system

Abstract

A system is provided.SOLUTION: The system includes means for a user to input a request using a device, means for a server to receive and analyze the user's request, means for the server to collect necessary information from relevant databases and external APIs, means for the server to generate content with a generative AI model based on the collected information, means for the server to review and modify the generated content, means for the server to deliver the final content to the user's device, and means for the user to confirm, edit, and publish the received content.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] In modern digital marketing, consistently creating high-quality content requires significant time, effort, and skill, posing a significant barrier to businesses and individuals. Small and medium-sized businesses, in particular, have limited resources, making it difficult to regularly create professional-level content. It is also important to ensure that such content does not violate international data privacy regulations or ethical guidelines. The present invention aims to address these challenges by utilizing generative AI technology to efficiently and effectively generate high-quality content. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means. First, a means is provided for a user to input a request using a terminal. Next, a means is provided for a server to receive and analyze the user's request. Next, a means is provided for the server to collect necessary information from relevant databases and external APIs. Next, a means is provided for a generative AI model on the server to generate content based on the collected information. A means is provided for the server to review the generated content and correct it as necessary. Finally, a means is provided for the server to deliver the final content to the user's terminal. Furthermore, a system is provided that includes a means for the user to review, edit, and publish the received content. The generative AI model generates content using natural language generation technology, and the server also includes a means to check whether the generated content complies with data privacy regulations and ethical guidelines.

[0006] A "user" is an entity that accesses the system using a terminal and makes a request for content generation.

[0007] A "terminal" is an electronic device through which a user accesses the system and enters and receives requests.

[0008] The "Server" is a central control unit that receives user requests, analyzes the data, and uses generative AI models to generate and deliver content.

[0009] A "request" is a specific requirement entered by a user, such as a topic or keyword for the content to be generated.

[0010] "Analysis" is the process by which the server understands the content of the user's request and obtains and generates the necessary information.

[0011] A "database" is a collection of information that a server accesses to gather the information it needs.

[0012] "External API" means an external application programming interface used by the Server to obtain additional information.

[0013] A "generative AI model" is an artificial intelligence model used by the server to generate high-quality text content based on collected data.

[0014] "Natural language generation technology" is a technology that enables artificial intelligence to generate human language, and is part of a generative AI model.

[0015] "Review" is the process by which the server checks the generated content and makes corrections if necessary.

[0016] "Data privacy regulations" are legal regulations aimed at protecting personal information.

[0017] "Ethical guidelines" are guidelines that set out the ethical standards that generative AI models must adhere to.

[0018] "Delivery" is the process by which the server sends the final content to the user's terminal.

[0019] "Review, edit and publish" refers to the process in which users review the content they receive, make corrections if necessary, and finally publish it. [Brief explanation of the drawings]

[0020] [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

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] The system of the present invention aims to automatically create content using generative AI technology. This system includes the following components:

[0042] User request input

[0043] User:

[0044] Users access the system using a terminal and log in to their account through a designated login screen. After logging in, users enter specific requests for new content. For example, a user can request "blog articles about healthy lifestyle habits."

[0045] Submitting a Request

[0046] Device:

[0047] The request entered by the user is sent to the server in the form of an HTTP POST request, which includes detailed information such as the topic and keywords.

[0048] Receiving and parsing the request

[0049] server:

[0050] The server analyzes the request received from the user to understand the request content, and determines which database or external API to retrieve information from.

[0051] Data collection

[0052] server:

[0053] The server collects data related to the specified topic from databases and external APIs, such as the latest research and statistics on healthy lifestyle habits.

[0054] Content generation using generative AI models

[0055] server:

[0056] The server-based generative AI model generates unique, high-quality content based on the collected data. Specifically, the generative AI model uses natural language generation technology to create article sentences. For example, it generates a paragraph-by-paragraph blog post about healthy lifestyle habits.

[0057] Content review and revision

[0058] server:

[0059] Automatically review generated content to correct inappropriate language and errors, and ensure that generated content complies with data privacy regulations and ethical guidelines.

[0060] Content Delivery

[0061] server:

[0062] Once the final content is confirmed, the server delivers it to the user's device, where it is displayed on the user's dashboard.

[0063] User review, editing and publishing

[0064] User:

[0065] The user can check the delivered content on their device, manually correct it if necessary, and then publish it on their website or blog.

[0066] Specific examples

[0067] 1. User Request

[0068] A user who runs a health blog uses a terminal to request "healthy breakfast recipes" from the system.

[0069] 2. Server analysis and preparation

[0070] The server receives the request and retrieves data related to a healthy breakfast from a database and an external API.

[0071] 3. Content Generation

[0072] The generative AI model uses the acquired data to generate detailed breakfast recipe articles, such as a recipe for an "oatmeal and fruit bowl" along with its nutritional information.

[0073] 4. Reviews and Shipping

[0074] The server automatically reviews the generated content and delivers it to users, ensuring that it complies with data privacy regulations and ethical guidelines.

[0075] 5. Final Review and Publication

[0076] Users can review and edit articles on their devices and publish them to their health blog.

[0077] In this way, the system of the present invention allows users to efficiently generate and publish high-quality content without requiring advanced technical skills.

[0078] The processing flow will be explained below.

[0079] Step 1:

[0080] The user accesses the Muse platform using a device and logs in to their account on the login screen. If successful, the user is taken to a form to request new content.

[0081] Step 2:

[0082] The user fills out a form on their device with a detailed request for the content they want generated, for example, by entering the topic "healthy breakfast recipes" and specific keywords.

[0083] Step 3:

[0084] The device sends the user's input request as an HTTP POST request to the server, which includes metadata such as topics and keywords.

[0085] Step 4:

[0086] The server receives the user's request, analyzes the format and content, and checks whether it is in the correct format. Specifically, it performs request validation.

[0087] Step 5:

[0088] The server queries an internal database or external API to gather relevant data, for example, data related to "healthy breakfast recipes."

[0089] Step 6:

[0090] The server preprocesses the collected data and converts it into a format that can be passed to the generative AI model, specifically by cleaning and tokenizing the text.

[0091] Step 7:

[0092] The server-based generative AI model generates unique, high-quality content based on the pre-processed data, for example by determining the flow of sentences and paragraph structure and generating the actual text.

[0093] Step 8:

[0094] The server reviews the generated content and makes corrections if necessary, including ensuring compliance with data privacy regulations and ethical guidelines.

[0095] Step 9:

[0096] The server delivers the final content to the user's terminal, allowing the user to receive the generated content.

[0097] Step 10:

[0098] The user checks the delivered content on their device, manually corrects it if necessary, and publishes the generated content on their own website or blog.

[0099] The above is a specific processing flow.

[0100] Example 1

[0101] 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."

[0102] In modern society, there is a growing need to generate content efficiently and with high quality. However, creating content on one's own requires advanced technical skills and a lot of time. Furthermore, it is difficult to ensure that the generated content complies with data privacy regulations and ethical guidelines. Therefore, there is a need for a system that allows users to easily generate, edit, and publish high-quality content.

[0103] 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.

[0104] In this invention, the server includes a means for a user to input a request using a terminal, a means for the terminal to send the user's request to the server in the form of an HTTP POST request, and a means for the server to receive and analyze the user's request, thereby enabling the user to easily make a content request and the server to quickly analyze it.

[0105] A "user" is a person who uses a terminal to access the system and enter a request for content generation.

[0106] A "terminal" is a device through which a user accesses the system and enters and transmits requests.

[0107] "Server" means a computer system that receives and analyzes user requests, collects relevant data, generates content using generative AI models, automatically reviews and corrects, and delivers the final content.

[0108] An "HTTP POST request" is a form of the HTTP protocol used to send data from a terminal to a server.

[0109] "Analysis" is the process by which the server understands the content of the request received from the user and determines which databases or external APIs to collect information from.

[0110] A "generative AI model" is an artificial intelligence system that uses natural language generation techniques to generate unique content based on data.

[0111] "Automated review" is a process in which server-generated content is automatically checked and corrected for inappropriate language or errors.

[0112] "Data privacy regulations" are legal regulations regarding the protection and handling of personal information.

[0113] "Ethical guidelines" are guidelines to ensure that content is socially and morally appropriate.

[0114] "Delivery" is the process by which the server sends the final content to the user's terminal.

[0115] "Publishing" is the act of a user making the final content available to the public on a platform such as their own website or blog.

[0116] This invention is a system that automatically creates content using generative AI technology. The system automates a series of processes: a user inputs a request via a terminal, and a server analyzes the request, collects data, generates content, reviews it, modifies it, and finally delivers it. Key technical elements include HTTP POST requests, a generative AI model that uses natural language generation technology, and an automated review system that complies with data privacy regulations and ethical guidelines.

[0117] Hardware and Software Configuration

[0118] The following hardware and software are required to implement the system:

[0119] User device: Refers to devices that can connect to the Internet, such as PCs, smartphones, and tablets.

[0120] Server: Uses high-performance computers or cloud-based servers to receive user requests, process the data, and generate content using generative AI models.

[0121] Generative AI models: For example, OpenAI's GPT series for natural language generation technology.

[0122] System Operation Overview

[0123] User request input

[0124] The user uses a terminal to access the system's login screen and enters their user ID and password to log in to their account. After logging in, the user enters a specific request for new content, for example, "blog articles about healthy lifestyle habits." After completing the request, the user clicks the submit button.

[0125] Submitting a Request

[0126] The device sends the user's request to the server in the form of an HTTP POST request, which includes detailed information such as topics and keywords.

[0127] The server receives and analyzes the request

[0128] The server receives the HTTP POST request, analyzes its contents, and uses a program to analyze the request, such as the Python Flask framework, to determine which database or external API to collect information from.

[0129] Data collection

[0130] The server collects data related to the specified topic from databases and external APIs, for example, by sending an API request to get the latest research data and statistics on healthy lifestyle habits.

[0131] Content generation using generative AI models

[0132] The server processes the collected data and converts it into a format that can be input into the generative AI model. After cleansing and normalizing the data, a prompt is input into the generative AI model. For example, "Please recommend some healthy breakfast recipes. Please recommend some simple and nutritious dishes using oatmeal." The generative AI model generates unique content based on the prompt, and the generated text is organized into paragraphs as blog posts.

[0133] Content review and revision

[0134] Automatically review generated content to correct inappropriate language and errors, and ensure that generated content complies with data privacy regulations and ethical guidelines, ensuring users can consume content with confidence.

[0135] Content Delivery

[0136] Once the final content is verified, the server delivers it to the user's device, where it is displayed on the user's dashboard.

[0137] User review, editing and publishing

[0138] Users can check the delivered content on their devices and manually edit it if necessary, then publish it on their own website or blog.

[0139] Specific examples

[0140] 1. User Request

[0141] A user who runs a health blog uses a terminal to request "healthy breakfast recipes" from the system.

[0142] 2. Server analysis and preparation

[0143] The server receives the request and retrieves data related to a healthy breakfast from a database and an external API.

[0144] 3. Content Generation

[0145] The generative AI model uses the acquired data to generate detailed breakfast recipe articles, such as a recipe for an "oatmeal and fruit bowl" along with its nutritional information.

[0146] 4. Reviews and Shipping

[0147] The server automatically reviews the generated content and delivers it to users, ensuring that it complies with data privacy regulations and ethical guidelines.

[0148] 5. Final Review and Publication

[0149] Users can review and edit articles on their devices and publish them to their health blog.

[0150] Example prompt sentence:

[0151] "I'd like some healthy breakfast recipes. Can you recommend some easy, nutritious options using oatmeal?"

[0152] As described above, this system allows users to efficiently generate and publish high-quality content without requiring advanced technical skills.

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

[0154] Step 1:

[0155] User request input

[0156] A user accesses the system's login screen using a terminal, enters their user ID and password to log in to their account, and then specifically enters a request for new content, for example, "a blog post about healthy lifestyle habits," and clicks the submit button.

[0157] Input: User ID, password, content request

[0158] Output: The input request data

[0159] Step 2:

[0160] Submitting a Request

[0161] The device sends the user's request to the server in the form of an HTTP POST request, with detailed information such as topics and keywords included in the JSON format.

[0162] Input: User request data

[0163] Output: HTTP POST request sent to the server

[0164] Step 3:

[0165] Receiving and parsing the request

[0166] The server receives the HTTP POST request, analyzes the request, for example using the Python Flask framework, and determines which databases or external APIs to collect information from.

[0167] Input: HTTP POST request

[0168] Output: Parsed request data, data sources to be collected

[0169] Step 4:

[0170] Data collection

[0171] The server collects data related to a specified topic from a database or external API, for example, by sending an API request to get the latest research data and statistics on healthy lifestyle habits.

[0172] Input: Data source to be collected

[0173] Output: Collected data

[0174] Step 5:

[0175] Content generation using generative AI models

[0176] The server processes the collected data and converts it into a format that can be input into the generative AI model. During this process, the data is cleansed and normalized. Next, a prompt is input into the generative AI model. For example, "Please recommend some healthy breakfast recipes. Please recommend some easy and nutritious menus using oatmeal." The generative AI model generates unique content based on the prompt.

[0177] Input: Collected data, prompt statement

[0178] Output: Generated content

[0179] Step 6:

[0180] Content review and revision

[0181] The server automatically reviews generated content, correcting inappropriate language and errors, and ensuring that generated content complies with data privacy regulations and ethical guidelines.

[0182] Input: Generated content

[0183] Output: The modified content

[0184] Step 7:

[0185] Content Delivery

[0186] The server delivers the final content to the user's device, where it is displayed on the user's dashboard.

[0187] Input: Modified content

[0188] Output: Content delivered to the user's device

[0189] Step 8:

[0190] User review, editing and publishing

[0191] The user checks the delivered content on the device and manually corrects it if necessary, then publishes it on their website or blog.

[0192] Input: Streamed content

[0193] Output: Published content

[0194] (Application example 1)

[0195] 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."

[0196] The challenge is to solve the problem of how users can efficiently generate, edit, and publish high-quality content without requiring advanced technical skills, and to automatically check whether the generated content complies with data privacy regulations and ethical guidelines.

[0197] 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.

[0198] In this invention, the server includes: means for a user to input a request using a computing terminal; means for the server to receive and analyze the user's request; means for the server to collect necessary information from a related database or an external interface; means for the server to generate content using a generative machine learning model based on the collected information; means for the server to review and modify the generated content; means for the server to deliver the final content to the user's computing terminal; means for the user to review, edit, and publish the content received; means for the server to send a request from the computing terminal based on the request input by the user; and means for the server to deliver the content generated in response to the request to the computing terminal and for the user to review, edit, save, and share. This enables users to easily generate, review, edit, and share high-quality content using their computing terminals, and also enables automatic verification of whether the generated content complies with data privacy regulations and ethical guidelines.

[0199] "Computing terminal" means a device used by a User to input requests and to review, edit, and publish received content.

[0200] "Server" means a computer system that receives and analyzes user requests, collects necessary information, generates content using a generative machine learning model, reviews and modifies it, and delivers the final content to users.

[0201] A "user request" is a content that indicates the theme or topic of the content that the user wants to generate.

[0202] A "relational database" is a data storage system that stores information necessary for generating content.

[0203] An "external interface" is a means of communication for exchanging data with external information sources other than the relational database.

[0204] A "generative machine learning model" is an advanced algorithm or model that generates content based on collected information.

[0205] "Review" is the process of checking and correcting generated content for errors or inappropriate language.

[0206] "Final Content" is the content delivered to users after review and revision is complete.

[0207] "Reviewing, editing, and publishing" refers to the user viewing the received content, making changes as necessary, and publishing it on the Internet or elsewhere.

[0208] A "means for sending a request" is a method or mechanism by which a user sends a request from a computing terminal to a server.

[0209] "Means to review, edit, save and share content" means the methods and mechanisms by which user-generated content can be reviewed, modified as needed, saved and shared with other users and platforms.

[0210] To implement this invention, it is necessary to appropriately configure the computer terminal, server, generative machine learning model, related database, and external interface. In this section, the specific operation of the system will be described.

[0211] First, a user uses a computing device to input a request including the theme or topic of the content they want to generate. For example, they input a specific theme such as "healthy breakfast recipes." The request input by the user is sent from the device to the server. This transmission is performed using an HTTP POST request.

[0212] The server analyzes the requests received from the user and accesses databases and external interfaces to gather relevant information. The server then retrieves the necessary data based on the user's request. This data may include the latest research data, statistics, and other relevant information.

[0213] Next, a server-based generative machine learning model generates unique, high-quality content based on the collected information. This generation uses advanced natural language generation technologies, such as OpenAI's GPT-3. The generated content is then reviewed on-site to correct errors and inappropriate language and ensure compliance with data privacy regulations and ethical guidelines.

[0214] The generated content is delivered from the server to the user's computer device. The delivered content is displayed in an application on the user's device, and the user can view, edit, and publish it. For example, if a user runs a health blog, they can publish generated content such as an "oatmeal and fruit bowl recipe" as is.

[0215] Specific examples

[0216] An example of a prompt might be:

[0217] text

[0218] "Enter the topic of the content you want to create: Healthy Breakfast Recipes"

[0219] Based on this prompt, the generative machine learning model generates specific content such as:

[0220] text

[0221] "How to make an oatmeal and fruit bowl:

[0222] 1 cup oatmeal

[0223] 2 cups milk

[0224] 1 teaspoon honey

[0225] 1 banana

[0226] 1 / 2 cup blueberries

[0227] Almonds (as needed)

[0228] procedure:

[0229] 1. Place the oatmeal and milk in a saucepan and bring to a boil over medium heat.

[0230] 2. Stir well and add honey.

[0231] 3. Thinly slice the banana and top with the blueberries.

[0232] 4. Finally, add the almonds to finish.

[0233] Users can view this content on their devices and modify, save, or publish it as needed.

[0234] In this way, the system of the present invention allows users to efficiently create, review, edit, and share high-quality content without requiring advanced technical skills, and can automatically verify that the content complies with data privacy regulations and ethical guidelines.

[0235] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0236] Step 1:

[0237] A user uses a computing device to enter a request, which includes the theme or topic of the content they want to generate. For example, they might enter the text "healthy breakfast recipes." The request is stored locally on the device and is then ready to be sent to the server.

[0238] Step 2:

[0239] The terminal sends the request entered by the user to the server in the form of an HTTP POST request. When sending the request, data including the request content and topic is passed to the server. The server receives this request and waits for analysis.

[0240] Step 3:

[0241] The server analyzes the received request, extracting the request content by topic or keyword, and determining which databases or external interfaces should be used to retrieve the information. Based on the results of this analysis, it establishes procedures for collecting the relevant data.

