system

Abstract

We provide the system. [Solution] An input method for the user to input the conditions for generating the target content, A selection means for selecting a necessary generation algorithm based on the generation conditions received from the input means, A generation means that generates content using a generation algorithm selected by the selection means, A customization method for adjusting the generated content according to user requests, An optimization method that analyzes and optimizes the performance of generated content, A means of collecting user feedback and using it to improve the next generation algorithm, A system that includes this.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 No. 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In content creation, it has been difficult to efficiently and quickly generate various forms of content with conventional methods, and there have been problems particularly in terms of multi-language support and individual customization. Also, a great deal of human resources are required for optimization to maximize the performance of content. Due to these problems, it has been restricted to realize timely and effective content provision required by enterprises and individuals. 【Means for Solving the Problems】 【0005】 This invention provides a system for efficiently generating content by automatically selecting and utilizing the necessary generation algorithms based on user-inputted goals. This system includes means for customizing the generated content, allowing for flexible adjustments to language and style. Furthermore, it features a function to analyze and optimize the performance of the generated content, improving its effectiveness using search engine optimization (SEO) technology. It also includes an update mechanism for collecting and learning from user feedback and continuously improving the generation algorithms, enabling the provision of content that is always adapted to the latest market needs. 【0006】 A "user" is the entity that uses this system to generate content and is responsible for inputting goals and conditions. 【0007】 An "input method" is an interface that allows the user to provide the system with the conditions for generating the target content. 【0008】 A "generative algorithm" is a set of computational steps or rules used to generate content. 【0009】 "Selection means" refers to a device or software component that has the function of selecting the optimal generation algorithm based on the generation conditions entered by the user. 【0010】 "Generation means" refers to the function used to actually generate content using the selected generation algorithm. 【0011】 "Customization means" refers to a device or software component that has the function of adjusting or modifying generated content based on user requests. 【0012】 An "optimization means" is a device or software component that has the function of evaluating the performance of generated content and making modifications to maximize its effectiveness. 【0013】 An "update mechanism" is a device or software component that has the function of accumulating user feedback and utilizing it as information to improve the system's generation algorithm. 【0014】 "System" refers to a comprehensive set of functions that include the above-mentioned means and work together to achieve efficient content generation. [Brief explanation of the drawing] 【0015】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined. 【Mode for Carrying Out the Invention】 【0016】 Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings. 【0017】 First, the language used in the following description will be explained. 【0018】 In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc. 【0019】 In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0020】 In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes. 【0021】 In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0022】 In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0023】 [First Embodiment] 【0024】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0025】 As shown in Figure 1, the 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. 【0026】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0027】 The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0028】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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. 【0029】 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor. 【0030】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54. 【0031】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0032】 As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30. 【0033】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0034】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0035】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0036】 This invention provides a system that allows users to quickly and efficiently generate content in various formats. Users access the system via a terminal and input the conditions for generating the target content. For example, a user can input, "I want to create a blog post introducing a new product." 【0037】 The server analyzes this input and determines which generation algorithm is most appropriate. If text generation is required, the server selects and applies a text generation algorithm. Based on the selected algorithm, the server generates content and adjusts its content and style as needed. This adjustment is done using customization methods and is translated into a tone and language that aligns with the user's instructions. 【0038】 Furthermore, the generated content is evaluated by optimization tools, and performance is improved using SEO (Search Engine Optimization) techniques. Users can review the generated content and make corrections or approvals as needed. The feedback is then sent to the server, and the update tool uses this feedback to improve the generation algorithm. 【0039】 Specifically, if a user wants to generate advertising copy highlighting the benefits of a home appliance, the server will incorporate content emphasizing the product's features and provide text in a friendly tone. If image generation is required, visually appealing product images will also be generated. This allows companies to consistently provide content that can be immediately used in their marketing activities. 【0040】 In this way, users can create high-quality content with minimal effort through the system and effectively communicate information to consumers. The automated selection, generation, and optimization processes of the servers enable faster and more efficient content generation than traditional methods. 【0041】 The following describes the processing flow. 【0042】 Step 1: 【0043】 Users input their content generation goals using their devices. For example, they might write specific requests in the form, such as "I want to generate a blog post about a new product." 【0044】 Step 2: 【0045】 The user's input is sent from the terminal to the server. The server analyzes this input using natural language processing technology to determine the subject of the goal and the required content format. 【0046】 Step 3: 【0047】 The server selects the optimal generation algorithm based on the analysis results. Depending on the type of content required, the algorithm will be chosen to generate text, images, or both. 【0048】 Step 4: 【0049】 The server applies a selected generation algorithm to produce content that aligns with the purpose. In the case of text generation, the generation algorithm creates the text; in the case of image generation, it produces visuals that conform to the specified theme. 【0050】 Step 5: 【0051】 To further adapt the generated content to the user's specified requirements, the server uses customization methods to adjust the content. The tone of the text, the color scheme of images, and other elements are modified to match the user's preferences. 【0052】 Step 6: 【0053】 The server analyzes and improves the performance of the generated content using optimization techniques, and then implements SEO measures. This process makes adjustments to improve the content's ranking in search engines. 【0054】 Step 7: 【0055】 The server sends the final content to the terminal and requests the user to review and provide feedback. The user reviews the content, makes corrections or approves it, and provides feedback as needed. 【0056】 Step 8: 【0057】 The server receives user feedback and uses it to improve the algorithm through update mechanisms. This will further improve the accuracy and efficiency of future content generation. 【0058】 (Example 1) 【0059】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0060】 When generating information in diverse formats quickly and efficiently, users are required to obtain high-quality content without any hassle. However, conventional methods have problems with efficient processes, such as the time required to select generation algorithms and optimize the generated information. As a result, it has been difficult to respond quickly in situations where a large amount of content needs to be delivered intensively in a short period of time. 【0061】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0062】 In this invention, the server includes a receiving means for the user to input the generation conditions for the target information, a determination means for selecting the necessary generation algorithm based on the generation conditions received from the receiving means, and a construction means for generating information using the generation algorithm selected by the determination means. This allows the user to efficiently generate high-quality information and customize or optimize it as needed. 【0063】 A "reception mechanism" is an element that provides an interface for the user to input the conditions for generating target information into the system. 【0064】 A "decision-making element" is an element that has the function of selecting the most appropriate generation algorithm based on the generation conditions received from the reception element. 【0065】 A "construction method" is an element that executes the process of generating the required information using a selected generation algorithm. 【0066】 A "coordination mechanism" is an element that has the function of customizing the generated information according to the user's requests. 【0067】 An "evaluation tool" is an element that has the function of analyzing the performance of generated information and optimizing it as needed. 【0068】 "Improvement measures" refer to elements that collect user feedback and incorporate it into improvements to the next generation algorithm. 【0069】 An "analysis tool" is an element that has the function of interpreting the input content from a reception tool using natural language processing technology. 【0070】 A "processing means" is an element that has the function of appropriately processing a prompt sentence using a generative AI model and obtaining the generation result. 【0071】 This invention provides a system that allows users to quickly and efficiently generate information in various formats. Users access the system using a terminal and input the conditions for generating the target information. For example, a user might input, "I want to create a blog post introducing a new product." 【0072】 The server uses natural language processing techniques to analyze the user's input. By utilizing this technology, the server accurately understands the intent of the input and selects the appropriate generation algorithm. In this process, the server uses a generation AI model to appropriately process the prompt text. Specifically, it can use text generation AI models such as OpenAI's GPT series. 【0073】 The generated information is adjusted by the server according to the user's requests. Using customization methods, the server appropriately adjusts the tone and language of the information to produce a final output that matches the user's specified conditions. Furthermore, this generated information is optimized by the server, and performance is improved using SEO techniques. This optimization enhances the discoverability of the information online. 【0074】 As a concrete example, consider a scenario where a user wants to generate advertising copy that highlights the benefits of a home appliance. The user inputs "I want to create advertising copy for a vacuum cleaner. Emphasize the benefits." into the terminal. The server analyzes this input and generates text in a friendly tone. For example, the content might read, "This vacuum cleaner is 20% off for a limited time! It's lightweight, easy to use, and can easily clean any room!" 【0075】 This invention enables users to generate high-quality information with minimal effort and effectively communicate it to consumers. The server's automated selection, generation, and optimization processes enable faster and more efficient information generation than conventional methods. 【0076】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0077】 Step 1: 【0078】 The user uses a terminal to input the conditions for generating the target information. For example, the user might input "I want to create a blog post introducing a new product" into the terminal. This input is sent to the server. The input is natural language text, and the following processing is performed based on it. 【0079】 Step 2: 【0080】 The server analyzes the input received from the user. Using natural language processing techniques, it interprets the intent of the input text and understands the generation conditions. The input data represents the conditions for text generation, and this analysis allows for the selection of an appropriate generation algorithm for the output. 【0081】 Step 3: 【0082】 The server selects an appropriate generation algorithm based on the analyzed generation conditions. For example, the server might select a text generation model such as the GPT series as the generation AI model. This selected generation algorithm becomes the input for the next processing step. 【0083】 Step 4: 【0084】 The server generates information using a selected generation algorithm. It sends a prompt to the generation AI model and receives output from the model. At this stage, initial content is generated based on the generation conditions. For example, the prompt might be, "Please generate a blog post introducing a new vacuum cleaner. Please make it approachable and engaging, including its features, convenience, and price." 【0085】 Step 5: 【0086】 The server adjusts the generated information according to the user's requests. This adjustment is performed using customization methods, changing the tone and language to match the user's instructions. The output is information that has been adjusted to meet the user's needs. 【0087】 Step 6: 【0088】 The server optimizes the generated information. Using SEO techniques, it optimizes the information to improve its online discoverability. This optimization improves performance on search engines. The output is the final, SEO-enhanced information. 【0089】 Step 7: 【0090】 Users review the generated and optimized information on their devices and make corrections or approvals as needed. User feedback is sent to the server, which serves as the basis for improving the algorithm in the next generation cycle. 【0091】 (Application Example 1) 【0092】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0093】 In modern digital content creation and distribution, there is a need to quickly and efficiently generate diverse forms of information and deliver them to target followers and subscribers in an appropriate manner. However, traditional systems have presented challenges such as complex and time-consuming content generation and optimization processes, as well as difficulty in effectively improving the performance of the generated content. This has made it difficult for companies and individuals to maintain a competitive edge in their digital communication strategies. 【0094】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0095】 In this invention, the server includes an input means for the user to input the generation conditions for the target information material, a selection means for selecting the necessary generation algorithm based on the generation conditions received from the input means, and a distribution means for distributing the generated information material to the user's followers or subscribers. This enables the user to quickly generate high-quality information material with minimal effort and effectively distribute it to their target audience. 【0096】 A "user" is an entity that uses a content generation system to input the conditions for generating information materials and then utilizes the generated information materials. 【0097】 "Information material" refers to digital content such as text and images that are generated based on input generation conditions. 【0098】 An "input method" is an interface used by the user to communicate generation conditions to the system. 【0099】 "Generation conditions" refer to the criteria and requirements for generating informational materials based on the user's goals. 【0100】 "Selection method" refers to a process or function for selecting a generation algorithm suitable for the input generation conditions. 【0101】 A "generative algorithm" is a computational method or mathematical model used to create informational material. 【0102】 A "generation method" is a mechanism for actually generating information material using a generation algorithm. 【0103】 "Customization means" refers to a method or function for adjusting the generated information material according to the user's requirements. 【0104】 "Optimization methods" refer to techniques and methods for analyzing and improving the performance of generated information material. 【0105】 "Distribution method" refers to the methods and technologies used to deliver generated informational material to a user's followers or subscribers. 【0106】 The implementation of this invention revolves around a digital content generation process conducted between an information processing terminal and a server system. The terminal provides an interface for the user to input the conditions for the content they wish to generate. The input includes keywords, the tone of the text, the attributes of the target audience, etc., and the server processes this information accordingly. 【0107】 Within the server, the input generation conditions are analyzed, and the most suitable generation algorithm is automatically selected. At this stage, for example, OpenAI's GPT model is used for text generation, and a generation model such as DALL-E is used for image generation as needed. The selected algorithm generates the information material, and then the content and style are adjusted according to the user's requests. 【0108】 The generated information material is analyzed using optimization techniques. Here, SEO optimization techniques utilizing deep learning are applied, particularly aiming to improve performance in digital communication. This optimization is a crucial step in ensuring that the information material maximizes its effectiveness on online platforms. 