system

Abstract

We provide the system. [Solution] A means of embedding invisible identification information into the generated information, A means for tracking the number of times generated information is generated and its usage, A means of calculating and distributing profits based on contribution, Means for providing the generated information to the user, A system that includes this.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Since the identification management of the copyright holders for the generated information is not carried out, it is difficult to fairly distribute the profits. In addition, it is necessary to provide an environment in which users can use it with confidence.

Means for Solving the Problems

[0005] The present invention embeds identification information invisible in the generated information to manage the copyright holder information. In addition, by providing a system that tracks the number of generations and usage status of the generated information, calculates the profit based on the contribution degree, and distributes it by a non-linear algorithm, these problems are solved.

[0006] The "generated information" refers to the content newly created by the generative AI model.

[0007] "Invisible identification information" refers to information about copyright holders that is embedded in content using steganography techniques and whose existence is not recognized during normal use.

[0008] "Generation count" refers to the number of times a particular piece of content has been created by the generation AI.

[0009] "Usage status" refers to data that tracks how and to what extent the generated information is being used.

[0010] "Contribution" refers to a numerical representation of the degree of contribution made by each copyright holder and developer involved in the creation of the content.

[0011] A "nonlinear calculation algorithm" refers to a calculation method that uses more complex functions, rather than simple linear functions, to calculate and distribute profits.

[0012] A "system" refers to a collection of automated processes or functions combined to achieve a specific purpose. [Brief explanation of the drawing]

[0013] [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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 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 Example 2 when an 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 an emotion engine is combined.

Mode for Carrying Out the Invention

[0014] 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.

[0015] First, the language used in the following description will be explained.

[0016] 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.

[0017] 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.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] An effective way to implement the invention is to construct a content generation AI management system. This system embeds invisible identification information into the generated information, protecting the rights of copyright holders while fairly distributing revenue.

[0035] System Configuration

[0036] User: The user inputs information through the interface of the generative AI service. The interface is typically provided via a web browser or a dedicated application. The user sends a request to the generative AI by specifying the prompts and detailed parameters required for content generation.

[0037] Server: The server receives requests from users and activates a generative AI model to generate content according to the specified parameters. During the generation process, the server embeds invisible identification information into the generated content. This identification information includes data to ensure traceability, such as copyright holder information and a unique ID based on the generation parameters.

[0038] Terminal: The terminal receives the results of processing user requests and displays the generated content. The terminal also provides functionality to allow users to review and edit the generated content.

[0039] Specific example

[0040] 1. Novel Generation: The server receives the novel plot from the user and generates chapters and sections based on the specified style and tone. The generated text is embedded with an invisible watermark containing copyright information. It is then displayed to the user via their terminal, where they can review the content and edit it if necessary.

[0041] 2. Music Content Production: Users input their preferred genre, tempo, and instruments to be used into the server. The server generates music tracks using a music generation AI model and embeds identification information. The generated music is delivered to the user via their device, and they can listen to it.

[0042] By implementing this system, copyright holders can ensure their creative works are properly managed, and users can enjoy a safe environment for using generated content. Revenue is accurately calculated based on contribution through the system's tracking mechanism, enabling a new business model powered by AI.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user inputs the necessary data (prompts, parameters, etc.) to provide to the generating AI and sends the request to the server through the interface on the terminal.

[0046] Step 2:

[0047] The terminal converts the data entered by the user into the appropriate format and sends it to the server according to the communication protocol.

[0048] Step 3:

[0049] The server analyzes the request received from the terminal and launches a generation AI model according to the specified conditions. The AI ​​model generates content based on the user's request.

[0050] Step 4:

[0051] The server embeds invisible identifiers into the generated content using steganography techniques. This identifier includes copyright holder identification information and metadata about the generation process.

[0052] Step 5:

[0053] The server stores the generated content and related data (such as metadata and identification information used during generation) in a database.

[0054] Step 6:

[0055] The server sends the generated content to the terminal. The terminal receives this data and displays it in an appropriate format for user access.

[0056] Step 7:

[0057] The user reviews the displayed content and edits it as needed. These edits are saved on the device.

[0058] Step 8:

[0059] The server tracks content usage and analyzes the data for revenue sharing. It retains revenue calculated based on contribution and performs procedures to distribute it appropriately to copyright holders.

[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] The challenge lies in ensuring copyright protection and fair revenue sharing in content generation using generative AI. Specifically, it is necessary to add transparent identification information to the generated content, track its generation history and usage, and provide appropriate revenue sharing based on contribution. Furthermore, it is essential to ensure the convenience of users being able to review and edit the generated content.

[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 inputting information using an information processing device, means for generating content based on received prompts and parameters using a generation AI model, means for embedding invisible identification information into the generated content, means for displaying and making editable the generated content on a terminal device, means for tracking the number of times the generated content has been generated and its usage status, and means for calculating and distributing revenue based on contribution. This ensures traceability for the generated content and enables fair and efficient revenue distribution and improved convenience for content use.

[0065] An "information processing device" is a general term for electronic devices that have functions such as data input, calculation processing, and information output.

[0066] A "generative AI model" is an artificial intelligence system that incorporates an algorithm that automatically generates content based on given data and prompt text.

[0067] A "prompt" is input data that serves as hints and guidelines necessary for a generative AI model to generate content.

[0068] "Identification information" refers to information used to identify specific data or objects, and typically includes unique IDs and owner information.

[0069] "Terminal device" refers to a computer or electronic device used by a user to directly operate and input / output information.

[0070] "Tracking generation count and usage" is the process of recording and managing how many times generated content has been created and how it has been used.

[0071] "Revenue calculation based on contribution" is a method of fairly calculating revenue by evaluating the contribution of each involved element and participant.

[0072] A "nonlinear computation algorithm" refers to a computation method that uses complex computation techniques where the input and output are not linearly related, and which particularly takes into account the interactions and non-uniformity between elements.

[0073] This invention aims to efficiently create and manage content through a content generation AI management system.

[0074] Hardware and software to be used:

[0075] Users utilize information processing devices to generate content. These devices are typically personal computers, smartphones, or tablets, and they access the AI ​​service interface via a web browser or dedicated application.

[0076] The server processes requests and generates content using a generative AI model. This AI model utilizes natural language processing and machine learning techniques to automatically generate content based on diverse data.

[0077] Generative AI models operate in cloud computing or on-premises environments and perform complex data processing and calculations.

[0078] Data processing or data manipulation:

[0079] The server receives a prompt message from the user, parses its contents, and determines the necessary generation parameters.

[0080] The generated content automatically has an invisible watermark embedded by the server as identification information. This watermark contributes to maintaining traceability and protecting the rights of copyright holders.

[0081] The device provides users with the ability to view content and perform simple edits as needed.

[0082] Specific example:

[0083] A user wants to generate the first chapter of a novel and enters a prompt such as "a medieval fantasy adventure story." Based on this prompt, a server-side AI model operates and automatically generates text that reflects the specified style and tone. The resulting novel text is watermarked with identifying information.

[0084] This system allows users to quickly generate high-quality content while simultaneously achieving fair revenue sharing and copyright protection.

[0085] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0086] Step 1:

[0087] The user accesses the AI ​​generation service using their device. Through the user interface, they input a prompt (e.g., "A medieval fantasy adventure story") and set the detailed parameters required for generation (e.g., word count, style). This input data is sent to the server as a generation request.

[0088] Step 2:

[0089] The server parses the prompt and parameters received from the user. This analysis extracts the information necessary to generate the requested content and prepares it for the generation AI model. The server configures the model appropriately based on the input data and then invokes the generation AI model.