[0242] Step 4:

[0243] The server accesses relevant databases and external interfaces to gather the necessary information, such as the latest research data and statistics related to healthy breakfast recipes. This gathering process involves extracting and integrating the data using appropriate queries.

[0244] Step 5:

[0245] The server passes the collected data to a generative machine learning model. The model used here is a natural language generation technology such as OpenAI's GPT-3. Based on the given data, the model generates unique, high-quality content on the specified topic. This generation process automatically creates topic-related sentence structure and content.

[0246] Step 6:

[0247] The server reviews the generated content, a process that checks the generated text for inappropriate language and errors, corrects them, and checks whether the generated content complies with data privacy regulations and ethical guidelines.

[0248] Step 7:

[0249] The server delivers the final content to the computer terminal. The delivered content is sent to the terminal in the form of a series of data and presented to the user. The terminal receives this data and displays it for the user to view.

[0250] Step 8:

[0251] Users can check the received content on their devices and edit it as needed. After editing, users can save the content and publish it on their own website or blog. They can also share the edited content with other platforms and users.

[0252] Through these steps, users can use generative AI models to quickly generate, review, edit, and publish high-quality content. For example, by using the prompt "Please enter the theme of the content you want to create: Healthy breakfast recipes," users can efficiently create the content they want.

[0253] 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.

[0254] The system of the present invention aims to automatically create more personalized, high-quality content by combining generative AI technology and an emotion engine. The system includes the following components:

[0255] User request input and emotion recognition

[0256] User:

[0257] Access the system using a terminal and log in to your account on the login screen. After logging in, you will be taken to the new content request form.

[0258] Emotion Engine:

[0259] The emotion engine recognizes the user's emotional state by analyzing the user's facial expression, tone of voice, writing style, etc. while they are inputting a request on the device. For example, if the user is inputting with a positive emotion, the emotion engine will send that information to the server.

[0260] Sending requests and transferring emotional data

[0261] Device:

[0262] The request entered by the user and the emotional data analyzed by the emotion engine are sent to the server as an HTTP POST request, which includes topics, keywords, and emotional data.

[0263] Receiving and parsing the request

[0264] server:

[0265] The server receives the user's request and emotional data, analyzes the request, validates the request, and determines how to adjust the tone of the content based on the emotional data.

[0266] Data collection

[0267] server:

[0268] The server gathers the necessary information related to the request from an internal database or external API, for example, fetching data for "healthy breakfast recipes."

[0269] Content generation based on emotional data

[0270] server:

[0271] A generative AI model generates unique, high-quality content based on collected data and sentiment data. If the sentiment data is positive, the tone of the article will also be positive. For example, a recipe will be introduced using uplifting language.

[0272] Content review and revision

[0273] server:

[0274] Review generated content and make corrections as needed. The server ensures that generated content complies with data privacy regulations and ethical guidelines.

[0275] Content Delivery

[0276] server:

[0277] The content that has undergone final confirmation and tone adjustment information based on the emotion data are delivered to the user's terminal, allowing the user to receive the generated content.

[0278] User review, editing and publishing

[0279] User:

[0280] Check the content delivered on the device, manually correct it if necessary, and finally publish the generated content on your own website or blog.

[0281] Specific examples

[0282] 1. User Request

[0283] A user who runs a health blog uses a device to request "healthy breakfast recipes" from the system. At the same time, the user expresses positive emotions.

[0284] 2. Emotion analysis using an emotion engine

[0285] The emotion engine recognizes positive emotions from the user's facial expressions and tone of voice and sends that data to the server.

[0286] 3. Data collection and content generation by the server

[0287] The server collects relevant data, and the generative AI model uses that data to generate breakfast recipe posts with a positive tone, such as "This oatmeal recipe will fuel your day!"

[0288] 4. Review and Distribution

[0289] The server automatically reviews the generated articles to ensure they comply with data privacy regulations and ethical guidelines before delivering them to the user's device.

[0290] 5. Final Review and Publication

[0291] Users review and edit articles and publish them to the health blog.

[0292] In this way, the system of the present invention, which combines an emotion engine, enables users to efficiently generate and publish high-quality content that reflects their own emotions.

[0293] The processing flow will be explained below.

[0294] Step 1:

[0295] The user accesses the "System" using a terminal and logs in to their account on the login screen. If successful, they are taken to a form to request new content.

[0296] Step 2:

[0297] The emotion engine analyzes the user's facial expressions, tone of voice, writing style, etc. to recognize the user's emotional state. For example, it can detect whether the user is smiling or speaking in a calm voice.

[0298] Step 3:

[0299] A user enters a specific content request on a device, for example, by entering the topic "healthy breakfast recipes" and specific keywords.

[0300] Step 4:

[0301] The terminal transmits the request input by the user and the emotion data analyzed by the emotion engine as an HTTP POST request to the server.

[0302] Step 5:

[0303] The server receives the user request and sentiment data, then validates the request to ensure it is well-formed, specifically by checking that it contains the required topics and keywords.

[0304] Step 6:

[0305] The server determines the tone and style of the content it generates based on the emotion data. For example, if the user expresses positive emotions, the tone of the article will also be set to be positive.

[0306] Step 7:

[0307] The server gathers relevant information from internal databases and external APIs, for example, retrieving the latest data and statistics for "healthy breakfast recipes."

[0308] Step 8:

[0309] The server preprocesses the collected information and converts it into a format that can be fed to the generative AI model, specifically by cleaning and tokenizing the data.

[0310] Step 9:

[0311] A server-based generative AI model generates content based on the pre-processed data and sentiment data, such as a detailed breakfast recipe with uplifting language.

[0312] Step 10:

[0313] Reviewing server-generated content and correcting it where necessary, ensuring compliance with data privacy regulations and ethical guidelines.

[0314] Step 11:

[0315] The server delivers the content that has undergone final confirmation to the user's device, where it is displayed on the user's dashboard.

[0316] Step 12:

[0317] The user can then review the content delivered on their device, manually edit it if necessary, and finally publish the resulting content on their own website or blog.

[0318] Specific examples

[0319] 1. User Requests and Emotion Recognition

[0320] A user uses a device to request a "healthy breakfast recipe" from the system, and the emotion engine recognizes the user's positive emotion (smiling).

[0321] 2. Data collection and content generation

[0322] The server collects relevant information from databases and external APIs, and the generative AI model generates a recipe post for "Oatmeal and Fruit Bowl" with a positive tone, such as "This oatmeal recipe will energize your day!"

[0323] 3. Review, Finalize, and Publish

[0324] The server reviews the content, and the user confirms and edits the article sent to the device and publishes it on the health blog.

[0325] In this way, the system of the present invention, which combines an emotion engine, enables users to efficiently generate and publish high-quality content that reflects their own emotions.

[0326] Example 2

[0327] 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."

[0328] Conventional content generation systems have struggled to automatically generate personalized, high-quality content that takes user emotions into account. This can result in a failure to provide content that addresses user needs and emotions, leading to a decline in user engagement and satisfaction. Furthermore, the quality and tone of the generated content can be inconsistent, which can undermine its reliability and usefulness.

[0329] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving and analyzing requests and emotional data input by a user, means for collecting necessary information from a related database or an external API, and means for generating content using a generative AI model based on the collected information and emotional data. This makes it possible to automatically provide high-quality content with a tone and content that matches the emotional state of the user.

[0330] A "terminal" is an electronic device such as a computer or smartphone that allows a user to input and send requests.

[0331] "Emotion data" is data that indicates the user's emotional state and is generated by analyzing the user's facial expression, tone of voice, writing style, etc.

[0332] A "server" is a computer system that receives and analyzes user requests and emotion data.

[0333] A "generative AI model" is an artificial intelligence model that generates unique, high-quality content based on collected data and emotional data.

[0334] A "relevant database" is an internal or external source of information that the server accesses to gather required information.

[0335] An "external API" is an application programming interface used to obtain information from other services.

[0336] "Review" is the process of checking generated content for linguistic accuracy, grammar, and relevance, and making corrections as needed.

[0337] "Data privacy regulations" are legal and ethical guidelines for protecting users' personal information.

[0338] "Ethical guidelines" are moral and ethical standards that should be observed during the content creation process.

[0339] "Content" refers to information such as text, articles, recipes, etc. created by generative AI models.

[0340] The present invention provides a system for automatically generating personalized, high-quality content that reflects a user's emotions. This system operates by combining generative AI technology with an emotion engine. A specific embodiment of this system will be described below.

[0341] First, a user accesses the system using a terminal and logs in to their account on the login screen. Once logged in, the user is taken to a form to request new content and enters a request, such as "healthy breakfast recipes." At this time, the emotion engine analyzes the user's facial expressions and tone of voice to generate emotion data, such as positive or negative. The emotion engine incorporates hardware such as a facial recognition camera and microphone, as well as machine learning models.

[0342] Next, the device sends the user's input request and emotional data to the server as an HTTP POST request. The request includes topics, keywords, and emotional data. The server receives the HTTP request and analyzes the request content and emotional data. It checks the syntax of the request and adjusts the tone of the generated content based on the emotional data.

[0343] The server collects the necessary information through relevant databases and external APIs. For example, it calls RecipeAPI to get information related to "healthy breakfast recipes." This collected data is then converted into a format that can be passed to the generative AI model.

[0344] The generative AI model generates unique, high-quality content based on collected data and sentiment data. If the sentiment data is positive, the generated content will also have a positive tone. For example, an article might be generated that reads, "This oatmeal recipe will start your morning off right!"

[0345] The generated content is reviewed on the server for language accuracy, grammar, and relevance, and an automated filtering system is used to ensure compliance with data privacy regulations and ethical guidelines. After any necessary corrections are made, the final content is delivered to the user's device.

[0346] The user can then review the content on their device, manually edit it if necessary, and publish the finalized content to their website or blog.

[0347] This invention enables users to efficiently create and publish high-quality content that reflects their own emotional state.

[0348] Prompt Sentence Examples

[0349] "Generate articles with a positive tone and healthy breakfast recipes."

[0350] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0351] Step 1:

[0352] The user enters a request

[0353] User: Access the system using a terminal and log in to their account on the login screen. After logging in, they will be taken to the request entry form.

[0354] Input: Login credentials, request (e.g. "healthy breakfast recipes").

[0355] Output: Login success notification, request data.

[0356] Specific action: Enter "healthy breakfast recipes" into the admin panel of a health blog and submit the form.

[0357] Step 2:

[0358] emotion recognition

[0359] Emotion engine: While the user is typing a request, the facial recognition camera and microphone are used to collect the user's facial expressions and tone of voice to generate emotion data.

[0360] Input: User's facial expression data, tone of voice data.

[0361] Output: Sentiment data (e.g., positive).

[0362] Specific behavior: Detects whether the user is smiling when typing and generates positive emotion data.

[0363] Step 3:

[0364] Sending requests and emotion data

[0365] Terminal: The request and emotion data entered by the user are sent to the server as an HTTP POST request.

[0366] Input: Request content, emotion data.

[0367] Output: HTTP POST request.

[0368] Specific behavior: By pressing the send button, the "healthy breakfast recipe" and positive emotion data are sent to the server.

[0369] Step 4:

[0370] Receiving and parsing the request

[0371] Server: Receives HTTP requests, analyzes the request content and sentiment data, performs syntax checks, and validates the content.

[0372] Input: HTTP POST request (request content, emotion data).

[0373] Output: Parsing results, syntax check results.

[0374] What it does: Ensures that requests for "healthy breakfast recipes" are submitted in the correct format.

[0375] Step 5:

[0376] Data collection

[0377] Server: Gathers information related to the request from an internal database or external API, for example, fetching data about "healthy breakfast recipes."

[0378] Input: Your request ("healthy breakfast recipes").

[0379] Output: A dataset of related information.

[0380] Specific behavior: Calls the Recipe API and retrieves breakfast recipe data.

[0381] Step 6:

[0382] Content generation based on emotional data

[0383] Server: The generative AI model generates unique, high-quality content based on collected data and sentiment data. If the sentiment data is positive, the tone of the article will also be positive.

[0384] Input: Relevant information dataset, sentiment data.

[0385] Output: Generated content (e.g., a breakfast recipe article with a positive tone).

[0386] What it does: Generates an article that says, "This oatmeal recipe will start your morning off right!"

[0387] Step 7:

[0388] Content review and revision

[0389] Server: Review the generated content, checking for language accuracy, grammar, and relevance, and making corrections as needed.

[0390] Input: Generated content.

[0391] Output: The revised content.

[0392] Specific Actions: Ensure automated tools comply with data privacy regulations and ethical guidelines.

[0393] Step 8:

[0394] Content Delivery

[0395] Server: Deliver the final content to the user's device. Delivery formats can be selected, such as email or notification.

[0396] Input: The revised content.

[0397] Output: Delivery notification or delivery data.

[0398] Specific operation: Notify the user that an article has arrived on their device.

[0399] Step 9:

[0400] User review, editing and publishing

[0401] User: Review the delivered content, make manual corrections as needed, and then publish the final, reviewed content.

[0402] Input: The delivered content.

[0403] Output: Published content.

[0404] What it does: Post an article to your blog or website.

[0405] (Application example 2)

[0406] 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."

[0407] Current content generation systems struggle to provide personalized content that reflects a user's emotional state. Conventional systems generate content based on user requests, but do not consider the user's emotions or moods in the process, making it difficult to increase user satisfaction. Furthermore, content generation that incorporates emotional data must comply with data privacy regulations and ethical guidelines.

[0408] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0409] In this invention, the server includes a means having an engine for analyzing a user's facial expressions and tone of voice to recognize emotions, a means for receiving and analyzing user requests and emotional data, and a means for generating content using a generative AI model based on the collected information and emotional data, thereby enabling the automatic generation of personalized content that reflects the user's emotional state.

[0410] A "user request" is a request or desire that a user inputs into the system using a terminal.

[0411] "Facial expressions and tone of voice" refers to the characteristics of the user's facial expressions and voice, and is information analyzed by the emotion engine.

[0412] An "emotion engine" is a software and hardware system that analyzes a user's facial expressions, tone of voice, writing style, etc. to recognize the user's emotional state.

[0413] "Server" refers to a computer system that receives user requests and emotional data, analyzes them, collects information, generates content, reviews, modifies, and distributes them.

[0414] A "generative AI model" is an artificial intelligence algorithm that generates unique, high-quality content based on collected data and emotional data.

[0415] "Content" refers to information and creative works such as text, images, audio, and video, which are generated based on user requests and emotional data.

[0416] "Review" is the process by which the server checks the generated content and makes corrections as necessary.

[0417] "Modification" is the act of improving or changing the content of the generated content.

[0418] "Data privacy regulations" are laws and regulations regarding the protection of users' personal information and data.

[0419] "Ethical guidelines" are ethical standards and guidelines regarding content creation and information collection.

[0420] "Delivery" refers to the act of the server sending the final content to the user's terminal.

[0421] A "terminal" is a device that allows a user to access the system, input requests, and check content.

[0422] "Natural language generation technology" is a technology that enables a generative AI model to generate content in language that humans can understand based on user requests and emotional data.

[0423] The system of the present invention combines a user terminal, an emotion engine, a server, and a generative AI model to automatically generate and deliver high-quality content based on the user's emotional state. Below, we will explain the details of each element and how they work.

[0424] User request input and emotion recognition

[0425] A user accesses the system using a terminal and first logs in to their account on the login screen. After logging in, they are taken to a form to request new content. At this time, the terminal uses a camera and microphone to provide the user's facial expressions and tone of voice to the emotion engine in real time.

[0426] Emotion analysis using an emotion engine

[0427] The emotion engine recognizes the user's emotional state by analyzing facial expressions, tone of voice, and writing style while the user is typing a request. This emotional data is classified as "positive" or "negative," for example, and sent to the server.

[0428] Receiving and parsing the request

[0429] The server receives the user's request and emotional data as an HTTP POST request, analyzes them, and determines the tone of the content to be generated based on the request and emotional data.

[0430] Data collection

[0431] The server gathers the necessary information related to the request from internal databases and external APIs, for example, fetching data for "latest news" from the web.

[0432] Content generation based on emotional data

[0433] A generative AI model generates unique, high-quality content based on collected data and sentiment data. If the sentiment data is positive, the tone of the article will also be positive. For example, "This news will brighten your day!"

[0434] Content review and revision

[0435] The server reviews the generated content and makes corrections as necessary. It also checks whether the generated content complies with data privacy regulations and ethical guidelines, ensuring there are no issues.

[0436] Content Delivery

[0437] The final confirmed content is delivered from the server to the user's terminal, allowing the user to receive the generated content.

[0438] User review, editing and publishing

[0439] Users can review the content delivered on their devices and manually edit it if necessary, and finally publish the generated content on their own website or blog.

[0440] Specific examples

[0441] For example, a user may request that they are interested in "Today's News," and emotion analysis is performed using the device's camera and microphone. If the user inputs the request with a smile, the emotion engine evaluates the information as "positive." Based on this information, the generative AI model generates a news article with a positive tone. The article may include phrases such as "Today's News Will Make Your Day Even Better!"

[0442] Example prompts for generative AI models

[0443] "Create a latest news article in a positive tone."

[0444] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0445] Step 1:

[0446] A user accesses the system using a terminal and logs in to their account on the login screen. User authentication information is entered here, and if authentication is successful, a request input form for new content is displayed.

[0447] Step 2:

[0448] The user enters the desired topic in the request input form. At this time, the device's camera and microphone are used to capture the user's facial expressions and tone of voice in real time and send them to the emotion engine. The input is the user's topic request and emotion data, and the output is the request data that integrates these data.

[0449] Step 3:

[0450] The emotion engine analyzes the user's facial expressions, tone of voice, and writing style to recognize the user's emotional state. As a result of the analysis, emotion data is generated and classified as, for example, "positive" or "negative." The output is the recognized emotion data.

[0451] Step 4:

[0452] The device sends the user's request and emotion data to the server as an HTTP POST request. In this step, the integrated request data is sent to the server.

[0453] Step 5:

[0454] The server receives the user request and emotional data and analyzes it. The input is the aggregated request data, and the output is the analysis result. The analysis includes request validation and determines the tone of the content based on the emotional data.

[0455] Step 6:

[0456] The server collects the necessary information related to the request from internal databases and external APIs. The input is the parsed request and the output is the collected data. For example, retrieving information about "latest news" from the web.

[0457] Step 7:

[0458] The generative AI model generates unique, high-quality content based on collected data and sentiment data. The input is collected data and sentiment data, and the output is generated content. If the sentiment data is positive, the tone of the article will also be positive.

[0459] Step 8:

[0460] The server reviews the generated content and makes corrections as needed. The input is the generated content and the output is the reviewed content. The server ensures that the generated content complies with data privacy regulations and ethical guidelines.