【0109】 Subsequently, the information material is rapidly distributed to designated followers and subscribers via distribution channels. This process is supported by a distribution infrastructure utilizing cloud computing technology. The servers receive user feedback after distribution and use it to improve the algorithms. 【0110】 As a concrete example, consider a scenario where a user inputs, "I want to create a blog post promoting a home cooking event next weekend." The server analyzes this request and generates a blog post that includes an overview of the event, how to participate, and suggested recipes. In addition, a visual image of the dishes is created using an image generation function. An example of a prompt would be, "Please create a blog post promoting a home cooking event next weekend. The post should include the event date, location, benefits of participating, and recommended recipes." 【0111】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0112】 Step 1: 【0113】 The terminal receives the user's requirements for the information material to be generated. Here, the user enters keywords, tone, and target audience attributes for the content to be generated. The entered data is sent to the server in an appropriate format for use in the next processing step. 【0114】 Step 2: 【0115】 The server analyzes the received generation conditions. Using natural language processing techniques, it grasps the intent of the input text data and identifies the necessary generation algorithm. Based on this analysis, it selects the optimal generation AI model (e.g., GPT for text generation, DALL-E for image generation). 【0116】 Step 3: 【0117】 A selection mechanism within the server calls the selected generative AI model and generates informational materials. Based on the input data, the selected algorithm generates text and images. The generated informational materials are temporarily stored on the server. 【0118】 Step 4: 【0119】 The generated information material is customized by the server according to the user's requests. Here, the content is adjusted to match the specified tone and style. For example, the writing style may be changed to a more casual style, or specific keywords may be added. 【0120】 Step 5: 【0121】 The server analyzes the information material generated using optimization techniques and performs SEO optimization. Search engine optimization (SEO) technologies are utilized to improve the performance of the generated content. This increases visibility and influence on online platforms. 【0122】 Step 6: 【0123】 The completed information material is distributed to the user's followers or subscribers via the server's distribution system. Cloud computing technology is used during distribution to ensure efficient and rapid delivery to the target audience. 【0124】 Step 7: 【0125】 The next phase involves collecting user feedback. Users input their opinions and suggestions for improvement regarding the delivered content through their devices. These devices send this feedback to the server, which is then used to improve the algorithm for future updates. This step is important because it helps improve throughput and optimize personalization. 【0126】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0127】 This invention provides a content generation system that can recognize user emotions and adapt the tone and style of the generated content based on those emotions. The user accesses the system using a terminal and inputs goals and conditions for content generation. For example, consider a case where the user inputs the goal, "I want to generate a promotional video for my brand." 【0128】 The server receives the input information and uses an emotion engine to analyze the user's emotional state. This analysis makes it possible to recognize what emotional state the user is in. For example, if the server recognizes that the user is excited, that emotion will be reflected in the generated content. 【0129】 Next, the server selects the optimal generation algorithm based on the analysis results. This selection reflects the user's intent and emotional state, ensuring that a content style that matches the user's emotions is chosen. The generation mechanism then creates specific text and visual content, which is adjusted according to the user's emotions. At this stage, the customization mechanism adjusts the content based on the results of the emotion engine. 【0130】 The generated content is analyzed using optimization tools, and SEO optimization is performed as needed. This process ensures that the content performs at its best on the digital platform. Finally, the server-generated content is delivered to the user via their device, and user review and feedback are requested. 【0131】 For example, if a user desires a calm tone when generating a post introducing a new product, the emotion engine will read the user's calm mood and adjust the tone of the text and visual elements accordingly. In this way, personalized content that matches the individual user's mood is quickly and efficiently generated. This feature enables businesses and creators to create more effective content that appeals to the emotions of their target audience. 【0132】 The following describes the processing flow. 【0133】 Step 1: 【0134】 Users use their devices to input their goals and conditions for content generation. For example, they might set the goal to "generate video advertisements for a new product." 【0135】 Step 2: 【0136】 The terminal sends user input information to the server. This transmission includes details about the goal and generation conditions. 【0137】 Step 3: 【0138】 The server analyzes the received information and activates the emotion engine. The emotion engine uses natural language processing technology to extract emotions from the user's text input and recognize the user's current emotional state. For example, if a user frequently uses words like "excited," a positive emotion will be recognized. 【0139】 Step 4: 【0140】 The server selects a generation algorithm based on emotional information obtained from the emotion engine. This selection is made with consideration to creating a tone and style that is appropriate for the user's emotional state. In the case of positive emotions, content with a bright and cheerful style is selected. 【0141】 Step 5: 【0142】 The server executes a selected generation algorithm to automatically generate content. For example, in the case of video generation, it adds visual effects and inserts audio and music based on a pre-prepared template. 【0143】 Step 6: 【0144】 The generated content is further refined through customization methods to match the user's emotions. Specifically, the colors, fonts, and tone of the narration are adjusted to create the atmosphere the user expects. 【0145】 Step 7: 【0146】 Next, the server analyzes the generated content using optimization techniques. Search engine optimization (SEO) technologies are applied, and settings are configured to maximize visibility and effectiveness, especially on digital platforms. 【0147】 Step 8: 【0148】 The final content is sent from the server to the terminal, where the user reviews the generated result. This provides an opportunity to offer feedback on the content. 【0149】 Step 9: 【0150】 Feedback collected from devices is returned to the server and used to improve future content generation algorithms through update mechanisms. This feedback information facilitates the evolution of the system to more accurately meet user needs. 【0151】 (Example 2) 【0152】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0153】 Conventional content generation technologies generate content uniformly without considering user emotions, making it difficult to provide personalized content tailored to the individual needs of users. Furthermore, the generated content is not sufficiently optimized to maximize performance on digital platforms, potentially leading to a decline in the quality of the user experience. 【0154】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0155】 In this invention, the server includes an information input means for the user to input target content generation conditions, an emotion analysis means for analyzing the user's emotional state, and an algorithm selection means for selecting the optimal generation algorithm. This enables the rapid generation of high-quality content that responds to the user's emotions and requests, as well as performance optimization. 【0156】 An "information input means" is an interface that provides users with the conditions for generating their target content. 【0157】 "Emotion analysis means" refers to a technology or process for determining a user's emotional state based on data received from them. 【0158】 An "algorithm selection means" is a device that has the function of selecting a generation algorithm that is suitable for the user's emotional state and requests. 【0159】 "Content generation means" refers to a technology or device that generates specific content based on a selected algorithm. 【0160】 "Content customization means" refers to a function that allows the generated content to be adjusted according to the user's emotional state and requests. 【0161】 "Performance optimization means" refers to methods or techniques for analyzing and optimizing the performance of generated content. 【0162】 An "information update mechanism" is a system that collects user feedback and uses it to improve the next generation process. 【0163】 This invention provides a system that, based on information input by the user regarding the conditions for generating target content, determines the user's emotional state and generates optimal content. 【0164】 The user accesses an information input method using a terminal and inputs prompt sentences to the generating AI model according to their purpose. For example, a prompt sentence such as "Please generate a new product introduction post in a calm tone" can be provided. 【0165】 The server processes the prompt message received from the terminal using sentiment analysis tools to analyze the user's emotional state. This analysis utilizes natural language processing technology to understand the emotional state in a way that is appropriate for the user's target content and style of expression. Subsequently, based on the analysis results, the server selects the optimal generation algorithm using algorithm selection tools. This is done based on both the user's emotional state and the generation conditions. 【0166】 The selected generation algorithm is used by the content generation means to generate specific content. The generated content encompasses a wide range of elements, including text and visuals, and is adjusted according to the user's emotional state through the content customization means. This process generates personalized content for each individual user. 【0167】 The generated content undergoes optimization processing to maximize its performance on digital platforms through performance optimization techniques. This processing includes search engine optimization (SEO) technologies to improve the visibility and effectiveness of the content. 【0168】 Ultimately, the generated content is delivered to the user via their device, and the user reviews it. Furthermore, the information update mechanism collects user feedback, which is used to improve the content generation process for the next time, resulting in higher quality content. 【0169】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0170】 Step 1: 【0171】 The user accesses an information input device via a terminal and enters a prompt message for the generating AI model. This entered prompt message indicates the user's target content generation conditions, including the specific type of content and expression style. An example of input is, "Generate a new product introduction post in a calm tone." At this stage, the terminal sends the prompt message to the server. 【0172】 Step 2: 【0173】 The server processes the prompt message received from the terminal using sentiment analysis. The sentiment analysis analyzes the content of the prompt message using natural language processing techniques to identify the user's emotional state. In this process, a language model extracts emotional keywords and phrases and classifies the emotional state based on them. The output is the user's emotional state (e.g., calm, excited). 【0174】 Step 3: 【0175】 The server uses an algorithm selection mechanism to choose the optimal generation algorithm based on the output of the sentiment analysis mechanism. Here, an algorithm that considers both the emotional state and the generation goal is selected. For example, if a calm tone is required, templates and styles for that purpose are applied. The selected algorithm is then used in the next content generation phase. 【0176】 Step 4: 【0177】 The server uses the algorithm selected by the algorithm selection mechanism to generate specific content through the content generation mechanism. This process utilizes a generation AI model to automatically create text and visual elements. The generated content is adjusted to reflect the user's emotional state and is faithful to the user's input conditions. 【0178】 Step 5: 【0179】 The generated content is evaluated using performance optimization techniques, and is optimized particularly for improving performance on digital platforms. SEO techniques are used to enhance content visibility through keyword placement and tagging. As a result, optimized content is generated. 【0180】 Step 6: 【0181】 The server sends optimized content to the terminal and provides it to the user. The user reviews the provided content and provides feedback as needed. This feedback is collected by an information update mechanism and used to improve the next generation process. 【0182】 (Application Example 2) 【0183】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal". 【0184】 Modern information delivery systems face the challenge of generating effective information that appeals to users' emotions because they do not adequately personalize content according to the user's emotional state. Furthermore, while performance optimization is necessary for generated content to be utilized to its fullest potential on digital platforms, there is a lack of efficient means to achieve this. 【0185】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0186】 In this invention, the server includes an input means for the user to input the conditions for generating target information, a selection means for selecting the necessary generation method based on the generation conditions received from the input means, and an adjustment means for adjusting the generated information according to the user's emotional state using identifier analysis. This makes it possible to efficiently generate and optimize information that matches the user's emotions. 【0187】 A "user" is an individual or organization that utilizes an information generation system. 【0188】 "Information generation conditions" are requirements that specify the content and characteristics of the information that the user wants to achieve. 【0189】 An "input method" is an interface for users to register information generation conditions with the system. 【0190】 A "generation method" is a technique or algorithm for assembling information based on given conditions. 【0191】 "Selection means" refers to an apparatus or method that determines the most appropriate generation method based on the input conditions. 【0192】 "Information" refers to digital content created based on the user's conditions and emotional state. 【0193】 "Identifier parsing" refers to the processing of identification information used to recognize a user's emotional state. 【0194】 "Emotional state" is an indicator that shows the user's current psychological state. 【0195】 "Adjustment means" refers to a device or method for changing the style and tone of generated information to match the user's emotional state. 【0196】 "Performance optimization" is the process of improving generated information so that it functions efficiently on a digital platform. 【0197】 This invention provides a system that dynamically generates and adjusts information in response to a user's emotions. This system is implemented using a terminal such as a smartphone or smart glasses. Specifically, it begins with the user inputting the conditions for the target information generation into the terminal. 【0198】 First, the terminal sends the input conditions to the server. Based on these conditions, the server selects an appropriate generation method and generates the information. The server uses an emotion recognition API (for example, an external cloud service that performs facial recognition) to analyze the user's emotional state. Based on the analyzed emotion data, a generation AI model is used to adjust the tone and style of the information. Tools such as content generation AI and image editing software are used to adjust the information. 【0199】 The generated information is further optimized by the server and its performance on the digital platform is improved using information retrieval technology. This ensures that information that matches the user's emotions is delivered most effectively. The optimized information is then transferred to the user's device and used. 【0200】 As a concrete example, consider a scenario where a user is using their smartphone in a shopping mall. The camera captures their facial expression, and the server recognizes this as happiness. Based on this emotional data, the server generates advertising content such as "Weekend Special Sale to Double Your Shopping Fun" and displays it on the user's smartphone. 【0201】 An example of a prompt message might be: "The user is currently smiling. Generate special and interesting ad content that shows he is enjoying himself." 【0202】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0203】 Step 1: 【0204】 The user uses a terminal to input the conditions for information generation. The entered conditions (e.g., the type and tone of the advertisement to be generated) are sent directly to the server. Input: Conditions for information generation; Output: Sending of conditions to the server. 【0205】 Step 2: 【0206】 The server selects a generation method based on the received conditions. A predefined algorithm is used for this selection to determine the optimal generation process. Input: Information generation conditions; Output: Selected generation method. 【0207】 Step 3: 【0208】 Next, the server sends the user's image data, captured by the device, to an emotion recognition API in the cloud to analyze the user's emotional state. This API uses facial recognition technology to identify emotions from the user's facial expressions. Input: Image data, Output: User's emotional state. 【0209】 Step 4: 【0210】 Based on the emotional state, the server uses a generative AI model to generate information (such as advertising content). The content and tone are adjusted to match the emotion, and the information is constructed. Here, the generative AI model uses prompt sentences to perform the generation. Input: Emotional state, prompt sentence; Output: Generated information. 