[0090] Step 3:

[0091] The server activates a generative AI model and generates content using the prompt text and extracted parameters. During this process, the AI ​​performs natural language processing and automatically constructs content that matches the specified style and tone through data calculations. The generated content is obtained as output data.

[0092] Step 4:

[0093] The server embeds invisible identifying information into the generated content. This is implemented as a watermark containing copyright holder information and generation date and time, based on a specified algorithm. This process adds an identifying feature to the generated data.

[0094] Step 5:

[0095] The server sends content with embedded identification information to the device. After the data transfer is complete, the user can visually confirm the generated content through the user interface on the device.

[0096] Step 6:

[0097] Users can review the generated content displayed on their device and edit it as needed, such as correcting typos or adjusting sentences. After reviewing, users can save or export the edited content.

[0098] (Application Example 1)

[0099] 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."

[0100] In the creation and distribution of digital content, it is difficult to ensure copyright protection and fair distribution of profits. Furthermore, there is a need to ensure effective traceability to prevent the misuse of generated content. Under these circumstances, it is necessary to develop an environment where users can safely verify and widely share the content they generate.

[0101] 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.

[0102] In this invention, the server includes means for embedding invisible identification data in the generated digital information, means for tracking the number of times the generated digital information has been generated and its usage, and means for calculating and distributing rewards based on contribution. This enables copyright protection and fair distribution of profits.

[0103] "Digital information" refers to content and data created in electronic format, including text, music, and video generated by generative AI models.

[0104] "Identification data" refers to invisible data embedded in generated digital information, which allows for the identification of copyright holders and generation parameters.

[0105] "Traceability" refers to tracking the history of digital information from its creation to its use, and clarifying its usage and copyright ownership.

[0106] "Rewards" refer to monetary compensation distributed to users and copyright holders based on their contribution to the digital information created using the generative AI model.

[0107] A "smart device" refers to a portable, high-functioning information terminal, such as a smartphone, that is used by users to view and share digitally generated information.

[0108] In the system for realizing this invention, a server plays a central role. The user accesses the generative AI service interface via a smart device and inputs prompts and detailed parameters. An example of a prompt is "jazz, instrumentation: piano, saxophone, 5 minutes, energetic." Based on this input, the server activates the generative AI model and generates digital content that matches the specified parameters.

[0109] The server embeds invisible identification data into the generated content, clearly identifying copyright holders and generation conditions. This identification data ensures the traceability of the generated content and prevents misuse. The server also tracks the number of generations and usage, calculating and distributing rewards based on contribution. This ensures fair revenue sharing.

[0110] Users can review digital content they create using smart devices and edit or share it as needed. For example, they can listen to a music track they've created, review the result, and then send it to friends or family. Smart devices such as smartphones and tablets leverage their computing power to provide an interface for users to instantly access and utilize the digital information they generate.

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] The user inputs prompts and detailed parameters for the AI ​​generation model through the smart device interface. These prompts might be phrases like "Jazz, Instrumentation: Piano, Saxophone, 5 mins, Energetic." This sends a specific music generation request to the server.

[0114] Step 2:

[0115] The server analyzes the received prompt message and activates an AI model based on the specified style and tone. Using the prompt message in the input data, the generating AI model calculates appropriate digital content. The output is a music track in the style desired by the user. The server internally uses the AI ​​model to generate content suitable for the prompt.

[0116] Step 3:

[0117] The server embeds invisible identification data into the generated digital content. This identification data includes copyright holder information and generation conditions, forming the basis for traceability. The output of the server at this stage is content with the identification data embedded.

[0118] Step 4:

[0119] The server records the number of times generated content is created and its usage, and uses this information to calculate rewards based on contribution. The server collects this information and prepares the basic data for appropriate reward distribution. The output is the calculated reward information.

[0120] Step 5:

[0121] The terminal receives digital content, including identification data provided by the user, and provides an interface that allows the user to view, edit, and share it. Using a smart device, the user can review the generated music track and modify or share it as needed. The output is user-manipulable digital content.

[0122] 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.

[0123] This invention improves the accuracy of content presentation and revenue distribution by incorporating an emotion engine that recognizes user emotions into a content generation AI management system. This system has the functionality to embed invisible identifying information into generated information and to track the number of times information is generated and its usage. Furthermore, it has a means for calculating and distributing revenue based on contribution, and uses the emotion engine to enable user-responsive interaction.

[0124] System Configuration

[0125] User: Users access the system using a device that includes an interface for recognizing emotions. User reactions and feedback are continuously collected.

[0126] Device: The device is equipped with an emotion engine that uses sensors such as cameras and microphones to analyze the user's emotions (e.g., joy, sadness, surprise, etc.) in real time. The user's emotion data is used to adjust how the generated information is delivered.

[0127] Server: The server optimizes the display of generated content based on sentiment data sent from the terminal. It manages features to make the user experience more personalized based on identification information and generated content. It also combines sentiment data with other usage data to perform more sophisticated usage analysis and revenue sharing.

[0128] Specific example

[0129] 1. In the case of educational content: When a user uses an AI-generated educational program, the emotion engine analyzes in real time whether the user is interested in the content. Based on this information, the server adjusts the learning pace and difficulty level to provide an environment in which the user can continue learning with enthusiasm.

[0130] 2. Applications in the entertainment sector: Analyze the emotional responses of users who use movie and music content, highlighting points of interest and suggesting next recommended content. This improves the viewing experience and extracts usage patterns to maximize revenue.

[0131] As described above, by incorporating an emotion engine, the AI-generated content management system can improve the user experience and achieve efficient and fair revenue distribution. This embodiment of the invention presents a new methodology for the generation and provision of digital content.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The user inputs and sends a request to the generating AI through the terminal's interface. The terminal converts this request into the appropriate format and prepares it for transmission to the server.

[0135] Step 2:

[0136] The server analyzes the request data received from the terminal and activates the generative AI model according to the specified conditions. The generative AI model generates content based on the user's input.

[0137] Step 3:

[0138] The server embeds invisible identifying information into the generated content using steganography techniques. This information includes identifying information related to the copyright holder and generation parameters.

[0139] Step 4:

[0140] The server sends the generated content back to the terminal and saves it to the database. Initial setup for tracking user activity is also performed at this stage.

[0141] Step 5:

[0142] The device activates an emotion engine and monitors the user's emotions in real time through its camera and microphone. The device analyzes the collected emotion data and sends it to a server.

[0143] Step 6:

[0144] The server adjusts how generated content is displayed based on sentiment data sent from the device. For example, if the user shows no interest, it displays options to suggest other relevant content.

[0145] Step 7:

[0146] Users view or use the provided content and obtain an experience that matches their emotions. If necessary, users can send feedback to the server via their device.

[0147] Step 8:

[0148] The server combines collected sentiment data with other usage data to calculate revenue sharing based on contribution. Each time revenue is generated, the management system is updated to ensure it is distributed appropriately to copyright holders.

[0149] (Example 2)

[0150] 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".

[0151] Traditional content management systems have struggled to optimize individual user experiences while considering user emotions and to ensure fair revenue sharing. In particular, dynamic interaction and channel optimization, which combine generated information with user emotion data, has been difficult to achieve due to technical limitations.

[0152] 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.

[0153] In this invention, the server includes means for embedding invisible identification information into the generated information, means for tracking the number of times the generated information is generated and its usage, and means for calculating and distributing revenue based on contribution. This enables the optimization of individual experiences based on user sentiment data and fair revenue distribution.

[0154] "Generated information" refers to digital content and data generated by artificial intelligence models.

