system

Abstract

We provide the system. [Solution] A means of receiving instructions from the user and extracting data for content generation, A means of analyzing received data using natural language processing technology and identifying key information, A means of generating content using a generative AI based on identified key information, A means of sending the generated content to the user, A system that includes this.

Description

Technical Field

[0001] The technology of this 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 that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional content marketing, it has been difficult for companies to create content such as high-quality and customized advertisements, blog articles, and social media posts quickly and efficiently. In particular, generating content adapted for various digital channels requires a lot of human resources and time, and there is a problem that the cost also increases. Furthermore, it has not been easy to perform customization according to the needs of individual companies while ensuring the quality of the content.

Means for Solving the Problems

[0005] This invention solves the above problems by providing a means for receiving instructions from a user and extracting data for content generation based on those instructions. Furthermore, it includes a means for analyzing the data using natural language processing technology to identify key information, and generates content using a generation AI based on that key information. The generated content is sent to the user, and can be modified and regenerated as needed. In addition, by providing a storage means for saving the generated content, it becomes reusable, enabling efficient content creation. As a result, companies can generate high-quality customized content in a short time and conduct marketing activities efficiently.

[0006] A "user" refers to an individual or legal entity that operates the system and provides instructions for content generation.

[0007] "Instructions" refer to information entered by the user regarding their requests and conditions concerning the type, purpose, and style of content they generate.

[0008] "Content creation" refers to the process of creating text and media that can be used for marketing activities, such as advertisements, blog posts, and social media posts.

[0009] "Data extraction" refers to the act of extracting information necessary for content generation from instructions received from the user.

[0010] "Natural language processing technology" refers to the technology that enables computers to understand human language and perform analysis and conversion.

[0011] "Key information" refers to important elements or characteristics that play a central role in content creation.

[0012] "Generative AI" refers to a program that uses artificial intelligence technology to automatically generate content based on user instructions.

[0013] "Sending" refers to the act of delivering the generated content to the user's device.

[0014] "Correction" refers to the act of incorporating user feedback and changes to the generated content.

[0015] "Regeneration" refers to the process of regenerating content that has already been created, based on user requests for modifications.

[0016] "Storage means" refers to a storage device or service for saving generated content and making it reusable later. [Brief explanation of the drawing]

[0017] [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]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It 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 Embodiment 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.

Modes for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0019] First, the language used in the following description will be explained.

[0020] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0021] 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.

[0022] 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.

[0023] 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).

[0024] 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."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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".

[0038] This invention is a system for supporting a company's content marketing activities, utilizing generative AI technology to automatically generate content based on user instructions. This system mainly consists of three main components: a server, a terminal, and a generative AI.

[0039] First, the user uses a terminal to input instructions specifying the type, purpose, keywords, tone, length, and other details of the content they want to generate. For example, if the user wants to create a promotional ad for a new product, they might input instructions such as, "I want to generate ad copy that highlights the product's features." This input is then sent to the server via the terminal.

[0040] Next, the server analyzes the received instructions. This analysis uses natural language processing techniques to extract important keywords and intentions from the user's input and format the analysis results into parameters necessary to pass them on to the generating AI.

[0041] Subsequently, based on the formatted data, the generative AI generates content. The generative AI retrieves relevant information from the database and automatically constructs text that matches the tone and style. For example, if the user requests a friendly tone, the generative AI will generate copy accordingly.

[0042] The generated content is then sent back to the user's device by the server. The user can review the provided content and request corrections as needed. The correction requests are sent back to the server for re-analysis and regeneration.

[0043] Furthermore, the generated content is saved to storage for later reuse. This allows users to generate content multiple times in a short period of time based on similar instructions.

[0044] This invention enables companies to efficiently generate high-quality, customized content applicable to various digital channels while significantly reducing the time and cost associated with content creation.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] The user uses their device to input instructions such as the type, purpose, keywords, tone, and length of the content they want to generate. The instructions are immediately sent to the server after input.

[0048] Step 2:

[0049] The server analyzes the instructions it receives. Using natural language processing techniques, it extracts important keywords and context, and formats them as parameters for content generation.

[0050] Step 3:

[0051] The server passes formatted parameters to the generating AI. The generating AI retrieves relevant information from the database and generates content that matches the specified tone and style.

[0052] Step 4:

[0053] The content generated by the AI ​​is sent to the server. The server packages that content and sends it to the user's device.

[0054] Step 5:

[0055] The user reviews the content received on their device. If there are any parts they are not satisfied with, they can send instructions to the server for correction.

[0056] Step 6:

[0057] The server receives the correction instructions and performs analysis and regeneration again. It then regenerates the content reflecting the corrections and sends it to the user again.

[0058] Step 7:

[0059] The final approved content is saved on the device. This saved content can be easily accessed later for use in marketing campaigns.

[0060] (Example 1)

[0061] 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."

[0062] In recent years, efficient and high-quality content generation has become a crucial factor in determining a company's competitiveness in the content market. However, existing methods are time-consuming and costly, and it is difficult to respond flexibly to individual content needs. Furthermore, it is difficult to accurately reflect the tone and style that users desire, making it impossible to quickly create content that matches a company's brand image. There is a need to solve these problems and automate and improve the accuracy of content generation.

[0063] 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.

[0064] In this invention, the server includes means for receiving instructions from a user via a terminal and extracting information for content generation; means for analyzing the received information using natural language processing technology, identifying important information, and forming prompt sentences suitable for a generative AI model; and means for automatically generating content using the generative AI model based on the identified important information. This enables companies to generate high-quality, customized content in a short time and respond quickly to market needs.

[0065] A "terminal" is an electronic device used by users to input information and exchange instructions and data with a server.

[0066] A "server" is a central processing unit that analyzes information received from users and generates content in conjunction with a generation AI model.

[0067] A "user" is an entity that provides instructions to the system via a terminal for content generation tailored to a specific purpose.

[0068] "Natural language processing technology" is a technology that interprets user instructions linguistically and extracts keywords and intentions that are relevant to the purpose.

[0069] "Important information" refers to keywords and parameters required when the AI ​​model generates content based on user instructions.

[0070] A "generative AI model" is an algorithm that automatically generates content based on a specified prompt.

[0071] A "prompt statement" is an instruction that a generative AI model uses as a basis for generating content.

[0072] "Automatic generation" refers to the process by which a system creates content based on pre-set conditions and specifications, without requiring manual input.

[0073] "Content" refers to a collection of information generated to meet the user's needs and objectives, and includes text and written documents.

[0074] This invention is a system for businesses to efficiently generate high-quality content. The system primarily consists of a server, terminals, and a generation AI model, with users providing content generation instructions via the terminals.

[0075] The user inputs specific instructions, such as the type, purpose, keywords, tone, and length of the content they want to generate, through an interface on their device. The device converts these instructions into digital data and sends it to the server.

[0076] The server processes the received data using natural language processing techniques to extract important information. This analysis can be performed using open-source natural language processing libraries (e.g., spaCy, NLTK). Based on this important information, the server forms prompt sentences suitable for the generative AI model. For this generative AI model, advanced algorithms such as the GPT (Generative Pretrained Transformer) series are used.

[0077] The generative AI model retrieves information from relevant databases based on the generated prompt text and automatically generates content that matches the specified tone and style. For example, if a user enters the prompt text, "Create advertising copy for a new environmentally friendly product in a friendly tone," the generative AI model will construct advertising copy that conforms to that tone.

[0078] The generated content is sent back to the user's device via the server, where the user can review it. If necessary, the user can request revisions, and the server can then analyze and regenerate the content again. This process allows users to efficiently obtain customized content tailored to their company's brand image and needs. In particular, to quickly respond to individual projects and campaigns, the generated content is stored on the server and can be reused.

[0079] In this way, this invention helps companies quickly and effectively deliver optimal content for digital channels.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The user uses a terminal to enter details about the content they want to generate. These details include the content type, purpose, keywords, tone, and length. This information is organized digitally as data packets and sent to the server.

[0083] Step 2:

[0084] The server analyzes data packets received from the user. Using natural language processing techniques, it extracts important information and keywords from the digital data. In this process, it utilizes open-source natural language processing libraries to perform data computation and information extraction, and to form prompt sentences suitable for the generative AI model.

[0085] Step 3:

[0086] The generative AI model receives prompt text generated by the server. Based on this prompt text, the generative AI model collects necessary information from relevant databases and automatically generates content based on the specified tone and style. The generated content is returned to the server as a digital output.

[0087] Step 4:

[0088] The server receives the content sent from the generating AI model and resends this content to the user's device. The user can review the content on their device and provide additional correction instructions if it does not meet their expectations.

[0089] Step 5:

[0090] When a user enters a correction instruction, the device sends the correction to the server. The server then analyzes the correction data again and forms the necessary new prompt sentences. Finally, it uses a generative AI model to regenerate the content and sends the new content to the user's device.

[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 content marketing, companies need to generate advertising content quickly and effectively. However, traditional generation processes often involve manual processes, resulting in time-consuming and costly methods. Furthermore, generating target-specific content in real time was difficult in situations where immediate response capabilities are required in sales environments.

[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 means for receiving instructions from the user and extracting information for content generation, means for analyzing the received information and identifying specific information using natural language processing technology, and means for creating content using a generation AI based on the identified specific information. This enables sales representatives to generate appropriate advertising content in real time on-site using a portable display device and present it to clients immediately.

[0096] "A means of receiving instructions from the user and extracting information for content generation" refers to a means of receiving elements related to the content that the user wishes to generate, and then extracting the necessary information.

[0097] "A means of analyzing received information and identifying specific information using natural language processing technology" refers to a means of analyzing information provided by the user and utilizing natural language processing technology to identify important keywords and intentions.

[0098] "Means of creating content using generative AI based on identified specific information" refers to methods of automatically creating text and expressions that match the tone and style, using generative AI technology based on specific information.

[0099] "Means of providing the created content to users" refers to means of communicating the generated content so that users can review it.

[0100] "Means of providing real-time generated content to the visual eye via a portable display device" refers to means of providing generated content as visual information immediately using portable devices such as smart glasses or head-mounted displays.

[0101] "Means for receiving user feedback on modifications to generated content and regenerating it" refers to a means of receiving user instructions for modifications to generated content and regenerating the content based on those instructions.

[0102] "Means of storage for recording generated content" refers to means of saving generated content in a database or storage device and retaining it for later use or reference.

[0103] The system that realizes this invention mainly consists of a server, a terminal used by the user, and a portable display device. Specific examples include smart glasses and head-mounted displays. The user uses the terminal to input information related to the content they want to generate, such as purpose, tone, and keywords. This information is sent to the server and analyzed using natural language processing technology. Based on the specific information extracted through the analysis, the generating AI combines the relevant information to create content.

[0104] The generated content is delivered from the server to the user's terminal and, if necessary, is also provided visually in real time via a portable display device. This system enables sales representatives and others to instantly generate appropriate content on-site and present it quickly to clients.

[0105] As a concrete example, consider the case of generating advertising copy for a new smartphone. Sales representatives can use smart glasses to view the generated content in real time and present content tailored to the client's needs on the spot. An example of a prompt sentence to input into the generation AI model is, "Advertising copy for a new smartphone. Use a friendly tone and emphasize long battery life and a high-resolution camera." In this way, the system enables rapid content generation and supports immediate responses in the sales field.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The user uses a terminal to input information about the content they want to generate. This information includes the purpose, tone, keywords, and prompts. The user's role is clear, as the input information is sent to the server.