[0461] Step 9:

[0462] The server delivers the final reviewed content to the user's device. The input is the reviewed content and the output is the content sent to the user's device.

[0463] Step 10:

[0464] The user checks the distributed content on their device and manually corrects it if necessary. The input is the distributed content, and the output is the final published content. The user then publishes the corrected content on their own website or blog.

[0465] 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.

[0466] 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.

[0467] 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.

[0468] [Second embodiment]

[0469] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0470] 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.

[0471] 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).

[0472] 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.

[0473] 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.

[0474] 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).

[0475] 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.

[0476] 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.

[0477] 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.

[0478] 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.

[0479] 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.

[0480] 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."

[0481] The system of the present invention aims to automatically create content using generative AI technology. This system includes the following components:

[0482] User request input

[0483] User:

[0484] Users access the system using a terminal and log in to their account through a designated login screen. After logging in, users enter specific requests for new content. For example, a user can request "blog articles about healthy lifestyle habits."

[0485] Submitting a Request

[0486] Device:

[0487] The request entered by the user is sent to the server in the form of an HTTP POST request, which includes detailed information such as the topic and keywords.

[0488] Receiving and parsing the request

[0489] server:

[0490] The server analyzes the request received from the user to understand the request content, and determines which database or external API to retrieve information from.

[0491] Data collection

[0492] server:

[0493] The server collects data related to the specified topic from databases and external APIs, such as the latest research and statistics on healthy lifestyle habits.

[0494] Content generation using generative AI models

[0495] server:

[0496] The server-based generative AI model generates unique, high-quality content based on the collected data. Specifically, the generative AI model uses natural language generation technology to create article sentences. For example, it generates a paragraph-by-paragraph blog post about healthy lifestyle habits.

[0497] Content review and revision

[0498] server:

[0499] Automatically review generated content to correct inappropriate language and errors, and ensure that generated content complies with data privacy regulations and ethical guidelines.

[0500] Content Delivery

[0501] server:

[0502] Once the final content is confirmed, the server delivers it to the user's device, where it is displayed on the user's dashboard.

[0503] User review, editing and publishing

[0504] User:

[0505] The user can check the delivered content on their device, manually correct it if necessary, and then publish it on their website or blog.

[0506] Specific examples

[0507] 1. User Request

[0508] A user who runs a health blog uses a terminal to request "healthy breakfast recipes" from the system.

[0509] 2. Server analysis and preparation

[0510] The server receives the request and retrieves data related to a healthy breakfast from a database and an external API.

[0511] 3. Content Generation

[0512] The generative AI model uses the acquired data to generate detailed breakfast recipe articles, such as a recipe for an "oatmeal and fruit bowl" along with its nutritional information.

[0513] 4. Reviews and Shipping

[0514] The server automatically reviews the generated content and delivers it to users, ensuring that it complies with data privacy regulations and ethical guidelines.

[0515] 5. Final Review and Publication

[0516] Users can review and edit articles on their devices and publish them to their health blog.

[0517] In this way, the system of the present invention allows users to efficiently generate and publish high-quality content without requiring advanced technical skills.

[0518] The processing flow will be explained below.

[0519] Step 1:

[0520] The user accesses the Muse platform using a device and logs in to their account on the login screen. If successful, the user is taken to a form to request new content.

[0521] Step 2:

[0522] The user fills out a form on their device with a detailed request for the content they want generated, for example, by entering the topic "healthy breakfast recipes" and specific keywords.

[0523] Step 3:

[0524] The device sends the user's input request as an HTTP POST request to the server, which includes metadata such as topics and keywords.

[0525] Step 4:

[0526] The server receives the user's request, analyzes the format and content, and checks whether it is in the correct format. Specifically, it performs request validation.

[0527] Step 5:

[0528] The server queries an internal database or external API to gather relevant data, for example, data related to "healthy breakfast recipes."

[0529] Step 6:

[0530] The server preprocesses the collected data and converts it into a format that can be passed to the generative AI model, specifically by cleaning and tokenizing the text.

[0531] Step 7:

[0532] The server-based generative AI model generates unique, high-quality content based on the pre-processed data, for example by determining the flow of sentences and paragraph structure and generating the actual text.

[0533] Step 8:

[0534] The server reviews the generated content and makes corrections if necessary, including ensuring compliance with data privacy regulations and ethical guidelines.

[0535] Step 9:

[0536] The server delivers the final content to the user's terminal, allowing the user to receive the generated content.

[0537] Step 10:

[0538] The user checks the delivered content on their device, manually corrects it if necessary, and publishes the generated content on their own website or blog.

[0539] The above is a specific processing flow.

[0540] Example 1

[0541] 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."

[0542] In modern society, there is a growing need to generate content efficiently and with high quality. However, creating content on one's own requires advanced technical skills and a lot of time. Furthermore, it is difficult to ensure that the generated content complies with data privacy regulations and ethical guidelines. Therefore, there is a need for a system that allows users to easily generate, edit, and publish high-quality content.

[0543] 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.

[0544] In this invention, the server includes a means for a user to input a request using a terminal, a means for the terminal to send the user's request to the server in the form of an HTTP POST request, and a means for the server to receive and analyze the user's request, thereby enabling the user to easily make a content request and the server to quickly analyze it.

[0545] A "user" is a person who uses a terminal to access the system and enter a request for content generation.

[0546] A "terminal" is a device through which a user accesses the system and enters and transmits requests.

[0547] "Server" means a computer system that receives and analyzes user requests, collects relevant data, generates content using generative AI models, automatically reviews and corrects, and delivers the final content.

[0548] An "HTTP POST request" is a form of the HTTP protocol used to send data from a terminal to a server.

[0549] "Analysis" is the process by which the server understands the content of the request received from the user and determines which databases or external APIs to collect information from.

[0550] A "generative AI model" is an artificial intelligence system that uses natural language generation techniques to generate unique content based on data.

[0551] "Automated review" is a process in which server-generated content is automatically checked and corrected for inappropriate language or errors.

[0552] "Data privacy regulations" are legal regulations regarding the protection and handling of personal information.

[0553] "Ethical guidelines" are guidelines to ensure that content is socially and morally appropriate.

[0554] "Delivery" is the process by which the server sends the final content to the user's terminal.

[0555] "Publishing" is the act of a user making the final content available to the public on a platform such as their own website or blog.

[0556] This invention is a system that automatically creates content using generative AI technology. The system automates a series of processes: a user inputs a request via a terminal, and a server analyzes the request, collects data, generates content, reviews it, modifies it, and finally delivers it. Key technical elements include HTTP POST requests, a generative AI model that uses natural language generation technology, and an automated review system that complies with data privacy regulations and ethical guidelines.

[0557] Hardware and Software Configuration

[0558] The following hardware and software are required to implement the system:

[0559] User device: Refers to devices that can connect to the Internet, such as PCs, smartphones, and tablets.

[0560] Server: Uses high-performance computers or cloud-based servers to receive user requests, process the data, and generate content using generative AI models.

[0561] Generative AI models: For example, OpenAI's GPT series for natural language generation technology.

[0562] System Operation Overview

[0563] User request input

[0564] The user uses a terminal to access the system's login screen and enters their user ID and password to log in to their account. After logging in, the user enters a specific request for new content, for example, "blog articles about healthy lifestyle habits." After completing the request, the user clicks the submit button.

[0565] Submitting a Request

[0566] The device sends the user's request to the server in the form of an HTTP POST request, which includes detailed information such as topics and keywords.

[0567] The server receives and analyzes the request

[0568] The server receives the HTTP POST request, analyzes its contents, and uses a program to analyze the request, such as the Python Flask framework, to determine which database or external API to collect information from.

[0569] Data collection

[0570] The server collects data related to the specified topic from databases and external APIs, for example, by sending an API request to get the latest research data and statistics on healthy lifestyle habits.

[0571] Content generation using generative AI models

[0572] The server processes the collected data and converts it into a format that can be input into the generative AI model. After cleansing and normalizing the data, a prompt is input into the generative AI model. For example, "Please recommend some healthy breakfast recipes. Please recommend some simple and nutritious dishes using oatmeal." The generative AI model generates unique content based on the prompt, and the generated text is organized into paragraphs as blog posts.

[0573] Content review and revision

[0574] Automatically review generated content to correct inappropriate language and errors, and ensure that generated content complies with data privacy regulations and ethical guidelines, ensuring users can consume content with confidence.

[0575] Content Delivery

[0576] Once the final content is verified, the server delivers it to the user's device, where it is displayed on the user's dashboard.

[0577] User review, editing and publishing

[0578] Users can check the delivered content on their devices and manually edit it if necessary, then publish it on their own website or blog.

[0579] Specific examples

[0580] 1. User Request

[0581] A user who runs a health blog uses a terminal to request "healthy breakfast recipes" from the system.

[0582] 2. Server analysis and preparation

[0583] The server receives the request and retrieves data related to a healthy breakfast from a database and an external API.

[0584] 3. Content Generation

[0585] The generative AI model uses the acquired data to generate detailed breakfast recipe articles, such as a recipe for an "oatmeal and fruit bowl" along with its nutritional information.

[0586] 4. Reviews and Shipping

[0587] The server automatically reviews the generated content and delivers it to users, ensuring that it complies with data privacy regulations and ethical guidelines.

[0588] 5. Final Review and Publication

[0589] Users can review and edit articles on their devices and publish them to their health blog.

[0590] Example prompt sentence:

[0591] "I'd like some healthy breakfast recipes. Can you recommend some easy, nutritious options using oatmeal?"

[0592] As described above, this system allows users to efficiently generate and publish high-quality content without requiring advanced technical skills.

[0593] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0594] Step 1:

[0595] User request input

[0596] A user accesses the system's login screen using a terminal, enters their user ID and password to log in to their account, and then specifically enters a request for new content, for example, "a blog post about healthy lifestyle habits," and clicks the submit button.

[0597] Input: User ID, password, content request

[0598] Output: The input request data

[0599] Step 2:

[0600] Submitting a Request

[0601] The device sends the user's request to the server in the form of an HTTP POST request, with detailed information such as topics and keywords included in the JSON format.

[0602] Input: User request data

[0603] Output: HTTP POST request sent to the server

[0604] Step 3:

[0605] Receiving and parsing the request

[0606] The server receives the HTTP POST request, analyzes the request, for example using the Python Flask framework, and determines which databases or external APIs to collect information from.

[0607] Input: HTTP POST request

[0608] Output: Parsed request data, data sources to be collected

[0609] Step 4:

[0610] Data collection

[0611] The server collects data related to a specified topic from a database or external API, for example, by sending an API request to get the latest research data and statistics on healthy lifestyle habits.

[0612] Input: Data source to be collected

[0613] Output: Collected data

[0614] Step 5:

[0615] Content generation using generative AI models

[0616] The server processes the collected data and converts it into a format that can be input into the generative AI model. During this process, the data is cleansed and normalized. Next, a prompt is input into the generative AI model. For example, "Please recommend some healthy breakfast recipes. Please recommend some easy and nutritious menus using oatmeal." The generative AI model generates unique content based on the prompt.

[0617] Input: Collected data, prompt statement

[0618] Output: Generated content

[0619] Step 6:

[0620] Content review and revision

[0621] The server automatically reviews generated content, correcting inappropriate language and errors, and ensuring that generated content complies with data privacy regulations and ethical guidelines.

[0622] Input: Generated content

[0623] Output: The modified content

[0624] Step 7:

[0625] Content Delivery

[0626] The server delivers the final content to the user's device, where it is displayed on the user's dashboard.

[0627] Input: Modified content

[0628] Output: Content delivered to the user's device

[0629] Step 8:

[0630] User review, editing and publishing

[0631] The user checks the delivered content on the device and manually corrects it if necessary, then publishes it on their website or blog.

[0632] Input: Streamed content

[0633] Output: Published content

[0634] (Application example 1)

[0635] 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."

[0636] The challenge is to solve the problem of how users can efficiently generate, edit, and publish high-quality content without requiring advanced technical skills, and to automatically check whether the generated content complies with data privacy regulations and ethical guidelines.

[0637] 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.

[0638] In this invention, the server includes: means for a user to input a request using a computing terminal; means for the server to receive and analyze the user's request; means for the server to collect necessary information from a related database or an external interface; means for the server to generate content using a generative machine learning model based on the collected information; means for the server to review and modify the generated content; means for the server to deliver the final content to the user's computing terminal; means for the user to review, edit, and publish the content received; means for the server to send a request from the computing terminal based on the request input by the user; and means for the server to deliver the content generated in response to the request to the computing terminal and for the user to review, edit, save, and share. This enables users to easily generate, review, edit, and share high-quality content using their computing terminals, and also enables automatic verification of whether the generated content complies with data privacy regulations and ethical guidelines.

[0639] "Computing terminal" means a device used by a User to input requests and to review, edit, and publish received content.

[0640] "Server" means a computer system that receives and analyzes user requests, collects necessary information, generates content using a generative machine learning model, reviews and modifies it, and delivers the final content to users.

[0641] A "user request" is a content that indicates the theme or topic of the content that the user wants to generate.

[0642] A "relational database" is a data storage system that stores information necessary for generating content.

[0643] An "external interface" is a means of communication for exchanging data with external information sources other than the relational database.

[0644] A "generative machine learning model" is an advanced algorithm or model that generates content based on collected information.

[0645] "Review" is the process of checking and correcting generated content for errors or inappropriate language.

[0646] "Final Content" is the content delivered to users after review and revision is complete.

[0647] "Reviewing, editing, and publishing" refers to the user viewing the received content, making changes as necessary, and publishing it on the Internet or elsewhere.

[0648] A "means for sending a request" is a method or mechanism by which a user sends a request from a computing terminal to a server.

[0649] "Means to review, edit, save and share content" means the methods and mechanisms by which user-generated content can be reviewed, modified as needed, saved and shared with other users and platforms.

[0650] To implement this invention, it is necessary to appropriately configure the computer terminal, server, generative machine learning model, related database, and external interface. In this section, the specific operation of the system will be described.

[0651] First, a user uses a computing device to input a request including the theme or topic of the content they want to generate. For example, they input a specific theme such as "healthy breakfast recipes." The request input by the user is sent from the device to the server. This transmission is performed using an HTTP POST request.

[0652] The server analyzes the requests received from the user and accesses databases and external interfaces to gather relevant information. The server then retrieves the necessary data based on the user's request. This data may include the latest research data, statistics, and other relevant information.

[0653] Next, a server-based generative machine learning model generates unique, high-quality content based on the collected information. This generation uses advanced natural language generation technologies, such as OpenAI's GPT-3. The generated content is then reviewed on-site to correct errors and inappropriate language and ensure compliance with data privacy regulations and ethical guidelines.

[0654] The generated content is delivered from the server to the user's computer device. The delivered content is displayed in an application on the user's device, and the user can view, edit, and publish it. For example, if a user runs a health blog, they can publish generated content such as an "oatmeal and fruit bowl recipe" as is.

[0655] Specific examples

[0656] An example of a prompt might be:

[0657] text

[0658] "Enter the topic of the content you want to create: Healthy Breakfast Recipes"

[0659] Based on this prompt, the generative machine learning model generates specific content such as:

[0660] text

[0661] "How to make an oatmeal and fruit bowl:

[0662] 1 cup oatmeal

[0663] 2 cups milk

[0664] 1 teaspoon honey

[0665] 1 banana

[0666] 1 / 2 cup blueberries

[0667] Almonds (as needed)

[0668] procedure:

[0669] 1. Place the oatmeal and milk in a saucepan and bring to a boil over medium heat.

[0670] 2. Stir well and add honey.

[0671] 3. Thinly slice the banana and top with the blueberries.

[0672] 4. Finally, add the almonds to finish.

[0673] Users can view this content on their devices and modify, save, or publish it as needed.

[0674] In this way, the system of the present invention allows users to efficiently create, review, edit, and share high-quality content without requiring advanced technical skills, and can automatically verify that the content complies with data privacy regulations and ethical guidelines.

[0675] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0676] Step 1:

[0677] A user uses a computing device to enter a request, which includes the theme or topic of the content they want to generate. For example, they might enter the text "healthy breakfast recipes." The request is stored locally on the device and is then ready to be sent to the server.

[0678] Step 2:

[0679] The terminal sends the request entered by the user to the server in the form of an HTTP POST request. When sending the request, data including the request content and topic is passed to the server. The server receives this request and waits for analysis.

[0680] Step 3:

[0681] The server analyzes the received request, extracting the request content by topic or keyword, and determining which databases or external interfaces should be used to retrieve the information. Based on the results of this analysis, it establishes procedures for collecting the relevant data.

[0682] Step 4:

[0683] The server accesses relevant databases and external interfaces to gather the necessary information, such as the latest research data and statistics related to healthy breakfast recipes. This gathering process involves extracting and integrating the data using appropriate queries.

[0684] Step 5:

[0685] The server passes the collected data to a generative machine learning model. The model used here is a natural language generation technology such as OpenAI's GPT-3. Based on the given data, the model generates unique, high-quality content on the specified topic. This generation process automatically creates topic-related sentence structure and content.

[0686] Step 6:

[0687] The server reviews the generated content, a process that checks the generated text for inappropriate language and errors, corrects them, and checks whether the generated content complies with data privacy regulations and ethical guidelines.

[0688] Step 7:

[0689] The server delivers the final content to the computer terminal. The delivered content is sent to the terminal in the form of a series of data and presented to the user. The terminal receives this data and displays it for the user to view.

[0690] Step 8:

[0691] Users can check the received content on their devices and edit it as needed. After editing, users can save the content and publish it on their own website or blog. They can also share the edited content with other platforms and users.

[0692] Through these steps, users can use generative AI models to quickly generate, review, edit, and publish high-quality content. For example, by using the prompt "Please enter the theme of the content you want to create: Healthy breakfast recipes," users can efficiently create the content they want.

[0693] 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.

[0694] The system of the present invention aims to automatically create more personalized, high-quality content by combining generative AI technology and an emotion engine. The system includes the following components:

[0695] User request input and emotion recognition

[0696] User:

[0697] Access the system using a terminal and log in to your account on the login screen. After logging in, you will be taken to the new content request form.

[0698] Emotion Engine:

[0699] The emotion engine recognizes the user's emotional state by analyzing the user's facial expression, tone of voice, writing style, etc. while they are inputting a request on the device. For example, if the user is inputting with a positive emotion, the emotion engine will send that information to the server.

[0700] Sending requests and transferring emotional data

[0701] Device:

[0702] The request entered by the user and the emotional data analyzed by the emotion engine are sent to the server as an HTTP POST request, which includes topics, keywords, and emotional data.