[0155] "Invisible identification information" refers to information that enables tracking of specific data or information embedded in a form that is not directly displayed to the user.

[0156] "Generation count" refers to a metric used to track how many times a particular piece of information or content is generated.

[0157] "Usage status" refers to data that shows how and to what extent information and content generated within the system are being used.

[0158] "Contribution" refers to an indicator that shows how much each user or content creator contributed to revenue.

[0159] "Real-time analysis" refers to the process of immediately processing collected data and deriving results.

[0160] "Optimizing individual experiences" refers to the process of delivering more specific and personalized services and content based on each user's profile and behavior.

[0161] "Dynamic adjustment" refers to a process that automatically changes settings and conditions according to the current situation.

[0162] This invention is a system based on a generative AI content management system that provides a personalized content experience using user emotion data. Specifically, it uses a terminal equipped with an emotion engine to collect user reactions and feedback in real time and send them to a server. The server analyzes the user's emotion data using a generative AI model and dynamically provides optimized content.

[0163] Hardware and software

[0164] The device is equipped with sensors such as a camera and microphone, and has an emotion engine built into it. This allows it to analyze the user's facial expressions and voice in real time. The emotion engine installed in the device has the function of classifying the user's emotions into categories such as joy, sadness, and surprise, and generating data.

[0165] The server integrates generated sentiment data and content identification information, and uses algorithms to identify and provide the most suitable content. The server runs a generative AI model, which generates customized content for each user based on past usage and sentiment data.

[0166] Specific example

[0167] 1. In the case of educational content:

[0168] The user is using an educational application equipped with an emotion engine. The server analyzes whether the user is interested in the problem and adjusts the difficulty level of the learning content based on that information. For example, if the user looks bored, the server will provide a more challenging problem. A specific example of a prompt might be, "What should be provided next when this user is feeling bored?"

[0169] 2. Applications in the entertainment field:

[0170] For users of movie and music streaming services, the device analyzes the user's emotions. If surprise or excitement is detected, this information is sent to the server and used to recommend the next content to watch. For example, a possible prompt message might be, "Based on the scene that excited the user the most, select a recommended title to watch next."

[0171] In this way, personalized user experiences and fair revenue sharing are realized through emotional data analysis. This invention opens up new possibilities for content provision.

[0172] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0173] Step 1:

[0174] The user selects content using the device. The input includes the user's selected content ID and the video and audio data obtained at that moment. The device uses its built-in camera and microphone to capture the user's facial expressions and voice tone in real time from this data and analyze their emotions. The output is the analyzed emotion data.

[0175] Step 2:

[0176] The device sends the sentiment data acquired in Step 1 to the server. The inputs are user identification information and sentiment data. The server receives the data and uses a generative AI model to analyze it and determine which content will interest the user. The output is a list of optimal content candidates analyzed by the generative AI model.

[0177] Step 3:

[0178] The server selects content tailored to the user's interests and emotions based on the analysis results from a generative AI model. Inputs include user emotion data, content IDs, and usage history. The server uses an algorithm to optimize the user experience by listing personalized content. The output is a personalized content list.

[0179] Step 4:

[0180] The server sends optimized content to the terminal and provides it to the user. The input is the content list generated in step 3. The terminal displays recommended content to the user. Specifically, content that the user has shown interest in is displayed preferentially on the screen. The output is the provided content and the associated user reaction data.

[0181] Step 5:

[0182] The server calculates and distributes revenue based on the user's viewing behavior and the new sentiment data obtained in step 4. Inputs include the user's viewing history data, sentiment data, and usage data. The server uses a non-linear calculation algorithm to ensure fair revenue distribution to each content provider. The output is revenue distribution information for each content provider.

[0183] (Application Example 2)

[0184] 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 device 14 will be referred to as the "terminal."

[0185] Traditional content management systems have struggled to present content that takes user emotions into account, resulting in limited personalized experiences. Furthermore, revenue sharing has lacked fairness and transparency. Therefore, there is a need for a new system that analyzes user emotions in real time and improves the user experience by providing optimal content presentation methods accordingly.

[0186] 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.

[0187] In this invention, the server includes means for embedding invisible identification data in the generated data, means for tracking the number of times the generated data is generated and its usage, means for calculating and distributing profits based on contribution, means for analyzing user emotions and optimizing the content presentation method, and means for suggesting relevant content based on user emotion data. This enables the presentation of personalized content that responds to user emotions and fair revenue distribution.

[0188] "Generated data" refers to information or content that is automatically created using AI.

[0189] "Invisible identification data" refers to identifiers embedded in an invisible way, used to identify the source and rights holders of content and data.

[0190] "Contribution" refers to a numerical representation of the extent to which each participant contributed to the overall outcome.

[0191] "Profit sharing" is the process of allocating and distributing profits obtained based on generated data to multiple stakeholders according to their respective contributions.

[0192] "User emotion analysis" is a technology that uses sensors and algorithms to detect and analyze a user's emotions in real time.

[0193] "Means of optimizing content presentation methods" refer to processes or technologies for delivering content in the most appropriate way, based on user preferences and emotional states.

[0194] "Methods for suggesting relevant content" refer to methods for recommending content that is likely to be of interest to a user based on their past usage history and current emotional state.

[0195] To implement this invention, integration of a server, a user terminal, and an emotion analysis engine is required. The user accesses the generated content using a terminal equipped with a camera and microphone. The emotion engine installed in the terminal analyzes the user's facial expressions and voice tone in real time and generates emotion data.

[0196] The server receives this sentiment data and uses a generative AI model to determine the optimal way to present content based on the user's emotions. Specifically, it identifies points to emphasize in the content being viewed based on the sentiment data and selects relevant content to suggest next. The server also embeds invisible identification data into the generated information and tracks the number of times it is generated and its usage. Furthermore, it calculates and distributes profits fairly based on contribution.

[0197] For example, if a user shows signs of boredom while watching a movie, the emotion engine recognizes this situation, and the server suggests an action movie as the next recommended content. Another example of a prompt to the generative AI model for this process is, "Identify whether the user is enjoying themselves and suggest content that will enhance their entertainment experience." Such a system can make the user experience more personalized and improve user satisfaction.

[0198] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0199] Step 1:

[0200] Users view content using a device equipped with a camera and microphone. During this stage, the device captures the user's facial expressions and voice. Inputs include camera images and audio data, while outputs include the user's facial expressions and voice tone. Sensor information is acquired in real time and sent to an emotion analysis engine.

[0201] Step 2:

[0202] The device uses an emotion analysis engine to analyze the user's emotions from acquired facial and voice data. Specifically, it identifies emotions such as joy, sadness, and surprise based on subtle facial movements and voice pitch. The input is the data acquired in step 1, and the output is the emotion data as a result of the analysis.

[0203] Step 3:

[0204] The server receives sentiment data sent from the terminal and uses a generative AI model to calculate the optimal way to present content based on the user's emotions. Here, the input is sentiment data and information about the content currently being viewed, and the output is the updated content presentation method. The generative AI model learns from past data and generates prompts to provide the user with more engaging content.

[0205] Step 4:

[0206] The server embeds invisible identification data into the content and tracks its generation count and usage. This allows it to record how often specific content is used. The input is the generated content, and the output is the content with the embedded identification data and usage statistics.

[0207] Step 5:

[0208] The server calculates revenue based on contribution and distributes it fairly to participants. This calculation is based on usage statistics and identification data. The input is tracked usage data, and the output is specific revenue information for each participant.