[0109] Step 2:

[0110] The server analyzes the information received from the user using natural language processing technology. This analysis identifies important parameters and keywords, and formats the data for transmission to the generating AI. The input is the information provided by the user, and the output is the formatted data.

[0111] Step 3:

[0112] The server sends formatted data to the generative AI to create content. The generative AI model retrieves relevant information from the database and automatically generates content that matches the required tone and style. The output is the generated content.

[0113] Step 4:

[0114] The server resends the generated content to the user's device. The user can review the generated content through their device and provide feedback for corrections as needed. The input is the generated content, and the output is the user's feedback and corrections.

[0115] Step 5:

[0116] When a user requests a correction, the server re-analyzes the correction information and uses a generation AI to regenerate the content. The input is the correction request, and the output is the regenerated content.

[0117] Step 6:

[0118] Ultimately, if the generated content satisfies the user, it is saved to storage. The server manages the saved content for future reference and reuse. The input is the final version of the content, and the output is the saved data.

[0119] 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.

[0120] This invention is a system that automatically generates content based on user instructions, and in particular, it incorporates an emotion engine to recognize the user's emotions and improve the quality of the generated content. This system mainly consists of four main components: a server, a terminal, a generation AI, and an emotion engine.

[0121] First, the user uses a device to input instructions about the content they want to generate. For example, when creating an advertisement for a new product, they might specify a request such as "I want to introduce the product in a tone that will delight customers." This input is then sent to the server via the device.

[0122] Next, the server analyzes the received instructions. Using natural language processing techniques, it extracts important keywords and context from the instructions and passes them to the emotion engine to recognize the user's emotions. This emotion information is then used by the generative AI to adjust the tone and style when creating content.

[0123] The generative AI generates content based on analyzed parameters and emotional information passed from the server. For example, if the emotion of joy is recognized, it can construct text in a bright and friendly tone that matches that emotion.

[0124] The generated content is sent to the user's device by the server. The user can review this content and send correction instructions to the server if necessary. The server receives the correction instructions and performs analysis and regeneration again.

[0125] Furthermore, the generated content is saved to storage, increasing its reusability. This allows users to quickly generate new content under similar conditions.

[0126] The emotion engine further tracks changes in the user's emotions and provides feedback throughout the entire generation process. This feedback allows the generation AI to create content that is more closely aligned with the user's emotions.

[0127] This invention enables companies to efficiently generate high-quality, customized content that takes user emotions into consideration during content creation, thereby making their marketing activities more effective.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The user uses their device to input instructions such as the type of content they want to generate, its purpose, keywords, tone, and emotional nuances. This input is then sent from the device to the server.

[0131] Step 2:

[0132] The server analyzes user instructions received using natural language processing technology and extracts the data necessary for content generation. This analyzed data is then passed to an emotion engine to recognize the user's emotions.

[0133] Step 3:

[0134] The server combines emotional information obtained from the emotion engine with analyzed data to shape the parameters for generating content. This includes the emotional tone and style intended by the user.

[0135] Step 4:

[0136] A generation AI on the server generates content based on formatted parameters. The generation AI retrieves relevant information from a database and constructs text that matches the user's emotions.

[0137] Step 5:

[0138] The server sends the generated content to the terminal. The user can review the content on the terminal and enter instructions for corrections to any parts they are not satisfied with.

[0139] Step 6:

[0140] The server receives the correction instructions and performs analysis and regeneration again. The regenerated content, reflecting the corrections, is then sent back to the terminal.

[0141] Step 7:

[0142] The final approved content is saved on the device and retained in cloud or local storage. The saved content can be reused later or used in other marketing activities.

[0143] Step 8:

[0144] The emotion engine continuously monitors changes in the user's emotions and provides feedback to the future content creation process. This enables content creation that responds to user emotions with greater accuracy.

[0145] (Example 2)

[0146] 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 as the "terminal".

[0147] In today's information society, the demand for personalized, high-quality content is rapidly increasing. However, traditional content creation methods struggle to reflect user emotions, resulting in difficulties in meeting user expectations. Furthermore, there is the challenge of low reusability of once-generated content, making it difficult to quickly adapt to new conditions.

[0148] 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.

[0149] In this invention, the server includes means for receiving instructions from the user and extracting information for content generation, means for analyzing the received information using natural language processing technology and identifying important information, and means for recognizing the user's emotions using an emotion engine based on the identified information. This enables the rapid generation and efficient reuse of personalized, high-quality content that reflects the user's emotions.

[0150] A "user" refers to someone who uses the system to give instructions for content generation.

[0151] "Instructions" refer to information that includes requirements and conditions regarding the content the user wants to generate.

[0152] "Content" refers to representations of information generated based on user instructions, including text and visual content.

[0153] "Natural language processing technology" refers to the technology that enables computers to understand, analyze, and generate human natural language.

[0154] "Important information" refers to keywords and context necessary for content creation.

[0155] An "emotion engine" refers to a technological element that recognizes emotions from user input and reflects them in content generation.

[0156] "Generative AI" refers to artificial intelligence technology that automatically generates content using machine learning techniques.

[0157] "Storage device means" refers to a storage function for storing the generated content.

[0158] This invention is designed as a system that enables the automatic generation of personalized content. The system mainly consists of four main components: a server, a terminal, a generation AI, and an emotion engine.

[0159] The user uses a terminal to input instructions about the content they want to generate. These instructions include specific conditions and requests. For example, they might enter a prompt such as, "I want to introduce the product in a tone that will delight customers." This input is then sent from the terminal to the server.

[0160] The server uses natural language processing techniques to analyze the received instructions. It extracts important information, which the emotion engine then uses to recognize the user's emotions. This process may utilize open-source natural language processing libraries and emotion analysis software.

[0161] The generative AI generates content based on emotional information obtained from an emotion engine. For example, if a user requests the emotion of "joy," the generative AI will create text and images in a bright and friendly tone. The generative AI implements a commonly used AI model platform.

[0162] The generated content is sent from the server to the user's terminal, where the user can review it. If necessary, the user can provide further correction instructions, and the system will regenerate the content based on those instructions. The created content is also saved to storage for future reuse.

[0163] This system allows companies to quickly and efficiently generate content that takes user emotions into account, thereby improving the quality of their marketing activities.

[0164] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0165] Step 1:

[0166] The user uses a terminal to input instructions about the content they want to generate. For example, a prompt might say, "I want to introduce the product in a tone that will delight customers." This input data is sent to the terminal through the user interface and then transferred to the server.

[0167] Step 2:

[0168] The server receives the prompt message sent from the terminal and begins analysis using natural language processing techniques. It receives the prompt message as input data and uses a keyword extraction algorithm to identify important information and context. The data extracted through analysis is then passed on to the next sentiment recognition step.

[0169] Step 3:

[0170] The server sends the data obtained through analysis to the emotion engine to recognize the user's intended emotion. The input consists of analyzed keywords and context. The emotion engine generates emotion information using a recognition algorithm and uses it as output for the generative AI.

[0171] Step 4:

[0172] The generative AI generates content based on sentiment information and prompt analysis data provided by the server. It uses sentiment information and context as input and generates text and visual content using a machine learning model. The output content is optimized based on pre-set tone and style.

[0173] Step 5:

[0174] The server sends the content generated by the AI ​​to the user's device. The output content can be reviewed again through the user interface and is provided for review and feedback. The user can review the results and send instructions for corrections to the server as needed.

[0175] Step 6:

[0176] When a user enters instructions for correction, the terminal sends new instructions to the server. The server re-analyzes the instructions and makes corrections or additions to the prompts. If necessary, it uses a generative AI to regenerate the content and produce new output.

[0177] Step 7:

[0178] Finally, the server saves the final verified content to storage. This saving process makes the generated content reusable in the future, which is useful for generating new content under similar conditions. Metadata may also be saved during the saving process.

[0179] (Application Example 2)

[0180] 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".

[0181] In recent years, generating effective information that appeals to emotions has become increasingly important in the information society. Especially in fields like advertising, there is a demand for information that accurately captures the emotions of the target audience. However, generating optimal information that responds to the emotions of the audience quickly and efficiently is difficult. Traditional methods require considerable time and effort, making productivity improvements a challenge.

[0182] 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.

[0183] In this invention, the server includes means for receiving instructions from the user and extracting basic data for information generation; means for analyzing the received basic data using natural language processing technology and identifying important information; and means for generating information using a generation AI based on the identified important information and emotional information, and adjusting it using an emotion engine. This makes it possible to generate high-quality information that is tailored to the emotions of the recipient.

[0184] "Receiving instructions from the user" means obtaining information and requests entered by the user through the terminal.

[0185] "Means for extracting basic data for information generation" refers to technologies and devices that extract the elements and metadata necessary for information generation from data received from users.

[0186] "Natural language processing technology" is a system of technologies that enables computers to understand and analyze human language, and it primarily deals with text data.

[0187] "Means for identifying important information" refer to technologies and devices that analyze received data and select elements that play a central role in information generation.

[0188] "Means of generating information using generative AI" refers to technologies and devices that utilize artificial intelligence technology to create new information and content based on given important information and additional data.

[0189] An "emotion engine" is a program or algorithm that infers a user's emotional state based on received data and user information, and takes that emotion into consideration when generating information.

[0190] "A means of generating information using generative AI based on emotional information and adjusting it with an emotional engine" refers to a technology or process that reflects the user's emotions in the generated information and modifies it to the optimal content and tone.

[0191] The system of this invention mainly consists of a terminal, a server, a generative AI, and an emotion engine. The user first uses the terminal to input instructions regarding information generation. These instructions are intended to evoke a certain emotion, such as "I want to surprise people with advertisements." The terminal receives these instructions and sends them to the server.

[0192] When the server receives instructions from a user, it analyzes those instructions using natural language processing technology. The analysis uses tools such as the Google® Cloud Natural Language API to extract important information and identify emotions. This important information and emotional information is then used by a generative AI to generate information.

[0193] The generative AI utilizes OpenAI's GPT model and other technologies, and is responsible for the process of generating information based on identified key information and emotions. The generative AI generates information in text or other formats, and an emotion engine is used to refine that information. The emotion engine fine-tunes the tone and expression of the generated information to match the user's emotions, completing the final information. The completed information is sent from the server to the user's terminal, where the user can review it.

[0194] This system is particularly effective in generating information that heavily relies on emotions, such as advertising. For example, if a new product campaign advertisement targets emotions like "surprise" or "joy," the system automatically generates content tailored to those emotions.

[0195] An example of a prompt message might be: "Generate a campaign ad for the launch of a new smartphone. Include key features and special offers, using a tone that conveys feelings of surprise and excitement." This allows users to perform complex information generation processes in a simple manner.

[0196] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0197] Step 1:

[0198] The user enters instructions for information generation on a terminal. These instructions include the subject and emotional tone of the information to be generated. The terminal converts these instructions into a digital format, preparing them for transmission to the server.

[0199] Step 2:

[0200] The server receives instructions sent from the terminal. To analyze the received data, it uses natural language processing techniques to extract keywords and sentiment information. Tools such as the Google Cloud Natural Language API are used here, which clarify important information and emotional tone. As output, the extracted data is prepared as input for a generating AI.

[0201] Step 3:

[0202] The server generates information based on important and emotional information extracted using a generative AI. The generative AI used is OpenAI's GPT model, among others, which generates optimal information content according to the instructions. The generated content is temporarily stored on the server and then proceeds to an adjustment process by the emotion engine.