【0211】 Step 5: 【0212】 The generated information is optimized by the server. This process utilizes information retrieval technology and performs optimization to maximize performance on the digital platform. Input: generated information, Output: optimized information. 【0213】 Step 6: 【0214】 The optimized information is sent back to the device and displayed to the user. This allows the user to access personalized information that matches their emotions. Input: Optimized information, Output: Displayed to the user. 【0215】 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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. 【0216】 Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0217】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14. 【0218】 [Second Embodiment] 【0219】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0220】 As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server. 【0221】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0222】 The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52. 【0223】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0224】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0225】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0226】 Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0227】 The specific processing program 56 is an example of a "program" relating to the technology of this 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. 【0228】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0229】 In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0230】 Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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". 【0231】 This invention provides a system that allows users to quickly and efficiently generate content in various formats. Users access the system via a terminal and input the conditions for generating the target content. For example, a user can input, "I want to create a blog post introducing a new product." 【0232】 The server analyzes this input and determines which generation algorithm is most appropriate. If text generation is required, the server selects and applies a text generation algorithm. Based on the selected algorithm, the server generates content and adjusts its content and style as needed. This adjustment is done using customization methods and is translated into a tone and language that aligns with the user's instructions. 【0233】 Furthermore, the generated content is evaluated by optimization tools, and performance is improved using SEO (Search Engine Optimization) techniques. Users can review the generated content and make corrections or approvals as needed. The feedback is then sent to the server, and the update tool uses this feedback to improve the generation algorithm. 【0234】 Specifically, if a user wants to generate advertising copy highlighting the benefits of a home appliance, the server will incorporate content emphasizing the product's features and provide text in a friendly tone. If image generation is required, visually appealing product images will also be generated. This allows companies to consistently provide content that can be immediately used in their marketing activities. 【0235】 In this way, users can create high-quality content with minimal effort through the system and effectively communicate information to consumers. The automated selection, generation, and optimization processes of the servers enable faster and more efficient content generation than traditional methods. 【0236】 The following describes the processing flow. 【0237】 Step 1: 【0238】 Users input their content generation goals using their devices. For example, they might write specific requests in the form, such as "I want to generate a blog post about a new product." 【0239】 Step 2: 【0240】 The user's input is sent from the terminal to the server. The server analyzes this input using natural language processing technology to determine the subject of the goal and the required content format. 【0241】 Step 3: 【0242】 The server selects the optimal generation algorithm based on the analysis results. Depending on the type of content required, the algorithm will be chosen to generate text, images, or both. 【0243】 Step 4: 【0244】 The server applies a selected generation algorithm to produce content that aligns with the purpose. In the case of text generation, the generation algorithm creates the text; in the case of image generation, it produces visuals that conform to the specified theme. 【0245】 Step 5: 【0246】 To further adapt the generated content to the user's specified requirements, the server uses customization methods to adjust the content. The tone of the text, the color scheme of images, and other elements are modified to match the user's preferences. 【0247】 Step 6: 【0248】 The server analyzes and improves the performance of the generated content using optimization techniques, and then implements SEO measures. This process makes adjustments to improve the content's ranking in search engines. 【0249】 Step 7: 【0250】 The server sends the final content to the terminal and requests the user to review and provide feedback. The user reviews the content, makes corrections or approves it, and provides feedback as needed. 【0251】 Step 8: 【0252】 The server receives user feedback and uses it to improve the algorithm through update mechanisms. This will further improve the accuracy and efficiency of future content generation. 【0253】 (Example 1) 【0254】 Next, we will describe Example 1. 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." 【0255】 When generating information in diverse formats quickly and efficiently, users are required to obtain high-quality content without any hassle. However, conventional methods have problems with efficient processes, such as the time required to select generation algorithms and optimize the generated information. As a result, it has been difficult to respond quickly in situations where a large amount of content needs to be delivered intensively in a short period of time. 【0256】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0257】 In this invention, the server includes a receiving means for the user to input the generation conditions for the target information, a determination means for selecting the necessary generation algorithm based on the generation conditions received from the receiving means, and a construction means for generating information using the generation algorithm selected by the determination means. This allows the user to efficiently generate high-quality information and customize or optimize it as needed. 【0258】 A "reception mechanism" is an element that provides an interface for the user to input the conditions for generating target information into the system. 【0259】 A "decision-making element" is an element that has the function of selecting the most appropriate generation algorithm based on the generation conditions received from the reception element. 【0260】 A "construction method" is an element that executes the process of generating the required information using a selected generation algorithm. 【0261】 A "coordination mechanism" is an element that has the function of customizing the generated information according to the user's requests. 【0262】 An "evaluation tool" is an element that has the function of analyzing the performance of generated information and optimizing it as needed. 【0263】 "Improvement measures" refer to elements that collect user feedback and incorporate it into improvements to the next generation algorithm. 【0264】 An "analysis tool" is an element that has the function of interpreting the input content from a reception tool using natural language processing technology. 【0265】 A "processing means" is an element that has the function of appropriately processing a prompt sentence using a generative AI model and obtaining the generation result. 【0266】 This invention provides a system that allows users to quickly and efficiently generate information in various formats. Users access the system using a terminal and input the conditions for generating the target information. For example, a user might input, "I want to create a blog post introducing a new product." 【0267】 The server uses natural language processing techniques to analyze the user's input. By utilizing this technology, the server accurately understands the intent of the input and selects the appropriate generation algorithm. In this process, the server leverages a generation AI model to appropriately process the prompt text. Specifically, it can use text generation AI models such as OpenAI's GPT series. 【0268】 The generated information is adjusted by the server according to the user's requests. Using customization methods, the server appropriately adjusts the tone and language of the information to produce a final output that matches the user's specified conditions. Furthermore, this generated information is optimized by the server, and performance is improved using SEO techniques. This optimization enhances the discoverability of the information online. 【0269】 As a concrete example, consider a scenario where a user wants to generate advertising copy that highlights the benefits of a home appliance. The user inputs "I want to create advertising copy for a vacuum cleaner. Emphasize the benefits." into the terminal. The server analyzes this input and generates text in a friendly tone. For example, the content might read, "This vacuum cleaner is 20% off for a limited time! It's lightweight, easy to use, and can easily clean any room!" 【0270】 This invention enables users to generate high-quality information with minimal effort and effectively communicate it to consumers. The server's automated selection, generation, and optimization processes enable faster and more efficient information generation than conventional methods. 【0271】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0272】 Step 1: 【0273】 The user uses a terminal to input the conditions for generating the target information. For example, the user might input "I want to create a blog post introducing a new product" into the terminal. This input is sent to the server. The input is natural language text, and the following processing is performed based on it. 【0274】 Step 2: 【0275】 The server analyzes the input received from the user. Using natural language processing techniques, it interprets the intent of the input text and understands the generation conditions. The input data represents the conditions for text generation, and this analysis allows for the selection of an appropriate generation algorithm for the output. 【0276】 Step 3: 【0277】 The server selects an appropriate generation algorithm based on the analyzed generation conditions. For example, the server might select a text generation model such as the GPT series as the generation AI model. This selected generation algorithm becomes the input for the next processing step. 【0278】 Step 4: 【0279】 The server generates information using a selected generation algorithm. It sends a prompt to the generation AI model and receives output from the model. At this stage, initial content is generated based on the generation conditions. For example, the prompt might be, "Please generate a blog post introducing a new vacuum cleaner. Please make it approachable and engaging, including its features, convenience, and price." 【0280】 Step 5: 【0281】 The server adjusts the generated information according to the user's requests. This adjustment is performed using customization methods, changing the tone and language to match the user's instructions. The output is information that has been adjusted to meet the user's needs. 【0282】 Step 6: 【0283】 The server optimizes the generated information. Using SEO techniques, it optimizes the information to improve its online discoverability. This optimization improves performance on search engines. The output is the final, SEO-enhanced information. 【0284】 Step 7: 【0285】 The user checks the generated and optimized information on the terminal and makes corrections or approvals if necessary. The user's feedback is sent to the server. This feedback serves as the basic data for improving the algorithm in the next generation cycle. 【0286】 (Application Example 1) 【0287】 Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal". 【0288】 In the generation and distribution of modern digital content, it is required to generate various forms of information materials quickly and efficiently and distribute them in an appropriate form to targeted followers or subscribers. However, in conventional systems, the processes of content generation and optimization are complex and time-consuming, and it is difficult to effectively improve the performance of the generated content. As a result, there has been a problem that it is difficult for companies and individuals to maintain competitiveness in digital communication strategies. 【0289】 The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0290】 In this invention, the server includes an input means for the user to input the generation conditions of the target information material, a selection means for selecting the necessary generation algorithm based on the generation conditions received from the input means, and a distribution means for distributing the generated information material to the user's followers or subscribers. Thereby, the user can quickly generate high-quality information materials with minimal effort and effectively distribute them to the target audience. 【0291】 The "user" is the entity that inputs the generation conditions of the information material using the content generation system and uses the generated information material. 【0292】 "Information material" refers to digital content such as text and images that are generated based on input generation conditions. 【0293】 An "input method" is an interface used by the user to communicate generation conditions to the system. 【0294】 "Generation conditions" refer to the criteria and requirements for generating informational materials based on the user's goals. 【0295】 "Selection method" refers to a process or function for selecting a generation algorithm suitable for the input generation conditions. 【0296】 A "generative algorithm" is a computational method or mathematical model used to create informational material. 【0297】 A "generation method" is a mechanism for actually generating information material using a generation algorithm. 【0298】 "Customization means" refers to a method or function for adjusting the generated information material according to the user's requirements. 【0299】 "Optimization methods" refer to techniques and methods for analyzing and improving the performance of generated information material. 【0300】 "Distribution method" refers to the methods and technologies used to deliver generated informational material to a user's followers or subscribers. 【0301】 The implementation of this invention revolves around a digital content generation process conducted between an information processing terminal and a server system. The terminal provides an interface for the user to input the conditions for the content they wish to generate. The input includes keywords, the tone of the text, the attributes of the target audience, etc., and the server processes this information accordingly. 【0302】 Within the server, the input generation conditions are analyzed, and the most suitable generation algorithm is automatically selected. At this stage, for text generation, for example, the GPT model of OpenAI is used, and for image generation as needed, a generation model such as DALL-E is used. The selected algorithm generates information materials, and then the content and style are adjusted according to the user's requirements. 【0303】 The generated information materials are analyzed using optimization means. Here, SEO optimization technology leveraging deep learning is applied, aiming particularly at improving performance in digital communication. This optimization is an important step for the information materials to maximize their effects on online platforms. 【0304】 After that, the information materials are quickly distributed to the specified followers and subscribers via distribution means. This process is supported by a distribution infrastructure using cloud computing technology. The server receives feedback from users after distribution and uses it to improve the algorithm. 【0305】 As a specific example, consider the case where a user inputs "I want to create a blog post to promote a home cooking event for the next weekend". The server analyzes this request and generates a blog post including an overview of the event, how to participate, and recipe suggestions. In addition, a visual image of the dish is also created using the image generation function. An example of the prompt text would be "Please create a blog post to promote a home cooking event for the next weekend. The article should include the schedule, location, benefits of participation, and recommended recipes of the event." 【0306】 The flow of the specific process in Application Example 1 will be described using Figure 12. 【0307】 Step 1: 【0308】 The terminal receives the user's requirements for the information material to be generated. Here, the user enters keywords, tone, and target audience attributes for the content to be generated. The entered data is sent to the server in an appropriate format for use in the next processing step. 【0309】 Step 2: 【0310】 The server analyzes the received generation conditions. Using natural language processing techniques, it grasps the intent of the input text data and identifies the necessary generation algorithm. Based on this analysis, it selects the optimal generation AI model (e.g., GPT for text generation, DALL-E for image generation). 【0311】 Step 3: 【0312】 A selection mechanism within the server calls the selected generative AI model and generates informational materials. Based on the input data, the selected algorithm generates text and images. The generated informational materials are temporarily stored on the server. 【0313】 Step 4: 【0314】 The generated information material is customized by the server according to the user's requests. Here, the content is adjusted to match the specified tone and style. For example, the writing style may be changed to a more casual style, or specific keywords may be added. 【0315】 Step 5: 【0316】 The server analyzes the information material generated using optimization techniques and performs SEO optimization. Search engine optimization (SEO) technologies are utilized to improve the performance of the generated content. This increases visibility and influence on online platforms. 【0317】 Step 6: 【0318】 The completed information material is distributed to the user's followers or subscribers via the server's distribution system. Cloud computing technology is used during distribution to ensure efficient and rapid delivery to the target audience. 【0319】 Step 7: 【0320】 The next phase involves collecting user feedback. Users input their opinions and suggestions for improvement regarding the delivered content through their devices. These devices send this feedback to the server, which is then used to improve the algorithm for future updates. This step is important because it helps improve throughput and optimize personalization. 