[0209] Step 6:

[0210] The system presents user-optimized content and suggests related new content. Input is optimized content information from the server, and output is the presentation and suggestion of new content to the user. The terminal displays recommended content based on the user's sentiment data.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] [Second Embodiment]

[0215] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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.

[0220] 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).

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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".

[0227] An effective way to implement the invention is to construct a content generation AI management system. This system embeds invisible identification information into the generated information, protecting the rights of copyright holders while fairly distributing revenue.

[0228] System Configuration

[0229] User: The user inputs information through the interface of the generative AI service. The interface is typically provided via a web browser or a dedicated application. The user sends a request to the generative AI by specifying the prompts and detailed parameters required for content generation.

[0230] Server: The server receives requests from users and activates a generative AI model to generate content according to the specified parameters. During the generation process, the server embeds invisible identification information into the generated content. This identification information includes data to ensure traceability, such as copyright holder information and a unique ID based on the generation parameters.

[0231] Terminal: The terminal receives the results of processing user requests and displays the generated content. The terminal also provides functionality to allow users to review and edit the generated content.

[0232] Specific example

[0233] 1. Novel Generation: The server receives the novel plot from the user and generates chapters and sections based on the specified style and tone. The generated text is embedded with an invisible watermark containing copyright information. It is then displayed to the user via their terminal, where they can review the content and edit it if necessary.

[0234] 2. Music Content Production: Users input their preferred genre, tempo, and instruments to be used into the server. The server generates music tracks using a music generation AI model and embeds identification information. The generated music is delivered to the user via their device, and they can listen to it.

[0235] By implementing this system, copyright holders can ensure their creative works are properly managed, and users can enjoy a safe environment for using generated content. Revenue is accurately calculated based on contribution through the system's tracking mechanism, enabling a new business model powered by AI.

[0236] The following describes the processing flow.

[0237] Step 1:

[0238] The user inputs the necessary data (prompts, parameters, etc.) to provide to the generating AI and sends the request to the server through the interface on the terminal.

[0239] Step 2:

[0240] The terminal converts the data entered by the user into the appropriate format and sends it to the server according to the communication protocol.

[0241] Step 3:

[0242] The server analyzes the request received from the terminal and launches a generation AI model according to the specified conditions. The AI ​​model generates content based on the user's request.

[0243] Step 4:

[0244] The server embeds invisible identifiers into the generated content using steganography techniques. This identifier includes copyright holder identification information and metadata about the generation process.

[0245] Step 5:

[0246] The server stores the generated content and related data (such as metadata and identification information used during generation) in a database.

[0247] Step 6:

[0248] The server sends the generated content to the terminal. The terminal receives this data and displays it in an appropriate format for user access.

[0249] Step 7:

[0250] The user reviews the displayed content and edits it as needed. These edits are saved on the device.

[0251] Step 8:

[0252] The server tracks content usage and analyzes the data for revenue sharing. It retains revenue calculated based on contribution and performs procedures to distribute it appropriately to copyright holders.

[0253] (Example 1)

[0254] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0255] The challenge lies in ensuring copyright protection and fair revenue sharing in content generation using generative AI. Specifically, it is necessary to add transparent identification information to the generated content, track its generation history and usage, and provide appropriate revenue sharing based on contribution. Furthermore, it is essential to ensure the convenience of users being able to review and edit the generated content.

[0256] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0257] In this invention, the server includes means for inputting information using an information processing device, means for generating content based on received prompts and parameters using a generation AI model, means for embedding invisible identification information into the generated content, means for displaying and making editable the generated content on a terminal device, means for tracking the number of times the generated content has been generated and its usage status, and means for calculating and distributing revenue based on contribution. This ensures traceability for the generated content and enables fair and efficient revenue distribution and improved convenience for content use.

[0258] An "information processing device" is a general term for electronic devices that have functions such as data input, calculation processing, and information output.

[0259] A "generative AI model" is an artificial intelligence system that incorporates an algorithm that automatically generates content based on given data and prompt text.

[0260] A "prompt" is input data that serves as hints and guidelines necessary for a generative AI model to generate content.

[0261] "Identification information" refers to information used to identify specific data or objects, and typically includes unique IDs and owner information.

[0262] "Terminal device" refers to a computer or electronic device used by a user to directly operate and input / output information.

[0263] "Tracking generation count and usage" is the process of recording and managing how many times generated content has been created and how it has been used.

[0264] "Revenue calculation based on contribution" is a method of fairly calculating revenue by evaluating the contribution of each involved element and participant.

[0265] A "nonlinear computation algorithm" refers to a computation method that uses complex computation techniques where the input and output are not linearly related, and which particularly takes into account the interactions and non-uniformity between elements.

[0266] This invention aims to efficiently create and manage content through a content generation AI management system.

[0267] Hardware and software to be used:

[0268] Users utilize information processing devices to generate content. These devices are typically personal computers, smartphones, or tablets, and they access the AI ​​service interface via a web browser or dedicated application.

[0269] The server processes requests and generates content using a generative AI model. This AI model utilizes natural language processing and machine learning techniques to automatically generate content based on diverse data.

[0270] Generative AI models operate in cloud computing or on-premises environments and perform complex data processing and calculations.

[0271] Data processing or data manipulation:

[0272] The server receives a prompt message from the user, parses its contents, and determines the necessary generation parameters.

[0273] The generated content automatically has an invisible watermark embedded by the server as identification information. This watermark contributes to maintaining traceability and protecting the rights of copyright holders.

[0274] The device provides users with the ability to view content and perform simple edits as needed.

[0275] Specific example:

[0276] A user wants to generate the first chapter of a novel and enters a prompt such as "a medieval fantasy adventure story." Based on this prompt, a server-side AI model operates and automatically generates text that reflects the specified style and tone. The resulting novel text is watermarked with identifying information.

[0277] With this system, users can quickly generate high-quality content while achieving both fair revenue distribution and copyright protection.

[0278] The flow of the specific process in Example 1 will be described using FIG. 11.

[0279] Step 1:

[0280] The user accesses the generation AI service using a terminal. Through the user interface, the user inputs a prompt sentence (e.g., "Medieval fantasy adventure story") and sets detailed parameters required for generation (e.g., number of words, style). These input data are sent to the server as a generation request.

[0281] Step 2:

[0282] The server analyzes the prompt sentence and parameters received from the user. Through this analysis, the information required for the requested content generation is extracted and prepared to be passed to the generation AI model. The server makes appropriate settings based on the input data and calls the generation AI model.

[0283] Step 3:

[0284] The server activates the generation AI model and generates content using the prompt sentence and the extracted parameters. In this process, the AI performs natural language processing and automatically constructs content that matches the specified style and tone through data operations. The generated content is obtained as output data.

[0285] Step 4:

[0286] The server embeds identification information that is not visible in the generated content. This is implemented as a watermark containing the information of the copyright owner and the generation date and time based on the specified algorithm. Through this process, an output with an identification function is added to the generated data.

[0287] Step 5:

[0288] The server sends content with embedded identification information to the device. After the data transfer is complete, the user can visually confirm the generated content through the user interface on the device.

[0289] Step 6:

[0290] Users can review the generated content displayed on their device and edit it as needed, such as correcting typos or adjusting sentences. After reviewing, users can save or export the edited content.

[0291] (Application Example 1)

[0292] 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."

[0293] In the creation and distribution of digital content, it is difficult to ensure copyright protection and fair distribution of profits. Furthermore, there is a need to ensure effective traceability to prevent the misuse of generated content. Under these circumstances, it is necessary to develop an environment where users can safely verify and widely share the content they generate.

[0294] 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.