[0203] Step 4:

[0204] The emotion engine analyzes the generated information content and adjusts the tone and style based on the emotional information. At this stage, final adjustments are made to ensure that the generated information appropriately reflects the user's intended emotions. The adjusted information content is then finalized on the server.

[0205] Step 5:

[0206] Once the information content is adjusted, the server sends the final version to the terminal. The terminal displays the received information to the user, who then reviews the generated information. If the user wishes to make corrections, the feedback is sent back to the server, and the process from step 2 is repeated as needed.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] [Second Embodiment]

[0211] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0212] 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.

[0213] 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).

[0214] 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.

[0215] 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.

[0216] 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).

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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".

[0223] This invention is a system for supporting a company's content marketing activities, utilizing generative AI technology to automatically generate content based on user instructions. This system mainly consists of three main components: a server, a terminal, and a generative AI.

[0224] First, the user uses a terminal to input instructions specifying the type, purpose, keywords, tone, length, and other details of the content they want to generate. For example, if the user wants to create a promotional ad for a new product, they might input instructions such as, "I want to generate ad copy that highlights the product's features." This input is then sent to the server via the terminal.

[0225] Next, the server analyzes the received instructions. This analysis uses natural language processing techniques to extract important keywords and intentions from the user's input and format the analysis results into parameters necessary to pass them on to the generating AI.

[0226] Subsequently, based on the formatted data, the generative AI generates content. The generative AI retrieves relevant information from the database and automatically constructs text that matches the tone and style. For example, if the user requests a friendly tone, the generative AI will generate copy accordingly.

[0227] The generated content is then sent back to the user's device by the server. The user can review the provided content and request corrections as needed. The correction requests are sent back to the server for re-analysis and regeneration.

[0228] Furthermore, the generated content is saved to storage for later reuse. This allows users to generate content multiple times in a short period of time based on similar instructions.

[0229] This invention enables companies to efficiently generate high-quality, customized content applicable to various digital channels while significantly reducing the time and cost associated with content creation.

[0230] The following describes the processing flow.

[0231] Step 1:

[0232] The user uses their device to input instructions such as the type, purpose, keywords, tone, and length of the content they want to generate. The instructions are immediately sent to the server after input.

[0233] Step 2:

[0234] The server analyzes the instructions it receives. Using natural language processing techniques, it extracts important keywords and context, and formats them as parameters for content generation.

[0235] Step 3:

[0236] The server passes formatted parameters to the generating AI. The generating AI retrieves relevant information from the database and generates content that matches the specified tone and style.

[0237] Step 4:

[0238] The content generated by the AI ​​is sent to the server. The server packages that content and sends it to the user's device.

[0239] Step 5:

[0240] The user reviews the content received on their device. If there are any parts they are not satisfied with, they can send instructions to the server for correction.

[0241] Step 6:

[0242] The server receives the correction instructions and performs analysis and regeneration again. It then regenerates the content reflecting the corrections and sends it to the user again.

[0243] Step 7:

[0244] The final approved content is saved on the device. This saved content can be easily accessed later for use in marketing campaigns.

[0245] (Example 1)

[0246] 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."

[0247] In recent years, efficient and high-quality content generation has become a crucial factor in determining a company's competitiveness in the content market. However, existing methods are time-consuming and costly, and it is difficult to respond flexibly to individual content needs. Furthermore, it is difficult to accurately reflect the tone and style that users desire, making it impossible to quickly create content that matches a company's brand image. There is a need to solve these problems and automate and improve the accuracy of content generation.

[0248] 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.

[0249] In this invention, the server includes means for receiving instructions from a user via a terminal and extracting information for content generation; means for analyzing the received information using natural language processing technology, identifying important information, and forming prompt sentences suitable for a generative AI model; and means for automatically generating content using the generative AI model based on the identified important information. This enables companies to generate high-quality, customized content in a short time and respond quickly to market needs.

[0250] A "terminal" is an electronic device used by users to input information and exchange instructions and data with a server.

[0251] A "server" is a central processing unit that analyzes information received from users and generates content in conjunction with a generation AI model.

[0252] A "user" is an entity that provides instructions to the system via a terminal for content generation tailored to a specific purpose.

[0253] "Natural language processing technology" is a technology that interprets user instructions linguistically and extracts keywords and intentions that are relevant to the purpose.

[0254] "Important information" refers to keywords and parameters required when the AI ​​model generates content based on user instructions.

[0255] A "generative AI model" is an algorithm that automatically generates content based on a specified prompt.

[0256] A "prompt statement" is an instruction that a generative AI model uses as a basis for generating content.

[0257] "Automatic generation" refers to the process by which a system creates content based on pre-set conditions and specifications, without requiring manual input.

[0258] "Content" refers to a collection of information generated to meet the user's needs and objectives, and includes text and written documents.

[0259] This invention is a system for businesses to efficiently generate high-quality content. The system primarily consists of a server, terminals, and a generation AI model, with users providing content generation instructions via the terminals.

[0260] The user inputs specific instructions, such as the type, purpose, keywords, tone, and length of the content they want to generate, through an interface on their device. The device converts these instructions into digital data and sends it to the server.

[0261] The server processes the received data using natural language processing techniques to extract important information. This analysis can be performed using open-source natural language processing libraries (e.g., spaCy, NLTK). Based on this important information, the server forms prompt sentences suitable for the generative AI model. For this generative AI model, advanced algorithms such as the GPT (Generative Pretrained Transformer) series are used.

[0262] The generative AI model retrieves information from relevant databases based on the generated prompt text and automatically generates content that matches the specified tone and style. For example, if a user enters the prompt text, "Create advertising copy for a new environmentally friendly product in a friendly tone," the generative AI model will construct advertising copy that conforms to that tone.

[0263] The generated content is sent back to the user's device via the server, where the user can review it. If necessary, the user can request revisions, and the server can then analyze and regenerate the content again. This process allows users to efficiently obtain customized content tailored to their company's brand image and needs. In particular, to quickly respond to individual projects and campaigns, the generated content is stored on the server and can be reused.

[0264] In this way, this invention helps companies quickly and effectively deliver optimal content for digital channels.

[0265] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0266] Step 1:

[0267] The user uses a terminal to enter details about the content they want to generate. These details include the content type, purpose, keywords, tone, and length. This information is organized digitally as data packets and sent to the server.

[0268] Step 2:

[0269] The server analyzes data packets received from the user. Using natural language processing techniques, it extracts important information and keywords from the digital data. In this process, it utilizes open-source natural language processing libraries to perform data computation and information extraction, and to form prompt sentences suitable for the generative AI model.

[0270] Step 3:

[0271] The generative AI model receives prompt text generated by the server. Based on this prompt text, the generative AI model collects necessary information from relevant databases and automatically generates content based on the specified tone and style. The generated content is returned to the server as a digital output.

[0272] Step 4:

[0273] The server receives the content sent from the generating AI model and resends this content to the user's device. The user can review the content on their device and provide additional correction instructions if it does not meet their expectations.

[0274] Step 5:

[0275] When a user enters a correction instruction, the device sends the correction to the server. The server then analyzes the correction data again and forms the necessary new prompt sentences. Finally, it uses a generative AI model to regenerate the content and sends the new content to the user's device.

[0276] (Application Example 1)

[0277] 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 glasses 214 will be referred to as the "terminal."

[0278] In content marketing, companies need to generate advertising content quickly and effectively. However, traditional generation processes often involve manual processes, resulting in time-consuming and costly methods. Furthermore, generating target-specific content in real time was difficult in situations where immediate response capabilities are required in sales environments.

[0279] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following means.

[0280] In this invention, the server includes means for receiving an instruction from a user and extracting information for content generation, means for analyzing the received information using natural language processing technology to identify specific information, and means for creating content using a generation AI based on the identified specific information. As a result, a salesperson can use a portable display device to generate appropriate advertising content in real time at the scene and immediately present it to the client.

[0281] The means for "receiving an instruction from a user and extracting information for content generation" is a means for receiving an element related to the content that the user desires to generate by inputting the element and extracting the necessary information.

[0282] The means for "analyzing the received information using natural language processing technology to identify specific information" is a means for analyzing the information provided by the user and utilizing natural language processing technology to identify important keywords and intentions.

[0283] The means for "creating content using a generation AI based on the identified specific information" is a means for automatically creating sentences and expressions according to tone and style by utilizing generation AI technology based on the specific information.

[0284] The means for "providing the created content to the user" is a means for transmitting the generated content so that the user can confirm it.

[0285] The means for "providing the content generated in real time visually through a portable display device" is a means for immediately providing the generated content as visual information using a portable device such as smart glasses or a head-mounted display.

[0286] The "means for receiving modifications to the generated content from the user and performing regeneration" refers to means for receiving a modification instruction for the content generated by the user and regenerating the content based on the instruction.

[0287] The "storage means for recording the generated content" refers to means for storing the generated content in a database or storage and retaining it for later use or reference.

[0288] The system for implementing this invention mainly consists of a server, a terminal used by the user, and a portable display device. Specific examples include smart glasses and head-mounted displays. The user inputs information related to the content to be generated using the terminal, such as the purpose, tone, keywords, etc. This information is transmitted to the server and analyzed using natural language processing technology. Based on the specific information extracted by the analysis, the generation AI combines relevant information to create content.

[0289] The generated content is provided from the server to the user's terminal, and furthermore, visually provided in real time through the portable display device as needed. With this system, it is possible for sales staff, etc. to immediately generate appropriate content on-site and promptly present it to the client.

[0290] As a specific example of the explanation, consider the case of generating an advertisement copy for a new smartphone. The salesperson can use smart glasses to confirm the content generated in real time and present content according to the client's request on the spot. An example of the prompt sentence input to the generation AI model is "Advertisement copy for a new smartphone. In a friendly tone, emphasizing a long battery life and a high-resolution camera." In this way, the system enables rapid content generation and supports immediate response at the sales site.

[0291] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0292] Step 1:

[0293] The user uses a terminal to input information about the content they want to generate. This information includes the purpose, tone, keywords, and prompts. The user's role is clear, as the input information is sent to the server.

[0294] Step 2:

[0295] The server analyzes the information received from the user using natural language processing technology. This analysis identifies important parameters and keywords, and formats the data for transmission to the generating AI. The input is the information provided by the user, and the output is the formatted data.

[0296] Step 3:

[0297] The server sends formatted data to the generative AI to create content. The generative AI model retrieves relevant information from the database and automatically generates content that matches the required tone and style. The output is the generated content.

[0298] Step 4:

[0299] The server resends the generated content to the user's device. The user can review the generated content through their device and provide feedback for corrections as needed. The input is the generated content, and the output is the user's feedback and corrections.

[0300] Step 5:

[0301] When a user requests a correction, the server re-analyzes the correction information and uses a generation AI to regenerate the content. The input is the correction request, and the output is the regenerated content.

[0302] Step 6:

[0303] Finally, if the generated content satisfies the user, it is saved to storage. The server manages the saved content for future reference and reuse. The input is the final version of the content, and the output is the saved data.

[0304] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0305] The present invention is a system for automatically generating content based on an instruction from a user, and in particular, it combines an emotion engine to recognize the user's emotion and improve the quality of the generated content. This system is mainly composed of four main components: a server, a terminal, a generation AI, and an emotion engine.

[0306] First, the user uses the terminal to input an instruction regarding the content to be generated. For example, when creating an advertisement for a new product, a desire such as "want to introduce the product in a tone that gives customers joy" can be conveyed. This input is sent to the server through the terminal.