[0703] Receiving and parsing the request

[0704] server:

[0705] The server receives the user's request and emotional data, analyzes the request, validates the request, and determines how to adjust the tone of the content based on the emotional data.

[0706] Data collection

[0707] server:

[0708] The server gathers the necessary information related to the request from an internal database or external API, for example, fetching data for "healthy breakfast recipes."

[0709] Content generation based on emotional data

[0710] server:

[0711] A generative AI model generates unique, high-quality content based on collected data and sentiment data. If the sentiment data is positive, the tone of the article will also be positive. For example, a recipe will be introduced using uplifting language.

[0712] Content review and revision

[0713] server:

[0714] Review generated content and make corrections as needed. The server ensures that generated content complies with data privacy regulations and ethical guidelines.

[0715] Content Delivery

[0716] server:

[0717] The content that has undergone final confirmation and tone adjustment information based on the emotion data are delivered to the user's terminal, allowing the user to receive the generated content.

[0718] User review, editing and publishing

[0719] User:

[0720] Check the content delivered on the device, manually correct it if necessary, and finally publish the generated content on your own website or blog.

[0721] Specific examples

[0722] 1. User Request

[0723] A user who runs a health blog uses a device to request "healthy breakfast recipes" from the system. At the same time, the user expresses positive emotions.

[0724] 2. Emotion analysis using an emotion engine

[0725] The emotion engine recognizes positive emotions from the user's facial expressions and tone of voice and sends that data to the server.

[0726] 3. Data collection and content generation by the server

[0727] The server collects relevant data, and the generative AI model uses that data to generate breakfast recipe posts with a positive tone, such as "This oatmeal recipe will fuel your day!"

[0728] 4. Review and Distribution

[0729] The server automatically reviews the generated articles to ensure they comply with data privacy regulations and ethical guidelines before delivering them to the user's device.

[0730] 5. Final Review and Publication

[0731] Users review and edit articles and publish them to the health blog.

[0732] In this way, the system of the present invention, which combines an emotion engine, enables users to efficiently generate and publish high-quality content that reflects their own emotions.

[0733] The processing flow will be explained below.

[0734] Step 1:

[0735] The user accesses the "System" using a terminal and logs in to their account on the login screen. If successful, they are taken to a form to request new content.

[0736] Step 2:

[0737] The emotion engine analyzes the user's facial expressions, tone of voice, writing style, etc. to recognize the user's emotional state. For example, it can detect whether the user is smiling or speaking in a calm voice.

[0738] Step 3:

[0739] A user enters a specific content request on a device, for example, by entering the topic "healthy breakfast recipes" and specific keywords.

[0740] Step 4:

[0741] The terminal transmits the request input by the user and the emotion data analyzed by the emotion engine as an HTTP POST request to the server.

[0742] Step 5:

[0743] The server receives the user request and sentiment data, then validates the request to ensure it is well-formed, specifically by checking that it contains the required topics and keywords.

[0744] Step 6:

[0745] The server determines the tone and style of the content it generates based on the emotion data. For example, if the user expresses positive emotions, the tone of the article will also be set to be positive.

[0746] Step 7:

[0747] The server gathers relevant information from internal databases and external APIs, for example, retrieving the latest data and statistics for "healthy breakfast recipes."

[0748] Step 8:

[0749] The server preprocesses the collected information and converts it into a format that can be fed to the generative AI model, specifically by cleaning and tokenizing the data.

[0750] Step 9:

[0751] A server-based generative AI model generates content based on the pre-processed data and sentiment data, such as a detailed breakfast recipe with uplifting language.

[0752] Step 10:

[0753] Reviewing server-generated content and correcting it where necessary, ensuring compliance with data privacy regulations and ethical guidelines.

[0754] Step 11:

[0755] The server delivers the content that has undergone final confirmation to the user's device, where it is displayed on the user's dashboard.

[0756] Step 12:

[0757] The user can then review the content delivered on their device, manually edit it if necessary, and finally publish the resulting content on their own website or blog.

[0758] Specific examples

[0759] 1. User Requests and Emotion Recognition

[0760] A user uses a device to request a "healthy breakfast recipe" from the system, and the emotion engine recognizes the user's positive emotion (smiling).

[0761] 2. Data collection and content generation

[0762] The server collects relevant information from databases and external APIs, and the generative AI model generates a recipe post for "Oatmeal and Fruit Bowl" with a positive tone, such as "This oatmeal recipe will energize your day!"

[0763] 3. Review, Finalize, and Publish

[0764] The server reviews the content, and the user confirms and edits the article sent to the device and publishes it on the health blog.

[0765] In this way, the system of the present invention, which combines an emotion engine, enables users to efficiently generate and publish high-quality content that reflects their own emotions.

[0766] Example 2

[0767] 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."

[0768] Conventional content generation systems have struggled to automatically generate personalized, high-quality content that takes user emotions into account. This can result in a failure to provide content that addresses user needs and emotions, leading to a decline in user engagement and satisfaction. Furthermore, the quality and tone of the generated content can be inconsistent, which can undermine its reliability and usefulness.

[0769] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving and analyzing requests and emotional data input by a user, means for collecting necessary information from a related database or an external API, and means for generating content using a generative AI model based on the collected information and emotional data. This makes it possible to automatically provide high-quality content with a tone and content that matches the emotional state of the user.

[0770] A "terminal" is an electronic device such as a computer or smartphone that allows a user to input and send requests.

[0771] "Emotion data" is data that indicates the user's emotional state and is generated by analyzing the user's facial expression, tone of voice, writing style, etc.

[0772] A "server" is a computer system that receives and analyzes user requests and emotion data.

[0773] A "generative AI model" is an artificial intelligence model that generates unique, high-quality content based on collected data and emotional data.

[0774] A "relevant database" is an internal or external source of information that the server accesses to gather required information.

[0775] An "external API" is an application programming interface used to obtain information from other services.

[0776] "Review" is the process of checking generated content for linguistic accuracy, grammar, and relevance, and making corrections as needed.

[0777] "Data privacy regulations" are legal and ethical guidelines for protecting users' personal information.

[0778] "Ethical guidelines" are moral and ethical standards that should be observed during the content creation process.

[0779] "Content" refers to information such as text, articles, recipes, etc. created by generative AI models.

[0780] The present invention provides a system for automatically generating personalized, high-quality content that reflects a user's emotions. This system operates by combining generative AI technology with an emotion engine. A specific embodiment of this system will be described below.

[0781] First, a user accesses the system using a terminal and logs in to their account on the login screen. Once logged in, the user is taken to a form to request new content and enters a request, such as "healthy breakfast recipes." At this time, the emotion engine analyzes the user's facial expressions and tone of voice to generate emotion data, such as positive or negative. The emotion engine incorporates hardware such as a facial recognition camera and microphone, as well as machine learning models.

[0782] Next, the device sends the user's input request and emotional data to the server as an HTTP POST request. The request includes topics, keywords, and emotional data. The server receives the HTTP request and analyzes the request content and emotional data. It checks the syntax of the request and adjusts the tone of the generated content based on the emotional data.

[0783] The server collects the necessary information through relevant databases and external APIs. For example, it calls RecipeAPI to get information related to "healthy breakfast recipes." This collected data is then converted into a format that can be passed to the generative AI model.

[0784] The generative AI model generates unique, high-quality content based on collected data and sentiment data. If the sentiment data is positive, the generated content will also have a positive tone. For example, an article might be generated that reads, "This oatmeal recipe will start your morning off right!"

[0785] The generated content is reviewed on the server for language accuracy, grammar, and relevance, and an automated filtering system is used to ensure compliance with data privacy regulations and ethical guidelines. After any necessary corrections are made, the final content is delivered to the user's device.

[0786] The user can then review the content on their device, manually edit it if necessary, and publish the finalized content to their website or blog.

[0787] This invention enables users to efficiently create and publish high-quality content that reflects their own emotional state.

[0788] Prompt Sentence Examples

[0789] "Generate articles with a positive tone and healthy breakfast recipes."

[0790] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0791] Step 1:

[0792] The user enters a request

[0793] User: Access the system using a terminal and log in to their account on the login screen. After logging in, they will be taken to the request entry form.

[0794] Input: Login credentials, request (e.g. "healthy breakfast recipes").

[0795] Output: Login success notification, request data.

[0796] Specific action: Enter "healthy breakfast recipes" into the admin panel of a health blog and submit the form.

[0797] Step 2:

[0798] emotion recognition

[0799] Emotion engine: While the user is typing a request, the facial recognition camera and microphone are used to collect the user's facial expressions and tone of voice to generate emotion data.

[0800] Input: User's facial expression data, tone of voice data.

[0801] Output: Sentiment data (e.g., positive).

[0802] Specific behavior: Detects whether the user is smiling when typing and generates positive emotion data.

[0803] Step 3:

[0804] Sending requests and emotion data

[0805] Terminal: The request and emotion data entered by the user are sent to the server as an HTTP POST request.

[0806] Input: Request content, emotion data.

[0807] Output: HTTP POST request.

[0808] Specific behavior: By pressing the send button, the "healthy breakfast recipe" and positive emotion data are sent to the server.

[0809] Step 4:

[0810] Receiving and parsing the request

[0811] Server: Receives HTTP requests, analyzes the request content and sentiment data, performs syntax checks, and validates the content.

[0812] Input: HTTP POST request (request content, emotion data).

[0813] Output: Parsing results, syntax check results.

[0814] What it does: Ensures that requests for "healthy breakfast recipes" are submitted in the correct format.

[0815] Step 5:

[0816] Data collection

[0817] Server: Gathers information related to the request from an internal database or external API, for example, fetching data about "healthy breakfast recipes."

[0818] Input: Your request ("healthy breakfast recipes").

[0819] Output: A dataset of related information.

[0820] Specific behavior: Calls the Recipe API and retrieves breakfast recipe data.

[0821] Step 6:

[0822] Content generation based on emotional data

[0823] Server: The generative AI model generates unique, high-quality content based on collected data and sentiment data. If the sentiment data is positive, the tone of the article will also be positive.

[0824] Input: Relevant information dataset, sentiment data.

[0825] Output: Generated content (e.g., a breakfast recipe article with a positive tone).

[0826] What it does: Generates an article that says, "This oatmeal recipe will start your morning off right!"

[0827] Step 7:

[0828] Content review and revision

[0829] Server: Review the generated content, checking for language accuracy, grammar, and relevance, and making corrections as needed.

[0830] Input: Generated content.

[0831] Output: The revised content.

[0832] Specific Actions: Ensure automated tools comply with data privacy regulations and ethical guidelines.

[0833] Step 8:

[0834] Content Delivery

[0835] Server: Deliver the final content to the user's device. Delivery formats can be selected, such as email or notification.

[0836] Input: The revised content.

[0837] Output: Delivery notification or delivery data.

[0838] Specific operation: Notify the user that an article has arrived on their device.

[0839] Step 9:

[0840] User review, editing and publishing

[0841] User: Review the delivered content, make manual corrections as needed, and then publish the final, reviewed content.

[0842] Input: The delivered content.

[0843] Output: Published content.

[0844] What it does: Post an article to your blog or website.

[0845] (Application example 2)

[0846] 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."

[0847] Current content generation systems struggle to provide personalized content that reflects a user's emotional state. Conventional systems generate content based on user requests, but do not consider the user's emotions or moods in the process, making it difficult to increase user satisfaction. Furthermore, content generation that incorporates emotional data must comply with data privacy regulations and ethical guidelines.

[0848] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0849] In this invention, the server includes a means having an engine for analyzing a user's facial expressions and tone of voice to recognize emotions, a means for receiving and analyzing user requests and emotional data, and a means for generating content using a generative AI model based on the collected information and emotional data, thereby enabling the automatic generation of personalized content that reflects the user's emotional state.

[0850] A "user request" is a request or desire that a user inputs into the system using a terminal.

[0851] "Facial expressions and tone of voice" refers to the characteristics of the user's facial expressions and voice, and is information analyzed by the emotion engine.

[0852] An "emotion engine" is a software and hardware system that analyzes a user's facial expressions, tone of voice, writing style, etc. to recognize the user's emotional state.

[0853] "Server" refers to a computer system that receives user requests and emotional data, analyzes them, collects information, generates content, reviews, modifies, and distributes them.

[0854] A "generative AI model" is an artificial intelligence algorithm that generates unique, high-quality content based on collected data and emotional data.

[0855] "Content" refers to information and creative works such as text, images, audio, and video, which are generated based on user requests and emotional data.

[0856] "Review" is the process by which the server checks the generated content and makes corrections as necessary.

[0857] "Modification" is the act of improving or changing the content of the generated content.

[0858] "Data privacy regulations" are laws and regulations regarding the protection of users' personal information and data.

[0859] "Ethical guidelines" are ethical standards and guidelines regarding content creation and information collection.

[0860] "Delivery" refers to the act of the server sending the final content to the user's terminal.

[0861] A "terminal" is a device that allows a user to access the system, input requests, and check content.

[0862] "Natural language generation technology" is a technology that enables a generative AI model to generate content in language that humans can understand based on user requests and emotional data.

[0863] The system of the present invention combines a user terminal, an emotion engine, a server, and a generative AI model to automatically generate and deliver high-quality content based on the user's emotional state. Below, we will explain the details of each element and how they work.

[0864] User request input and emotion recognition

[0865] A user accesses the system using a terminal and first logs in to their account on the login screen. After logging in, they are taken to a form to request new content. At this time, the terminal uses a camera and microphone to provide the user's facial expressions and tone of voice to the emotion engine in real time.

[0866] Emotion analysis using an emotion engine

[0867] The emotion engine recognizes the user's emotional state by analyzing facial expressions, tone of voice, and writing style while the user is typing a request. This emotional data is classified as "positive" or "negative," for example, and sent to the server.

[0868] Receiving and parsing the request

[0869] The server receives the user's request and emotional data as an HTTP POST request, analyzes them, and determines the tone of the content to be generated based on the request and emotional data.

[0870] Data collection

[0871] The server gathers the necessary information related to the request from internal databases and external APIs, for example, fetching data for "latest news" from the web.

[0872] Content generation based on emotional data

[0873] A generative AI model generates unique, high-quality content based on collected data and sentiment data. If the sentiment data is positive, the tone of the article will also be positive. For example, "This news will brighten your day!"

[0874] Content review and revision

[0875] The server reviews the generated content and makes corrections as necessary. It also checks whether the generated content complies with data privacy regulations and ethical guidelines, ensuring there are no issues.

[0876] Content Delivery

[0877] The final confirmed content is delivered from the server to the user's terminal, allowing the user to receive the generated content.

[0878] User review, editing and publishing

[0879] Users can review the content delivered on their devices and manually edit it if necessary, and finally publish the generated content on their own website or blog.

[0880] Specific examples

[0881] For example, a user may request that they are interested in "Today's News," and emotion analysis is performed using the device's camera and microphone. If the user inputs the request with a smile, the emotion engine evaluates the information as "positive." Based on this information, the generative AI model generates a news article with a positive tone. The article may include phrases such as "Today's News Will Make Your Day Even Better!"

[0882] Example prompts for generative AI models

[0883] "Create a latest news article in a positive tone."

[0884] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0885] Step 1:

[0886] A user accesses the system using a terminal and logs in to their account on the login screen. User authentication information is entered here, and if authentication is successful, a request input form for new content is displayed.

[0887] Step 2:

[0888] The user enters the desired topic in the request input form. At this time, the device's camera and microphone are used to capture the user's facial expressions and tone of voice in real time and send them to the emotion engine. The input is the user's topic request and emotion data, and the output is the request data that integrates these data.

[0889] Step 3:

[0890] The emotion engine analyzes the user's facial expressions, tone of voice, and writing style to recognize the user's emotional state. As a result of the analysis, emotion data is generated and classified as, for example, "positive" or "negative." The output is the recognized emotion data.

[0891] Step 4:

[0892] The device sends the user's request and emotion data to the server as an HTTP POST request. In this step, the integrated request data is sent to the server.

[0893] Step 5:

[0894] The server receives the user request and emotional data and analyzes it. The input is the aggregated request data, and the output is the analysis result. The analysis includes request validation and determines the tone of the content based on the emotional data.

[0895] Step 6:

[0896] The server collects the necessary information related to the request from internal databases and external APIs. The input is the parsed request and the output is the collected data. For example, retrieving information about "latest news" from the web.

[0897] Step 7:

[0898] The generative AI model generates unique, high-quality content based on collected data and sentiment data. The input is collected data and sentiment data, and the output is generated content. If the sentiment data is positive, the tone of the article will also be positive.

[0899] Step 8:

[0900] The server reviews the generated content and makes corrections as needed. The input is the generated content and the output is the reviewed content. The server ensures that the generated content complies with data privacy regulations and ethical guidelines.

[0901] Step 9:

[0902] The server delivers the final reviewed content to the user's device. The input is the reviewed content and the output is the content sent to the user's device.

[0903] Step 10:

[0904] The user checks the distributed content on their device and manually corrects it if necessary. The input is the distributed content, and the output is the final published content. The user then publishes the corrected content on their own website or blog.

[0905] 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.

[0906] 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.

[0907] 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.

[0908] [Third embodiment]

[0909] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0910] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0911] 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).

[0912] 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.

[0913] 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.

[0914] 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).

[0915] 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.

[0916] 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.

[0917] 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.

[0918] 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.

[0919] 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.

[0920] 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."

[0921] The system of the present invention aims to automatically create content using generative AI technology. This system includes the following components:

[0922] User request input

[0923] User:

[0924] Users access the system using a terminal and log in to their account through a designated login screen. After logging in, users enter specific requests for new content. For example, a user can request "blog articles about healthy lifestyle habits."

[0925] Submitting a Request

[0926] Device:

[0927] The request entered by the user is sent to the server in the form of an HTTP POST request, which includes detailed information such as the topic and keywords.

[0928] Receiving and parsing the request

[0929] server:

[0930] The server analyzes the request received from the user to understand the request content, and determines which database or external API to retrieve information from.

[0931] Data collection

[0932] server:

[0933] The server collects data related to the specified topic from databases and external APIs, such as the latest research and statistics on healthy lifestyle habits.

[0934] Content generation using generative AI models

[0935] server:

[0936] The server-based generative AI model generates unique, high-quality content based on the collected data. Specifically, the generative AI model uses natural language generation technology to create article sentences. For example, it generates a paragraph-by-paragraph blog post about healthy lifestyle habits.

[0937] Content review and revision

[0938] server:

[0939] Automatically review generated content to correct inappropriate language and errors, and ensure that generated content complies with data privacy regulations and ethical guidelines.

[0940] Content Delivery

[0941] server:

[0942] Once the final content is confirmed, the server delivers it to the user's device, where it is displayed on the user's dashboard.