【0321】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0322】 This invention provides a content generation system that can recognize user emotions and adapt the tone and style of the generated content based on those emotions. The user accesses the system using a terminal and inputs goals and conditions for content generation. For example, consider a case where the user inputs the goal, "I want to generate a promotional video for my brand." 【0323】 The server receives the input information and uses an emotion engine to analyze the user's emotional state. This analysis makes it possible to recognize what emotional state the user is in. For example, if the server recognizes that the user is excited, that emotion will be reflected in the generated content. 【0324】 Next, the server selects the optimal generation algorithm based on the analysis results. This selection reflects the user's intent and emotional state, ensuring that a content style that matches the user's emotions is chosen. The generation mechanism then creates specific text and visual content, which is adjusted according to the user's emotions. At this stage, the customization mechanism adjusts the content based on the results of the emotion engine. 【0325】 The generated content is analyzed using optimization tools, and SEO optimization is performed as needed. This process ensures that the content performs at its best on the digital platform. Finally, the server-generated content is delivered to the user via their device, and user review and feedback are requested. 【0326】 For example, if a user desires a calm tone when generating a post introducing a new product, the emotion engine will read the user's calm mood and adjust the tone of the text and visual elements accordingly. In this way, personalized content that matches the individual user's mood is quickly and efficiently generated. This feature enables businesses and creators to create more effective content that appeals to the emotions of their target audience. 【0327】 The following describes the processing flow. 【0328】 Step 1: 【0329】 Users use their devices to input their goals and conditions for content generation. For example, they might set the goal to "generate video advertisements for a new product." 【0330】 Step 2: 【0331】 The terminal sends user input information to the server. This transmission includes details about the goal and generation conditions. 【0332】 Step 3: 【0333】 The server analyzes the received information and activates the emotion engine. The emotion engine uses natural language processing technology to extract emotions from the user's text input and recognize the user's current emotional state. For example, if a user frequently uses words like "excited," a positive emotion will be recognized. 【0334】 Step 4: 【0335】 The server selects a generation algorithm based on emotional information obtained from the emotion engine. This selection is made with consideration to creating a tone and style that is appropriate for the user's emotional state. In the case of positive emotions, content with a bright and cheerful style is selected. 【0336】 Step 5: 【0337】 The server executes a selected generation algorithm to automatically generate content. For example, in the case of video generation, it adds visual effects and inserts audio and music based on a pre-prepared template. 【0338】 Step 6: 【0339】 The generated content is further refined through customization methods to match the user's emotions. Specifically, the colors, fonts, and tone of the narration are adjusted to create the atmosphere the user expects. 【0340】 Step 7: 【0341】 Next, the server analyzes the generated content using optimization techniques. Search engine optimization (SEO) technologies are applied, and settings are configured to maximize visibility and effectiveness, especially on digital platforms. 【0342】 Step 8: 【0343】 The final content is sent from the server to the terminal, where the user reviews the generated result. This provides an opportunity to offer feedback on the content. 【0344】 Step 9: 【0345】 Feedback collected from devices is returned to the server and used to improve future content generation algorithms through update mechanisms. This feedback information facilitates the evolution of the system to more accurately meet user needs. 【0346】 (Example 2) 【0347】 Next, we will describe Example 2. 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". 【0348】 Conventional content generation technologies generate content uniformly without considering user emotions, making it difficult to provide personalized content tailored to the individual needs of users. Furthermore, the generated content is not sufficiently optimized to maximize performance on digital platforms, potentially leading to a decline in the quality of the user experience. 【0349】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0350】 In this invention, the server includes an information input means for the user to input target content generation conditions, an emotion analysis means for analyzing the user's emotional state, and an algorithm selection means for selecting the optimal generation algorithm. This enables the rapid generation of high-quality content that responds to the user's emotions and requests, as well as performance optimization. 【0351】 An "information input means" is an interface that provides users with the conditions for generating their target content. 【0352】 "Emotion analysis means" refers to a technology or process for determining a user's emotional state based on data received from them. 【0353】 An "algorithm selection means" is a device that has the function of selecting a generation algorithm that is suitable for the user's emotional state and requests. 【0354】 "Content generation means" refers to a technology or device that generates specific content based on a selected algorithm. 【0355】 "Content customization means" refers to a function that allows the generated content to be adjusted according to the user's emotional state and requests. 【0356】 "Performance optimization means" refers to methods or techniques for analyzing and optimizing the performance of generated content. 【0357】 An "information update mechanism" is a system that collects user feedback and uses it to improve the next generation process. 【0358】 This invention provides a system that, based on information input by the user regarding the conditions for generating target content, determines the user's emotional state and generates optimal content. 【0359】 The user accesses an information input method using a terminal and inputs prompt sentences to the generating AI model according to their purpose. For example, a prompt sentence such as "Please generate a new product introduction post in a calm tone" can be provided. 【0360】 The server processes the prompt message received from the terminal using sentiment analysis tools to analyze the user's emotional state. This analysis utilizes natural language processing technology to understand the emotional state in a way that is appropriate for the user's target content and style of expression. Subsequently, based on the analysis results, the server selects the optimal generation algorithm using algorithm selection tools. This is done based on both the user's emotional state and the generation conditions. 【0361】 The selected generation algorithm is used by the content generation means to generate specific content. The generated content encompasses a wide range of elements, including text and visuals, and is adjusted according to the user's emotional state through the content customization means. This process generates personalized content for each individual user. 【0362】 The generated content undergoes optimization processing to maximize its performance on digital platforms through performance optimization techniques. This processing includes search engine optimization (SEO) technologies to improve the visibility and effectiveness of the content. 【0363】 Ultimately, the generated content is delivered to the user via their device, and the user reviews it. Furthermore, the information update mechanism collects user feedback, which is used to improve the content generation process for the next time, resulting in higher quality content. 【0364】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0365】 Step 1: 【0366】 The user accesses an information input device via a terminal and enters a prompt message for the generating AI model. This entered prompt message indicates the user's target content generation conditions, including the specific type of content and expression style. An example of input is, "Generate a new product introduction post in a calm tone." At this stage, the terminal sends the prompt message to the server. 【0367】 Step 2: 【0368】 The server processes the prompt message received from the terminal using sentiment analysis. The sentiment analysis analyzes the content of the prompt message using natural language processing techniques to identify the user's emotional state. In this process, a language model extracts emotional keywords and phrases and classifies the emotional state based on them. The output is the user's emotional state (e.g., calm, excited). 【0369】 Step 3: 【0370】 The server uses an algorithm selection mechanism to choose the optimal generation algorithm based on the output of the sentiment analysis mechanism. Here, an algorithm that considers both the emotional state and the generation goal is selected. For example, if a calm tone is required, templates and styles for that purpose are applied. The selected algorithm is then used in the next content generation phase. 【0371】 Step 4: 【0372】 The server uses the algorithm selected by the algorithm selection mechanism to generate specific content through the content generation mechanism. This process utilizes a generation AI model to automatically create text and visual elements. The generated content is adjusted to reflect the user's emotional state and is faithful to the user's input conditions. 【0373】 Step 5: 【0374】 The generated content is evaluated using performance optimization techniques, and is optimized particularly for improving performance on digital platforms. SEO techniques are used to enhance content visibility through keyword placement and tagging. As a result, optimized content is generated. 【0375】 Step 6: 【0376】 The server sends optimized content to the terminal and provides it to the user. The user reviews the provided content and provides feedback as needed. This feedback is collected by an information update mechanism and used to improve the next generation process. 【0377】 (Application Example 2) 【0378】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0379】 Modern information delivery systems face the challenge of generating effective information that appeals to users' emotions because they do not adequately personalize content according to the user's emotional state. Furthermore, while performance optimization is necessary for generated content to be utilized to its fullest potential on digital platforms, there is a lack of efficient means to achieve this. 【0380】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0381】 In this invention, the server includes an input means for the user to input the conditions for generating target information, a selection means for selecting the necessary generation method based on the generation conditions received from the input means, and an adjustment means for adjusting the generated information according to the user's emotional state using identifier analysis. This makes it possible to efficiently generate and optimize information that matches the user's emotions. 【0382】 A "user" is an individual or organization that utilizes an information generation system. 【0383】 "Information generation conditions" are requirements that specify the content and characteristics of the information that the user wants to achieve. 【0384】 An "input method" is an interface for users to register information generation conditions with the system. 【0385】 A "generation method" is a technique or algorithm for assembling information based on given conditions. 【0386】 "Selection means" refers to an apparatus or method that determines the most appropriate generation method based on the input conditions. 【0387】 "Information" refers to digital content created based on the user's conditions and emotional state. 【0388】 "Identifier parsing" refers to the processing of identification information used to recognize a user's emotional state. 【0389】 "Emotional state" is an indicator that shows the user's current psychological state. 【0390】 "Adjustment means" refers to a device or method for changing the style and tone of generated information to match the user's emotional state. 【0391】 "Performance optimization" is the process of improving generated information so that it functions efficiently on a digital platform. 【0392】 This invention provides a system that dynamically generates and adjusts information in response to a user's emotions. This system is implemented using a terminal such as a smartphone or smart glasses. Specifically, it begins with the user inputting the conditions for the target information generation into the terminal. 【0393】 First, the terminal sends the input conditions to the server. Based on these conditions, the server selects an appropriate generation method and generates the information. The server uses an emotion recognition API (for example, an external cloud service that performs facial recognition) to analyze the user's emotional state. Based on the analyzed emotion data, a generation AI model is used to adjust the tone and style of the information. Tools such as content generation AI and image editing software are used to adjust the information. 【0394】 The generated information is further optimized by the server and its performance on the digital platform is improved using information retrieval technology. This ensures that information that matches the user's emotions is delivered most effectively. The optimized information is then transferred to the user's device and used. 【0395】 As a concrete example, consider a scenario where a user is using their smartphone in a shopping mall. The camera captures their facial expression, and the server recognizes this as happiness. Based on this emotional data, the server generates advertising content such as "Weekend Special Sale to Double Your Shopping Fun" and displays it on the user's smartphone. 【0396】 An example of a prompt message might be: "The user is currently smiling. Generate special and interesting ad content that shows he is enjoying himself." 【0397】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0398】 Step 1: 【0399】 The user uses a terminal to input the conditions for information generation. The entered conditions (e.g., the type and tone of the advertisement to be generated) are sent directly to the server. Input: Conditions for information generation; Output: Sending of conditions to the server. 【0400】 Step 2: 【0401】 The server selects a generation method based on the received conditions. A predefined algorithm is used for this selection to determine the optimal generation process. Input: Information generation conditions; Output: Selected generation method. 【0402】 Step 3: 【0403】 Next, the server sends the user's image data, captured by the device, to an emotion recognition API in the cloud to analyze the user's emotional state. This API uses facial recognition technology to identify emotions from the user's facial expressions. Input: Image data, Output: User's emotional state. 【0404】 Step 4: 【0405】 Based on the emotional state, the server uses a generative AI model to generate information (such as advertising content). The content and tone are adjusted to match the emotion, and the information is constructed. Here, the generative AI model uses prompt sentences to perform the generation. Input: Emotional state, prompt sentence; Output: Generated information. 【0406】 Step 5: 【0407】 The generated information is optimized by the server. This process utilizes information retrieval technology and performs optimization to maximize performance on the digital platform. Input: generated information, Output: optimized information. 【0408】 Step 6: 【0409】 The optimized information is sent back to the device and displayed to the user. This allows the user to access personalized information that matches their emotions. Input: Optimized information, Output: Displayed to the user. 【0410】 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0411】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0412】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214. 【0413】 [Third Embodiment] 【0414】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0415】 As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server. 【0416】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0417】 The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52. 【0418】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0419】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0420】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0421】 Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0422】 The specific processing program 56 is an example of a "program" relating to the technology of this 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. 【0423】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0424】 In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0425】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal". 【0426】 This invention provides a system that allows users to quickly and efficiently generate content in various formats. Users access the system via a terminal and input the conditions for generating the target content. For example, a user can input, "I want to create a blog post introducing a new product." 【0427】 The server analyzes this input and determines which generation algorithm is most appropriate. If text generation is required, the server selects and applies a text generation algorithm. Based on the selected algorithm, the server generates content and adjusts its content and style as needed. This adjustment is done using customization methods and is translated into a tone and language that aligns with the user's instructions. 【0428】 Furthermore, the generated content is evaluated by optimization tools, and performance is improved using SEO (Search Engine Optimization) techniques. Users can review the generated content and make corrections or approvals as needed. The feedback is then sent to the server, and the update tool uses this feedback to improve the generation algorithm. 【0429】 Specifically, if a user wants to generate advertising copy highlighting the benefits of a home appliance, the server will incorporate content emphasizing the product's features and provide text in a friendly tone. If image generation is required, visually appealing product images will also be generated. This allows companies to consistently provide content that can be immediately used in their marketing activities. 