[0307] Next, the server analyzes the received instruction. Using natural language processing technology, important keywords and contexts are extracted from the instruction and passed to the emotion engine to recognize the user's emotion. This emotion information is utilized for adjusting the tone and style when the generation AI creates content.

[0308] The generation AI generates content based on the analyzed parameters and emotion information passed from the server. For example, when a happy emotion is recognized, a bright and friendly tone can be used to construct the text.

[0309] The generated content is sent to the user's device by the server. The user can review this content and send correction instructions to the server if necessary. The server receives the correction instructions and performs analysis and regeneration again.

[0310] Furthermore, the generated content is saved to storage, increasing its reusability. This allows users to quickly generate new content under similar conditions.

[0311] The emotion engine further tracks changes in the user's emotions and provides feedback throughout the entire generation process. This feedback allows the generation AI to create content that is more closely aligned with the user's emotions.

[0312] This invention enables companies to efficiently generate high-quality, customized content that takes user emotions into consideration during content creation, thereby making their marketing activities more effective.

[0313] The following describes the processing flow.

[0314] Step 1:

[0315] The user uses their device to input instructions such as the type of content they want to generate, its purpose, keywords, tone, and emotional nuances. This input is then sent from the device to the server.

[0316] Step 2:

[0317] The server analyzes user instructions received using natural language processing technology and extracts the data necessary for content generation. This analyzed data is then passed to an emotion engine to recognize the user's emotions.

[0318] Step 3:

[0319] The server combines emotional information obtained from the emotion engine with analyzed data to shape the parameters for generating content. This includes the emotional tone and style intended by the user.

[0320] Step 4:

[0321] A generation AI on the server generates content based on formatted parameters. The generation AI retrieves relevant information from a database and constructs text that matches the user's emotions.

[0322] Step 5:

[0323] The server sends the generated content to the terminal. The user can review the content on the terminal and enter instructions for corrections to any parts they are not satisfied with.

[0324] Step 6:

[0325] The server receives the correction instructions and performs analysis and regeneration again. The regenerated content, reflecting the corrections, is then sent back to the terminal.

[0326] Step 7:

[0327] The final approved content is saved on the device and retained in cloud or local storage. The saved content can be reused later or used in other marketing activities.

[0328] Step 8:

[0329] The emotion engine continuously monitors changes in the user's emotions and provides feedback to the future content creation process. This enables content creation that responds to user emotions with greater accuracy.

[0330] (Example 2)

[0331] 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".

[0332] In today's information society, the demand for personalized, high-quality content is rapidly increasing. However, traditional content creation methods struggle to reflect user emotions, resulting in difficulties in meeting user expectations. Furthermore, there is the challenge of low reusability of once-generated content, making it difficult to quickly adapt to new conditions.

[0333] 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.

[0334] In this invention, the server includes means for receiving instructions from the user and extracting information for content generation, means for analyzing the received information using natural language processing technology and identifying important information, and means for recognizing the user's emotions using an emotion engine based on the identified information. This enables the rapid generation and efficient reuse of personalized, high-quality content that reflects the user's emotions.

[0335] A "user" refers to someone who uses the system to give instructions for content generation.

[0336] "Instructions" refer to information that includes requirements and conditions regarding the content the user wants to generate.

[0337] "Content" refers to representations of information generated based on user instructions, including text and visual content.

[0338] "Natural language processing technology" refers to the technology that enables computers to understand, analyze, and generate human natural language.

[0339] "Important information" refers to keywords and context necessary for content creation.

[0340] An "emotion engine" refers to a technological element that recognizes emotions from user input and reflects them in content generation.

[0341] "Generative AI" refers to artificial intelligence technology that automatically generates content using machine learning techniques.

[0342] "Storage device means" refers to a storage function for storing the generated content.

[0343] This invention is designed as a system that enables the automatic generation of personalized content. The system mainly consists of four main components: a server, a terminal, a generation AI, and an emotion engine.

[0344] The user uses a terminal to input instructions about the content they want to generate. These instructions include specific conditions and requests. For example, they might enter a prompt such as, "I want to introduce the product in a tone that will delight customers." This input is then sent from the terminal to the server.

[0345] The server uses natural language processing techniques to analyze the received instructions. It extracts important information, which the emotion engine then uses to recognize the user's emotions. This process may utilize open-source natural language processing libraries and emotion analysis software.

[0346] The generative AI generates content based on emotional information obtained from an emotion engine. For example, if a user requests the emotion of "joy," the generative AI will create text and images in a bright and friendly tone. The generative AI implements a commonly used AI model platform.

[0347] The generated content is sent from the server to the user's terminal, where the user can review it. If necessary, the user can provide further correction instructions, and the system will regenerate the content based on those instructions. The created content is also saved to storage for future reuse.

[0348] This system allows companies to quickly and efficiently generate content that takes user emotions into account, thereby improving the quality of their marketing activities.

[0349] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0350] Step 1:

[0351] The user uses a terminal to input instructions about the content they want to generate. For example, a prompt might say, "I want to introduce the product in a tone that will delight customers." This input data is sent to the terminal through the user interface and then transferred to the server.

[0352] Step 2:

[0353] The server receives the prompt message sent from the terminal and begins analysis using natural language processing techniques. It receives the prompt message as input data and uses a keyword extraction algorithm to identify important information and context. The data extracted through analysis is then passed on to the next sentiment recognition step.

[0354] Step 3:

[0355] The server sends the data obtained through analysis to the emotion engine to recognize the user's intended emotion. The input consists of analyzed keywords and context. The emotion engine generates emotion information using a recognition algorithm and uses it as output for the generative AI.

[0356] Step 4:

[0357] The generative AI generates content based on sentiment information and prompt analysis data provided by the server. It uses sentiment information and context as input and generates text and visual content using a machine learning model. The output content is optimized based on pre-set tone and style.

[0358] Step 5:

[0359] The server sends the content generated by the AI ​​to the user's device. The output content can be reviewed again through the user interface and is provided for review and feedback. The user can review the results and send instructions for corrections to the server as needed.

[0360] Step 6:

[0361] When a user enters instructions for correction, the terminal sends new instructions to the server. The server re-analyzes the instructions and makes corrections or additions to the prompts. If necessary, it uses a generative AI to regenerate the content and produce new output.

[0362] Step 7:

[0363] Finally, the server saves the final verified content to storage. This saving process makes the generated content reusable in the future, which is useful for generating new content under similar conditions. Metadata may also be saved during the saving process.

[0364] (Application Example 2)

[0365] 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."

[0366] In recent years, generating effective information that appeals to emotions has become increasingly important in the information society. Especially in fields like advertising, there is a demand for information that accurately captures the emotions of the target audience. However, generating optimal information that responds to the emotions of the audience quickly and efficiently is difficult. Traditional methods require considerable time and effort, making productivity improvements a challenge.

[0367] 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.

[0368] In this invention, the server includes means for receiving instructions from the user and extracting basic data for information generation; means for analyzing the received basic data using natural language processing technology and identifying important information; and means for generating information using a generation AI based on the identified important information and emotional information, and adjusting it using an emotion engine. This makes it possible to generate high-quality information that is tailored to the emotions of the recipient.

[0369] "Receiving instructions from the user" means obtaining information and requests entered by the user through the terminal.

[0370] "Means for extracting basic data for information generation" refers to technologies and devices that extract the elements and metadata necessary for information generation from data received from users.

[0371] "Natural language processing technology" is a system of technologies that enables computers to understand and analyze human language, and it primarily deals with text data.

[0372] "Means for identifying important information" refer to technologies and devices that analyze received data and select elements that play a central role in information generation.

[0373] "Means of generating information using generative AI" refers to technologies and devices that utilize artificial intelligence technology to create new information and content based on given important information and additional data.

[0374] An "emotion engine" is a program or algorithm that infers a user's emotional state based on received data and user information, and takes that emotion into consideration when generating information.

[0375] "A means of generating information using generative AI based on emotional information and adjusting it with an emotional engine" refers to a technology or process that reflects the user's emotions in the generated information and modifies it to the optimal content and tone.

[0376] The system of this invention mainly consists of a terminal, a server, a generative AI, and an emotion engine. The user first uses the terminal to input instructions regarding information generation. These instructions are intended to evoke a certain emotion, such as "I want to surprise people with advertisements." The terminal receives these instructions and sends them to the server.

[0377] When the server receives instructions from a user, it analyzes those instructions using natural language processing technology. The analysis uses tools such as the Google Cloud Natural Language API to extract important information and identify emotions. This important information and emotional information are then used by a generative AI to generate information.

[0378] The generative AI utilizes OpenAI's GPT model and is responsible for the process of generating information based on identified key information and emotions. The generative AI generates information in text or other formats, and an emotion engine is used to refine that information. The emotion engine fine-tunes the tone and expression of the generated information to match the user's emotions, completing the final information. The completed information is sent from the server to the user's terminal, where the user can review it.

[0379] This system is particularly effective in generating information that heavily relies on emotions, such as advertising. For example, if a new product campaign advertisement targets emotions like "surprise" or "joy," the system automatically generates content tailored to those emotions.

[0380] An example of a prompt message might be: "Generate a campaign ad for the launch of a new smartphone. Include key features and special offers, using a tone that conveys feelings of surprise and excitement." This allows users to perform complex information generation processes in a simple manner.

[0381] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0382] Step 1:

[0383] The user enters instructions for information generation on a terminal. These instructions include the subject and emotional tone of the information to be generated. The terminal converts these instructions into a digital format, preparing them for transmission to the server.

[0384] Step 2:

[0385] The server receives instructions sent from the terminal. To analyze the received data, it uses natural language processing techniques to extract keywords and sentiment information. Tools such as the Google Cloud Natural Language API are used here, which clarify important information and emotional tone. As output, the extracted data is prepared as input for a generating AI.

[0386] Step 3:

[0387] The server generates information based on important and emotional information extracted using a generative AI. The generative AI used is OpenAI's GPT model, among others, which generates optimal information content according to the instructions. The generated content is temporarily stored on the server and then proceeds to an adjustment process by the emotion engine.

[0388] Step 4:

[0389] The emotion engine analyzes the generated information content and adjusts the tone and style based on the emotional information. At this stage, final adjustments are made to ensure that the generated information appropriately reflects the user's intended emotions. The adjusted information content is then finalized on the server.

[0390] Step 5:

[0391] Once the information content is adjusted, the server sends the final version to the terminal. The terminal displays the received information to the user, who then reviews the generated information. If the user wishes to make corrections, the feedback is sent back to the server, and the process from step 2 is repeated as needed.

[0392] 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.

[0393] 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.

[0394] 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.

[0395] [Third Embodiment]

[0396] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0397] 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.

[0398] 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).

[0399] 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.

[0400] 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.

[0401] 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).

[0402] 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.

[0403] 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.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] 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".

[0408] This invention is a system for supporting a company's content marketing activities, utilizing generative AI technology to automatically generate content based on user instructions. This system mainly consists of three main components: a server, a terminal, and a generative AI.

[0409] First, the user uses a terminal to input instructions specifying the type, purpose, keywords, tone, length, and other details of the content they want to generate. For example, if the user wants to create a promotional ad for a new product, they might input instructions such as, "I want to generate ad copy that highlights the product's features." This input is then sent to the server via the terminal.

[0410] Next, the server analyzes the received instructions. This analysis uses natural language processing techniques to extract important keywords and intentions from the user's input and format the analysis results into parameters necessary to pass them on to the generating AI.