[0943] User review, editing and publishing

[0944] User:

[0945] The user can check the delivered content on their device, manually correct it if necessary, and then publish it on their website or blog.

[0946] Specific examples

[0947] 1. User Request

[0948] A user who runs a health blog uses a terminal to request "healthy breakfast recipes" from the system.

[0949] 2. Server analysis and preparation

[0950] The server receives the request and retrieves data related to a healthy breakfast from a database and an external API.

[0951] 3. Content Generation

[0952] The generative AI model uses the acquired data to generate detailed breakfast recipe articles, such as a recipe for an "oatmeal and fruit bowl" along with its nutritional information.

[0953] 4. Reviews and Shipping

[0954] The server automatically reviews the generated content and delivers it to users, ensuring that it complies with data privacy regulations and ethical guidelines.

[0955] 5. Final Review and Publication

[0956] Users can review and edit articles on their devices and publish them to their health blog.

[0957] In this way, the system of the present invention allows users to efficiently generate and publish high-quality content without requiring advanced technical skills.

[0958] The processing flow will be explained below.

[0959] Step 1:

[0960] The user accesses the Muse platform using a device and logs in to their account on the login screen. If successful, the user is taken to a form to request new content.

[0961] Step 2:

[0962] The user fills out a form on their device with a detailed request for the content they want generated, for example, by entering the topic "healthy breakfast recipes" and specific keywords.

[0963] Step 3:

[0964] The device sends the user's input request as an HTTP POST request to the server, which includes metadata such as topics and keywords.

[0965] Step 4:

[0966] The server receives the user's request, analyzes the format and content, and checks whether it is in the correct format. Specifically, it performs request validation.

[0967] Step 5:

[0968] The server queries an internal database or external API to gather relevant data, for example, data related to "healthy breakfast recipes."

[0969] Step 6:

[0970] The server preprocesses the collected data and converts it into a format that can be passed to the generative AI model, specifically by cleaning and tokenizing the text.

[0971] Step 7:

[0972] The server-based generative AI model generates unique, high-quality content based on the pre-processed data, for example by determining the flow of sentences and paragraph structure and generating the actual text.

[0973] Step 8:

[0974] The server reviews the generated content and makes corrections if necessary, including ensuring compliance with data privacy regulations and ethical guidelines.

[0975] Step 9:

[0976] The server delivers the final content to the user's terminal, allowing the user to receive the generated content.

[0977] Step 10:

[0978] The user checks the delivered content on their device, manually corrects it if necessary, and publishes the generated content on their own website or blog.

[0979] The above is a specific processing flow.

[0980] Example 1

[0981] 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."

[0982] In modern society, there is a growing need to generate content efficiently and with high quality. However, creating content on one's own requires advanced technical skills and a lot of time. Furthermore, it is difficult to ensure that the generated content complies with data privacy regulations and ethical guidelines. Therefore, there is a need for a system that allows users to easily generate, edit, and publish high-quality content.

[0983] 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.

[0984] In this invention, the server includes a means for a user to input a request using a terminal, a means for the terminal to send the user's request to the server in the form of an HTTP POST request, and a means for the server to receive and analyze the user's request, thereby enabling the user to easily make a content request and the server to quickly analyze it.

[0985] A "user" is a person who uses a terminal to access the system and enter a request for content generation.

[0986] A "terminal" is a device through which a user accesses the system and enters and transmits requests.

[0987] "Server" means a computer system that receives and analyzes user requests, collects relevant data, generates content using generative AI models, automatically reviews and corrects, and delivers the final content.

[0988] An "HTTP POST request" is a form of the HTTP protocol used to send data from a terminal to a server.

[0989] "Analysis" is the process by which the server understands the content of the request received from the user and determines which databases or external APIs to collect information from.

[0990] A "generative AI model" is an artificial intelligence system that uses natural language generation techniques to generate unique content based on data.

[0991] "Automated review" is a process in which server-generated content is automatically checked and corrected for inappropriate language or errors.

[0992] "Data privacy regulations" are legal regulations regarding the protection and handling of personal information.

[0993] "Ethical guidelines" are guidelines to ensure that content is socially and morally appropriate.

[0994] "Delivery" is the process by which the server sends the final content to the user's terminal.

[0995] "Publishing" is the act of a user making the final content available to the public on a platform such as their own website or blog.

[0996] This invention is a system that automatically creates content using generative AI technology. The system automates a series of processes: a user inputs a request via a terminal, and a server analyzes the request, collects data, generates content, reviews it, modifies it, and finally delivers it. Key technical elements include HTTP POST requests, a generative AI model that uses natural language generation technology, and an automated review system that complies with data privacy regulations and ethical guidelines.

[0997] Hardware and Software Configuration

[0998] The following hardware and software are required to implement the system:

[0999] User device: Refers to devices that can connect to the Internet, such as PCs, smartphones, and tablets.

[1000] Server: Uses high-performance computers or cloud-based servers to receive user requests, process the data, and generate content using generative AI models.

[1001] Generative AI models: For example, OpenAI's GPT series for natural language generation technology.

[1002] System Operation Overview

[1003] User request input

[1004] The user uses a terminal to access the system's login screen and enters their user ID and password to log in to their account. After logging in, the user enters a specific request for new content, for example, "blog articles about healthy lifestyle habits." After completing the request, the user clicks the submit button.

[1005] Submitting a Request

[1006] The device sends the user's request to the server in the form of an HTTP POST request, which includes detailed information such as topics and keywords.

[1007] The server receives and analyzes the request

[1008] The server receives the HTTP POST request, analyzes its contents, and uses a program to analyze the request, such as the Python Flask framework, to determine which database or external API to collect information from.

[1009] Data collection

[1010] The server collects data related to the specified topic from databases and external APIs, for example, by sending an API request to get the latest research data and statistics on healthy lifestyle habits.

[1011] Content generation using generative AI models

[1012] The server processes the collected data and converts it into a format that can be input into the generative AI model. After cleansing and normalizing the data, a prompt is input into the generative AI model. For example, "Please recommend some healthy breakfast recipes. Please recommend some simple and nutritious dishes using oatmeal." The generative AI model generates unique content based on the prompt, and the generated text is organized into paragraphs as blog posts.

[1013] Content review and revision

[1014] Automatically review generated content to correct inappropriate language and errors, and ensure that generated content complies with data privacy regulations and ethical guidelines, ensuring users can consume content with confidence.

[1015] Content Delivery

[1016] Once the final content is verified, the server delivers it to the user's device, where it is displayed on the user's dashboard.

[1017] User review, editing and publishing

[1018] Users can check the delivered content on their devices and manually edit it if necessary, then publish it on their own website or blog.

[1019] Specific examples

[1020] 1. User Request

[1021] A user who runs a health blog uses a terminal to request "healthy breakfast recipes" from the system.

[1022] 2. Server analysis and preparation

[1023] The server receives the request and retrieves data related to a healthy breakfast from a database and an external API.

[1024] 3. Content Generation

[1025] The generative AI model uses the acquired data to generate detailed breakfast recipe articles, such as a recipe for an "oatmeal and fruit bowl" along with its nutritional information.

[1026] 4. Reviews and Shipping

[1027] The server automatically reviews the generated content and delivers it to users, ensuring that it complies with data privacy regulations and ethical guidelines.

[1028] 5. Final Review and Publication

[1029] Users can review and edit articles on their devices and publish them to their health blog.

[1030] Example prompt sentence:

[1031] "I'd like some healthy breakfast recipes. Can you recommend some easy, nutritious options using oatmeal?"

[1032] As described above, this system allows users to efficiently generate and publish high-quality content without requiring advanced technical skills.

[1033] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1034] Step 1:

[1035] User request input

[1036] A user accesses the system's login screen using a terminal, enters their user ID and password to log in to their account, and then specifically enters a request for new content, for example, "a blog post about healthy lifestyle habits," and clicks the submit button.

[1037] Input: User ID, password, content request

[1038] Output: The input request data

[1039] Step 2:

[1040] Submitting a Request

[1041] The device sends the user's request to the server in the form of an HTTP POST request, with detailed information such as topics and keywords included in the JSON format.

[1042] Input: User request data

[1043] Output: HTTP POST request sent to the server

[1044] Step 3:

[1045] Receiving and parsing the request

[1046] The server receives the HTTP POST request, analyzes the request, for example using the Python Flask framework, and determines which databases or external APIs to collect information from.

[1047] Input: HTTP POST request

[1048] Output: Parsed request data, data sources to be collected

[1049] Step 4:

[1050] Data collection

[1051] The server collects data related to a specified topic from a database or external API, for example, by sending an API request to get the latest research data and statistics on healthy lifestyle habits.

[1052] Input: Data source to be collected

[1053] Output: Collected data

[1054] Step 5:

[1055] Content generation using generative AI models

[1056] The server processes the collected data and converts it into a format that can be input into the generative AI model. During this process, the data is cleansed and normalized. Next, a prompt is input into the generative AI model. For example, "Please recommend some healthy breakfast recipes. Please recommend some easy and nutritious menus using oatmeal." The generative AI model generates unique content based on the prompt.

[1057] Input: Collected data, prompt statement

[1058] Output: Generated content

[1059] Step 6:

[1060] Content review and revision

[1061] The server automatically reviews generated content, correcting inappropriate language and errors, and ensuring that generated content complies with data privacy regulations and ethical guidelines.

[1062] Input: Generated content

[1063] Output: The modified content

[1064] Step 7:

[1065] Content Delivery

[1066] The server delivers the final content to the user's device, where it is displayed on the user's dashboard.

[1067] Input: Modified content

[1068] Output: Content delivered to the user's device

[1069] Step 8:

[1070] User review, editing and publishing

[1071] The user checks the delivered content on the device and manually corrects it if necessary, then publishes it on their website or blog.

[1072] Input: Streamed content

[1073] Output: Published content

[1074] (Application example 1)

[1075] 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."

[1076] The challenge is to solve the problem of how users can efficiently generate, edit, and publish high-quality content without requiring advanced technical skills, and to automatically check whether the generated content complies with data privacy regulations and ethical guidelines.

[1077] 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.

[1078] In this invention, the server includes: means for a user to input a request using a computing terminal; means for the server to receive and analyze the user's request; means for the server to collect necessary information from a related database or an external interface; means for the server to generate content using a generative machine learning model based on the collected information; means for the server to review and modify the generated content; means for the server to deliver the final content to the user's computing terminal; means for the user to review, edit, and publish the content received; means for the server to send a request from the computing terminal based on the request input by the user; and means for the server to deliver the content generated in response to the request to the computing terminal and for the user to review, edit, save, and share. This enables users to easily generate, review, edit, and share high-quality content using their computing terminals, and also enables automatic verification of whether the generated content complies with data privacy regulations and ethical guidelines.

[1079] "Computing terminal" means a device used by a User to input requests and to review, edit, and publish received content.

[1080] "Server" means a computer system that receives and analyzes user requests, collects necessary information, generates content using a generative machine learning model, reviews and modifies it, and delivers the final content to users.

[1081] A "user request" is a content that indicates the theme or topic of the content that the user wants to generate.

[1082] A "relational database" is a data storage system that stores information necessary for generating content.

[1083] An "external interface" is a means of communication for exchanging data with external information sources other than the relational database.

[1084] A "generative machine learning model" is an advanced algorithm or model that generates content based on collected information.

[1085] "Review" is the process of checking and correcting generated content for errors or inappropriate language.

[1086] "Final Content" is the content delivered to users after review and revision is complete.

[1087] "Reviewing, editing, and publishing" refers to the user viewing the received content, making changes as necessary, and publishing it on the Internet or elsewhere.

[1088] A "means for sending a request" is a method or mechanism by which a user sends a request from a computing terminal to a server.

[1089] "Means to review, edit, save and share content" means the methods and mechanisms by which user-generated content can be reviewed, modified as needed, saved and shared with other users and platforms.

[1090] To implement this invention, it is necessary to appropriately configure the computer terminal, server, generative machine learning model, related database, and external interface. In this section, the specific operation of the system will be described.

[1091] First, a user uses a computing device to input a request including the theme or topic of the content they want to generate. For example, they input a specific theme such as "healthy breakfast recipes." The request input by the user is sent from the device to the server. This transmission is performed using an HTTP POST request.

[1092] The server analyzes the requests received from the user and accesses databases and external interfaces to gather relevant information. The server then retrieves the necessary data based on the user's request. This data may include the latest research data, statistics, and other relevant information.

[1093] Next, a server-based generative machine learning model generates unique, high-quality content based on the collected information. This generation uses advanced natural language generation technologies, such as OpenAI's GPT-3. The generated content is then reviewed on-site to correct errors and inappropriate language and ensure compliance with data privacy regulations and ethical guidelines.

[1094] The generated content is delivered from the server to the user's computer device. The delivered content is displayed in an application on the user's device, and the user can view, edit, and publish it. For example, if a user runs a health blog, they can publish generated content such as an "oatmeal and fruit bowl recipe" as is.

[1095] Specific examples

[1096] An example of a prompt might be:

[1097] text

[1098] "Enter the topic of the content you want to create: Healthy Breakfast Recipes"

[1099] Based on this prompt, the generative machine learning model generates specific content such as:

[1100] text

[1101] "How to make an oatmeal and fruit bowl:

[1102] 1 cup oatmeal

[1103] 2 cups milk

[1104] 1 teaspoon honey

[1105] 1 banana

[1106] 1 / 2 cup blueberries

[1107] Almonds (as needed)

[1108] procedure:

[1109] 1. Place the oatmeal and milk in a saucepan and bring to a boil over medium heat.

[1110] 2. Stir well and add honey.

[1111] 3. Thinly slice the banana and top with the blueberries.

[1112] 4. Finally, add the almonds to finish.

[1113] Users can view this content on their devices and modify, save, or publish it as needed.

[1114] In this way, the system of the present invention allows users to efficiently create, review, edit, and share high-quality content without requiring advanced technical skills, and can automatically verify that the content complies with data privacy regulations and ethical guidelines.

[1115] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1116] Step 1:

[1117] A user uses a computing device to enter a request, which includes the theme or topic of the content they want to generate. For example, they might enter the text "healthy breakfast recipes." The request is stored locally on the device and is then ready to be sent to the server.

[1118] Step 2:

[1119] The terminal sends the request entered by the user to the server in the form of an HTTP POST request. When sending the request, data including the request content and topic is passed to the server. The server receives this request and waits for analysis.

[1120] Step 3:

[1121] The server analyzes the received request, extracting the request content by topic or keyword, and determining which databases or external interfaces should be used to retrieve the information. Based on the results of this analysis, it establishes procedures for collecting the relevant data.

[1122] Step 4:

[1123] The server accesses relevant databases and external interfaces to gather the necessary information, such as the latest research data and statistics related to healthy breakfast recipes. This gathering process involves extracting and integrating the data using appropriate queries.

[1124] Step 5:

[1125] The server passes the collected data to a generative machine learning model. The model used here is a natural language generation technology such as OpenAI's GPT-3. Based on the given data, the model generates unique, high-quality content on the specified topic. This generation process automatically creates topic-related sentence structure and content.

[1126] Step 6:

[1127] The server reviews the generated content, a process that checks the generated text for inappropriate language and errors, corrects them, and checks whether the generated content complies with data privacy regulations and ethical guidelines.

[1128] Step 7:

[1129] The server delivers the final content to the computer terminal. The delivered content is sent to the terminal in the form of a series of data and presented to the user. The terminal receives this data and displays it for the user to view.

[1130] Step 8:

[1131] Users can check the received content on their devices and edit it as needed. After editing, users can save the content and publish it on their own website or blog. They can also share the edited content with other platforms and users.

[1132] Through these steps, users can use generative AI models to quickly generate, review, edit, and publish high-quality content. For example, by using the prompt "Please enter the theme of the content you want to create: Healthy breakfast recipes," users can efficiently create the content they want.

[1133] 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.

[1134] The system of the present invention aims to automatically create more personalized, high-quality content by combining generative AI technology and an emotion engine. The system includes the following components:

[1135] User request input and emotion recognition

[1136] User:

[1137] Access the system using a terminal and log in to your account on the login screen. After logging in, you will be taken to the new content request form.

[1138] Emotion Engine:

[1139] The emotion engine recognizes the user's emotional state by analyzing the user's facial expression, tone of voice, writing style, etc. while they are inputting a request on the device. For example, if the user is inputting with a positive emotion, the emotion engine will send that information to the server.

[1140] Sending requests and transferring emotional data

[1141] Device:

[1142] The request entered by the user and the emotional data analyzed by the emotion engine are sent to the server as an HTTP POST request, which includes topics, keywords, and emotional data.

[1143] Receiving and parsing the request

[1144] server:

[1145] The server receives the user's request and emotional data, analyzes the request, validates the request, and determines how to adjust the tone of the content based on the emotional data.

[1146] Data collection

[1147] server:

[1148] The server gathers the necessary information related to the request from an internal database or external API, for example, fetching data for "healthy breakfast recipes."

[1149] Content generation based on emotional data

[1150] server:

[1151] A generative AI model generates unique, high-quality content based on collected data and sentiment data. If the sentiment data is positive, the tone of the article will also be positive. For example, a recipe will be introduced using uplifting language.

[1152] Content review and revision

[1153] server:

[1154] Review generated content and make corrections as needed. The server ensures that generated content complies with data privacy regulations and ethical guidelines.

[1155] Content Delivery

[1156] server:

[1157] The content that has undergone final confirmation and tone adjustment information based on the emotion data are delivered to the user's terminal, allowing the user to receive the generated content.

[1158] User review, editing and publishing

[1159] User:

[1160] Check the content delivered on the device, manually correct it if necessary, and finally publish the generated content on your own website or blog.

[1161] Specific examples

[1162] 1. User Request

[1163] A user who runs a health blog uses a device to request "healthy breakfast recipes" from the system. At the same time, the user expresses positive emotions.

[1164] 2. Emotion analysis using an emotion engine

[1165] The emotion engine recognizes positive emotions from the user's facial expressions and tone of voice and sends that data to the server.

[1166] 3. Data collection and content generation by the server

[1167] The server collects relevant data, and the generative AI model uses that data to generate breakfast recipe posts with a positive tone, such as "This oatmeal recipe will fuel your day!"

[1168] 4. Review and Distribution

[1169] The server automatically reviews the generated articles to ensure they comply with data privacy regulations and ethical guidelines before delivering them to the user's device.

[1170] 5. Final Review and Publication

[1171] Users review and edit articles and publish them to the health blog.

[1172] In this way, the system of the present invention, which combines an emotion engine, enables users to efficiently generate and publish high-quality content that reflects their own emotions.