【0430】 In this way, users can create high-quality content with minimal effort through the system and effectively communicate information to consumers. The automated selection, generation, and optimization processes of the servers enable faster and more efficient content generation than traditional methods. 【0431】 The following describes the processing flow. 【0432】 Step 1: 【0433】 Users input their content generation goals using their devices. For example, they might write specific requests in the form, such as "I want to generate a blog post about a new product." 【0434】 Step 2: 【0435】 The user's input is sent from the terminal to the server. The server analyzes this input using natural language processing technology to determine the subject of the goal and the required content format. 【0436】 Step 3: 【0437】 The server selects the optimal generation algorithm based on the analysis results. Depending on the type of content required, the algorithm will be chosen to generate text, images, or both. 【0438】 Step 4: 【0439】 The server applies a selected generation algorithm to produce content that aligns with the purpose. In the case of text generation, the generation algorithm creates the text; in the case of image generation, it produces visuals that conform to the specified theme. 【0440】 Step 5: 【0441】 To further adapt the generated content to the user's specified requirements, the server uses customization methods to adjust the content. The tone of the text, the color scheme of images, and other elements are modified to match the user's preferences. 【0442】 Step 6: 【0443】 The server analyzes and improves the performance of the generated content using optimization techniques, and then implements SEO measures. This process makes adjustments to improve the content's ranking in search engines. 【0444】 Step 7: 【0445】 The server sends the final content to the terminal and requests the user to review and provide feedback. The user reviews the content, makes corrections or approves it, and provides feedback as needed. 【0446】 Step 8: 【0447】 The server receives user feedback and uses it to improve the algorithm through update mechanisms. This will further improve the accuracy and efficiency of future content generation. 【0448】 (Example 1) 【0449】 Next, we will describe Example 1. 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." 【0450】 When generating information in diverse formats quickly and efficiently, users are required to obtain high-quality content without any hassle. However, conventional methods have problems with efficient processes, such as the time required to select generation algorithms and optimize the generated information. As a result, it has been difficult to respond quickly in situations where a large amount of content needs to be delivered intensively in a short period of time. 【0451】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0452】 In this invention, the server includes a receiving means for the user to input the generation conditions for the target information, a determination means for selecting the necessary generation algorithm based on the generation conditions received from the receiving means, and a construction means for generating information using the generation algorithm selected by the determination means. This allows the user to efficiently generate high-quality information and customize or optimize it as needed. 【0453】 A "reception mechanism" is an element that provides an interface for the user to input the conditions for generating target information into the system. 【0454】 A "decision-making element" is an element that has the function of selecting the most appropriate generation algorithm based on the generation conditions received from the reception element. 【0455】 A "construction method" is an element that executes the process of generating the required information using a selected generation algorithm. 【0456】 A "coordination mechanism" is an element that has the function of customizing the generated information according to the user's requests. 【0457】 An "evaluation tool" is an element that has the function of analyzing the performance of generated information and optimizing it as needed. 【0458】 "Improvement measures" refer to elements that collect user feedback and incorporate it into improvements to the next generation algorithm. 【0459】 An "analysis tool" is an element that has the function of interpreting the input content from a reception tool using natural language processing technology. 【0460】 A "processing means" is an element that has the function of appropriately processing a prompt sentence using a generative AI model and obtaining the generation result. 【0461】 This invention provides a system that allows users to quickly and efficiently generate information in various formats. Users access the system using a terminal and input the conditions for generating the target information. For example, a user might input, "I want to create a blog post introducing a new product." 【0462】 The server uses natural language processing techniques to analyze the user's input. By utilizing this technology, the server accurately understands the intent of the input and selects the appropriate generation algorithm. In this process, the server leverages a generation AI model to appropriately process the prompt text. Specifically, it can use text generation AI models such as OpenAI's GPT series. 【0463】 The generated information is adjusted by the server according to the user's requests. Using customization methods, the server appropriately adjusts the tone and language of the information to produce a final output that matches the user's specified conditions. Furthermore, this generated information is optimized by the server, and performance is improved using SEO techniques. This optimization enhances the discoverability of the information online. 【0464】 As a concrete example, consider a scenario where a user wants to generate advertising copy that highlights the benefits of a home appliance. The user inputs "I want to create advertising copy for a vacuum cleaner. Emphasize the benefits." into the terminal. The server analyzes this input and generates text in a friendly tone. For example, the content might read, "This vacuum cleaner is 20% off for a limited time! It's lightweight, easy to use, and can easily clean any room!" 【0465】 This invention enables users to generate high-quality information with minimal effort and effectively communicate it to consumers. The server's automated selection, generation, and optimization processes enable faster and more efficient information generation than conventional methods. 【0466】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0467】 Step 1: 【0468】 The user uses a terminal to input the conditions for generating the target information. For example, the user might input "I want to create a blog post introducing a new product" into the terminal. This input is sent to the server. The input is natural language text, and the following processing is performed based on it. 【0469】 Step 2: 【0470】 The server analyzes the input received from the user. Using natural language processing techniques, it interprets the intent of the input text and understands the generation conditions. The input data represents the conditions for text generation, and this analysis allows for the selection of an appropriate generation algorithm for the output. 【0471】 Step 3: 【0472】 The server selects an appropriate generation algorithm based on the analyzed generation conditions. For example, the server might select a text generation model such as the GPT series as the generation AI model. This selected generation algorithm becomes the input for the next processing step. 【0473】 Step 4: 【0474】 The server generates information using a selected generation algorithm. It sends a prompt to the generation AI model and receives output from the model. At this stage, initial content is generated based on the generation conditions. For example, the prompt might be, "Please generate a blog post introducing a new vacuum cleaner. Please make it approachable and engaging, including its features, convenience, and price." 【0475】 Step 5: 【0476】 The server adjusts the generated information according to the user's requests. This adjustment is performed using customization methods, changing the tone and language to match the user's instructions. The output is information that has been adjusted to meet the user's needs. 【0477】 Step 6: 【0478】 The server optimizes the generated information. Using SEO techniques, it optimizes the information to improve its online discoverability. This optimization improves performance on search engines. The output is the final, SEO-enhanced information. 【0479】 Step 7: 【0480】 Users review the generated and optimized information on their devices and make corrections or approvals as needed. User feedback is sent to the server, which serves as the basis for improving the algorithm in the next generation cycle. 【0481】 (Application Example 1) 【0482】 Next, we will explain Application Example 1. In the following explanation, 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." 【0483】 In modern digital content creation and distribution, there is a need to quickly and efficiently generate diverse forms of information and deliver them to target followers and subscribers in an appropriate manner. However, traditional systems have presented challenges such as complex and time-consuming content generation and optimization processes, as well as difficulty in effectively improving the performance of the generated content. This has made it difficult for companies and individuals to maintain a competitive edge in their digital communication strategies. 【0484】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0485】 In this invention, the server includes an input means for the user to input the generation conditions for the target information material, a selection means for selecting the necessary generation algorithm based on the generation conditions received from the input means, and a distribution means for distributing the generated information material to the user's followers or subscribers. This enables the user to quickly generate high-quality information material with minimal effort and effectively distribute it to their target audience. 【0486】 A "user" is an entity that uses a content generation system to input the conditions for generating information materials and then utilizes the generated information materials. 【0487】 "Information material" refers to digital content such as text and images that are generated based on input generation conditions. 【0488】 An "input method" is an interface used by the user to communicate generation conditions to the system. 【0489】 "Generation conditions" refer to the criteria and requirements for generating informational materials based on the user's goals. 【0490】 "Selection method" refers to a process or function for selecting a generation algorithm suitable for the input generation conditions. 【0491】 A "generative algorithm" is a computational method or mathematical model used to create informational material. 【0492】 A "generation method" is a mechanism for actually generating information material using a generation algorithm. 【0493】 "Customization means" refers to a method or function for adjusting the generated information material according to the user's requirements. 【0494】 "Optimization methods" refer to techniques and methods for analyzing and improving the performance of generated information material. 【0495】 "Distribution method" refers to the methods and technologies used to deliver generated informational material to a user's followers or subscribers. 【0496】 The implementation of this invention revolves around a digital content generation process conducted between an information processing terminal and a server system. The terminal provides an interface for the user to input the conditions for the content they wish to generate. The input includes keywords, the tone of the text, the attributes of the target audience, etc., and the server processes this information accordingly. 【0497】 Within the server, the input generation conditions are analyzed, and the most suitable generation algorithm is automatically selected. At this stage, for example, OpenAI's GPT model is used for text generation, and a generation model such as DALL-E is used for image generation as needed. The selected algorithm generates the information material, and then the content and style are adjusted according to the user's requests. 【0498】 The generated information material is analyzed using optimization techniques. Here, SEO optimization techniques utilizing deep learning are applied, particularly aiming to improve performance in digital communication. This optimization is a crucial step in ensuring that the information material maximizes its effectiveness on online platforms. 【0499】 Subsequently, the information material is rapidly distributed to designated followers and subscribers via distribution channels. This process is supported by a distribution infrastructure utilizing cloud computing technology. The servers receive user feedback after distribution and use it to improve the algorithms. 【0500】 As a concrete example, consider a scenario where a user inputs, "I want to create a blog post promoting a home cooking event next weekend." The server analyzes this request and generates a blog post that includes an overview of the event, how to participate, and suggested recipes. In addition, a visual image of the dishes is created using an image generation function. An example of a prompt would be, "Please create a blog post promoting a home cooking event next weekend. The post should include the event date, location, benefits of participating, and recommended recipes." 【0501】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0502】 Step 1: 【0503】 The terminal receives the user's requirements for the information material to be generated. Here, the user enters keywords, tone, and target audience attributes for the content to be generated. The entered data is sent to the server in an appropriate format for use in the next processing step. 【0504】 Step 2: 【0505】 The server analyzes the received generation conditions. Using natural language processing techniques, it grasps the intent of the input text data and identifies the necessary generation algorithm. Based on this analysis, it selects the optimal generation AI model (e.g., GPT for text generation, DALL-E for image generation). 【0506】 Step 3: 【0507】 A selection mechanism within the server calls the selected generative AI model and generates informational materials. Based on the input data, the selected algorithm generates text and images. The generated informational materials are temporarily stored on the server. 【0508】 Step 4: 【0509】 The generated information material is customized by the server according to the user's requests. Here, the content is adjusted to match the specified tone and style. For example, the writing style may be changed to a more casual style, or specific keywords may be added. 【0510】 Step 5: 【0511】 The server analyzes the information material generated using optimization techniques and performs SEO optimization. Search engine optimization (SEO) technologies are utilized to improve the performance of the generated content. This increases visibility and influence on online platforms. 【0512】 Step 6: 【0513】 The completed information material is distributed to the user's followers or subscribers via the server's distribution system. Cloud computing technology is used during distribution to ensure efficient and rapid delivery to the target audience. 【0514】 Step 7: 【0515】 The next phase involves collecting user feedback. Users input their opinions and suggestions for improvement regarding the delivered content through their devices. These devices send this feedback to the server, which is then used to improve the algorithm for future updates. This step is important because it helps improve throughput and optimize personalization. 【0516】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0517】 This invention provides a content generation system that can recognize user emotions and adapt the tone and style of the generated content based on those emotions. The user accesses the system using a terminal and inputs goals and conditions for content generation. For example, consider a case where the user inputs the goal, "I want to generate a promotional video for my brand." 【0518】 The server receives the input information and uses an emotion engine to analyze the user's emotional state. This analysis makes it possible to recognize what emotional state the user is in. For example, if the server recognizes that the user is excited, that emotion will be reflected in the generated content. 【0519】 Next, the server selects the optimal generation algorithm based on the analysis results. This selection reflects the user's intent and emotional state, ensuring that a content style that matches the user's emotions is chosen. The generation mechanism then creates specific text and visual content, which is adjusted according to the user's emotions. At this stage, the customization mechanism adjusts the content based on the results of the emotion engine. 【0520】 The generated content is analyzed using optimization tools, and SEO optimization is performed as needed. This process ensures that the content performs at its best on the digital platform. Finally, the server-generated content is delivered to the user via their device, and user review and feedback are requested. 【0521】 For example, if a user desires a calm tone when generating a post introducing a new product, the emotion engine will read the user's calm mood and adjust the tone of the text and visual elements accordingly. In this way, personalized content that matches the individual user's mood is quickly and efficiently generated. This feature enables businesses and creators to create more effective content that appeals to the emotions of their target audience. 【0522】 The following describes the processing flow. 【0523】 Step 1: 【0524】 Users use their devices to input their goals and conditions for content generation. For example, they might set the goal to "generate video advertisements for a new product." 【0525】 Step 2: 【0526】 The terminal sends user input information to the server. This transmission includes details about the goal and generation conditions. 【0527】 Step 3: 【0528】 The server analyzes the received information and activates the emotion engine. The emotion engine uses natural language processing technology to extract emotions from the user's text input and recognize the user's current emotional state. For example, if a user frequently uses words like "excited," a positive emotion will be recognized. 【0529】 Step 4: 【0530】 The server selects a generation algorithm based on emotional information obtained from the emotion engine. This selection is made with consideration to creating a tone and style that is appropriate for the user's emotional state. In the case of positive emotions, content with a bright and cheerful style is selected. 