[0411] Subsequently, based on the formatted data, the generative AI generates content. The generative AI retrieves relevant information from the database and automatically constructs text that matches the tone and style. For example, if the user requests a friendly tone, the generative AI will generate copy accordingly.

[0412] The generated content is then sent back to the user's device by the server. The user can review the provided content and request corrections as needed. The correction requests are sent back to the server for re-analysis and regeneration.

[0413] Furthermore, the generated content is saved to storage for later reuse. This allows users to generate content multiple times in a short period of time based on similar instructions.

[0414] This invention enables companies to efficiently generate high-quality, customized content applicable to various digital channels while significantly reducing the time and cost associated with content creation.

[0415] The following describes the processing flow.

[0416] Step 1:

[0417] The user uses their device to input instructions such as the type, purpose, keywords, tone, and length of the content they want to generate. The instructions are immediately sent to the server after input.

[0418] Step 2:

[0419] The server analyzes the instructions it receives. Using natural language processing techniques, it extracts important keywords and context, and formats them as parameters for content generation.

[0420] Step 3:

[0421] The server passes formatted parameters to the generating AI. The generating AI retrieves relevant information from the database and generates content that matches the specified tone and style.

[0422] Step 4:

[0423] The content generated by the AI ​​is sent to the server. The server packages that content and sends it to the user's device.

[0424] Step 5:

[0425] The user reviews the content received on their device. If there are any parts they are not satisfied with, they can send instructions to the server for correction.

[0426] Step 6:

[0427] The server receives the correction instructions and performs analysis and regeneration again. It then regenerates the content reflecting the corrections and sends it to the user again.

[0428] Step 7:

[0429] The final approved content is saved on the device. This saved content can be easily accessed later for use in marketing campaigns.

[0430] (Example 1)

[0431] 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."

[0432] In recent years, efficient and high-quality content generation has become a crucial factor in determining a company's competitiveness in the content market. However, existing methods are time-consuming and costly, and it is difficult to respond flexibly to individual content needs. Furthermore, it is difficult to accurately reflect the tone and style that users desire, making it impossible to quickly create content that matches a company's brand image. There is a need to solve these problems and automate and improve the accuracy of content generation.

[0433] 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.

[0434] In this invention, the server includes means for receiving instructions from a user via a terminal and extracting information for content generation; means for analyzing the received information using natural language processing technology, identifying important information, and forming prompt sentences suitable for a generative AI model; and means for automatically generating content using the generative AI model based on the identified important information. This enables companies to generate high-quality, customized content in a short time and respond quickly to market needs.

[0435] A "terminal" is an electronic device used by users to input information and exchange instructions and data with a server.

[0436] A "server" is a central processing unit that analyzes information received from users and generates content in conjunction with a generation AI model.

[0437] A "user" is an entity that provides instructions to the system via a terminal for content generation tailored to a specific purpose.

[0438] "Natural language processing technology" is a technology that interprets user instructions linguistically and extracts keywords and intentions that are relevant to the purpose.

[0439] "Important information" refers to keywords and parameters required when the AI ​​model generates content based on user instructions.

[0440] A "generative AI model" is an algorithm that automatically generates content based on a specified prompt.

[0441] A "prompt statement" is an instruction that a generative AI model uses as a basis for generating content.

[0442] "Automatic generation" refers to the process by which a system creates content based on pre-set conditions and specifications, without requiring manual input.

[0443] "Content" refers to a collection of information generated to meet the user's needs and objectives, and includes text and written documents.

[0444] This invention is a system for businesses to efficiently generate high-quality content. The system primarily consists of a server, terminals, and a generation AI model, with users providing content generation instructions via the terminals.

[0445] The user inputs specific instructions, such as the type, purpose, keywords, tone, and length of the content they want to generate, through an interface on their device. The device converts these instructions into digital data and sends it to the server.

[0446] The server processes the received data using natural language processing techniques to extract important information. This analysis can be performed using open-source natural language processing libraries (e.g., spaCy, NLTK). Based on this important information, the server forms prompt sentences suitable for the generative AI model. For this generative AI model, advanced algorithms such as the GPT (Generative Pretrained Transformer) series are used.

[0447] The generative AI model retrieves information from relevant databases based on the generated prompt text and automatically generates content that matches the specified tone and style. For example, if a user enters the prompt text, "Create advertising copy for a new environmentally friendly product in a friendly tone," the generative AI model will construct advertising copy that conforms to that tone.

[0448] The generated content is sent back to the user's device via the server, where the user can review it. If necessary, the user can request revisions, and the server can then analyze and regenerate the content again. This process allows users to efficiently obtain customized content tailored to their company's brand image and needs. In particular, to quickly respond to individual projects and campaigns, the generated content is stored on the server and can be reused.

[0449] In this way, this invention helps companies quickly and effectively deliver optimal content for digital channels.

[0450] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0451] Step 1:

[0452] The user uses a terminal to enter details about the content they want to generate. These details include the content type, purpose, keywords, tone, and length. This information is organized digitally as data packets and sent to the server.

[0453] Step 2:

[0454] The server analyzes data packets received from the user. Using natural language processing techniques, it extracts important information and keywords from the digital data. In this process, it utilizes open-source natural language processing libraries to perform data computation and information extraction, and to form prompt sentences suitable for the generative AI model.

[0455] Step 3:

[0456] The generative AI model receives prompt text generated by the server. Based on this prompt text, the generative AI model collects necessary information from relevant databases and automatically generates content based on the specified tone and style. The generated content is returned to the server as a digital output.

[0457] Step 4:

[0458] The server receives the content sent from the generating AI model and resends this content to the user's device. The user can review the content on their device and provide additional correction instructions if it does not meet their expectations.

[0459] Step 5:

[0460] When a user enters a correction instruction, the device sends the correction to the server. The server then analyzes the correction data again and forms the necessary new prompt sentences. Finally, it uses a generative AI model to regenerate the content and sends the new content to the user's device.

[0461] (Application Example 1)

[0462] 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."

[0463] In content marketing, companies need to generate advertising content quickly and effectively. However, traditional generation processes often involve manual processes, resulting in time-consuming and costly methods. Furthermore, generating target-specific content in real time was difficult in situations where immediate response capabilities are required in sales environments.

[0464] 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.

[0465] In this invention, the server includes means for receiving instructions from the user and extracting information for content generation, means for analyzing the received information and identifying specific information using natural language processing technology, and means for creating content using a generation AI based on the identified specific information. This enables sales representatives to generate appropriate advertising content in real time on-site using a portable display device and present it to clients immediately.

[0466] "A means of receiving instructions from the user and extracting information for content generation" refers to a means of receiving elements related to the content that the user wishes to generate, and then extracting the necessary information.

[0467] "A means of analyzing received information and identifying specific information using natural language processing technology" refers to a means of analyzing information provided by the user and utilizing natural language processing technology to identify important keywords and intentions.

[0468] "Means of creating content using generative AI based on identified specific information" refers to methods of automatically creating text and expressions that match the tone and style, using generative AI technology based on specific information.

[0469] "Means of providing the created content to users" refers to means of communicating the generated content so that users can review it.

[0470] "Means of providing real-time generated content to the visual eye via a portable display device" refers to means of providing generated content as visual information immediately using portable devices such as smart glasses or head-mounted displays.

[0471] "Means for receiving user feedback on modifications to generated content and regenerating it" refers to a means of receiving user instructions for modifications to generated content and regenerating the content based on those instructions.

[0472] "Means of storage for recording generated content" refers to means of saving generated content in a database or storage device and retaining it for later use or reference.

[0473] The system that realizes this invention mainly consists of a server, a terminal used by the user, and a portable display device. Specific examples include smart glasses and head-mounted displays. The user uses the terminal to input information related to the content they want to generate, such as purpose, tone, and keywords. This information is sent to the server and analyzed using natural language processing technology. Based on the specific information extracted through the analysis, the generating AI combines the relevant information to create content.

[0474] The generated content is delivered from the server to the user's terminal and, if necessary, is also provided visually in real time via a portable display device. This system enables sales representatives and others to instantly generate appropriate content on-site and present it quickly to clients.

[0475] As a concrete example, consider the case of generating advertising copy for a new smartphone. Sales representatives can use smart glasses to view the generated content in real time and present content tailored to the client's needs on the spot. An example of a prompt sentence to input into the generation AI model is, "Advertising copy for a new smartphone. Use a friendly tone and emphasize long battery life and a high-resolution camera." In this way, the system enables rapid content generation and supports immediate responses in the sales field.

[0476] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0477] Step 1:

[0478] The user uses a terminal to input information about the content they want to generate. This information includes the purpose, tone, keywords, and prompts. The user's role is clear, as the input information is sent to the server.

[0479] Step 2:

[0480] The server analyzes the information received from the user using natural language processing technology. This analysis identifies important parameters and keywords, and formats the data for transmission to the generating AI. The input is the information provided by the user, and the output is the formatted data.

[0481] Step 3:

[0482] The server sends formatted data to the generative AI to create content. The generative AI model retrieves relevant information from the database and automatically generates content that matches the required tone and style. The output is the generated content.

[0483] Step 4:

[0484] The server resends the generated content to the user's device. The user can review the generated content through their device and provide feedback for corrections as needed. The input is the generated content, and the output is the user's feedback and corrections.

[0485] Step 5:

[0486] When a user requests a correction, the server re-analyzes the correction information and uses a generation AI to regenerate the content. The input is the correction request, and the output is the regenerated content.

[0487] Step 6:

[0488] Ultimately, if the generated content satisfies the user, it is saved to storage. The server manages the saved content for future reference and reuse. The input is the final version of the content, and the output is the saved data.

[0489] 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.

[0490] This invention is a system that automatically generates content based on user instructions, and in particular, it incorporates an emotion engine to recognize the user's emotions and improve the quality of the generated content. This system mainly consists of four main components: a server, a terminal, a generation AI, and an emotion engine.

[0491] First, the user uses a device to input instructions about the content they want to generate. For example, when creating an advertisement for a new product, they might specify a request such as "I want to introduce the product in a tone that will delight customers." This input is then sent to the server via the device.

[0492] Next, the server analyzes the received instructions. Using natural language processing techniques, it extracts important keywords and context from the instructions and passes them to the emotion engine to recognize the user's emotions. This emotion information is then used by the generative AI to adjust the tone and style when creating content.

[0493] The generative AI generates content based on analyzed parameters and emotional information passed from the server. For example, if the emotion of joy is recognized, it can construct text in a bright and friendly tone that matches that emotion.

[0494] The generated content is sent to the user's device by the server. The user can review this content and send correction instructions to the server if necessary. The server receives the correction instructions and performs analysis and regeneration again.

[0495] Furthermore, the generated content is saved to storage, increasing its reusability. This allows users to quickly generate new content under similar conditions.

[0496] The emotion engine further tracks changes in the user's emotions and provides feedback throughout the entire generation process. This feedback allows the generation AI to create content that is more closely aligned with the user's emotions.

[0497] This invention enables companies to efficiently generate high-quality, customized content that takes user emotions into consideration during content creation, thereby making their marketing activities more effective.

[0498] The following describes the processing flow.

[0499] Step 1:

[0500] The user uses their device to input instructions such as the type of content they want to generate, its purpose, keywords, tone, and emotional nuances. This input is then sent from the device to the server.

[0501] Step 2:

[0502] The server analyzes user instructions received using natural language processing technology and extracts the data necessary for content generation. This analyzed data is then passed to an emotion engine to recognize the user's emotions.

