format, and style of content the user wishes to generate;

[1744] A means for analyzing input information and identifying the type of content to be generated;

[1745] means for selecting and initializing an optimal generative artificial intelligence model based on the identified content type;

[1746] means for generating content using the initialized generative artificial intelligence model;

[1747] a means for providing the generated content to a user;

[1748] means for displaying the generated content in a smartphone application;

[1749] A system including:

[1750] (Claim 2)

[1751] 10. The system of claim 1, further comprising means for collecting user feedback and improving performance of the generative artificial intelligence model.

[1752] (Claim 3)

[1753] 10. The system of claim 1, further comprising means for a user to embed the generated content into a web page or a social networking service.

[1754] "Example 2: Combining Emotion Engines"

[1755] (Claim 1)

[1756] A means of textually inputting the content, format, and style of content the user wishes to generate;

[1757] A means for analyzing input information and identifying the type of content to be generated;

[1758] means for selecting and initializing an optimal generative artificial intelligence model based on the identified content type;

[1759] means for generating content using the initialized generative artificial intelligence model;

[1760] a means for providing the generated content to a user;

[1761] a means for recognizing an emotional state from a user's input text;

[1762] a means for adjusting the tone and style of generated content based on a perceived emotional state;

[1763] A system including:

[1764] (Claim 2)

[1765] 10. The system of claim 1, further comprising means for collecting user feedback and improving performance of the generative artificial intelligence model.

[1766] (Claim 3)

[1767] 10. The system of claim 1, further comprising means for a user to embed the generated content into a web page or a social networking service.

[1768] "Application example 2 when combining emotion engines"

[1769] (Claim 1)

[1770] A means of textually inputting the content, format, and style of content the user wishes to generate;

[1771] A means for analyzing input information and identifying the type of content to be generated;

[1772] means for selecting and initializing an optimal generative artificial intelligence model based on the identified content type;

[1773] means for generating content using the initialized generative artificial intelligence model;

[1774] a means for providing the generated content to a user;

[1775] means including an emotion engine for recognizing an emotional state of a user from input information;

[1776] a means for adjusting the tone and style of generated content based on a perceived emotional state;

[1777] A means of providing a simple interface that does not require users to have specific programming knowledge;

[1778] A system including:

[1779] (Claim 2)

[1780] 10. The system of claim 1, further comprising means for collecting user feedback and improving performance of the generative artificial intelligence model.

[1781] (Claim 3)

[1782] 10. The system of claim 1, further comprising means for a user to embed the generated content into a web page or a social networking service. [Explanation of symbols]

[1783] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of textually inputting the content, format, and style of content the user wishes to generate; A means for analyzing input information and identifying the type of content to be generated; means for selecting and initializing an optimal generative artificial intelligence model based on the identified content type; means for generating content using the initialized generative artificial intelligence model; a means for providing the generated content to a user; A system including:

2. The system of claim 1 , further comprising means for collecting user feedback and improving performance of the generative artificial intelligence model.

3. The system of claim 1 , further comprising means for a user to embed the generated content into a web page or a social networking service.

4. 2. The system according to claim 1, wherein the content generated based on the user's input includes text, images, audio, and video.

5. 10. The system of claim 1, further comprising means for providing a simple interface that does not require a user to have specific programming knowledge.

Images