[1173] The processing flow will be explained below.

[1174] Step 1:

[1175] The user accesses the "System" using a terminal and logs in to their account on the login screen. If successful, they are taken to a form to request new content.

[1176] Step 2:

[1177] The emotion engine analyzes the user's facial expressions, tone of voice, writing style, etc. to recognize the user's emotional state. For example, it can detect whether the user is smiling or speaking in a calm voice.

[1178] Step 3:

[1179] A user enters a specific content request on a device, for example, by entering the topic "healthy breakfast recipes" and specific keywords.

[1180] Step 4:

[1181] The terminal transmits the request input by the user and the emotion data analyzed by the emotion engine as an HTTP POST request to the server.

[1182] Step 5:

[1183] The server receives the user request and sentiment data, then validates the request to ensure it is well-formed, specifically by checking that it contains the required topics and keywords.

[1184] Step 6:

[1185] The server determines the tone and style of the content it generates based on the emotion data. For example, if the user expresses positive emotions, the tone of the article will also be set to be positive.

[1186] Step 7:

[1187] The server gathers relevant information from internal databases and external APIs, for example, retrieving the latest data and statistics for "healthy breakfast recipes."

[1188] Step 8:

[1189] The server preprocesses the collected information and converts it into a format that can be fed to the generative AI model, specifically by cleaning and tokenizing the data.

[1190] Step 9:

[1191] A server-based generative AI model generates content based on the pre-processed data and sentiment data, such as a detailed breakfast recipe with uplifting language.

[1192] Step 10:

[1193] Reviewing server-generated content and correcting it where necessary, ensuring compliance with data privacy regulations and ethical guidelines.

[1194] Step 11:

[1195] The server delivers the content that has undergone final confirmation to the user's device, where it is displayed on the user's dashboard.

[1196] Step 12:

[1197] The user can then review the content delivered on their device, manually edit it if necessary, and finally publish the resulting content on their own website or blog.

[1198] Specific examples

[1199] 1. User Requests and Emotion Recognition

[1200] A user uses a device to request a "healthy breakfast recipe" from the system, and the emotion engine recognizes the user's positive emotion (smiling).

[1201] 2. Data collection and content generation

[1202] The server collects relevant information from databases and external APIs, and the generative AI model generates a recipe post for "Oatmeal and Fruit Bowl" with a positive tone, such as "This oatmeal recipe will energize your day!"

[1203] 3. Review, Finalize, and Publish

[1204] The server reviews the content, and the user confirms and edits the article sent to the device and publishes it on the health blog.

[1205] In this way, the system of the present invention, which combines an emotion engine, enables users to efficiently generate and publish high-quality content that reflects their own emotions.

[1206] Example 2

[1207] 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."

[1208] Conventional content generation systems have struggled to automatically generate personalized, high-quality content that takes user emotions into account. This can result in a failure to provide content that addresses user needs and emotions, leading to a decline in user engagement and satisfaction. Furthermore, the quality and tone of the generated content can be inconsistent, which can undermine its reliability and usefulness.

[1209] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving and analyzing requests and emotional data input by a user, means for collecting necessary information from a related database or an external API, and means for generating content using a generative AI model based on the collected information and emotional data. This makes it possible to automatically provide high-quality content with a tone and content that matches the emotional state of the user.

[1210] A "terminal" is an electronic device such as a computer or smartphone that allows a user to input and send requests.

[1211] "Emotion data" is data that indicates the user's emotional state and is generated by analyzing the user's facial expression, tone of voice, writing style, etc.

[1212] A "server" is a computer system that receives and analyzes user requests and emotion data.

[1213] A "generative AI model" is an artificial intelligence model that generates unique, high-quality content based on collected data and emotional data.

[1214] A "relevant database" is an internal or external source of information that the server accesses to gather required information.

[1215] An "external API" is an application programming interface used to obtain information from other services.

[1216] "Review" is the process of checking generated content for linguistic accuracy, grammar, and relevance, and making corrections as needed.

[1217] "Data privacy regulations" are legal and ethical guidelines for protecting users' personal information.

[1218] "Ethical guidelines" are moral and ethical standards that should be observed during the content creation process.

[1219] "Content" refers to information such as text, articles, recipes, etc. created by generative AI models.

[1220] The present invention provides a system for automatically generating personalized, high-quality content that reflects a user's emotions. This system operates by combining generative AI technology with an emotion engine. A specific embodiment of this system will be described below.

[1221] First, a user accesses the system using a terminal and logs in to their account on the login screen. Once logged in, the user is taken to a form to request new content and enters a request, such as "healthy breakfast recipes." At this time, the emotion engine analyzes the user's facial expressions and tone of voice to generate emotion data, such as positive or negative. The emotion engine incorporates hardware such as a facial recognition camera and microphone, as well as machine learning models.

[1222] Next, the device sends the user's input request and emotional data to the server as an HTTP POST request. The request includes topics, keywords, and emotional data. The server receives the HTTP request and analyzes the request content and emotional data. It checks the syntax of the request and adjusts the tone of the generated content based on the emotional data.

[1223] The server collects the necessary information through relevant databases and external APIs. For example, it calls RecipeAPI to get information related to "healthy breakfast recipes." This collected data is then converted into a format that can be passed to the generative AI model.

[1224] The generative AI model generates unique, high-quality content based on collected data and sentiment data. If the sentiment data is positive, the generated content will also have a positive tone. For example, an article might be generated that reads, "This oatmeal recipe will start your morning off right!"

[1225] The generated content is reviewed on the server for language accuracy, grammar, and relevance, and an automated filtering system is used to ensure compliance with data privacy regulations and ethical guidelines. After any necessary corrections are made, the final content is delivered to the user's device.

[1226] The user can then review the content on their device, manually edit it if necessary, and publish the finalized content to their website or blog.

[1227] This invention enables users to efficiently create and publish high-quality content that reflects their own emotional state.

[1228] Prompt Sentence Examples

[1229] "Generate articles with a positive tone and healthy breakfast recipes."

[1230] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1231] Step 1:

[1232] The user enters a request

[1233] User: Access the system using a terminal and log in to their account on the login screen. After logging in, they will be taken to the request entry form.

[1234] Input: Login credentials, request (e.g. "healthy breakfast recipes").

[1235] Output: Login success notification, request data.

[1236] Specific action: Enter "healthy breakfast recipes" into the admin panel of a health blog and submit the form.

[1237] Step 2:

[1238] emotion recognition

[1239] Emotion engine: While the user is typing a request, the facial recognition camera and microphone are used to collect the user's facial expressions and tone of voice to generate emotion data.

[1240] Input: User's facial expression data, tone of voice data.

[1241] Output: Sentiment data (e.g., positive).

[1242] Specific behavior: Detects whether the user is smiling when typing and generates positive emotion data.

[1243] Step 3:

[1244] Sending requests and emotion data

[1245] Terminal: The request and emotion data entered by the user are sent to the server as an HTTP POST request.

[1246] Input: Request content, emotion data.

[1247] Output: HTTP POST request.

[1248] Specific behavior: By pressing the send button, the "healthy breakfast recipe" and positive emotion data are sent to the server.

[1249] Step 4:

[1250] Receiving and parsing the request

[1251] Server: Receives HTTP requests, analyzes the request content and sentiment data, performs syntax checks, and validates the content.

[1252] Input: HTTP POST request (request content, emotion data).

[1253] Output: Parsing results, syntax check results.

[1254] What it does: Ensures that requests for "healthy breakfast recipes" are submitted in the correct format.

[1255] Step 5:

[1256] Data collection

[1257] Server: Gathers information related to the request from an internal database or external API, for example, fetching data about "healthy breakfast recipes."

[1258] Input: Your request ("healthy breakfast recipes").

[1259] Output: A dataset of related information.

[1260] Specific behavior: Calls the Recipe API and retrieves breakfast recipe data.

[1261] Step 6:

[1262] Content generation based on emotional data

[1263] Server: The generative AI model generates unique, high-quality content based on collected data and sentiment data. If the sentiment data is positive, the tone of the article will also be positive.

[1264] Input: Relevant information dataset, sentiment data.

[1265] Output: Generated content (e.g., a breakfast recipe article with a positive tone).

[1266] What it does: Generates an article that says, "This oatmeal recipe will start your morning off right!"

[1267] Step 7:

[1268] Content review and revision

[1269] Server: Review the generated content, checking for language accuracy, grammar, and relevance, and making corrections as needed.

[1270] Input: Generated content.

[1271] Output: The revised content.

[1272] Specific Actions: Ensure automated tools comply with data privacy regulations and ethical guidelines.

[1273] Step 8:

[1274] Content Delivery

[1275] Server: Deliver the final content to the user's device. Delivery formats can be selected, such as email or notification.

[1276] Input: The revised content.

[1277] Output: Delivery notification or delivery data.

[1278] Specific operation: Notify the user that an article has arrived on their device.

[1279] Step 9:

[1280] User review, editing and publishing

[1281] User: Review the delivered content, make manual corrections as needed, and then publish the final, reviewed content.

[1282] Input: The delivered content.

[1283] Output: Published content.

[1284] What it does: Post an article to your blog or website.

[1285] (Application example 2)

[1286] 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."

[1287] Current content generation systems struggle to provide personalized content that reflects a user's emotional state. Conventional systems generate content based on user requests, but do not consider the user's emotions or moods in the process, making it difficult to increase user satisfaction. Furthermore, content generation that incorporates emotional data must comply with data privacy regulations and ethical guidelines.

[1288] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1289] In this invention, the server includes a means having an engine for analyzing a user's facial expressions and tone of voice to recognize emotions, a means for receiving and analyzing user requests and emotional data, and a means for generating content using a generative AI model based on the collected information and emotional data, thereby enabling the automatic generation of personalized content that reflects the user's emotional state.

[1290] A "user request" is a request or desire that a user inputs into the system using a terminal.

[1291] "Facial expressions and tone of voice" refers to the characteristics of the user's facial expressions and voice, and is information analyzed by the emotion engine.

[1292] An "emotion engine" is a software and hardware system that analyzes a user's facial expressions, tone of voice, writing style, etc. to recognize the user's emotional state.

[1293] "Server" refers to a computer system that receives user requests and emotional data, analyzes them, collects information, generates content, reviews, modifies, and distributes them.

[1294] A "generative AI model" is an artificial intelligence algorithm that generates unique, high-quality content based on collected data and emotional data.

[1295] "Content" refers to information and creative works such as text, images, audio, and video, which are generated based on user requests and emotional data.

[1296] "Review" is the process by which the server checks the generated content and makes corrections as necessary.

[1297] "Modification" is the act of improving or changing the content of the generated content.

[1298] "Data privacy regulations" are laws and regulations regarding the protection of users' personal information and data.

[1299] "Ethical guidelines" are ethical standards and guidelines regarding content creation and information collection.

[1300] "Delivery" refers to the act of the server sending the final content to the user's terminal.

[1301] A "terminal" is a device that allows a user to access the system, input requests, and check content.

[1302] "Natural language generation technology" is a technology that enables a generative AI model to generate content in language that humans can understand based on user requests and emotional data.

[1303] The system of the present invention combines a user terminal, an emotion engine, a server, and a generative AI model to automatically generate and deliver high-quality content based on the user's emotional state. Below, we will explain the details of each element and how they work.

[1304] User request input and emotion recognition

[1305] A user accesses the system using a terminal and first logs in to their account on the login screen. After logging in, they are taken to a form to request new content. At this time, the terminal uses a camera and microphone to provide the user's facial expressions and tone of voice to the emotion engine in real time.

[1306] Emotion analysis using an emotion engine

[1307] The emotion engine recognizes the user's emotional state by analyzing facial expressions, tone of voice, and writing style while the user is typing a request. This emotional data is classified as "positive" or "negative," for example, and sent to the server.

[1308] Receiving and parsing the request

[1309] The server receives the user's request and emotional data as an HTTP POST request, analyzes them, and determines the tone of the content to be generated based on the request and emotional data.

[1310] Data collection

[1311] The server gathers the necessary information related to the request from internal databases and external APIs, for example, fetching data for "latest news" from the web.

[1312] Content generation based on emotional data

[1313] A generative AI model generates unique, high-quality content based on collected data and sentiment data. If the sentiment data is positive, the tone of the article will also be positive. For example, "This news will brighten your day!"

[1314] Content review and revision

[1315] The server reviews the generated content and makes corrections as necessary. It also checks whether the generated content complies with data privacy regulations and ethical guidelines, ensuring there are no issues.

[1316] Content Delivery

[1317] The final confirmed content is delivered from the server to the user's terminal, allowing the user to receive the generated content.

[1318] User review, editing and publishing

[1319] Users can review the content delivered on their devices and manually edit it if necessary, and finally publish the generated content on their own website or blog.

[1320] Specific examples

[1321] For example, a user may request that they are interested in "Today's News," and emotion analysis is performed using the device's camera and microphone. If the user inputs the request with a smile, the emotion engine evaluates the information as "positive." Based on this information, the generative AI model generates a news article with a positive tone. The article may include phrases such as "Today's News Will Make Your Day Even Better!"

[1322] Example prompts for generative AI models

[1323] "Create a latest news article in a positive tone."

[1324] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1325] Step 1:

[1326] A user accesses the system using a terminal and logs in to their account on the login screen. User authentication information is entered here, and if authentication is successful, a request input form for new content is displayed.

[1327] Step 2:

[1328] The user enters the desired topic in the request input form. At this time, the device's camera and microphone are used to capture the user's facial expressions and tone of voice in real time and send them to the emotion engine. The input is the user's topic request and emotion data, and the output is the request data that integrates these data.

[1329] Step 3:

[1330] The emotion engine analyzes the user's facial expressions, tone of voice, and writing style to recognize the user's emotional state. As a result of the analysis, emotion data is generated and classified as, for example, "positive" or "negative." The output is the recognized emotion data.

[1331] Step 4:

[1332] The device sends the user's request and emotion data to the server as an HTTP POST request. In this step, the integrated request data is sent to the server.

[1333] Step 5:

[1334] The server receives the user request and emotional data and analyzes it. The input is the aggregated request data, and the output is the analysis result. The analysis includes request validation and determines the tone of the content based on the emotional data.

[1335] Step 6:

[1336] The server collects the necessary information related to the request from internal databases and external APIs. The input is the parsed request and the output is the collected data. For example, retrieving information about "latest news" from the web.

[1337] Step 7:

[1338] The generative AI model generates unique, high-quality content based on collected data and sentiment data. The input is collected data and sentiment data, and the output is generated content. If the sentiment data is positive, the tone of the article will also be positive.

[1339] Step 8:

[1340] The server reviews the generated content and makes corrections as needed. The input is the generated content and the output is the reviewed content. The server ensures that the generated content complies with data privacy regulations and ethical guidelines.

[1341] Step 9:

[1342] The server delivers the final reviewed content to the user's device. The input is the reviewed content and the output is the content sent to the user's device.

[1343] Step 10:

[1344] The user checks the distributed content on their device and manually corrects it if necessary. The input is the distributed content, and the output is the final published content. The user then publishes the corrected content on their own website or blog.

[1345] 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.

[1346] 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.

[1347] 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.

[1348] [Fourth embodiment]

[1349] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1350] 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.

[1351] 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).

[1352] 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.

[1353] 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.

[1354] 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).

[1355] 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.

[1356] 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.

[1357] 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.

[1358] 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.

[1359] 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.

[1360] 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.

[1361] 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."

[1362] The system of the present invention aims to automatically create content using generative AI technology. This system includes the following components:

[1363] User request input

[1364] User:

[1365] Users access the system using a terminal and log in to their account through a designated login screen. After logging in, users enter specific requests for new content. For example, a user can request "blog articles about healthy lifestyle habits."

[1366] Submitting a Request

[1367] Device:

[1368] The request entered by the user is sent to the server in the form of an HTTP POST request, which includes detailed information such as the topic and keywords.

[1369] Receiving and parsing the request

[1370] server:

[1371] The server analyzes the request received from the user to understand the request content, and determines which database or external API to retrieve information from.

[1372] Data collection

[1373] server:

[1374] The server collects data related to the specified topic from databases and external APIs, such as the latest research and statistics on healthy lifestyle habits.

[1375] Content generation using generative AI models

[1376] server:

[1377] The server-based generative AI model generates unique, high-quality content based on the collected data. Specifically, the generative AI model uses natural language generation technology to create article sentences. For example, it generates a paragraph-by-paragraph blog post about healthy lifestyle habits.

[1378] Content review and revision

[1379] server:

[1380] Automatically review generated content to correct inappropriate language and errors, and ensure that generated content complies with data privacy regulations and ethical guidelines.

[1381] Content Delivery

[1382] server:

[1383] Once the final content is confirmed, the server delivers it to the user's device, where it is displayed on the user's dashboard.

[1384] User review, editing and publishing

[1385] User:

[1386] The user can check the delivered content on their device, manually correct it if necessary, and then publish it on their website or blog.

[1387] Specific examples

[1388] 1. User Request

[1389] A user who runs a health blog uses a terminal to request "healthy breakfast recipes" from the system.

[1390] 2. Server analysis and preparation

[1391] The server receives the request and retrieves data related to a healthy breakfast from a database and an external API.

[1392] 3. Content Generation

[1393] The generative AI model uses the acquired data to generate detailed breakfast recipe articles, such as a recipe for an "oatmeal and fruit bowl" along with its nutritional information.

[1394] 4. Reviews and Shipping

[1395] The server automatically reviews the generated content and delivers it to users, ensuring that it complies with data privacy regulations and ethical guidelines.

[1396] 5. Final Review and Publication

[1397] Users can review and edit articles on their devices and publish them to their health blog.

[1398] In this way, the system of the present invention allows users to efficiently generate and publish high-quality content without requiring advanced technical skills.

[1399] The processing flow will be explained below.

[1400] Step 1:

[1401] The user accesses the Muse platform using a device and logs in to their account on the login screen. If successful, the user is taken to a form to request new content.

[1402] Step 2:

[1403] The user fills out a form on their device with a detailed request for the content they want generated, for example, by entering the topic "healthy breakfast recipes" and specific keywords.

[1404] Step 3:

[1405] The device sends the user's input request as an HTTP POST request to the server, which includes metadata such as topics and keywords.

[1406] Step 4:

[1407] The server receives the user's request, analyzes the format and content, and checks whether it is in the correct format. Specifically, it performs request validation.

[1408] Step 5:

[1409] The server queries an internal database or external API to gather relevant data, for example, data related to "healthy breakfast recipes."

[1410] Step 6:

[1411] The server preprocesses the collected data and converts it into a format that can be passed to the generative AI model, specifically by cleaning and tokenizing the text.