[0503] Step 3:

[0504] The server combines emotional information obtained from the emotion engine with analyzed data to shape the parameters for generating content. This includes the emotional tone and style intended by the user.

[0505] Step 4:

[0506] A generation AI on the server generates content based on formatted parameters. The generation AI retrieves relevant information from a database and constructs text that matches the user's emotions.

[0507] Step 5:

[0508] The server sends the generated content to the terminal. The user can review the content on the terminal and enter instructions for corrections to any parts they are not satisfied with.

[0509] Step 6:

[0510] The server receives the correction instructions and performs analysis and regeneration again. The regenerated content, reflecting the corrections, is then sent back to the terminal.

[0511] Step 7:

[0512] The final approved content is saved on the device and retained in cloud or local storage. The saved content can be reused later or used in other marketing activities.

[0513] Step 8:

[0514] The emotion engine continuously monitors changes in the user's emotions and provides feedback to the future content creation process. This enables content creation that responds to user emotions with greater accuracy.

[0515] (Example 2)

[0516] 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."

[0517] In today's information society, the demand for personalized, high-quality content is rapidly increasing. However, traditional content creation methods struggle to reflect user emotions, resulting in difficulties in meeting user expectations. Furthermore, there is the challenge of low reusability of once-generated content, making it difficult to quickly adapt to new conditions.

[0518] 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.

[0519] In this invention, the server includes means for receiving instructions from the user and extracting information for content generation, means for analyzing the received information using natural language processing technology and identifying important information, and means for recognizing the user's emotions using an emotion engine based on the identified information. This enables the rapid generation and efficient reuse of personalized, high-quality content that reflects the user's emotions.

[0520] A "user" refers to someone who uses the system to give instructions for content generation.

[0521] "Instructions" refer to information that includes requirements and conditions regarding the content the user wants to generate.

[0522] "Content" refers to representations of information generated based on user instructions, including text and visual content.

[0523] "Natural language processing technology" refers to the technology that enables computers to understand, analyze, and generate human natural language.

[0524] "Important information" refers to keywords and context necessary for content creation.

[0525] An "emotion engine" refers to a technological element that recognizes emotions from user input and reflects them in content generation.

[0526] "Generative AI" refers to artificial intelligence technology that automatically generates content using machine learning techniques.

[0527] "Storage device means" refers to a storage function for storing the generated content.

[0528] This invention is designed as a system that enables the automatic generation of personalized content. The system mainly consists of four main components: a server, a terminal, a generation AI, and an emotion engine.

[0529] The user uses a terminal to input instructions about the content they want to generate. These instructions include specific conditions and requests. For example, they might enter a prompt such as, "I want to introduce the product in a tone that will delight customers." This input is then sent from the terminal to the server.

[0530] The server uses natural language processing techniques to analyze the received instructions. It extracts important information, which the emotion engine then uses to recognize the user's emotions. This process may utilize open-source natural language processing libraries and emotion analysis software.

[0531] The generative AI generates content based on emotional information obtained from an emotion engine. For example, if a user requests the emotion of "joy," the generative AI will create text and images in a bright and friendly tone. The generative AI implements a commonly used AI model platform.

[0532] The generated content is sent from the server to the user's terminal, where the user can review it. If necessary, the user can provide further correction instructions, and the system will regenerate the content based on those instructions. The created content is also saved to storage for future reuse.

[0533] This system allows companies to quickly and efficiently generate content that takes user emotions into account, thereby improving the quality of their marketing activities.

[0534] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0535] Step 1:

[0536] The user uses a terminal to input instructions about the content they want to generate. For example, a prompt might say, "I want to introduce the product in a tone that will delight customers." This input data is sent to the terminal through the user interface and then transferred to the server.

[0537] Step 2:

[0538] The server receives the prompt message sent from the terminal and begins analysis using natural language processing techniques. It receives the prompt message as input data and uses a keyword extraction algorithm to identify important information and context. The data extracted through analysis is then passed on to the next sentiment recognition step.

[0539] Step 3:

[0540] The server sends the data obtained through analysis to the emotion engine to recognize the user's intended emotion. The input consists of analyzed keywords and context. The emotion engine generates emotion information using a recognition algorithm and uses it as output for the generative AI.

[0541] Step 4:

[0542] The generative AI generates content based on sentiment information and prompt analysis data provided by the server. It uses sentiment information and context as input and generates text and visual content using a machine learning model. The output content is optimized based on pre-set tone and style.

[0543] Step 5:

[0544] The server sends the content generated by the AI ​​to the user's device. The output content can be reviewed again through the user interface and is provided for review and feedback. The user can review the results and send instructions for corrections to the server as needed.

[0545] Step 6:

[0546] When a user enters instructions for correction, the terminal sends new instructions to the server. The server re-analyzes the instructions and makes corrections or additions to the prompts. If necessary, it uses a generative AI to regenerate the content and produce new output.

[0547] Step 7:

[0548] Finally, the server saves the final verified content to storage. This saving process makes the generated content reusable in the future, which is useful for generating new content under similar conditions. Metadata may also be saved during the saving process.

[0549] (Application Example 2)

[0550] 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."

[0551] In recent years, generating effective information that appeals to emotions has become increasingly important in the information society. Especially in fields like advertising, there is a demand for information that accurately captures the emotions of the target audience. However, generating optimal information that responds to the emotions of the audience quickly and efficiently is difficult. Traditional methods require considerable time and effort, making productivity improvements a challenge.

[0552] 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.

[0553] In this invention, the server includes means for receiving instructions from the user and extracting basic data for information generation; means for analyzing the received basic data using natural language processing technology and identifying important information; and means for generating information using a generation AI based on the identified important information and emotional information, and adjusting it using an emotion engine. This makes it possible to generate high-quality information that is tailored to the emotions of the recipient.

[0554] "Receiving instructions from the user" means obtaining information and requests entered by the user through the terminal.

[0555] "Means for extracting basic data for information generation" refers to technologies and devices that extract the elements and metadata necessary for information generation from data received from users.

[0556] "Natural language processing technology" is a system of technologies that enables computers to understand and analyze human language, and it primarily deals with text data.

[0557] "Means for identifying important information" refer to technologies and devices that analyze received data and select elements that play a central role in information generation.

[0558] "Means of generating information using generative AI" refers to technologies and devices that utilize artificial intelligence technology to create new information and content based on given important information and additional data.

[0559] An "emotion engine" is a program or algorithm that infers a user's emotional state based on received data and user information, and takes that emotion into consideration when generating information.

[0560] "A means of generating information using generative AI based on emotional information and adjusting it with an emotional engine" refers to a technology or process that reflects the user's emotions in the generated information and modifies it to the optimal content and tone.

[0561] The system of this invention mainly consists of a terminal, a server, a generative AI, and an emotion engine. The user first uses the terminal to input instructions regarding information generation. These instructions are intended to evoke a certain emotion, such as "I want to surprise people with advertisements." The terminal receives these instructions and sends them to the server.

[0562] When the server receives instructions from a user, it analyzes those instructions using natural language processing technology. The analysis uses tools such as the Google Cloud Natural Language API to extract important information and identify emotions. This important information and emotional information are then used by a generative AI to generate information.

[0563] The generative AI utilizes OpenAI's GPT model and is responsible for the process of generating information based on identified key information and emotions. The generative AI generates information in text or other formats, and an emotion engine is used to refine that information. The emotion engine fine-tunes the tone and expression of the generated information to match the user's emotions, completing the final information. The completed information is sent from the server to the user's terminal, where the user can review it.

[0564] This system is particularly effective in generating information that heavily relies on emotions, such as advertising. For example, if a new product campaign advertisement targets emotions like "surprise" or "joy," the system automatically generates content tailored to those emotions.

[0565] An example of a prompt message might be: "Generate a campaign ad for the launch of a new smartphone. Include key features and special offers, using a tone that conveys feelings of surprise and excitement." This allows users to perform complex information generation processes in a simple manner.

[0566] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0567] Step 1:

[0568] The user enters instructions for information generation on a terminal. These instructions include the subject and emotional tone of the information to be generated. The terminal converts these instructions into a digital format, preparing them for transmission to the server.

[0569] Step 2:

[0570] The server receives instructions sent from the terminal. To analyze the received data, it uses natural language processing techniques to extract keywords and sentiment information. Tools such as the Google Cloud Natural Language API are used here, which clarify important information and emotional tone. As output, the extracted data is prepared as input for a generating AI.

[0571] Step 3:

[0572] The server generates information based on important and emotional information extracted using a generative AI. The generative AI used is OpenAI's GPT model, among others, which generates optimal information content according to the instructions. The generated content is temporarily stored on the server and then proceeds to an adjustment process by the emotion engine.

[0573] Step 4:

[0574] The emotion engine analyzes the generated information content and adjusts the tone and style based on the emotional information. At this stage, final adjustments are made to ensure that the generated information appropriately reflects the user's intended emotions. The adjusted information content is then finalized on the server.

[0575] Step 5:

[0576] Once the information content is adjusted, the server sends the final version to the terminal. The terminal displays the received information to the user, who then reviews the generated information. If the user wishes to make corrections, the feedback is sent back to the server, and the process from step 2 is repeated as needed.

[0577] 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.

[0578] 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.

[0579] 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.

[0580] [Fourth Embodiment]

[0581] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0582] 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.

[0583] 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).

[0584] 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.

[0585] 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.

[0586] 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).

[0587] 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.

[0588] 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.

[0589] 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.

[0590] 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.

[0591] 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.

[0592] 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.

[0593] 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".

[0594] This invention is a system for supporting a company's content marketing activities, utilizing generative AI technology to automatically generate content based on user instructions. This system mainly consists of three main components: a server, a terminal, and a generative AI.

[0595] First, the user uses a terminal to input instructions specifying the type, purpose, keywords, tone, length, and other details of the content they want to generate. For example, if the user wants to create a promotional ad for a new product, they might input instructions such as, "I want to generate ad copy that highlights the product's features." This input is then sent to the server via the terminal.

[0596] Next, the server analyzes the received instructions. This analysis uses natural language processing techniques to extract important keywords and intentions from the user's input and format the analysis results into parameters necessary to pass them on to the generating AI.

[0597] Subsequently, based on the formatted data, the generative AI generates content. The generative AI retrieves relevant information from the database and automatically constructs text that matches the tone and style. For example, if the user requests a friendly tone, the generative AI will generate copy accordingly.

[0598] The generated content is then sent back to the user's device by the server. The user can review the provided content and request corrections as needed. The correction requests are sent back to the server for re-analysis and regeneration.

[0599] Furthermore, the generated content is saved to storage for later reuse. This allows users to generate content multiple times in a short period of time based on similar instructions.

[0600] This invention enables companies to efficiently generate high-quality, customized content applicable to various digital channels while significantly reducing the time and cost associated with content creation.

[0601] The following describes the processing flow.

[0602] Step 1:

[0603] The user uses their device to input instructions such as the type, purpose, keywords, tone, and length of the content they want to generate. The instructions are immediately sent to the server after input.

[0604] Step 2:

[0605] The server analyzes the instructions it receives. Using natural language processing techniques, it extracts important keywords and context, and formats them as parameters for content generation.

[0606] Step 3:

[0607] The server passes formatted parameters to the generating AI. The generating AI retrieves relevant information from the database and generates content that matches the specified tone and style.

[0608] Step 4:

[0609] The content generated by the AI ​​is sent to the server. The server packages that content and sends it to the user's device.

[0610] Step 5:

[0611] The user reviews the content received on their device. If there are any parts they are not satisfied with, they can send instructions to the server for correction.