[0295] In this invention, the server includes means for embedding invisible identification data in the generated digital information, means for tracking the number of times the generated digital information has been generated and its usage, and means for calculating and distributing rewards based on contribution. This enables copyright protection and fair distribution of profits.

[0296] "Digital information" refers to content and data created in electronic format, including text, music, and video generated by generative AI models.

[0297] "Identification data" refers to invisible data embedded in generated digital information, which allows for the identification of copyright holders and generation parameters.

[0298] "Traceability" refers to tracking the history of digital information from its creation to its use, and clarifying its usage and copyright ownership.

[0299] "Rewards" refer to monetary compensation distributed to users and copyright holders based on their contribution to the digital information created using the generative AI model.

[0300] A "smart device" refers to a portable, high-functioning information terminal, such as a smartphone, that is used by users to view and share digitally generated information.

[0301] In the system for realizing this invention, a server plays a central role. The user accesses the generative AI service interface via a smart device and inputs prompts and detailed parameters. An example of a prompt is "jazz, instrumentation: piano, saxophone, 5 minutes, energetic." Based on this input, the server activates the generative AI model and generates digital content that matches the specified parameters.

[0302] The server embeds invisible identification data into the generated content, clearly identifying copyright holders and generation conditions. This identification data ensures the traceability of the generated content and prevents misuse. The server also tracks the number of generations and usage, calculating and distributing rewards based on contribution. This ensures fair revenue sharing.

[0303] The user can view the digital content generated using a smart device and edit or share it as needed. For example, after listening to a music track generated by the user and checking the finished product, operations such as sending it to friends or family can be performed. Smart devices such as smartphones and tablets utilize their computing power to provide an interface for immediately using the digital information generated by the user.

[0304] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0305] Step 1:

[0306] The user inputs a prompt sentence and detailed parameters for the generation AI model through the interface of the smart device. The input at this time is a prompt sentence such as "Jazz, instrument composition: piano, saxophone, 5 minutes, energetic". As a result, a specific music generation request is sent to the server.

[0307] Step 2:

[0308] The server analyzes the received prompt sentence and activates the AI model based on the specified style and tone. Using the prompt sentence of the input data, the generation AI model calculates appropriate digital content. The output is a music track along the style desired by the user. The server utilizes the AI model internally to generate content suitable for the prompt.

[0309] Step 3:

[0310] The server embeds identification data that is invisible in the generated digital content. This identification data includes copyright holder information and generation conditions and serves as the basis for traceability. The output of this stage of the server is the content with the identification data embedded.

[0311] Step 4:

[0312] The server records the number of times generated content is created and its usage, and uses this information to calculate rewards based on contribution. The server collects this information and prepares the basic data for appropriate reward distribution. The output is the calculated reward information.

[0313] Step 5:

[0314] The terminal receives digital content, including identification data provided by the user, and provides an interface that allows the user to view, edit, and share it. Using a smart device, the user can review the generated music track and modify or share it as needed. The output is user-manipulable digital content.

[0315] 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.

[0316] This invention improves the accuracy of content presentation and revenue distribution by incorporating an emotion engine that recognizes user emotions into a content generation AI management system. This system has the functionality to embed invisible identifying information into generated information and to track the number of times information is generated and its usage. Furthermore, it has a means for calculating and distributing revenue based on contribution, and uses the emotion engine to enable user-responsive interaction.

[0317] System Configuration

[0318] User: Users access the system using a device that includes an interface for recognizing emotions. User reactions and feedback are continuously collected.

[0319] Device: The device is equipped with an emotion engine that uses sensors such as cameras and microphones to analyze the user's emotions (e.g., joy, sadness, surprise, etc.) in real time. The user's emotion data is used to adjust how the generated information is delivered.

[0320] Server: The server optimizes the display of generated content based on sentiment data sent from the terminal. It manages features to make the user experience more personalized based on identification information and generated content. It also combines sentiment data with other usage data to perform more sophisticated usage analysis and revenue sharing.

[0321] Specific example

[0322] 1. In the case of educational content: When a user uses an AI-generated educational program, the emotion engine analyzes in real time whether the user is interested in the content. Based on this information, the server adjusts the learning pace and difficulty level to provide an environment in which the user can continue learning with enthusiasm.

[0323] 2. Applications in the entertainment sector: Analyze the emotional responses of users who use movie and music content, highlighting points of interest and suggesting next recommended content. This improves the viewing experience and extracts usage patterns to maximize revenue.

[0324] As described above, by incorporating an emotion engine, the AI-generated content management system can improve the user experience and achieve efficient and fair revenue distribution. This embodiment of the invention presents a new methodology for the generation and provision of digital content.

[0325] The following describes the processing flow.

[0326] Step 1:

[0327] The user inputs and sends a request to the generating AI through the terminal's interface. The terminal converts this request into the appropriate format and prepares it for transmission to the server.

[0328] Step 2:

[0329] The server analyzes the request data received from the terminal and activates the generative AI model according to the specified conditions. The generative AI model generates content based on the user's input.

[0330] Step 3:

[0331] The server embeds invisible identifying information into the generated content using steganography techniques. This information includes identifying information related to the copyright holder and generation parameters.

[0332] Step 4:

[0333] The server sends the generated content back to the terminal and saves it to the database. Initial setup for tracking user activity is also performed at this stage.

[0334] Step 5:

[0335] The device activates an emotion engine and monitors the user's emotions in real time through its camera and microphone. The device analyzes the collected emotion data and sends it to a server.

[0336] Step 6:

[0337] The server adjusts how generated content is displayed based on sentiment data sent from the device. For example, if the user shows no interest, it displays options to suggest other relevant content.

[0338] Step 7:

[0339] Users view or use the provided content and obtain an experience that matches their emotions. If necessary, users can send feedback to the server via their device.

[0340] Step 8:

[0341] The server combines collected sentiment data with other usage data to calculate revenue sharing based on contribution. Each time revenue is generated, the management system is updated to ensure it is distributed appropriately to copyright holders.

[0342] (Example 2)

[0343] 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".

[0344] Traditional content management systems have struggled to optimize individual user experiences while considering user emotions and to ensure fair revenue sharing. In particular, dynamic interaction and channel optimization, which combine generated information with user emotion data, has been difficult to achieve due to technical limitations.

[0345] 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.

[0346] In this invention, the server includes means for embedding invisible identification information into the generated information, means for tracking the number of times the generated information is generated and its usage, and means for calculating and distributing revenue based on contribution. This enables the optimization of individual experiences based on user sentiment data and fair revenue distribution.

[0347] "Generated information" refers to digital content and data generated by artificial intelligence models.

[0348] "Invisible identification information" refers to information that enables tracking of specific data or information embedded in a form that is not directly displayed to the user.

[0349] "Generation count" refers to a metric used to track how many times a particular piece of information or content is generated.

[0350] "Usage status" refers to data that shows how and to what extent information and content generated within the system are being used.

[0351] "Contribution" refers to an indicator that shows how much each user or content creator contributed to revenue.

[0352] "Real-time analysis" refers to the process of immediately processing collected data and deriving results.

[0353] "Optimizing individual experiences" refers to the process of delivering more specific and personalized services and content based on each user's profile and behavior.

[0354] "Dynamic adjustment" refers to a process that automatically changes settings and conditions according to the current situation.

[0355] This invention is a system based on a generative AI content management system that provides a personalized content experience using user emotion data. Specifically, it uses a terminal equipped with an emotion engine to collect user reactions and feedback in real time and send them to a server. The server analyzes the user's emotion data using a generative AI model and dynamically provides optimized content.