[1412] Step 7:

[1413] The server-based generative AI model generates unique, high-quality content based on the pre-processed data, for example by determining the flow of sentences and paragraph structure and generating the actual text.

[1414] Step 8:

[1415] The server reviews the generated content and makes corrections if necessary, including ensuring compliance with data privacy regulations and ethical guidelines.

[1416] Step 9:

[1417] The server delivers the final content to the user's terminal, allowing the user to receive the generated content.

[1418] Step 10:

[1419] The user checks the delivered content on their device, manually corrects it if necessary, and publishes the generated content on their own website or blog.

[1420] The above is a specific processing flow.

[1421] Example 1

[1422] 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."

[1423] In modern society, there is a growing need to generate content efficiently and with high quality. However, creating content on one's own requires advanced technical skills and a lot of time. Furthermore, it is difficult to ensure that the generated content complies with data privacy regulations and ethical guidelines. Therefore, there is a need for a system that allows users to easily generate, edit, and publish high-quality content.

[1424] 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.

[1425] In this invention, the server includes a means for a user to input a request using a terminal, a means for the terminal to send the user's request to the server in the form of an HTTP POST request, and a means for the server to receive and analyze the user's request, thereby enabling the user to easily make a content request and the server to quickly analyze it.

[1426] A "user" is a person who uses a terminal to access the system and enter a request for content generation.

[1427] A "terminal" is a device through which a user accesses the system and enters and transmits requests.

[1428] "Server" means a computer system that receives and analyzes user requests, collects relevant data, generates content using generative AI models, automatically reviews and corrects, and delivers the final content.

[1429] An "HTTP POST request" is a form of the HTTP protocol used to send data from a terminal to a server.

[1430] "Analysis" is the process by which the server understands the content of the request received from the user and determines which databases or external APIs to collect information from.

[1431] A "generative AI model" is an artificial intelligence system that uses natural language generation techniques to generate unique content based on data.

[1432] "Automated review" is a process in which server-generated content is automatically checked and corrected for inappropriate language or errors.

[1433] "Data privacy regulations" are legal regulations regarding the protection and handling of personal information.

[1434] "Ethical guidelines" are guidelines to ensure that content is socially and morally appropriate.

[1435] "Delivery" is the process by which the server sends the final content to the user's terminal.

[1436] "Publishing" is the act of a user making the final content available to the public on a platform such as their own website or blog.

[1437] This invention is a system that automatically creates content using generative AI technology. The system automates a series of processes: a user inputs a request via a terminal, and a server analyzes the request, collects data, generates content, reviews it, modifies it, and finally delivers it. Key technical elements include HTTP POST requests, a generative AI model that uses natural language generation technology, and an automated review system that complies with data privacy regulations and ethical guidelines.

[1438] Hardware and Software Configuration

[1439] The following hardware and software are required to implement the system:

[1440] User device: Refers to devices that can connect to the Internet, such as PCs, smartphones, and tablets.

[1441] Server: Uses high-performance computers or cloud-based servers to receive user requests, process the data, and generate content using generative AI models.

[1442] Generative AI models: For example, OpenAI's GPT series for natural language generation technology.

[1443] System Operation Overview

[1444] User request input

[1445] The user uses a terminal to access the system's login screen and enters their user ID and password to log in to their account. After logging in, the user enters a specific request for new content, for example, "blog articles about healthy lifestyle habits." After completing the request, the user clicks the submit button.

[1446] Submitting a Request

[1447] The device sends the user's request to the server in the form of an HTTP POST request, which includes detailed information such as topics and keywords.

[1448] The server receives and analyzes the request

[1449] The server receives the HTTP POST request, analyzes its contents, and uses a program to analyze the request, such as the Python Flask framework, to determine which database or external API to collect information from.

[1450] Data collection

[1451] The server collects data related to the specified topic from databases and external APIs, for example, by sending an API request to get the latest research data and statistics on healthy lifestyle habits.

[1452] Content generation using generative AI models

[1453] The server processes the collected data and converts it into a format that can be input into the generative AI model. After cleansing and normalizing the data, a prompt is input into the generative AI model. For example, "Please recommend some healthy breakfast recipes. Please recommend some simple and nutritious dishes using oatmeal." The generative AI model generates unique content based on the prompt, and the generated text is organized into paragraphs as blog posts.

[1454] Content review and revision

[1455] Automatically review generated content to correct inappropriate language and errors, and ensure that generated content complies with data privacy regulations and ethical guidelines, ensuring users can consume content with confidence.

[1456] Content Delivery

[1457] Once the final content is verified, the server delivers it to the user's device, where it is displayed on the user's dashboard.

[1458] User review, editing and publishing

[1459] Users can check the delivered content on their devices and manually edit it if necessary, then publish it on their own website or blog.

[1460] Specific examples

[1461] 1. User Request

[1462] A user who runs a health blog uses a terminal to request "healthy breakfast recipes" from the system.

[1463] 2. Server analysis and preparation

[1464] The server receives the request and retrieves data related to a healthy breakfast from a database and an external API.

[1465] 3. Content Generation

[1466] The generative AI model uses the acquired data to generate detailed breakfast recipe articles, such as a recipe for an "oatmeal and fruit bowl" along with its nutritional information.

[1467] 4. Reviews and Shipping

[1468] The server automatically reviews the generated content and delivers it to users, ensuring that it complies with data privacy regulations and ethical guidelines.

[1469] 5. Final Review and Publication

[1470] Users can review and edit articles on their devices and publish them to their health blog.

[1471] Example prompt sentence:

[1472] "I'd like some healthy breakfast recipes. Can you recommend some easy, nutritious options using oatmeal?"

[1473] As described above, this system allows users to efficiently generate and publish high-quality content without requiring advanced technical skills.

[1474] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1475] Step 1:

[1476] User request input

[1477] A user accesses the system's login screen using a terminal, enters their user ID and password to log in to their account, and then specifically enters a request for new content, for example, "a blog post about healthy lifestyle habits," and clicks the submit button.

[1478] Input: User ID, password, content request

[1479] Output: The input request data

[1480] Step 2:

[1481] Submitting a Request

[1482] The device sends the user's request to the server in the form of an HTTP POST request, with detailed information such as topics and keywords included in the JSON format.

[1483] Input: User request data

[1484] Output: HTTP POST request sent to the server

[1485] Step 3:

[1486] Receiving and parsing the request

[1487] The server receives the HTTP POST request, analyzes the request, for example using the Python Flask framework, and determines which databases or external APIs to collect information from.

[1488] Input: HTTP POST request

[1489] Output: Parsed request data, data sources to be collected

[1490] Step 4:

[1491] Data collection

[1492] The server collects data related to a specified topic from a database or external API, for example, by sending an API request to get the latest research data and statistics on healthy lifestyle habits.

[1493] Input: Data source to be collected

[1494] Output: Collected data

[1495] Step 5:

[1496] Content generation using generative AI models

[1497] The server processes the collected data and converts it into a format that can be input into the generative AI model. During this process, the data is cleansed and normalized. Next, a prompt is input into the generative AI model. For example, "Please recommend some healthy breakfast recipes. Please recommend some easy and nutritious menus using oatmeal." The generative AI model generates unique content based on the prompt.

[1498] Input: Collected data, prompt statement

[1499] Output: Generated content

[1500] Step 6:

[1501] Content review and revision

[1502] The server automatically reviews generated content, correcting inappropriate language and errors, and ensuring that generated content complies with data privacy regulations and ethical guidelines.

[1503] Input: Generated content

[1504] Output: The modified content

[1505] Step 7:

[1506] Content Delivery

[1507] The server delivers the final content to the user's device, where it is displayed on the user's dashboard.

[1508] Input: Modified content

[1509] Output: Content delivered to the user's device

[1510] Step 8:

[1511] User review, editing and publishing

[1512] The user checks the delivered content on the device and manually corrects it if necessary, then publishes it on their website or blog.

[1513] Input: Streamed content

[1514] Output: Published content

[1515] (Application example 1)

[1516] 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."

[1517] The challenge is to solve the problem of how users can efficiently generate, edit, and publish high-quality content without requiring advanced technical skills, and to automatically check whether the generated content complies with data privacy regulations and ethical guidelines.

[1518] 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.

[1519] In this invention, the server includes: means for a user to input a request using a computing terminal; means for the server to receive and analyze the user's request; means for the server to collect necessary information from a related database or an external interface; means for the server to generate content using a generative machine learning model based on the collected information; means for the server to review and modify the generated content; means for the server to deliver the final content to the user's computing terminal; means for the user to review, edit, and publish the content received; means for the server to send a request from the computing terminal based on the request input by the user; and means for the server to deliver the content generated in response to the request to the computing terminal and for the user to review, edit, save, and share. This enables users to easily generate, review, edit, and share high-quality content using their computing terminals, and also enables automatic verification of whether the generated content complies with data privacy regulations and ethical guidelines.

[1520] "Computing terminal" means a device used by a User to input requests and to review, edit, and publish received content.

[1521] "Server" means a computer system that receives and analyzes user requests, collects necessary information, generates content using a generative machine learning model, reviews and modifies it, and delivers the final content to users.

[1522] A "user request" is a content that indicates the theme or topic of the content that the user wants to generate.

[1523] A "relational database" is a data storage system that stores information necessary for generating content.

[1524] An "external interface" is a means of communication for exchanging data with external information sources other than the relational database.

[1525] A "generative machine learning model" is an advanced algorithm or model that generates content based on collected information.

[1526] "Review" is the process of checking and correcting generated content for errors or inappropriate language.

[1527] "Final Content" is the content delivered to users after review and revision is complete.

[1528] "Reviewing, editing, and publishing" refers to the user viewing the received content, making changes as necessary, and publishing it on the Internet or elsewhere.

[1529] A "means for sending a request" is a method or mechanism by which a user sends a request from a computing terminal to a server.

[1530] "Means to review, edit, save and share content" means the methods and mechanisms by which user-generated content can be reviewed, modified as needed, saved and shared with other users and platforms.

[1531] To implement this invention, it is necessary to appropriately configure the computer terminal, server, generative machine learning model, related database, and external interface. In this section, the specific operation of the system will be described.

[1532] First, a user uses a computing device to input a request including the theme or topic of the content they want to generate. For example, they input a specific theme such as "healthy breakfast recipes." The request input by the user is sent from the device to the server. This transmission is performed using an HTTP POST request.

[1533] The server analyzes the requests received from the user and accesses databases and external interfaces to gather relevant information. The server then retrieves the necessary data based on the user's request. This data may include the latest research data, statistics, and other relevant information.

[1534] Next, a server-based generative machine learning model generates unique, high-quality content based on the collected information. This generation uses advanced natural language generation technologies, such as OpenAI's GPT-3. The generated content is then reviewed on-site to correct errors and inappropriate language and ensure compliance with data privacy regulations and ethical guidelines.

[1535] The generated content is delivered from the server to the user's computer device. The delivered content is displayed in an application on the user's device, and the user can view, edit, and publish it. For example, if a user runs a health blog, they can publish generated content such as an "oatmeal and fruit bowl recipe" as is.

[1536] Specific examples

[1537] An example of a prompt might be:

[1538] text

[1539] "Enter the topic of the content you want to create: Healthy Breakfast Recipes"

[1540] Based on this prompt, the generative machine learning model generates specific content such as:

[1541] text

[1542] "How to make an oatmeal and fruit bowl:

[1543] 1 cup oatmeal

[1544] 2 cups milk

[1545] 1 teaspoon honey

[1546] 1 banana

[1547] 1 / 2 cup blueberries

[1548] Almonds (as needed)

[1549] procedure:

[1550] 1. Place the oatmeal and milk in a saucepan and bring to a boil over medium heat.

[1551] 2. Stir well and add honey.

[1552] 3. Thinly slice the banana and top with the blueberries.

[1553] 4. Finally, add the almonds to finish.

[1554] Users can view this content on their devices and modify, save, or publish it as needed.

[1555] In this way, the system of the present invention allows users to efficiently create, review, edit, and share high-quality content without requiring advanced technical skills, and can automatically verify that the content complies with data privacy regulations and ethical guidelines.

[1556] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1557] Step 1:

[1558] A user uses a computing device to enter a request, which includes the theme or topic of the content they want to generate. For example, they might enter the text "healthy breakfast recipes." The request is stored locally on the device and is then ready to be sent to the server.

[1559] Step 2:

[1560] The terminal sends the request entered by the user to the server in the form of an HTTP POST request. When sending the request, data including the request content and topic is passed to the server. The server receives this request and waits for analysis.

[1561] Step 3:

[1562] The server analyzes the received request, extracting the request content by topic or keyword, and determining which databases or external interfaces should be used to retrieve the information. Based on the results of this analysis, it establishes procedures for collecting the relevant data.

[1563] Step 4:

[1564] The server accesses relevant databases and external interfaces to gather the necessary information, such as the latest research data and statistics related to healthy breakfast recipes. This gathering process involves extracting and integrating the data using appropriate queries.

[1565] Step 5:

[1566] The server passes the collected data to a generative machine learning model. The model used here is a natural language generation technology such as OpenAI's GPT-3. Based on the given data, the model generates unique, high-quality content on the specified topic. This generation process automatically creates topic-related sentence structure and content.

[1567] Step 6:

[1568] The server reviews the generated content, a process that checks the generated text for inappropriate language and errors, corrects them, and checks whether the generated content complies with data privacy regulations and ethical guidelines.

[1569] Step 7:

[1570] The server delivers the final content to the computer terminal. The delivered content is sent to the terminal in the form of a series of data and presented to the user. The terminal receives this data and displays it for the user to view.

[1571] Step 8:

[1572] Users can check the received content on their devices and edit it as needed. After editing, users can save the content and publish it on their own website or blog. They can also share the edited content with other platforms and users.

[1573] Through these steps, users can use generative AI models to quickly generate, review, edit, and publish high-quality content. For example, by using the prompt "Please enter the theme of the content you want to create: Healthy breakfast recipes," users can efficiently create the content they want.

[1574] 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.

[1575] The system of the present invention aims to automatically create more personalized, high-quality content by combining generative AI technology and an emotion engine. The system includes the following components:

[1576] User request input and emotion recognition

[1577] User:

[1578] Access the system using a terminal and log in to your account on the login screen. After logging in, you will be taken to the new content request form.

[1579] Emotion Engine:

[1580] The emotion engine recognizes the user's emotional state by analyzing the user's facial expression, tone of voice, writing style, etc. while they are inputting a request on the device. For example, if the user is inputting with a positive emotion, the emotion engine will send that information to the server.

[1581] Sending requests and transferring emotional data

[1582] Device:

[1583] The request entered by the user and the emotional data analyzed by the emotion engine are sent to the server as an HTTP POST request, which includes topics, keywords, and emotional data.

[1584] Receiving and parsing the request

[1585] server:

[1586] The server receives the user's request and emotional data, analyzes the request, validates the request, and determines how to adjust the tone of the content based on the emotional data.

[1587] Data collection

[1588] server:

[1589] The server gathers the necessary information related to the request from an internal database or external API, for example, fetching data for "healthy breakfast recipes."

[1590] Content generation based on emotional data

[1591] server:

[1592] A generative AI model generates unique, high-quality content based on collected data and sentiment data. If the sentiment data is positive, the tone of the article will also be positive. For example, a recipe will be introduced using uplifting language.

[1593] Content review and revision

[1594] server:

[1595] Review generated content and make corrections as needed. The server ensures that generated content complies with data privacy regulations and ethical guidelines.

[1596] Content Delivery

[1597] server:

[1598] The content that has undergone final confirmation and tone adjustment information based on the emotion data are delivered to the user's terminal, allowing the user to receive the generated content.

[1599] User review, editing and publishing

[1600] User:

[1601] Check the content delivered on the device, manually correct it if necessary, and finally publish the generated content on your own website or blog.

[1602] Specific examples

[1603] 1. User Request

[1604] A user who runs a health blog uses a device to request "healthy breakfast recipes" from the system. At the same time, the user expresses positive emotions.

[1605] 2. Emotion analysis using an emotion engine

[1606] The emotion engine recognizes positive emotions from the user's facial expressions and tone of voice and sends that data to the server.

[1607] 3. Data collection and content generation by the server

[1608] The server collects relevant data, and the generative AI model uses that data to generate breakfast recipe posts with a positive tone, such as "This oatmeal recipe will fuel your day!"

[1609] 4. Review and Distribution

[1610] The server automatically reviews the generated articles to ensure they comply with data privacy regulations and ethical guidelines before delivering them to the user's device.

[1611] 5. Final Review and Publication

[1612] Users review and edit articles and publish them to the health blog.

[1613] In this way, the system of the present invention, which combines an emotion engine, enables users to efficiently generate and publish high-quality content that reflects their own emotions.

[1614] The processing flow will be explained below.

[1615] Step 1:

[1616] The user accesses the "System" using a terminal and logs in to their account on the login screen. If successful, they are taken to a form to request new content.

[1617] Step 2:

[1618] The emotion engine analyzes the user's facial expressions, tone of voice, writing style, etc. to recognize the user's emotional state. For example, it can detect whether the user is smiling or speaking in a calm voice.

[1619] Step 3:

[1620] A user enters a specific content request on a device, for example, by entering the topic "healthy breakfast recipes" and specific keywords.

[1621] Step 4:

[1622] The terminal transmits the request input by the user and the emotion data analyzed by the emotion engine as an HTTP POST request to the server.

[1623] Step 5:

[1624] The server receives the user request and sentiment data, then validates the request to ensure it is well-formed, specifically by checking that it contains the required topics and keywords.

[1625] Step 6:

[1626] The server determines the tone and style of the content it generates based on the emotion data. For example, if the user expresses positive emotions, the tone of the article will also be set to be positive.

[1627] Step 7:

[1628] The server gathers relevant information from internal databases and external APIs, for example, retrieving the latest data and statistics for "healthy breakfast recipes."

[1629] Step 8:

[1630] The server preprocesses the collected information and converts it into a format that can be fed to the generative AI model, specifically by cleaning and tokenizing the data.

[1631] Step 9:

[1632] A server-based generative AI model generates content based on the pre-processed data and sentiment data, such as a detailed breakfast recipe with uplifting language.

[1633] Step 10:

[1634] Reviewing server-generated content and correcting it where necessary, ensuring compliance with data privacy regulations and ethical guidelines.

[1635] Step 11:

[1636] The server delivers the content that has undergone final confirmation to the user's device, where it is displayed on the user's dashboard.

[1637] Step 12:

[1638] The user can then review the content delivered on their device, manually edit it if necessary, and finally publish the resulting content on their own website or blog.