[0612] Step 6:

[0613] The server receives the correction instructions and performs analysis and regeneration again. It then regenerates the content reflecting the corrections and sends it to the user again.

[0614] Step 7:

[0615] The final approved content is saved on the device. This saved content can be easily accessed later for use in marketing campaigns.

[0616] (Example 1)

[0617] 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".

[0618] In recent years, efficient and high-quality content generation has become a crucial factor in determining a company's competitiveness in the content market. However, existing methods are time-consuming and costly, and it is difficult to respond flexibly to individual content needs. Furthermore, it is difficult to accurately reflect the tone and style that users desire, making it impossible to quickly create content that matches a company's brand image. There is a need to solve these problems and automate and improve the accuracy of content generation.

[0619] 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.

[0620] In this invention, the server includes means for receiving instructions from a user via a terminal and extracting information for content generation; means for analyzing the received information using natural language processing technology, identifying important information, and forming prompt sentences suitable for a generative AI model; and means for automatically generating content using the generative AI model based on the identified important information. This enables companies to generate high-quality, customized content in a short time and respond quickly to market needs.

[0621] A "terminal" is an electronic device used by users to input information and exchange instructions and data with a server.

[0622] A "server" is a central processing unit that analyzes information received from users and generates content in conjunction with a generation AI model.

[0623] A "user" is an entity that provides instructions to the system via a terminal for content generation tailored to a specific purpose.

[0624] "Natural language processing technology" is a technology that interprets user instructions linguistically and extracts keywords and intentions that are relevant to the purpose.

[0625] "Important information" refers to keywords and parameters required when the AI ​​model generates content based on user instructions.

[0626] A "generative AI model" is an algorithm that automatically generates content based on a specified prompt.

[0627] A "prompt statement" is an instruction that a generative AI model uses as a basis for generating content.

[0628] "Automatic generation" refers to the process by which a system creates content based on pre-set conditions and specifications, without requiring manual input.

[0629] "Content" refers to a collection of information generated to meet the user's needs and objectives, and includes text and written documents.

[0630] This invention is a system for businesses to efficiently generate high-quality content. The system primarily consists of a server, terminals, and a generation AI model, with users providing content generation instructions via the terminals.

[0631] The user inputs specific instructions, such as the type, purpose, keywords, tone, and length of the content they want to generate, through an interface on their device. The device converts these instructions into digital data and sends it to the server.

[0632] The server processes the received data using natural language processing techniques to extract important information. This analysis can be performed using open-source natural language processing libraries (e.g., spaCy, NLTK). Based on this important information, the server forms prompt sentences suitable for the generative AI model. For this generative AI model, advanced algorithms such as the GPT (Generative Pretrained Transformer) series are used.

[0633] The generative AI model retrieves information from relevant databases based on the generated prompt text and automatically generates content that matches the specified tone and style. For example, if a user enters the prompt text, "Create advertising copy for a new environmentally friendly product in a friendly tone," the generative AI model will construct advertising copy that conforms to that tone.

[0634] The generated content is sent back to the user's device via the server, where the user can review it. If necessary, the user can request revisions, and the server can then analyze and regenerate the content again. This process allows users to efficiently obtain customized content tailored to their company's brand image and needs. In particular, to quickly respond to individual projects and campaigns, the generated content is stored on the server and can be reused.

[0635] In this way, this invention helps companies quickly and effectively deliver optimal content for digital channels.

[0636] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0637] Step 1:

[0638] The user uses a terminal to enter details about the content they want to generate. These details include the content type, purpose, keywords, tone, and length. This information is organized digitally as data packets and sent to the server.

[0639] Step 2:

[0640] The server analyzes data packets received from the user. Using natural language processing techniques, it extracts important information and keywords from the digital data. In this process, it utilizes open-source natural language processing libraries to perform data computation and information extraction, and to form prompt sentences suitable for the generative AI model.

[0641] Step 3:

[0642] The generative AI model receives prompt text generated by the server. Based on this prompt text, the generative AI model collects necessary information from relevant databases and automatically generates content based on the specified tone and style. The generated content is returned to the server as a digital output.

[0643] Step 4:

[0644] The server receives the content sent from the generating AI model and resends this content to the user's device. The user can review the content on their device and provide additional correction instructions if it does not meet their expectations.

[0645] Step 5:

[0646] When a user enters a correction instruction, the device sends the correction to the server. The server then analyzes the correction data again and forms the necessary new prompt sentences. Finally, it uses a generative AI model to regenerate the content and sends the new content to the user's device.

[0647] (Application Example 1)

[0648] 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".

[0649] In content marketing, companies need to generate advertising content quickly and effectively. However, traditional generation processes often involve manual processes, resulting in time-consuming and costly methods. Furthermore, generating target-specific content in real time was difficult in situations where immediate response capabilities are required in sales environments.

[0650] 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.

[0651] In this invention, the server includes means for receiving instructions from the user and extracting information for content generation, means for analyzing the received information and identifying specific information using natural language processing technology, and means for creating content using a generation AI based on the identified specific information. This enables sales representatives to generate appropriate advertising content in real time on-site using a portable display device and present it to clients immediately.

[0652] "A means of receiving instructions from the user and extracting information for content generation" refers to a means of receiving elements related to the content that the user wishes to generate, and then extracting the necessary information.

[0653] "A means of analyzing received information and identifying specific information using natural language processing technology" refers to a means of analyzing information provided by the user and utilizing natural language processing technology to identify important keywords and intentions.

[0654] "Means of creating content using generative AI based on identified specific information" refers to methods of automatically creating text and expressions that match the tone and style, using generative AI technology based on specific information.

[0655] "Means of providing the created content to users" refers to means of communicating the generated content so that users can review it.

[0656] "Means of providing real-time generated content to the visual eye via a portable display device" refers to means of providing generated content as visual information immediately using portable devices such as smart glasses or head-mounted displays.

[0657] "Means for receiving user feedback on modifications to generated content and regenerating it" refers to a means of receiving user instructions for modifications to generated content and regenerating the content based on those instructions.

[0658] "Means of storage for recording generated content" refers to means of saving generated content in a database or storage device and retaining it for later use or reference.

[0659] The system that realizes this invention mainly consists of a server, a terminal used by the user, and a portable display device. Specific examples include smart glasses and head-mounted displays. The user uses the terminal to input information related to the content they want to generate, such as purpose, tone, and keywords. This information is sent to the server and analyzed using natural language processing technology. Based on the specific information extracted through the analysis, the generating AI combines the relevant information to create content.

[0660] The generated content is delivered from the server to the user's terminal and, if necessary, is also provided visually in real time via a portable display device. This system enables sales representatives and others to instantly generate appropriate content on-site and present it quickly to clients.

[0661] As a concrete example, consider the case of generating advertising copy for a new smartphone. Sales representatives can use smart glasses to view the generated content in real time and present content tailored to the client's needs on the spot. An example of a prompt sentence to input into the generation AI model is, "Advertising copy for a new smartphone. Use a friendly tone and emphasize long battery life and a high-resolution camera." In this way, the system enables rapid content generation and supports immediate responses in the sales field.

[0662] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0663] Step 1:

[0664] The user uses a terminal to input information about the content they want to generate. This information includes the purpose, tone, keywords, and prompts. The user's role is clear, as the input information is sent to the server.

[0665] Step 2:

[0666] The server analyzes the information received from the user using natural language processing technology. This analysis identifies important parameters and keywords, and formats the data for transmission to the generating AI. The input is the information provided by the user, and the output is the formatted data.

[0667] Step 3:

[0668] The server sends formatted data to the generative AI to create content. The generative AI model retrieves relevant information from the database and automatically generates content that matches the required tone and style. The output is the generated content.

[0669] Step 4:

[0670] The server resends the generated content to the user's device. The user can review the generated content through their device and provide feedback for corrections as needed. The input is the generated content, and the output is the user's feedback and corrections.

[0671] Step 5:

[0672] When a user requests a correction, the server re-analyzes the correction information and uses a generation AI to regenerate the content. The input is the correction request, and the output is the regenerated content.

[0673] Step 6:

[0674] Ultimately, if the generated content satisfies the user, it is saved to storage. The server manages the saved content for future reference and reuse. The input is the final version of the content, and the output is the saved data.

[0675] 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.

[0676] This invention is a system that automatically generates content based on user instructions, and in particular, it incorporates an emotion engine to recognize the user's emotions and improve the quality of the generated content. This system mainly consists of four main components: a server, a terminal, a generation AI, and an emotion engine.

[0677] First, the user uses a device to input instructions about the content they want to generate. For example, when creating an advertisement for a new product, they might specify a request such as "I want to introduce the product in a tone that will delight customers." This input is then sent to the server via the device.

[0678] Next, the server analyzes the received instructions. Using natural language processing techniques, it extracts important keywords and context from the instructions and passes them to the emotion engine to recognize the user's emotions. This emotion information is then used by the generative AI to adjust the tone and style when creating content.

[0679] The generative AI generates content based on analyzed parameters and emotional information passed from the server. For example, if the emotion of joy is recognized, it can construct text in a bright and friendly tone that matches that emotion.

[0680] The generated content is sent to the user's device by the server. The user can review this content and send correction instructions to the server if necessary. The server receives the correction instructions and performs analysis and regeneration again.

[0681] Furthermore, the generated content is saved to storage, increasing its reusability. This allows users to quickly generate new content under similar conditions.

[0682] The emotion engine further tracks changes in the user's emotions and provides feedback throughout the entire generation process. This feedback allows the generation AI to create content that is more closely aligned with the user's emotions.

[0683] This invention enables companies to efficiently generate high-quality, customized content that takes user emotions into consideration during content creation, thereby making their marketing activities more effective.

[0684] The following describes the processing flow.

[0685] Step 1:

[0686] The user uses their device to input instructions such as the type of content they want to generate, its purpose, keywords, tone, and emotional nuances. This input is then sent from the device to the server.

[0687] Step 2:

[0688] The server analyzes user instructions received using natural language processing technology and extracts the data necessary for content generation. This analyzed data is then passed to an emotion engine to recognize the user's emotions.

[0689] Step 3:

[0690] The server combines emotional information obtained from the emotion engine with analyzed data to shape the parameters for generating content. This includes the emotional tone and style intended by the user.

[0691] Step 4:

[0692] A generation AI on the server generates content based on formatted parameters. The generation AI retrieves relevant information from a database and constructs text that matches the user's emotions.

[0693] Step 5:

[0694] The server sends the generated content to the terminal. The user can review the content on the terminal and enter instructions for corrections to any parts they are not satisfied with.

[0695] Step 6:

[0696] The server receives the correction instructions and performs analysis and regeneration again. The regenerated content, reflecting the corrections, is then sent back to the terminal.

[0697] Step 7:

[0698] The final approved content is saved on the device and retained in cloud or local storage. The saved content can be reused later or used in other marketing activities.

[0699] Step 8:

[0700] The emotion engine continuously monitors changes in the user's emotions and provides feedback to the future content creation process. This enables content creation that responds to user emotions with greater accuracy.

[0701] (Example 2)

[0702] 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".

[0703] In today's information society, the demand for personalized, high-quality content is rapidly increasing. However, traditional content creation methods struggle to reflect user emotions, resulting in difficulties in meeting user expectations. Furthermore, there is the challenge of low reusability of once-generated content, making it difficult to quickly adapt to new conditions.

[0704] 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.