[0356] Hardware and software

[0357] The device is equipped with sensors such as a camera and microphone, and has an emotion engine built into it. This allows it to analyze the user's facial expressions and voice in real time. The emotion engine installed in the device has the function of classifying the user's emotions into categories such as joy, sadness, and surprise, and generating data.

[0358] The server integrates generated sentiment data and content identification information, and uses algorithms to identify and provide the most suitable content. The server runs a generative AI model, which generates customized content for each user based on past usage and sentiment data.

[0359] Specific example

[0360] 1. In the case of educational content:

[0361] The user is using an educational application equipped with an emotion engine. The server analyzes whether the user is interested in the problem and adjusts the difficulty level of the learning content based on that information. For example, if the user looks bored, the server will provide a more challenging problem. A specific example of a prompt might be, "What should be provided next when this user is feeling bored?"

[0362] 2. Applications in the entertainment field:

[0363] For users of movie and music streaming services, the device analyzes the user's emotions. If surprise or excitement is detected, this information is sent to the server and used to recommend the next content to watch. For example, a possible prompt message might be, "Based on the scene that excited the user the most, select a recommended title to watch next."

[0364] In this way, personalized user experiences and fair revenue sharing are realized through emotional data analysis. This invention opens up new possibilities for content provision.

[0365] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0366] Step 1:

[0367] The user selects content using the device. The input includes the user's selected content ID and the video and audio data obtained at that moment. The device uses its built-in camera and microphone to capture the user's facial expressions and voice tone in real time from this data and analyze their emotions. The output is the analyzed emotion data.

[0368] Step 2:

[0369] The device sends the sentiment data acquired in Step 1 to the server. The inputs are user identification information and sentiment data. The server receives the data and uses a generative AI model to analyze it and determine which content will interest the user. The output is a list of optimal content candidates analyzed by the generative AI model.

[0370] Step 3:

[0371] The server selects content tailored to the user's interests and emotions based on the analysis results from a generative AI model. Inputs include user emotion data, content IDs, and usage history. The server uses an algorithm to optimize the user experience by listing personalized content. The output is a personalized content list.

[0372] Step 4:

[0373] The server sends optimized content to the terminal and provides it to the user. The input is the content list generated in step 3. The terminal displays recommended content to the user. Specifically, content that the user has shown interest in is displayed preferentially on the screen. The output is the provided content and the associated user reaction data.

[0374] Step 5:

[0375] The server calculates and distributes revenue based on the user's viewing behavior and the new sentiment data obtained in step 4. Inputs include the user's viewing history data, sentiment data, and usage data. The server uses a non-linear calculation algorithm to ensure fair revenue distribution to each content provider. The output is revenue distribution information for each content provider.

[0376] (Application Example 2)

[0377] 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 as the "terminal".

[0378] Traditional content management systems have struggled to present content that takes user emotions into account, resulting in limited personalized experiences. Furthermore, revenue sharing has lacked fairness and transparency. Therefore, there is a need for a new system that analyzes user emotions in real time and improves the user experience by providing optimal content presentation methods accordingly.

[0379] 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.

[0380] In this invention, the server includes means for embedding invisible identification data in the generated data, means for tracking the number of times the generated data is generated and its usage, means for calculating and distributing profits based on contribution, means for analyzing user emotions and optimizing the content presentation method, and means for suggesting relevant content based on user emotion data. This enables the presentation of personalized content that responds to user emotions and fair revenue distribution.

[0381] "Generated data" refers to information or content that is automatically created using AI.

[0382] "Invisible identification data" refers to identifiers embedded in an invisible way, used to identify the source and rights holders of content and data.

[0383] "Contribution" refers to a numerical representation of the extent to which each participant contributed to the overall outcome.

[0384] "Profit sharing" is the process of allocating and distributing profits obtained based on generated data to multiple stakeholders according to their respective contributions.

[0385] "User emotion analysis" is a technology that uses sensors and algorithms to detect and analyze a user's emotions in real time.

[0386] "Means of optimizing content presentation methods" refer to processes or technologies for delivering content in the most appropriate way, based on user preferences and emotional states.

[0387] "Methods for suggesting relevant content" refer to methods for recommending content that is likely to be of interest to a user based on their past usage history and current emotional state.

[0388] To implement this invention, integration of a server, a user terminal, and an emotion analysis engine is required. The user accesses the generated content using a terminal equipped with a camera and microphone. The emotion engine installed in the terminal analyzes the user's facial expressions and voice tone in real time and generates emotion data.

[0389] The server receives this sentiment data and uses a generative AI model to determine the optimal way to present content based on the user's emotions. Specifically, it identifies points to emphasize in the content being viewed based on the sentiment data and selects relevant content to suggest next. The server also embeds invisible identification data into the generated information and tracks the number of times it is generated and its usage. Furthermore, it calculates and distributes profits fairly based on contribution.

[0390] For example, if a user shows signs of boredom while watching a movie, the emotion engine recognizes this situation, and the server suggests an action movie as the next recommended content. Another example of a prompt to the generative AI model for this process is, "Identify whether the user is enjoying themselves and suggest content that will enhance their entertainment experience." Such a system can make the user experience more personalized and improve user satisfaction.

[0391] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0392] Step 1:

[0393] Users view content using a device equipped with a camera and microphone. During this stage, the device captures the user's facial expressions and voice. Inputs include camera images and audio data, while outputs include the user's facial expressions and voice tone. Sensor information is acquired in real time and sent to an emotion analysis engine.

[0394] Step 2:

[0395] The device uses an emotion analysis engine to analyze the user's emotions from acquired facial and voice data. Specifically, it identifies emotions such as joy, sadness, and surprise based on subtle facial movements and voice pitch. The input is the data acquired in step 1, and the output is the emotion data as a result of the analysis.

[0396] Step 3:

[0397] The server receives sentiment data sent from the terminal and uses a generative AI model to calculate the optimal way to present content based on the user's emotions. Here, the input is sentiment data and information about the content currently being viewed, and the output is the updated content presentation method. The generative AI model learns from past data and generates prompts to provide the user with more engaging content.

[0398] Step 4:

[0399] The server embeds invisible identification data into the content and tracks its generation count and usage. This allows it to record how often specific content is used. The input is the generated content, and the output is the content with the embedded identification data and usage statistics.

[0400] Step 5:

[0401] The server calculates revenue based on contribution and distributes it fairly to participants. This calculation is based on usage statistics and identification data. The input is tracked usage data, and the output is specific revenue information for each participant.

[0402] Step 6:

[0403] The system presents user-optimized content and suggests related new content. Input is optimized content information from the server, and output is the presentation and suggestion of new content to the user. The terminal displays recommended content based on the user's sentiment data.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] [Third Embodiment]

[0408] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0409] 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.

[0410] 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).

[0411] 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.

[0412] 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.

[0413] 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).

[0414] 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.

[0415] 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.

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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".

[0420] An effective way to implement the invention is to construct a content generation AI management system. This system embeds invisible identification information into the generated information, protecting the rights of copyright holders while fairly distributing revenue.

[0421] System Configuration

[0422] User: The user inputs information through the interface of the generative AI service. The interface is typically provided via a web browser or a dedicated application. The user sends a request to the generative AI by specifying the prompts and detailed parameters required for content generation.

[0423] Server: The server receives requests from users and activates a generative AI model to generate content according to the specified parameters. During the generation process, the server embeds invisible identification information into the generated content. This identification information includes data to ensure traceability, such as copyright holder information and a unique ID based on the generation parameters.