【0531】 Step 5: 【0532】 The server executes a selected generation algorithm to automatically generate content. For example, in the case of video generation, it adds visual effects and inserts audio and music based on a pre-prepared template. 【0533】 Step 6: 【0534】 The generated content is further refined through customization methods to match the user's emotions. Specifically, the colors, fonts, and tone of the narration are adjusted to create the atmosphere the user expects. 【0535】 Step 7: 【0536】 Next, the server analyzes the generated content using optimization techniques. Search engine optimization (SEO) technologies are applied, and settings are configured to maximize visibility and effectiveness, especially on digital platforms. 【0537】 Step 8: 【0538】 The final content is sent from the server to the terminal, where the user reviews the generated result. This provides an opportunity to offer feedback on the content. 【0539】 Step 9: 【0540】 Feedback collected from devices is returned to the server and used to improve future content generation algorithms through update mechanisms. This feedback information facilitates the evolution of the system to more accurately meet user needs. 【0541】 (Example 2) 【0542】 Next, we will describe Example 2. 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." 【0543】 Conventional content generation technologies generate content uniformly without considering user emotions, making it difficult to provide personalized content tailored to the individual needs of users. Furthermore, the generated content is not sufficiently optimized to maximize performance on digital platforms, potentially leading to a decline in the quality of the user experience. 【0544】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0545】 In this invention, the server includes an information input means for the user to input target content generation conditions, an emotion analysis means for analyzing the user's emotional state, and an algorithm selection means for selecting the optimal generation algorithm. This enables the rapid generation of high-quality content that responds to the user's emotions and requests, as well as performance optimization. 【0546】 An "information input means" is an interface that provides users with the conditions for generating their target content. 【0547】 "Emotion analysis means" refers to a technology or process for determining a user's emotional state based on data received from them. 【0548】 An "algorithm selection means" is a device that has the function of selecting a generation algorithm that is suitable for the user's emotional state and requests. 【0549】 "Content generation means" refers to a technology or device that generates specific content based on a selected algorithm. 【0550】 "Content customization means" refers to a function that allows the generated content to be adjusted according to the user's emotional state and requests. 【0551】 "Performance optimization means" refers to methods or techniques for analyzing and optimizing the performance of generated content. 【0552】 An "information update mechanism" is a system that collects user feedback and uses it to improve the next generation process. 【0553】 This invention provides a system that, based on information input by the user regarding the conditions for generating target content, determines the user's emotional state and generates optimal content. 【0554】 The user accesses an information input method using a terminal and inputs prompt sentences to the generating AI model according to their purpose. For example, a prompt sentence such as "Please generate a new product introduction post in a calm tone" can be provided. 【0555】 The server processes the prompt message received from the terminal using sentiment analysis tools to analyze the user's emotional state. This analysis utilizes natural language processing technology to understand the emotional state in a way that is appropriate for the user's target content and style of expression. Subsequently, based on the analysis results, the server selects the optimal generation algorithm using algorithm selection tools. This is done based on both the user's emotional state and the generation conditions. 【0556】 The selected generation algorithm is used by the content generation means to generate specific content. The generated content encompasses a wide range of elements, including text and visuals, and is adjusted according to the user's emotional state through the content customization means. This process generates personalized content for each individual user. 【0557】 The generated content undergoes optimization processing to maximize its performance on digital platforms through performance optimization techniques. This processing includes search engine optimization (SEO) technologies to improve the visibility and effectiveness of the content. 【0558】 Ultimately, the generated content is delivered to the user via their device, and the user reviews it. Furthermore, the information update mechanism collects user feedback, which is used to improve the content generation process for the next time, resulting in higher quality content. 【0559】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0560】 Step 1: 【0561】 The user accesses an information input device via a terminal and enters a prompt message for the generating AI model. This entered prompt message indicates the user's target content generation conditions, including the specific type of content and expression style. An example of input is, "Generate a new product introduction post in a calm tone." At this stage, the terminal sends the prompt message to the server. 【0562】 Step 2: 【0563】 The server processes the prompt message received from the terminal using sentiment analysis. The sentiment analysis analyzes the content of the prompt message using natural language processing techniques to identify the user's emotional state. In this process, a language model extracts emotional keywords and phrases and classifies the emotional state based on them. The output is the user's emotional state (e.g., calm, excited). 【0564】 Step 3: 【0565】 The server uses an algorithm selection mechanism to choose the optimal generation algorithm based on the output of the sentiment analysis mechanism. Here, an algorithm that considers both the emotional state and the generation goal is selected. For example, if a calm tone is required, templates and styles for that purpose are applied. The selected algorithm is then used in the next content generation phase. 【0566】 Step 4: 【0567】 The server uses the algorithm selected by the algorithm selection mechanism to generate specific content through the content generation mechanism. This process utilizes a generation AI model to automatically create text and visual elements. The generated content is adjusted to reflect the user's emotional state and is faithful to the user's input conditions. 【0568】 Step 5: 【0569】 The generated content is evaluated using performance optimization techniques, and is optimized particularly for improving performance on digital platforms. SEO techniques are used to enhance content visibility through keyword placement and tagging. As a result, optimized content is generated. 【0570】 Step 6: 【0571】 The server sends optimized content to the terminal and provides it to the user. The user reviews the provided content and provides feedback as needed. This feedback is collected by an information update mechanism and used to improve the next generation process. 【0572】 (Application Example 2) 【0573】 Next, we will explain application example 2. In the following explanation, 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." 【0574】 Modern information delivery systems face the challenge of generating effective information that appeals to users' emotions because they do not adequately personalize content according to the user's emotional state. Furthermore, while performance optimization is necessary for generated content to be utilized to its fullest potential on digital platforms, there is a lack of efficient means to achieve this. 【0575】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0576】 In this invention, the server includes an input means for the user to input the conditions for generating target information, a selection means for selecting the necessary generation method based on the generation conditions received from the input means, and an adjustment means for adjusting the generated information according to the user's emotional state using identifier analysis. This makes it possible to efficiently generate and optimize information that matches the user's emotions. 【0577】 A "user" is an individual or organization that utilizes an information generation system. 【0578】 "Information generation conditions" are requirements that specify the content and characteristics of the information that the user wants to achieve. 【0579】 An "input method" is an interface for users to register information generation conditions with the system. 【0580】 A "generation method" is a technique or algorithm for assembling information based on given conditions. 【0581】 "Selection means" refers to an apparatus or method that determines the most appropriate generation method based on the input conditions. 【0582】 "Information" refers to digital content created based on the user's conditions and emotional state. 【0583】 "Identifier parsing" refers to the processing of identification information used to recognize a user's emotional state. 【0584】 "Emotional state" is an indicator that shows the user's current psychological state. 【0585】 "Adjustment means" refers to a device or method for changing the style and tone of generated information to match the user's emotional state. 【0586】 "Performance optimization" is the process of improving generated information so that it functions efficiently on a digital platform. 【0587】 This invention provides a system that dynamically generates and adjusts information in response to a user's emotions. This system is implemented using a terminal such as a smartphone or smart glasses. Specifically, it begins with the user inputting the conditions for the target information generation into the terminal. 【0588】 First, the terminal sends the input conditions to the server. Based on these conditions, the server selects an appropriate generation method and generates the information. The server uses an emotion recognition API (for example, an external cloud service that performs facial recognition) to analyze the user's emotional state. Based on the analyzed emotion data, a generation AI model is used to adjust the tone and style of the information. Tools such as content generation AI and image editing software are used to adjust the information. 【0589】 The generated information is further optimized by the server and its performance on the digital platform is improved using information retrieval technology. This ensures that information that matches the user's emotions is delivered most effectively. The optimized information is then transferred to the user's device and used. 【0590】 As a concrete example, consider a scenario where a user is using their smartphone in a shopping mall. The camera captures their facial expression, and the server recognizes this as happiness. Based on this emotional data, the server generates advertising content such as "Weekend Special Sale to Double Your Shopping Fun" and displays it on the user's smartphone. 【0591】 An example of a prompt message might be: "The user is currently smiling. Generate special and interesting ad content that shows he is enjoying himself." 【0592】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0593】 Step 1: 【0594】 The user uses a terminal to input the conditions for information generation. The entered conditions (e.g., the type and tone of the advertisement to be generated) are sent directly to the server. Input: Conditions for information generation; Output: Sending of conditions to the server. 【0595】 Step 2: 【0596】 The server selects a generation method based on the received conditions. A predefined algorithm is used for this selection to determine the optimal generation process. Input: Information generation conditions; Output: Selected generation method. 【0597】 Step 3: 【0598】 Next, the server sends the user's image data, captured by the device, to an emotion recognition API in the cloud to analyze the user's emotional state. This API uses facial recognition technology to identify emotions from the user's facial expressions. Input: Image data, Output: User's emotional state. 【0599】 Step 4: 【0600】 Based on the emotional state, the server uses a generative AI model to generate information (such as advertising content). The content and tone are adjusted to match the emotion, and the information is constructed. Here, the generative AI model uses prompt sentences to perform the generation. Input: Emotional state, prompt sentence; Output: Generated information. 【0601】 Step 5: 【0602】 The generated information is optimized by the server. This process utilizes information retrieval technology and performs optimization to maximize performance on the digital platform. Input: generated information, Output: optimized information. 【0603】 Step 6: 【0604】 The optimized information is sent back to the device and displayed to the user. This allows the user to access personalized information that matches their emotions. Input: Optimized information, Output: Displayed to the user. 【0605】 The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0606】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0607】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314. 【0608】 [Fourth Embodiment] 【0609】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0610】 As shown in Figure 7, the 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. 【0611】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0612】 The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52. 【0613】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0614】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0615】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0616】 The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes. 【0617】 Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0618】 The specific processing program 56 is an example of a "program" relating to the technology of this 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. 【0619】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0620】 In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0621】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0622】 This invention provides a system that allows users to quickly and efficiently generate content in various formats. Users access the system via a terminal and input the conditions for generating the target content. For example, a user can input, "I want to create a blog post introducing a new product." 【0623】 The server analyzes this input and determines which generation algorithm is most appropriate. If text generation is required, the server selects and applies a text generation algorithm. Based on the selected algorithm, the server generates content and adjusts its content and style as needed. This adjustment is done using customization methods and is translated into a tone and language that aligns with the user's instructions. 【0624】 Furthermore, the generated content is evaluated by optimization tools, and performance is improved using SEO (Search Engine Optimization) techniques. Users can review the generated content and make corrections or approvals as needed. The feedback is then sent to the server, and the update tool uses this feedback to improve the generation algorithm. 【0625】 Specifically, if a user wants to generate advertising copy highlighting the benefits of a home appliance, the server will incorporate content emphasizing the product's features and provide text in a friendly tone. If image generation is required, visually appealing product images will also be generated. This allows companies to consistently provide content that can be immediately used in their marketing activities. 【0626】 In this way, users can create high-quality content with minimal effort through the system and effectively communicate information to consumers. The automated selection, generation, and optimization processes of the servers enable faster and more efficient content generation than traditional methods. 【0627】 The following describes the processing flow. 【0628】 Step 1: 【0629】 Users input their content generation goals using their devices. For example, they might write specific requests in the form, such as "I want to generate a blog post about a new product." 【0630】 Step 2: 【0631】 The user's input is sent from the terminal to the server. The server analyzes this input using natural language processing technology to determine the subject of the goal and the required content format. 【0632】 Step 3: 【0633】 The server selects the optimal generation algorithm based on the analysis results. Depending on the type of content required, the algorithm will be chosen to generate text, images, or both. 【0634】 Step 4: 【0635】 The server applies a selected generation algorithm to produce content that aligns with the purpose. In the case of text generation, the generation algorithm creates the text; in the case of image generation, it produces visuals that conform to the specified theme. 【0636】 Step 5: 【0637】 To further adapt the generated content to the user's specified requirements, the server uses customization methods to adjust the content. The tone of the text, the color scheme of images, and other elements are modified to match the user's preferences. 【0638】 Step 6: 【0639】 The server analyzes and improves the performance of the generated content using optimization techniques, and then implements SEO measures. This process makes adjustments to improve the content's ranking in search engines. 【0640】 Step 7: 【0641】 The server sends the final content to the terminal and requests the user to review and provide feedback. The user reviews the content, makes corrections or approves it, and provides feedback as needed. 【0642】 Step 8: 【0643】 The server receives user feedback and uses it to improve the algorithm through update mechanisms. This will further improve the accuracy and efficiency of future content generation. 【0644】 (Example 1) 【0645】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0646】 When generating information in diverse formats quickly and efficiently, users are required to obtain high-quality content without any hassle. However, conventional methods have problems with efficient processes, such as the time required to select generation algorithms and optimize the generated information. As a result, it has been difficult to respond quickly in situations where a large amount of content needs to be delivered intensively in a short period of time. 