[0705] In this invention, the server includes means for receiving instructions from the user and extracting information for content generation, means for analyzing the received information using natural language processing technology and identifying important information, and means for recognizing the user's emotions using an emotion engine based on the identified information. This enables the rapid generation and efficient reuse of personalized, high-quality content that reflects the user's emotions.

[0706] A "user" refers to someone who uses the system to give instructions for content generation.

[0707] "Instructions" refer to information that includes requirements and conditions regarding the content the user wants to generate.

[0708] "Content" refers to representations of information generated based on user instructions, including text and visual content.

[0709] "Natural language processing technology" refers to the technology that enables computers to understand, analyze, and generate human natural language.

[0710] "Important information" refers to keywords and context necessary for content creation.

[0711] An "emotion engine" refers to a technological element that recognizes emotions from user input and reflects them in content generation.

[0712] "Generative AI" refers to artificial intelligence technology that automatically generates content using machine learning techniques.

[0713] "Storage device means" refers to a storage function for storing the generated content.

[0714] This invention is designed as a system that enables the automatic generation of personalized content. The system mainly consists of four main components: a server, a terminal, a generation AI, and an emotion engine.

[0715] The user uses a terminal to input instructions about the content they want to generate. These instructions include specific conditions and requests. For example, they might enter a prompt such as, "I want to introduce the product in a tone that will delight customers." This input is then sent from the terminal to the server.

[0716] The server uses natural language processing techniques to analyze the received instructions. It extracts important information, which the emotion engine then uses to recognize the user's emotions. This process may utilize open-source natural language processing libraries and emotion analysis software.

[0717] The generative AI generates content based on emotional information obtained from an emotion engine. For example, if a user requests the emotion of "joy," the generative AI will create text and images in a bright and friendly tone. The generative AI implements a commonly used AI model platform.

[0718] The generated content is sent from the server to the user's terminal, where the user can review it. If necessary, the user can provide further correction instructions, and the system will regenerate the content based on those instructions. The created content is also saved to storage for future reuse.

[0719] This system allows companies to quickly and efficiently generate content that takes user emotions into account, thereby improving the quality of their marketing activities.

[0720] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0721] Step 1:

[0722] The user uses a terminal to input instructions about the content they want to generate. For example, a prompt might say, "I want to introduce the product in a tone that will delight customers." This input data is sent to the terminal through the user interface and then transferred to the server.

[0723] Step 2:

[0724] The server receives the prompt message sent from the terminal and begins analysis using natural language processing techniques. It receives the prompt message as input data and uses a keyword extraction algorithm to identify important information and context. The data extracted through analysis is then passed on to the next sentiment recognition step.

[0725] Step 3:

[0726] The server sends the data obtained through analysis to the emotion engine to recognize the user's intended emotion. The input consists of analyzed keywords and context. The emotion engine generates emotion information using a recognition algorithm and uses it as output for the generative AI.

[0727] Step 4:

[0728] The generative AI generates content based on sentiment information and prompt analysis data provided by the server. It uses sentiment information and context as input and generates text and visual content using a machine learning model. The output content is optimized based on pre-set tone and style.

[0729] Step 5:

[0730] The server sends the content generated by the AI ​​to the user's device. The output content can be reviewed again through the user interface and is provided for review and feedback. The user can review the results and send instructions for corrections to the server as needed.

[0731] Step 6:

[0732] When a user enters instructions for correction, the terminal sends new instructions to the server. The server re-analyzes the instructions and makes corrections or additions to the prompts. If necessary, it uses a generative AI to regenerate the content and produce new output.

[0733] Step 7:

[0734] Finally, the server saves the final verified content to storage. This saving process makes the generated content reusable in the future, which is useful for generating new content under similar conditions. Metadata may also be saved during the saving process.

[0735] (Application Example 2)

[0736] 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".

[0737] In recent years, generating effective information that appeals to emotions has become increasingly important in the information society. Especially in fields like advertising, there is a demand for information that accurately captures the emotions of the target audience. However, generating optimal information that responds to the emotions of the audience quickly and efficiently is difficult. Traditional methods require considerable time and effort, making productivity improvements a challenge.

[0738] 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.

[0739] In this invention, the server includes means for receiving instructions from the user and extracting basic data for information generation; means for analyzing the received basic data using natural language processing technology and identifying important information; and means for generating information using a generation AI based on the identified important information and emotional information, and adjusting it using an emotion engine. This makes it possible to generate high-quality information that is tailored to the emotions of the recipient.

[0740] "Receiving instructions from the user" means obtaining information and requests entered by the user through the terminal.

[0741] "Means for extracting basic data for information generation" refers to technologies and devices that extract the elements and metadata necessary for information generation from data received from users.

[0742] "Natural language processing technology" is a system of technologies that enables computers to understand and analyze human language, and it primarily deals with text data.

[0743] "Means for identifying important information" refer to technologies and devices that analyze received data and select elements that play a central role in information generation.

[0744] "Means of generating information using generative AI" refers to technologies and devices that utilize artificial intelligence technology to create new information and content based on given important information and additional data.

[0745] An "emotion engine" is a program or algorithm that infers a user's emotional state based on received data and user information, and takes that emotion into consideration when generating information.

[0746] "A means of generating information using generative AI based on emotional information and adjusting it with an emotional engine" refers to a technology or process that reflects the user's emotions in the generated information and modifies it to the optimal content and tone.

[0747] The system of this invention mainly consists of a terminal, a server, a generative AI, and an emotion engine. The user first uses the terminal to input instructions regarding information generation. These instructions are intended to evoke a certain emotion, such as "I want to surprise people with advertisements." The terminal receives these instructions and sends them to the server.

[0748] When the server receives instructions from a user, it analyzes those instructions using natural language processing technology. The analysis uses tools such as the Google Cloud Natural Language API to extract important information and identify emotions. This important information and emotional information are then used by a generative AI to generate information.

[0749] The generative AI utilizes OpenAI's GPT model and is responsible for the process of generating information based on identified key information and emotions. The generative AI generates information in text or other formats, and an emotion engine is used to refine that information. The emotion engine fine-tunes the tone and expression of the generated information to match the user's emotions, completing the final information. The completed information is sent from the server to the user's terminal, where the user can review it.

[0750] This system is particularly effective in generating information that heavily relies on emotions, such as advertising. For example, if a new product campaign advertisement targets emotions like "surprise" or "joy," the system automatically generates content tailored to those emotions.

[0751] An example of a prompt message might be: "Generate a campaign ad for the launch of a new smartphone. Include key features and special offers, using a tone that conveys feelings of surprise and excitement." This allows users to perform complex information generation processes in a simple manner.

[0752] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0753] Step 1:

[0754] The user enters instructions for information generation on a terminal. These instructions include the subject and emotional tone of the information to be generated. The terminal converts these instructions into a digital format, preparing them for transmission to the server.

[0755] Step 2:

[0756] The server receives instructions sent from the terminal. To analyze the received data, it uses natural language processing techniques to extract keywords and sentiment information. Tools such as the Google Cloud Natural Language API are used here, which clarify important information and emotional tone. As output, the extracted data is prepared as input for a generating AI.

[0757] Step 3:

[0758] The server generates information based on important and emotional information extracted using a generative AI. The generative AI used is OpenAI's GPT model, among others, which generates optimal information content according to the instructions. The generated content is temporarily stored on the server and then proceeds to an adjustment process by the emotion engine.

[0759] Step 4:

[0760] The emotion engine analyzes the generated information content and adjusts the tone and style based on the emotional information. At this stage, final adjustments are made to ensure that the generated information appropriately reflects the user's intended emotions. The adjusted information content is then finalized on the server.

[0761] Step 5:

[0762] Once the information content is adjusted, the server sends the final version to the terminal. The terminal displays the received information to the user, who then reviews the generated information. If the user wishes to make corrections, the feedback is sent back to the server, and the process from step 2 is repeated as needed.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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."

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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 to be incorporated by reference.

[0784] The following is further disclosed regarding the embodiments described above.

[0785] (Claim 1)

[0786] A means of receiving instructions from the user and extracting data for content generation,

[0787] A means of analyzing received data using natural language processing technology and identifying key information,

[0788] A means of generating content using a generative AI based on identified key information,

[0789] A means of sending the generated content to the user,

[0790] A system that includes this.

[0791] (Claim 2)

[0792] The system according to claim 1, further comprising means for receiving modifications to generated content from a user and regenerating the content.

[0793] (Claim 3)

[0794] The system according to claim 1, further comprising storage means for storing generated content.

[0795] "Example 1"

[0796] (Claim 1)

[0797] A means for receiving instructions from the user via a terminal and extracting information for content generation,

[0798] A means for analyzing received information using natural language processing technology, identifying important information, and forming prompt sentences suitable for a generative AI model,

[0799] A means of automatically generating content using a generative AI model based on identified important information,

[0800] A means of sending automatically generated content to the user's device and obtaining confirmation from the user,

[0801] A system that includes this.

[0802] (Claim 2)

[0803] The system according to claim 1, further comprising a process for receiving modifications to generated content from a user and performing re-analysis and regeneration.

[0804] (Claim 3)

[0805] The system according to claim 1, wherein the generated content is stored in a data storage means and made reusable.

[0806] "Application Example 1"

[0807] (Claim 1)

[0808] A means of receiving instructions from the user and extracting information for content generation,

[0809] A means for analyzing received information using natural language processing technology and identifying specific information,

[0810] A means of creating content using a generative AI based on identified specific information,

[0811] Means of providing the created content to users,

[0812] A means of providing visually generated content in real time via a portable display device,

[0813] A system that includes this.

[0814] (Claim 2)

[0815] The system according to claim 1, further comprising means for receiving modifications to the generated content from a user and regenerating it.

[0816] (Claim 3)

[0817] The system according to claim 1, further comprising a storage means for recording the generated content.

[0818] "Example 2 of combining an emotion engine"

[0819] (Claim 1)

[0820] A means of receiving instructions from the user and extracting information for content generation,

[0821] A means of analyzing received information using natural language processing technology and identifying important information,

[0822] A means of recognizing the user's emotions using an emotion engine based on identified information,

[0823] A means of generating content using generative AI based on recognized emotional information,

[0824] A means of sending the generated content to the user,

[0825] A system that includes this.

[0826] (Claim 2)

[0827] The system according to claim 1, further comprising means for receiving modifications to generated content from a user and regenerating the content.

[0828] (Claim 3)

[0829] The system according to claim 1, further comprising a storage device for storing generated content.

[0830] "Application example 2 when combining with an emotional engine"

[0831] (Claim 1)

[0832] A means for receiving instructions from the user and extracting basic data for information generation,

[0833] A means of analyzing received basic data using natural language processing technology and identifying important information,

[0834] A means of generating information using a generative AI based on identified important information and emotional information, and adjusting it using an emotional engine,

[0835] A means of transmitting the generated information to a person,

[0836] An information generation system that includes this.

[0837] (Claim 2)

[0838] The information generation system according to claim 1, further comprising means for receiving corrections to generated information from a person and regenerating the information.

[0839] (Claim 3)

[0840] The information generation system according to claim 1, further comprising storage means for storing the generated information in a storage device. [Explanation of Symbols]

[0841] 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

1. A means of receiving instructions from the user and extracting data for content generation, A means of analyzing received data using natural language processing technology and identifying key information, A means of generating content using a generative AI based on identified key information, A means of sending the generated content to the user, A system that includes this.

2. The system according to claim 1, further comprising means for receiving modifications to generated content from a user and regenerating the content.

3. The system according to claim 1, further comprising storage means for storing generated content.

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