[0424] Terminal: The terminal receives the results of processing user requests and displays the generated content. The terminal also provides functionality to allow users to review and edit the generated content.

[0425] Specific example

[0426] 1. Novel Generation: The server receives the novel plot from the user and generates chapters and sections based on the specified style and tone. The generated text is embedded with an invisible watermark containing copyright information. It is then displayed to the user via their terminal, where they can review the content and edit it if necessary.

[0427] 2. Music Content Production: Users input their preferred genre, tempo, and instruments to be used into the server. The server generates music tracks using a music generation AI model and embeds identification information. The generated music is delivered to the user via their device, and they can listen to it.

[0428] By implementing this system, copyright holders can ensure their creative works are properly managed, and users can enjoy a safe environment for using generated content. Revenue is accurately calculated based on contribution through the system's tracking mechanism, enabling a new business model powered by AI.

[0429] The following describes the processing flow.

[0430] Step 1:

[0431] The user inputs the necessary data (prompts, parameters, etc.) to provide to the generating AI and sends the request to the server through the interface on the terminal.

[0432] Step 2:

[0433] The terminal converts the data entered by the user into the appropriate format and sends it to the server according to the communication protocol.

[0434] Step 3:

[0435] The server analyzes the request received from the terminal and launches a generation AI model according to the specified conditions. The AI ​​model generates content based on the user's request.

[0436] Step 4:

[0437] The server embeds invisible identifiers into the generated content using steganography techniques. This identifier includes copyright holder identification information and metadata about the generation process.

[0438] Step 5:

[0439] The server stores the generated content and related data (such as metadata and identification information used during generation) in a database.

[0440] Step 6:

[0441] The server sends the generated content to the terminal. The terminal receives this data and displays it in an appropriate format for user access.

[0442] Step 7:

[0443] The user reviews the displayed content and edits it as needed. These edits are saved on the device.

[0444] Step 8:

[0445] The server tracks content usage and analyzes the data for revenue sharing. It retains revenue calculated based on contribution and performs procedures to distribute it appropriately to copyright holders.

[0446] (Example 1)

[0447] 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."

[0448] The challenge lies in ensuring copyright protection and fair revenue sharing in content generation using generative AI. Specifically, it is necessary to add transparent identification information to the generated content, track its generation history and usage, and provide appropriate revenue sharing based on contribution. Furthermore, it is essential to ensure the convenience of users being able to review and edit the generated content.

[0449] 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.

[0450] In this invention, the server includes means for inputting information using an information processing device, means for generating content based on received prompts and parameters using a generation AI model, means for embedding invisible identification information into the generated content, means for displaying and making editable the generated content on a terminal device, means for tracking the number of times the generated content has been generated and its usage status, and means for calculating and distributing revenue based on contribution. This ensures traceability for the generated content and enables fair and efficient revenue distribution and improved convenience for content use.

[0451] An "information processing device" is a general term for electronic devices that have functions such as data input, calculation processing, and information output.

[0452] A "generative AI model" is an artificial intelligence system that incorporates an algorithm that automatically generates content based on given data and prompt text.

[0453] A "prompt" is input data that serves as hints and guidelines necessary for a generative AI model to generate content.

[0454] "Identification information" refers to information used to identify specific data or objects, and typically includes unique IDs and owner information.

[0455] "Terminal device" refers to a computer or electronic device used by a user to directly operate and input / output information.

[0456] "Tracking generation count and usage" is the process of recording and managing how many times generated content has been created and how it has been used.

[0457] "Revenue calculation based on contribution" is a method of fairly calculating revenue by evaluating the contribution of each involved element and participant.

[0458] A "nonlinear computation algorithm" refers to a computation method that uses complex computation techniques where the input and output are not linearly related, and which particularly takes into account the interactions and non-uniformity between elements.

[0459] This invention aims to efficiently create and manage content through a content generation AI management system.

[0460] Hardware and software to be used:

[0461] Users utilize information processing devices to generate content. These devices are typically personal computers, smartphones, or tablets, and they access the AI ​​service interface via a web browser or dedicated application.

[0462] The server processes requests and generates content using a generative AI model. This AI model utilizes natural language processing and machine learning techniques to automatically generate content based on diverse data.

[0463] Generative AI models operate in cloud computing or on-premises environments and perform complex data processing and calculations.

[0464] Data processing or data manipulation:

[0465] The server receives a prompt message from the user, parses its contents, and determines the necessary generation parameters.

[0466] The generated content automatically has an invisible watermark embedded by the server as identification information. This watermark contributes to maintaining traceability and protecting the rights of copyright holders.

[0467] The device provides users with the ability to view content and perform simple edits as needed.

[0468] Specific example:

[0469] A user wants to generate the first chapter of a novel and enters a prompt such as "a medieval fantasy adventure story." Based on this prompt, a server-side AI model operates and automatically generates text that reflects the specified style and tone. The resulting novel text is watermarked with identifying information.

[0470] This system allows users to quickly generate high-quality content while simultaneously achieving fair revenue sharing and copyright protection.

[0471] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0472] Step 1:

[0473] The user accesses the AI ​​generation service using their device. Through the user interface, they input a prompt (e.g., "A medieval fantasy adventure story") and set the detailed parameters required for generation (e.g., word count, style). This input data is sent to the server as a generation request.

[0474] Step 2:

[0475] The server parses the prompt and parameters received from the user. This analysis extracts the information necessary to generate the requested content and prepares it for the generation AI model. The server configures the model appropriately based on the input data and then invokes the generation AI model.

[0476] Step 3:

[0477] The server activates a generative AI model and generates content using the prompt text and extracted parameters. During this process, the AI ​​performs natural language processing and automatically constructs content that matches the specified style and tone through data calculations. The generated content is obtained as output data.

[0478] Step 4:

[0479] The server embeds invisible identifying information into the generated content. This is implemented as a watermark containing copyright holder information and generation date and time, based on a specified algorithm. This process adds an identifying feature to the generated data.

[0480] Step 5:

[0481] The server sends content with embedded identification information to the device. After the data transfer is complete, the user can visually confirm the generated content through the user interface on the device.

[0482] Step 6:

[0483] Users can review the generated content displayed on their device and edit it as needed, such as correcting typos or adjusting sentences. After reviewing, users can save or export the edited content.

[0484] (Application Example 1)

[0485] 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."

[0486] In the creation and distribution of digital content, it is difficult to ensure copyright protection and fair distribution of profits. Furthermore, there is a need to ensure effective traceability to prevent the misuse of generated content. Under these circumstances, it is necessary to develop an environment where users can safely verify and widely share the content they generate.

[0487] 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.

[0488] In this invention, the server includes means for embedding invisible identification data in the generated digital information, means for tracking the number of times the generated digital information has been generated and its usage, and means for calculating and distributing rewards based on contribution. This enables copyright protection and fair distribution of profits.

[0489] "Digital information" refers to content and data created in electronic format, including text, music, and video generated by generative AI models.

[0490] "Identification data" refers to invisible data embedded in generated digital information, which allows for the identification of copyright holders and generation parameters.

[0491] "Traceability" refers to tracking the history of digital information from its creation to its use, and clarifying its usage and copyright ownership.

[0492] "Rewards" refer to monetary compensation distributed to users and copyright holders based on their contribution to the digital information created using the generative AI model.

[0493] A "smart device" refers to a portable, high-functioning information terminal, such as a smartphone, that is used by users to view and share digitally generated information.

[0494] In the system for realizing this invention, a server plays a central role. The user accesses the generative AI service interface via a smart device and inputs prompts and detailed parameters. An example of a prompt is "jazz, instrumentation: piano, saxophone, 5 minutes, energetic." Based on this input, the server activates the generative AI model and generates digital content that matches the specified parameters.