[1639] Specific examples

[1640] 1. User Requests and Emotion Recognition

[1641] A user uses a device to request a "healthy breakfast recipe" from the system, and the emotion engine recognizes the user's positive emotion (smiling).

[1642] 2. Data collection and content generation

[1643] The server collects relevant information from databases and external APIs, and the generative AI model generates a recipe post for "Oatmeal and Fruit Bowl" with a positive tone, such as "This oatmeal recipe will energize your day!"

[1644] 3. Review, Finalize, and Publish

[1645] The server reviews the content, and the user confirms and edits the article sent to the device and publishes it on the health blog.

[1646] In this way, the system of the present invention, which combines an emotion engine, enables users to efficiently generate and publish high-quality content that reflects their own emotions.

[1647] Example 2

[1648] 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."

[1649] Conventional content generation systems have struggled to automatically generate personalized, high-quality content that takes user emotions into account. This can result in a failure to provide content that addresses user needs and emotions, leading to a decline in user engagement and satisfaction. Furthermore, the quality and tone of the generated content can be inconsistent, which can undermine its reliability and usefulness.

[1650] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving and analyzing requests and emotional data input by a user, means for collecting necessary information from a related database or an external API, and means for generating content using a generative AI model based on the collected information and emotional data. This makes it possible to automatically provide high-quality content with a tone and content that matches the emotional state of the user.

[1651] A "terminal" is an electronic device such as a computer or smartphone that allows a user to input and send requests.

[1652] "Emotion data" is data that indicates the user's emotional state and is generated by analyzing the user's facial expression, tone of voice, writing style, etc.

[1653] A "server" is a computer system that receives and analyzes user requests and emotion data.

[1654] A "generative AI model" is an artificial intelligence model that generates unique, high-quality content based on collected data and emotional data.

[1655] A "relevant database" is an internal or external source of information that the server accesses to gather required information.

[1656] An "external API" is an application programming interface used to obtain information from other services.

[1657] "Review" is the process of checking generated content for linguistic accuracy, grammar, and relevance, and making corrections as needed.

[1658] "Data privacy regulations" are legal and ethical guidelines for protecting users' personal information.

[1659] "Ethical guidelines" are moral and ethical standards that should be observed during the content creation process.

[1660] "Content" refers to information such as text, articles, recipes, etc. created by generative AI models.

[1661] The present invention provides a system for automatically generating personalized, high-quality content that reflects a user's emotions. This system operates by combining generative AI technology with an emotion engine. A specific embodiment of this system will be described below.

[1662] First, a user accesses the system using a terminal and logs in to their account on the login screen. Once logged in, the user is taken to a form to request new content and enters a request, such as "healthy breakfast recipes." At this time, the emotion engine analyzes the user's facial expressions and tone of voice to generate emotion data, such as positive or negative. The emotion engine incorporates hardware such as a facial recognition camera and microphone, as well as machine learning models.

[1663] Next, the device sends the user's input request and emotional data to the server as an HTTP POST request. The request includes topics, keywords, and emotional data. The server receives the HTTP request and analyzes the request content and emotional data. It checks the syntax of the request and adjusts the tone of the generated content based on the emotional data.

[1664] The server collects the necessary information through relevant databases and external APIs. For example, it calls RecipeAPI to get information related to "healthy breakfast recipes." This collected data is then converted into a format that can be passed to the generative AI model.

[1665] The generative AI model generates unique, high-quality content based on collected data and sentiment data. If the sentiment data is positive, the generated content will also have a positive tone. For example, an article might be generated that reads, "This oatmeal recipe will start your morning off right!"

[1666] The generated content is reviewed on the server for language accuracy, grammar, and relevance, and an automated filtering system is used to ensure compliance with data privacy regulations and ethical guidelines. After any necessary corrections are made, the final content is delivered to the user's device.

[1667] The user can then review the content on their device, manually edit it if necessary, and publish the finalized content to their website or blog.

[1668] This invention enables users to efficiently create and publish high-quality content that reflects their own emotional state.

[1669] Prompt Sentence Examples

[1670] "Generate articles with a positive tone and healthy breakfast recipes."

[1671] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1672] Step 1:

[1673] The user enters a request

[1674] User: Access the system using a terminal and log in to their account on the login screen. After logging in, they will be taken to the request entry form.

[1675] Input: Login credentials, request (e.g. "healthy breakfast recipes").

[1676] Output: Login success notification, request data.

[1677] Specific action: Enter "healthy breakfast recipes" into the admin panel of a health blog and submit the form.

[1678] Step 2:

[1679] emotion recognition

[1680] Emotion engine: While the user is typing a request, the facial recognition camera and microphone are used to collect the user's facial expressions and tone of voice to generate emotion data.

[1681] Input: User's facial expression data, tone of voice data.

[1682] Output: Sentiment data (e.g., positive).

[1683] Specific behavior: Detects whether the user is smiling when typing and generates positive emotion data.

[1684] Step 3:

[1685] Sending requests and emotion data

[1686] Terminal: The request and emotion data entered by the user are sent to the server as an HTTP POST request.

[1687] Input: Request content, emotion data.

[1688] Output: HTTP POST request.

[1689] Specific behavior: By pressing the send button, the "healthy breakfast recipe" and positive emotion data are sent to the server.

[1690] Step 4:

[1691] Receiving and parsing the request

[1692] Server: Receives HTTP requests, analyzes the request content and sentiment data, performs syntax checks, and validates the content.

[1693] Input: HTTP POST request (request content, emotion data).

[1694] Output: Parsing results, syntax check results.

[1695] What it does: Ensures that requests for "healthy breakfast recipes" are submitted in the correct format.

[1696] Step 5:

[1697] Data collection

[1698] Server: Gathers information related to the request from an internal database or external API, for example, fetching data about "healthy breakfast recipes."

[1699] Input: Your request ("healthy breakfast recipes").

[1700] Output: A dataset of related information.

[1701] Specific behavior: Calls the Recipe API and retrieves breakfast recipe data.

[1702] Step 6:

[1703] Content generation based on emotional data

[1704] Server: The generative AI model generates unique, high-quality content based on collected data and sentiment data. If the sentiment data is positive, the tone of the article will also be positive.

[1705] Input: Relevant information dataset, sentiment data.

[1706] Output: Generated content (e.g., a breakfast recipe article with a positive tone).

[1707] What it does: Generates an article that says, "This oatmeal recipe will start your morning off right!"

[1708] Step 7:

[1709] Content review and revision

[1710] Server: Review the generated content, checking for language accuracy, grammar, and relevance, and making corrections as needed.

[1711] Input: Generated content.

[1712] Output: The revised content.

[1713] Specific Actions: Ensure automated tools comply with data privacy regulations and ethical guidelines.

[1714] Step 8:

[1715] Content Delivery

[1716] Server: Deliver the final content to the user's device. Delivery formats can be selected, such as email or notification.

[1717] Input: The revised content.

[1718] Output: Delivery notification or delivery data.

[1719] Specific operation: Notify the user that an article has arrived on their device.

[1720] Step 9:

[1721] User review, editing and publishing

[1722] User: Review the delivered content, make manual corrections as needed, and then publish the final, reviewed content.

[1723] Input: The delivered content.

[1724] Output: Published content.

[1725] What it does: Post an article to your blog or website.

[1726] (Application example 2)

[1727] 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."

[1728] Current content generation systems struggle to provide personalized content that reflects a user's emotional state. Conventional systems generate content based on user requests, but do not consider the user's emotions or moods in the process, making it difficult to increase user satisfaction. Furthermore, content generation that incorporates emotional data must comply with data privacy regulations and ethical guidelines.

[1729] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1730] In this invention, the server includes a means having an engine for analyzing a user's facial expressions and tone of voice to recognize emotions, a means for receiving and analyzing user requests and emotional data, and a means for generating content using a generative AI model based on the collected information and emotional data, thereby enabling the automatic generation of personalized content that reflects the user's emotional state.

[1731] A "user request" is a request or desire that a user inputs into the system using a terminal.

[1732] "Facial expressions and tone of voice" refers to the characteristics of the user's facial expressions and voice, and is information analyzed by the emotion engine.

[1733] An "emotion engine" is a software and hardware system that analyzes a user's facial expressions, tone of voice, writing style, etc. to recognize the user's emotional state.

[1734] "Server" refers to a computer system that receives user requests and emotional data, analyzes them, collects information, generates content, reviews, modifies, and distributes them.

[1735] A "generative AI model" is an artificial intelligence algorithm that generates unique, high-quality content based on collected data and emotional data.

[1736] "Content" refers to information and creative works such as text, images, audio, and video, which are generated based on user requests and emotional data.

[1737] "Review" is the process by which the server checks the generated content and makes corrections as necessary.

[1738] "Modification" is the act of improving or changing the content of the generated content.

[1739] "Data privacy regulations" are laws and regulations regarding the protection of users' personal information and data.

[1740] "Ethical guidelines" are ethical standards and guidelines regarding content creation and information collection.

[1741] "Delivery" refers to the act of the server sending the final content to the user's terminal.

[1742] A "terminal" is a device that allows a user to access the system, input requests, and check content.

[1743] "Natural language generation technology" is a technology that enables a generative AI model to generate content in language that humans can understand based on user requests and emotional data.

[1744] The system of the present invention combines a user terminal, an emotion engine, a server, and a generative AI model to automatically generate and deliver high-quality content based on the user's emotional state. Below, we will explain the details of each element and how they work.

[1745] User request input and emotion recognition

[1746] A user accesses the system using a terminal and first logs in to their account on the login screen. After logging in, they are taken to a form to request new content. At this time, the terminal uses a camera and microphone to provide the user's facial expressions and tone of voice to the emotion engine in real time.

[1747] Emotion analysis using an emotion engine

[1748] The emotion engine recognizes the user's emotional state by analyzing facial expressions, tone of voice, and writing style while the user is typing a request. This emotional data is classified as "positive" or "negative," for example, and sent to the server.

[1749] Receiving and parsing the request

[1750] The server receives the user's request and emotional data as an HTTP POST request, analyzes them, and determines the tone of the content to be generated based on the request and emotional data.

[1751] Data collection

[1752] The server gathers the necessary information related to the request from internal databases and external APIs, for example, fetching data for "latest news" from the web.

[1753] Content generation based on emotional data

[1754] A generative AI model generates unique, high-quality content based on collected data and sentiment data. If the sentiment data is positive, the tone of the article will also be positive. For example, "This news will brighten your day!"

[1755] Content review and revision

[1756] The server reviews the generated content and makes corrections as necessary. It also checks whether the generated content complies with data privacy regulations and ethical guidelines, ensuring there are no issues.

[1757] Content Delivery

[1758] The final confirmed content is delivered from the server to the user's terminal, allowing the user to receive the generated content.

[1759] User review, editing and publishing

[1760] Users can review the content delivered on their devices and manually edit it if necessary, and finally publish the generated content on their own website or blog.

[1761] Specific examples

[1762] For example, a user may request that they are interested in "Today's News," and emotion analysis is performed using the device's camera and microphone. If the user inputs the request with a smile, the emotion engine evaluates the information as "positive." Based on this information, the generative AI model generates a news article with a positive tone. The article may include phrases such as "Today's News Will Make Your Day Even Better!"

[1763] Example prompts for generative AI models

[1764] "Create a latest news article in a positive tone."

[1765] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1766] Step 1:

[1767] A user accesses the system using a terminal and logs in to their account on the login screen. User authentication information is entered here, and if authentication is successful, a request input form for new content is displayed.

[1768] Step 2:

[1769] The user enters the desired topic in the request input form. At this time, the device's camera and microphone are used to capture the user's facial expressions and tone of voice in real time and send them to the emotion engine. The input is the user's topic request and emotion data, and the output is the request data that integrates these data.

[1770] Step 3:

[1771] The emotion engine analyzes the user's facial expressions, tone of voice, and writing style to recognize the user's emotional state. As a result of the analysis, emotion data is generated and classified as, for example, "positive" or "negative." The output is the recognized emotion data.

[1772] Step 4:

[1773] The device sends the user's request and emotion data to the server as an HTTP POST request. In this step, the integrated request data is sent to the server.

[1774] Step 5:

[1775] The server receives the user request and emotional data and analyzes it. The input is the aggregated request data, and the output is the analysis result. The analysis includes request validation and determines the tone of the content based on the emotional data.

[1776] Step 6:

[1777] The server collects the necessary information related to the request from internal databases and external APIs. The input is the parsed request and the output is the collected data. For example, retrieving information about "latest news" from the web.

[1778] Step 7:

[1779] The generative AI model generates unique, high-quality content based on collected data and sentiment data. The input is collected data and sentiment data, and the output is generated content. If the sentiment data is positive, the tone of the article will also be positive.

[1780] Step 8:

[1781] The server reviews the generated content and makes corrections as needed. The input is the generated content and the output is the reviewed content. The server ensures that the generated content complies with data privacy regulations and ethical guidelines.

[1782] Step 9:

[1783] The server delivers the final reviewed content to the user's device. The input is the reviewed content and the output is the content sent to the user's device.

[1784] Step 10:

[1785] The user checks the distributed content on their device and manually corrects it if necessary. The input is the distributed content, and the output is the final published content. The user then publishes the corrected content on their own website or blog.

[1786] 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.

[1787] 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.

[1788] 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.

[1789] 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.

[1790] 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.

[1791] 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.

[1792] 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).

[1793] 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.

[1794] 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."

[1795] 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.

[1796] 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).

[1797] 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.

[1798] 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.

[1799] 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.

[1800] 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.

[1801] 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.

[1802] 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.

[1803] 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.

[1804] 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.

[1805] 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.

[1806] 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.

[1807] The following is further disclosed regarding the above embodiment.

[1808] (Claim 1)

[1809] a means for a user to input a request using a terminal;

[1810] a means by which the server receives and analyzes the user's request;

[1811] A means for the server to gather the necessary information from relevant databases and external APIs;

[1812] A means for generating content using a generative AI model based on the information collected by the server;

[1813] a means for reviewing and modifying the server-generated content; and

[1814] A means for the server to deliver the final content to the user's terminal;

[1815] A system that includes a means for users to review, edit and publish received content.

[1816] (Claim 2)

[1817] 10. The system of claim 1, wherein the generative AI model comprises means for generating the content using natural language generation techniques.

[1818] (Claim 3)

[1819] 10. The system of claim 1, further comprising means for verifying that the server-generated content complies with data privacy regulations and ethical guidelines.

[1820] "Example 1"

[1821] (Claim 1)

[1822] a means for a user to input a request using a terminal;

[1823] a means for the terminal to send a user request to a server in the form of an HTTP POST request;

[1824] a means by which the server receives and analyzes the user's request;

[1825] A means for the server to gather the necessary information from relevant databases and external APIs;

[1826] A means for generating content using a generative AI model based on the information collected by the server;

[1827] a means for the server to perform automated review and correction;

[1828] A means for the server to deliver the final content to the user's terminal;

[1829] A system that includes a means for users to review, edit and publish received content.

[1830] (Claim 2)

[1831] 10. The system of claim 1, wherein the generative AI model comprises means for generating the content using natural language generation techniques.

[1832] (Claim 3)

[1833] 10. The system of claim 1, further comprising means for verifying that the server-generated content complies with data privacy regulations and ethical guidelines.

[1834] "Application Example 1"

[1835] (Claim 1)

[1836] means for a user to input a request using a computing terminal;

[1837] a means by which the server receives and analyzes the user's request;

[1838] A means for the server to gather the necessary information from relevant databases and external interfaces; and

[1839] A means for generating content using a generative machine learning model based on the information collected by the server;

[1840] a means for reviewing and modifying the server-generated content; and

[1841] means by which the server delivers the final content to the user's computing terminal;

[1842] A means for users to review, edit and publish the content they receive;

[1843] means for transmitting a request from a computer terminal to a server based on a request input by a user;

[1844] A system in which a server delivers content generated in response to requests to a computer terminal, and includes a means for users to view, edit, save, and share it.

[1845] (Claim 2)

[1846] 10. The system of claim 1, wherein the generative machine learning model comprises means for generating the content using natural language generation techniques.

[1847] (Claim 3)

[1848] 10. The system of claim 1, further comprising means for verifying that the server-generated content complies with data privacy regulations and ethical guidelines.

[1849] "Example 2: Combining Emotion Engines"

[1850] (Claim 1)

[1851] a means for a user to input a request using a terminal;

[1852] A means for the terminal to analyze the user's facial expression and tone of voice and generate emotion data;

[1853] a means for the terminal to transmit a user's request and emotion data to a server;

[1854] A means for the server to receive and analyze the user's request and emotion data;

[1855] A means for the server to gather the necessary information from relevant databases and external APIs;

[1856] A means for generating content using a generative AI model based on the information and emotion data collected by the server;

[1857] a means for reviewing and modifying the server-generated content; and

[1858] A means for the server to deliver the final content to the user's terminal;

[1859] A system that includes a means for users to review, edit and publish received content.

[1860] (Claim 2)

[1861] 10. The system of claim 1, wherein the generative AI model comprises means for generating the content using natural language generation techniques.

[1862] (Claim 3)

[1863] 10. The system of claim 1, further comprising means for verifying that the server-generated content complies with data privacy regulations and ethical guidelines.

[1864] "Application example 2 when combining emotion engines"

[1865] (Claim 1)

[1866] a means for a user to input a request using a terminal;

[1867] means for analyzing a user's facial expressions and tone of voice to recognize emotions;

[1868] A means for the server to receive and analyze user requests and emotion data;

[1869] A means for the server to gather the necessary information from relevant databases and external APIs;

[1870] A means for generating content using a generative AI model based on the information and emotion data collected by the server;

[1871] a means for reviewing and modifying the server-generated content; and

[1872] A means for the server to deliver the final content to the user's terminal;

[1873] A system that includes a means for users to review, edit and publish received content.

[1874] (Claim 2)

[1875] 10. The system of claim 1, wherein the generative AI model comprises means for generating content using natural language generation techniques to adjust tone based on emotion data.

[1876] (Claim 3)

[1877] 10. The system of claim 1, further comprising means for verifying that the server-generated content complies with data privacy regulations and ethical guidelines. [Explanation of symbols]

[1878] 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 for a user to input a request using a terminal; a means by which the server receives and analyzes the user's request; A means for the server to gather the necessary information from relevant databases and external APIs; A means for generating content using a generative AI model based on the information collected by the server; a means for reviewing and modifying the server-generated content; and A means for the server to deliver the final content to the user's terminal; A system that includes a means for users to review, edit, and publish received content.

2. The system of claim 1 , wherein the generative AI model includes means for generating content using natural language generation techniques.

3. 10. The system of claim 1, further comprising means for verifying that the server-generated content complies with data privacy regulations and ethical guidelines.

Images