【0647】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0648】 In this invention, the server includes a receiving means for the user to input the generation conditions for the target information, a determination means for selecting the necessary generation algorithm based on the generation conditions received from the receiving means, and a construction means for generating information using the generation algorithm selected by the determination means. This allows the user to efficiently generate high-quality information and customize or optimize it as needed. 【0649】 A "reception mechanism" is an element that provides an interface for the user to input the conditions for generating target information into the system. 【0650】 A "decision-making element" is an element that has the function of selecting the most appropriate generation algorithm based on the generation conditions received from the reception element. 【0651】 A "construction method" is an element that executes the process of generating the required information using a selected generation algorithm. 【0652】 A "coordination mechanism" is an element that has the function of customizing the generated information according to the user's requests. 【0653】 An "evaluation tool" is an element that has the function of analyzing the performance of generated information and optimizing it as needed. 【0654】 "Improvement measures" refer to elements that collect user feedback and incorporate it into improvements to the next generation algorithm. 【0655】 An "analysis tool" is an element that has the function of interpreting the input content from a reception tool using natural language processing technology. 【0656】 A "processing means" is an element that has the function of appropriately processing a prompt sentence using a generative AI model and obtaining the generation result. 【0657】 This invention provides a system that allows users to quickly and efficiently generate information in various formats. Users access the system using a terminal and input the conditions for generating the target information. For example, a user might input, "I want to create a blog post introducing a new product." 【0658】 The server uses natural language processing techniques to analyze the user's input. By utilizing this technology, the server accurately understands the intent of the input and selects the appropriate generation algorithm. In this process, the server leverages a generation AI model to appropriately process the prompt text. Specifically, it can use text generation AI models such as OpenAI's GPT series. 【0659】 The generated information is adjusted by the server according to the user's requests. Using customization methods, the server appropriately adjusts the tone and language of the information to produce a final output that matches the user's specified conditions. Furthermore, this generated information is optimized by the server, and performance is improved using SEO techniques. This optimization enhances the discoverability of the information online. 【0660】 As a concrete example, consider a scenario where a user wants to generate advertising copy that highlights the benefits of a home appliance. The user inputs "I want to create advertising copy for a vacuum cleaner. Emphasize the benefits." into the terminal. The server analyzes this input and generates text in a friendly tone. For example, the content might read, "This vacuum cleaner is 20% off for a limited time! It's lightweight, easy to use, and can easily clean any room!" 【0661】 This invention enables users to generate high-quality information with minimal effort and effectively communicate it to consumers. The server's automated selection, generation, and optimization processes enable faster and more efficient information generation than conventional methods. 【0662】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0663】 Step 1: 【0664】 The user uses a terminal to input the conditions for generating the target information. For example, the user might input "I want to create a blog post introducing a new product" into the terminal. This input is sent to the server. The input is natural language text, and the following processing is performed based on it. 【0665】 Step 2: 【0666】 The server analyzes the input received from the user. Using natural language processing techniques, it interprets the intent of the input text and understands the generation conditions. The input data represents the conditions for text generation, and this analysis allows for the selection of an appropriate generation algorithm for the output. 【0667】 Step 3: 【0668】 The server selects an appropriate generation algorithm based on the analyzed generation conditions. For example, the server might select a text generation model such as the GPT series as the generation AI model. This selected generation algorithm becomes the input for the next processing step. 【0669】 Step 4: 【0670】 The server generates information using a selected generation algorithm. It sends a prompt to the generation AI model and receives output from the model. At this stage, initial content is generated based on the generation conditions. For example, the prompt might be, "Please generate a blog post introducing a new vacuum cleaner. Please make it approachable and engaging, including its features, convenience, and price." 【0671】 Step 5: 【0672】 The server adjusts the generated information according to the user's requests. This adjustment is performed using customization methods, changing the tone and language to match the user's instructions. The output is information that has been adjusted to meet the user's needs. 【0673】 Step 6: 【0674】 The server optimizes the generated information. Using SEO techniques, it optimizes the information to improve its online discoverability. This optimization improves performance on search engines. The output is the final, SEO-enhanced information. 【0675】 Step 7: 【0676】 Users review the generated and optimized information on their devices and make corrections or approvals as needed. User feedback is sent to the server, which serves as the basis for improving the algorithm in the next generation cycle. 【0677】 (Application Example 1) 【0678】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0679】 In modern digital content creation and distribution, there is a need to quickly and efficiently generate diverse forms of information and deliver them to target followers and subscribers in an appropriate manner. However, traditional systems have presented challenges such as complex and time-consuming content generation and optimization processes, as well as difficulty in effectively improving the performance of the generated content. This has made it difficult for companies and individuals to maintain a competitive edge in their digital communication strategies. 【0680】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0681】 In this invention, the server includes an input means for the user to input the generation conditions for the target information material, a selection means for selecting the necessary generation algorithm based on the generation conditions received from the input means, and a distribution means for distributing the generated information material to the user's followers or subscribers. This enables the user to quickly generate high-quality information material with minimal effort and effectively distribute it to their target audience. 【0682】 A "user" is an entity that uses a content generation system to input the conditions for generating information materials and then utilizes the generated information materials. 【0683】 "Information material" refers to digital content such as text and images that are generated based on input generation conditions. 【0684】 An "input method" is an interface used by the user to communicate generation conditions to the system. 【0685】 "Generation conditions" refer to the criteria and requirements for generating informational materials based on the user's goals. 【0686】 "Selection method" refers to a process or function for selecting a generation algorithm suitable for the input generation conditions. 【0687】 A "generative algorithm" is a computational method or mathematical model used to create informational material. 【0688】 A "generation method" is a mechanism for actually generating information material using a generation algorithm. 【0689】 "Customization means" refers to a method or function for adjusting the generated information material according to the user's requirements. 【0690】 "Optimization methods" refer to techniques and methods for analyzing and improving the performance of generated information material. 【0691】 "Distribution method" refers to the methods and technologies used to deliver generated informational material to a user's followers or subscribers. 【0692】 The implementation of this invention revolves around a digital content generation process conducted between an information processing terminal and a server system. The terminal provides an interface for the user to input the conditions for the content they wish to generate. The input includes keywords, the tone of the text, the attributes of the target audience, etc., and the server processes this information accordingly. 【0693】 Within the server, the input generation conditions are analyzed, and the most suitable generation algorithm is automatically selected. At this stage, for example, OpenAI's GPT model is used for text generation, and a generation model such as DALL-E is used for image generation as needed. The selected algorithm generates the information material, and then the content and style are adjusted according to the user's requests. 【0694】 The generated information material is analyzed using optimization techniques. Here, SEO optimization techniques utilizing deep learning are applied, particularly aiming to improve performance in digital communication. This optimization is a crucial step in ensuring that the information material maximizes its effectiveness on online platforms. 【0695】 Subsequently, the information material is rapidly distributed to designated followers and subscribers via distribution channels. This process is supported by a distribution infrastructure utilizing cloud computing technology. The servers receive user feedback after distribution and use it to improve the algorithms. 【0696】 As a concrete example, consider a scenario where a user inputs, "I want to create a blog post promoting a home cooking event next weekend." The server analyzes this request and generates a blog post that includes an overview of the event, how to participate, and suggested recipes. In addition, a visual image of the dishes is created using an image generation function. An example of a prompt would be, "Please create a blog post promoting a home cooking event next weekend. The post should include the event date, location, benefits of participating, and recommended recipes." 【0697】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0698】 Step 1: 【0699】 The terminal receives the user's requirements for the information material to be generated. Here, the user enters keywords, tone, and target audience attributes for the content to be generated. The entered data is sent to the server in an appropriate format for use in the next processing step. 【0700】 Step 2: 【0701】 The server analyzes the received generation conditions. Using natural language processing techniques, it grasps the intent of the input text data and identifies the necessary generation algorithm. Based on this analysis, it selects the optimal generation AI model (e.g., GPT for text generation, DALL-E for image generation). 【0702】 Step 3: 【0703】 A selection mechanism within the server calls the selected generative AI model and generates informational materials. Based on the input data, the selected algorithm generates text and images. The generated informational materials are temporarily stored on the server. 【0704】 Step 4: 【0705】 The generated information material is customized by the server according to the user's requests. Here, the content is adjusted to match the specified tone and style. For example, the writing style may be changed to a more casual style, or specific keywords may be added. 【0706】 Step 5: 【0707】 The server analyzes the information material generated using optimization techniques and performs SEO optimization. Search engine optimization (SEO) technologies are utilized to improve the performance of the generated content. This increases visibility and influence on online platforms. 【0708】 Step 6: 【0709】 The completed information material is distributed to the user's followers or subscribers via the server's distribution system. Cloud computing technology is used during distribution to ensure efficient and rapid delivery to the target audience. 【0710】 Step 7: 【0711】 The next phase involves collecting user feedback. Users input their opinions and suggestions for improvement regarding the delivered content through their devices. These devices send this feedback to the server, which is then used to improve the algorithm for future updates. This step is important because it helps improve throughput and optimize personalization. 【0712】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0713】 This invention provides a content generation system that can recognize user emotions and adapt the tone and style of the generated content based on those emotions. The user accesses the system using a terminal and inputs goals and conditions for content generation. For example, consider a case where the user inputs the goal, "I want to generate a promotional video for my brand." 【0714】 The server receives the input information and uses an emotion engine to analyze the user's emotional state. This analysis makes it possible to recognize what emotional state the user is in. For example, if the server recognizes that the user is excited, that emotion will be reflected in the generated content. 【0715】 Next, the server selects the optimal generation algorithm based on the analysis results. This selection reflects the user's intent and emotional state, ensuring that a content style that matches the user's emotions is chosen. The generation mechanism then creates specific text and visual content, which is adjusted according to the user's emotions. At this stage, the customization mechanism adjusts the content based on the results of the emotion engine. 【0716】 The generated content is analyzed using optimization tools, and SEO optimization is performed as needed. This process ensures that the content performs at its best on the digital platform. Finally, the server-generated content is delivered to the user via their device, and user review and feedback are requested. 【0717】 For example, if a user desires a calm tone when generating a post introducing a new product, the emotion engine will read the user's calm mood and adjust the tone of the text and visual elements accordingly. In this way, personalized content that matches the individual user's mood is quickly and efficiently generated. This feature enables businesses and creators to create more effective content that appeals to the emotions of their target audience. 【0718】 The following describes the processing flow. 【0719】 Step 1: 【0720】 Users use their devices to input their goals and conditions for content generation. For example, they might set the goal to "generate video advertisements for a new product." 【0721】 Step 2: 【0722】 The terminal sends user input information to the server. This transmission includes details about the goal and generation conditions. 【0723】 Step 3: 【0724】 The server analyzes the received information and activates the emotion engine. The emotion engine uses natural language processing technology to extract emotions from the user's text input and recognize the user's current emotional state. For example, if a user frequently uses words like "excited," a positive emotion will be recognized. 【0725】 Step 4: 【0726】 The server selects a generation algorithm based on emotional information obtained from the emotion engine. This selection is made with consideration to creating a tone and style that is appropriate for the user's emotional state. In the case of positive emotions, content with a bright and cheerful style is selected. 【0727】 Step 5: 【0728】 The server executes a selected generation algorithm to automatically generate content. For example, in the case of video generation, it adds visual effects and inserts audio and music based on a pre-prepared template. 【0729】 Step 6: 【0730】 The generated content is further refined through customization methods to match the user's emotions. Specifically, the colors, fonts, and tone of the narration are adjusted to create the atmosphere the user expects. 【0731】 Step 7: 【0732】 Next, the server analyzes the generated content using optimization techniques. Search engine optimization (SEO) technologies are applied, and settings are configured to maximize visibility and effectiveness, especially on digital platforms. 【0733】 Step 8: 【0734】 The final content is sent from the server to the terminal, where the user reviews the generated result. This provides an opportunity to offer feedback on the content. 【0735】 Step 9: 【0736】 Feedback collected from devices is returned to the server and used to improve future content generation algorithms through update mechanisms. This feedback information facilitates the evolution of the system to more accurately meet user needs. 【0737】 (Example 2) 【0738】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0739】 Conventional content generation technologies generate content uniformly without considering user emotions, making it difficult to provide personalized content tailored to the individual needs of users. Furthermore, the generated content is not sufficiently optimized to maximize performance on digital platforms, potentially leading to a decline in the quality of the user experience. 【0740】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0741】 In this invention, the server includes an information input means for the user to input target content generation conditions, an emotion analysis means for analyzing the user's emotional state, and an algorithm selection means for selecting the optimal generation algorithm. This enables the rapid generation of high-quality content that responds to the user's emotions and requests, as well as performance optimization. 【0742】 An "information input means" is an interface that provides users with the conditions for generating their target content. 【0743】 "Emotion analysis means" refers to a technology or process for determining a user's emotional state based on data received from them. 【0744】 An "algorithm selection means" is a device that has the function of selecting a generation algorithm that is suitable for the user's emotional state and requests. 【0745】 "Content generation means" refers to a technology or device that generates specific content based on a selected algorithm. 【0746】 "Content customization means" refers to a function that allows the generated content to be adjusted according to the user's emotional state and requests. 【0747】 "Performance optimization means" refers to methods or techniques for analyzing and optimizing the performance of generated content. 【0748】 An "information update mechanism" is a system that collects user feedback and uses it to improve the next generation process. 【0749】 This invention provides a system that, based on information input by the user regarding the conditions for generating target content, determines the user's emotional state and generates optimal content. 【0750】 The user accesses an information input method using a terminal and inputs prompt sentences to the generating AI model according to their purpose. For example, a prompt sentence such as "Please generate a new product introduction post in a calm tone" can be provided. 【0751】 The server processes the prompt message received from the terminal using sentiment analysis tools to analyze the user's emotional state. This analysis utilizes natural language processing technology to understand the emotional state in a way that is appropriate for the user's target content and style of expression. Subsequently, based on the analysis results, the server selects the optimal generation algorithm using algorithm selection tools. This is done based on both the user's emotional state and the generation conditions. 【0752】 The selected generation algorithm is used by the content generation means to generate specific content. The generated content encompasses a wide range of elements, including text and visuals, and is adjusted according to the user's emotional state through the content customization means. This process generates personalized content for each individual user. 【0753】 The generated content undergoes optimization processing to maximize its performance on digital platforms through performance optimization techniques. This processing includes search engine optimization (SEO) technologies to improve the visibility and effectiveness of the content. 【0754】 Ultimately, the generated content is delivered to the user via their device, and the user reviews it. Furthermore, the information update mechanism collects user feedback, which is used to improve the content generation process for the next time, resulting in higher quality content. 【0755】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0756】 Step 1: 【0757】 The user accesses an information input device via a terminal and enters a prompt message for the generating AI model. This entered prompt message indicates the user's target content generation conditions, including the specific type of content and expression style. An example of input is, "Generate a new product introduction post in a calm tone." At this stage, the terminal sends the prompt message to the server. 【0758】 Step 2: 【0759】 The server processes the prompt message received from the terminal using sentiment analysis. The sentiment analysis analyzes the content of the prompt message using natural language processing techniques to identify the user's emotional state. In this process, a language model extracts emotional keywords and phrases and classifies the emotional state based on them. The output is the user's emotional state (e.g., calm, excited). 【0760】 Step 3: 【0761】 The server uses an algorithm selection mechanism to choose the optimal generation algorithm based on the output of the sentiment analysis mechanism. Here, an algorithm that considers both the emotional state and the generation goal is selected. For example, if a calm tone is required, templates and styles for that purpose are applied. The selected algorithm is then used in the next content generation phase. 【0762】 Step 4: 【0763】 The server uses the algorithm selected by the algorithm selection mechanism to generate specific content through the content generation mechanism. This process utilizes a generation AI model to automatically create text and visual elements. The generated content is adjusted to reflect the user's emotional state and is faithful to the user's input conditions. 【0764】 Step 5: 【0765】 The generated content is evaluated using performance optimization techniques, and is optimized particularly for improving performance on digital platforms. SEO techniques are used to enhance content visibility through keyword placement and tagging. As a result, optimized content is generated. 【0766】 Step 6: 【0767】 The server sends optimized content to the terminal and provides it to the user. The user reviews the provided content and provides feedback as needed. This feedback is collected by an information update mechanism and used to improve the next generation process. 【0768】 (Application Example 2) 【0769】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0770】 Modern information delivery systems face the challenge of generating effective information that appeals to users' emotions because they do not adequately personalize content according to the user's emotional state. Furthermore, while performance optimization is necessary for generated content to be utilized to its fullest potential on digital platforms, there is a lack of efficient means to achieve this. 【0771】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0772】 In this invention, the server includes an input means for the user to input the conditions for generating target information, a selection means for selecting the necessary generation method based on the generation conditions received from the input means, and an adjustment means for adjusting the generated information according to the user's emotional state using identifier analysis. This makes it possible to efficiently generate and optimize information that matches the user's emotions. 【0773】 A "user" is an individual or organization that utilizes an information generation system. 【0774】 "Information generation conditions" are requirements that specify the content and characteristics of the information that the user wants to achieve. 【0775】 An "input method" is an interface for users to register information generation conditions with the system. 【0776】 A "generation method" is a technique or algorithm for assembling information based on given conditions. 【0777】 "Selection means" refers to an apparatus or method that determines the most appropriate generation method based on the input conditions. 【0778】 "Information" refers to digital content created based on the user's conditions and emotional state. 【0779】 "Identifier parsing" refers to the processing of identification information used to recognize a user's emotional state. 【0780】 "Emotional state" is an indicator that shows the user's current psychological state. 【0781】 "Adjustment means" refers to a device or method for changing the style and tone of generated information to match the user's emotional state. 【0782】 "Performance optimization" is the process of improving generated information so that it functions efficiently on a digital platform. 【0783】 This invention provides a system that dynamically generates and adjusts information in response to a user's emotions. This system is implemented using a terminal such as a smartphone or smart glasses. Specifically, it begins with the user inputting the conditions for the target information generation into the terminal. 【0784】 First, the terminal sends the input conditions to the server. Based on these conditions, the server selects an appropriate generation method and generates the information. The server uses an emotion recognition API (for example, an external cloud service that performs facial recognition) to analyze the user's emotional state. Based on the analyzed emotion data, a generation AI model is used to adjust the tone and style of the information. Tools such as content generation AI and image editing software are used to adjust the information. 【0785】 The generated information is further optimized by the server and its performance on the digital platform is improved using information retrieval technology. This ensures that information that matches the user's emotions is delivered most effectively. The optimized information is then transferred to the user's device and used. 【0786】 As a concrete example, consider a scenario where a user is using their smartphone in a shopping mall. The camera captures their facial expression, and the server recognizes this as happiness. Based on this emotional data, the server generates advertising content such as "Weekend Special Sale to Double Your Shopping Fun" and displays it on the user's smartphone. 【0787】 An example of a prompt message might be: "The user is currently smiling. Generate special and interesting ad content that shows he is enjoying himself." 【0788】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0789】 Step 1: 【0790】 The user uses a terminal to input the conditions for information generation. The entered conditions (e.g., the type and tone of the advertisement to be generated) are sent directly to the server. Input: Conditions for information generation; Output: Sending of conditions to the server. 【0791】 Step 2: 【0792】 The server selects a generation method based on the received conditions. A predefined algorithm is used for this selection to determine the optimal generation process. Input: Information generation conditions; Output: Selected generation method. 【0793】 Step 3: 【0794】 Next, the server sends the user's image data, captured by the device, to an emotion recognition API in the cloud to analyze the user's emotional state. This API uses facial recognition technology to identify emotions from the user's facial expressions. Input: Image data, Output: User's emotional state. 【0795】 Step 4: 【0796】 Based on the emotional state, the server uses a generative AI model to generate information (such as advertising content). The content and tone are adjusted to match the emotion, and the information is constructed. Here, the generative AI model uses prompt sentences to perform the generation. Input: Emotional state, prompt sentence; Output: Generated information. 【0797】 Step 5: 【0798】 The generated information is optimized by the server. This process utilizes information retrieval technology and performs optimization to maximize performance on the digital platform. Input: generated information, Output: optimized information. 【0799】 Step 6: 【0800】 The optimized information is sent back to the device and displayed to the user. This allows the user to access personalized information that matches their emotions. Input: Optimized information, Output: Displayed to the user. 【0801】 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0802】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0803】 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0804】 Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion. 【0805】 Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together. 【0806】 These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression. 【0807】 The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become. 【0808】 Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant. 【0809】 The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more." 【0810】 The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values. 【0811】 The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format. 【0812】 In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data. 【0813】 In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56. 【0814】 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. 【0815】 Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56. 【0816】 The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory. 【0817】 The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor. 【0818】 Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources. 【0819】 Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose. 【0820】 The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above. 【0821】 All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference. 【0822】 The following is further disclosed regarding the embodiments described above. 【0823】 (Claim 1) 【0824】 An input method for the user to input the conditions for generating the target content, 【0825】 A selection means for selecting a necessary generation algorithm based on the generation conditions received from the input means, 【0826】 A generation means that generates content using a generation algorithm selected by the selection means, 【0827】 A customization method for adjusting the generated content according to user requests, 【0828】 An optimization method that analyzes and optimizes the performance of generated content, 【0829】 A means of collecting user feedback and using it to improve the next generation algorithm, 【0830】 A system that includes this. 【0831】 (Claim 2) 【0832】 The system according to claim 1, wherein the optimization means improves the performance of generated content using search engine optimization technology. 【0833】 (Claim 3) 【0834】 The system according to claim 1, wherein the customization means is capable of adjusting the language and style of content based on user requirements. 【0835】 "Example 1" 【0836】 (Claim 1) 【0837】 A reception mechanism in which the user inputs the conditions for generating the target information, 【0838】 A determination means for selecting a necessary generation algorithm based on the generation conditions received from the aforementioned receiving means, 【0839】 A construction means that generates information using a generation algorithm selected by the aforementioned determination means, 【0840】 A means for adjusting the generated information according to the user's request, 【0841】 An evaluation method for analyzing and optimizing the performance of generated information, 【0842】 A means of improvement that collects user feedback and uses it to improve the next generation algorithm, 【0843】 An analysis method that interprets input content using natural language processing technology, 【0844】 A processing means for processing prompt sentences using a generative AI model, 【0845】 A system that includes this. 【0846】 (Claim 2) 【0847】 The system according to claim 1, wherein the evaluation means improves the performance of generated information using information retrieval technology. 【0848】 (Claim 3) 【0849】 The system according to claim 1, wherein the adjustment means is capable of adjusting the language and expression style of information based on the user's requirements. 【0850】 "Application Example 1" 【0851】 (Claim 1) 【0852】 An input means for the user to input the conditions for generating the target information material, 【0853】 A selection means for selecting a necessary generation algorithm based on the generation conditions received from the input means, 【0854】 A generation means that generates information material using a generation algorithm selected by the selection means, 【0855】 A customization method for adjusting the generated information material according to the user's requirements, 【0856】 An optimization method that analyzes and optimizes the performance of the generated information material, 【0857】 A means of collecting user feedback and using it to improve the next generation algorithm, 【0858】 A distribution method for delivering generated information materials to the user's followers or subscribers, 【0859】 A system that includes this. 【0860】 (Claim 2) 【0861】 The system according to claim 1, wherein the optimization means improves the performance of generated information material using digital communication optimization technology. 【0862】 (Claim 3) 【0863】 The system according to claim 1, wherein the customization means can adjust the language and style of expression of the information material based on user requirements and can also include visual elements. 【0864】 "Example 2 of combining an emotion engine" 【0865】 (Claim 1) 【0866】 An information input method in which the user inputs the target content generation conditions, 【0867】 An emotion analysis means analyzes the user's emotional state based on the generation conditions received from the information input means, 【0868】 An algorithm selection means for selecting the optimal generation algorithm based on the emotional state determined by the emotion analysis means, 【0869】 Content generation means that generates specific content using the generation algorithm selected by the algorithm selection means, 【0870】 A method for customizing generated content to adjust it according to the user's emotional state, 【0871】 A performance optimization method that analyzes the performance of generated content and optimizes it using search engine optimization techniques, 【0872】 A means of updating information to collect user feedback and use it to improve the next generation process, 【0873】 A system that includes this. 【0874】 (Claim 2) 【0875】 The system according to claim 1, wherein the performance optimization means maximizes content performance on a digital platform. 【0876】 (Claim 3) 【0877】 The system according to claim 1, wherein the content customization means is capable of adjusting the language and style of expression of the content based on the user's emotional state and requests. 【0878】 "Application example 2 when combining with an emotional engine" 【0879】 (Claim 1) 【0880】 An input means for the user to input the conditions for generating target information, 【0881】 A selection means for selecting the necessary generation method based on the generation conditions received from the input means, 【0882】 A generation means that generates information using the generation method selected by the selection means, 【0883】 An adjustment means that adjusts the generated information according to the user's emotional state using identifier analysis, 【0884】 An optimization method that evaluates and optimizes the performance of the generated information, 【0885】 A means of collecting user feedback and using it to improve the next generation method, 【0886】 A system that includes this. 【0887】 (Claim 2) 【0888】 The system according to claim 1, wherein the optimization means improves the performance of generated information using information retrieval technology. 【0889】 (Claim 3) 【0890】 The system according to claim 1, wherein the adjustment means is capable of adjusting the description and expression format of information based on the user's emotional state. [Explanation of Symbols] 【0891】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] An input method for the user to input the conditions for generating the target content, A selection means for selecting a necessary generation algorithm based on the generation conditions received from the input means, A generation means that generates content using a generation algorithm selected by the selection means, A customization method for adjusting the generated content according to user requests, An optimization method that analyzes and optimizes the performance of generated content, A means of collecting user feedback and using it to improve the next generation algorithm, A system that includes this. [Claim 2] The system according to claim 1, wherein the optimization means improves the performance of generated content using search engine optimization technology. [Claim 3] The system according to claim 1, wherein the customization means is capable of adjusting the language and style of content based on user requirements.

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