[0495] The server embeds invisible identification data into the generated content, clearly identifying copyright holders and generation conditions. This identification data ensures the traceability of the generated content and prevents misuse. The server also tracks the number of generations and usage, calculating and distributing rewards based on contribution. This ensures fair revenue sharing.

[0496] Users can review digital content they create using smart devices and edit or share it as needed. For example, they can listen to a music track they've created, review the result, and then send it to friends or family. Smart devices such as smartphones and tablets leverage their computing power to provide an interface for users to instantly access and utilize the digital information they generate.

[0497] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0498] Step 1:

[0499] The user inputs prompts and detailed parameters for the AI ​​generation model through the smart device interface. These prompts might be phrases like "Jazz, Instrumentation: Piano, Saxophone, 5 mins, Energetic." This sends a specific music generation request to the server.

[0500] Step 2:

[0501] The server analyzes the received prompt message and activates an AI model based on the specified style and tone. Using the prompt message in the input data, the generating AI model calculates appropriate digital content. The output is a music track in the style desired by the user. The server internally uses the AI ​​model to generate content suitable for the prompt.

[0502] Step 3:

[0503] The server embeds invisible identification data into the generated digital content. This identification data includes copyright holder information and generation conditions, forming the basis for traceability. The output of the server at this stage is content with the identification data embedded.

[0504] Step 4:

[0505] The server records the number of times generated content is created and its usage, and uses this information to calculate rewards based on contribution. The server collects this information and prepares the basic data for appropriate reward distribution. The output is the calculated reward information.

[0506] Step 5:

[0507] The terminal receives digital content, including identification data provided by the user, and provides an interface that allows the user to view, edit, and share it. Using a smart device, the user can review the generated music track and modify or share it as needed. The output is user-manipulable digital content.

[0508] 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.

[0509] This invention improves the accuracy of content presentation and revenue distribution by incorporating an emotion engine that recognizes user emotions into a content generation AI management system. This system has the functionality to embed invisible identifying information into generated information and to track the number of times information is generated and its usage. Furthermore, it has a means for calculating and distributing revenue based on contribution, and uses the emotion engine to enable user-responsive interaction.

[0510] System Configuration

[0511] User: Users access the system using a device that includes an interface for recognizing emotions. User reactions and feedback are continuously collected.

[0512] Device: The device is equipped with an emotion engine that uses sensors such as cameras and microphones to analyze the user's emotions (e.g., joy, sadness, surprise, etc.) in real time. The user's emotion data is used to adjust how the generated information is delivered.

[0513] Server: The server optimizes the display of generated content based on sentiment data sent from the terminal. It manages features to make the user experience more personalized based on identification information and generated content. It also combines sentiment data with other usage data to perform more sophisticated usage analysis and revenue sharing.

[0514] Specific example

[0515] 1. In the case of educational content: When a user uses an AI-generated educational program, the emotion engine analyzes in real time whether the user is interested in the content. Based on this information, the server adjusts the learning pace and difficulty level to provide an environment in which the user can continue learning with enthusiasm.

[0516] 2. Applications in the entertainment sector: Analyze the emotional responses of users who use movie and music content, highlighting points of interest and suggesting next recommended content. This improves the viewing experience and extracts usage patterns to maximize revenue.

[0517] As described above, by incorporating an emotion engine, the AI-generated content management system can improve the user experience and achieve efficient and fair revenue distribution. This embodiment of the invention presents a new methodology for the generation and provision of digital content.

[0518] The following describes the processing flow.

[0519] Step 1:

[0520] The user inputs and sends a request to the generating AI through the terminal's interface. The terminal converts this request into the appropriate format and prepares it for transmission to the server.

[0521] Step 2:

[0522] The server analyzes the request data received from the terminal and activates the generative AI model according to the specified conditions. The generative AI model generates content based on the user's input.

[0523] Step 3:

[0524] The server embeds invisible identifying information into the generated content using steganography techniques. This information includes identifying information related to the copyright holder and generation parameters.

[0525] Step 4:

[0526] The server sends the generated content back to the terminal and saves it to the database. Initial setup for tracking user activity is also performed at this stage.

[0527] Step 5:

[0528] The device activates an emotion engine and monitors the user's emotions in real time through its camera and microphone. The device analyzes the collected emotion data and sends it to a server.

[0529] Step 6:

[0530] The server adjusts how generated content is displayed based on sentiment data sent from the device. For example, if the user shows no interest, it displays options to suggest other relevant content.

[0531] Step 7:

[0532] Users view or use the provided content and obtain an experience that matches their emotions. If necessary, users can send feedback to the server via their device.

[0533] Step 8:

[0534] The server combines collected sentiment data with other usage data to calculate revenue sharing based on contribution. Each time revenue is generated, the management system is updated to ensure it is distributed appropriately to copyright holders.

[0535] (Example 2)

[0536] 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."

[0537] Traditional content management systems have struggled to optimize individual user experiences while considering user emotions and to ensure fair revenue sharing. In particular, dynamic interaction and channel optimization, which combine generated information with user emotion data, has been difficult to achieve due to technical limitations.

[0538] 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.

[0539] In this invention, the server includes means for embedding invisible identification information into the generated information, means for tracking the number of times the generated information is generated and its usage, and means for calculating and distributing revenue based on contribution. This enables the optimization of individual experiences based on user sentiment data and fair revenue distribution.

[0540] "Generated information" refers to digital content and data generated by artificial intelligence models.

[0541] "Invisible identification information" refers to information that enables tracking of specific data or information embedded in a form that is not directly displayed to the user.

[0542] "Generation count" refers to a metric used to track how many times a particular piece of information or content is generated.

[0543] "Usage status" refers to data that shows how and to what extent information and content generated within the system are being used.

[0544] "Contribution" refers to an indicator that shows how much each user or content creator contributed to revenue.

[0545] "Real-time analysis" refers to the process of immediately processing collected data and deriving results.

[0546] "Optimizing individual experiences" refers to the process of delivering more specific and personalized services and content based on each user's profile and behavior.

[0547] "Dynamic adjustment" refers to a process that automatically changes settings and conditions according to the current situation.

[0548] This invention is a system based on a generative AI content management system that provides a personalized content experience using user emotion data. Specifically, it uses a terminal equipped with an emotion engine to collect user reactions and feedback in real time and send them to a server. The server analyzes the user's emotion data using a generative AI model and dynamically provides optimized content.

[0549] Hardware and software

[0550] The device is equipped with sensors such as a camera and microphone, and has an emotion engine built into it. This allows it to analyze the user's facial expressions and voice in real time. The emotion engine installed in the device has the function of classifying the user's emotions into categories such as joy, sadness, and surprise, and generating data.

[0551] The server integrates generated sentiment data and content identification information, and uses algorithms to identify and provide the most suitable content. The server runs a generative AI model, which generates customized content for each user based on past usage and sentiment data.

[0552] Specific example

[0553] 1. In the case of educational content:

[0554] The user is using an educational application equipped with an emotion engine. The server analyzes whether the user is interested in the problem and adjusts the difficulty level of the learning content based on that information. For example, if the user looks bored, the server will provide a more challenging problem. A specific example of a prompt might be, "What should be provided next when this user is feeling bored?"

[0555] 2. Applications in the entertainment field:

[0556] For users of movie and music streaming services, the device analyzes the user's emotions. If surprise or excitement is detected, this information is sent to the server and used to recommend the next content to watch. For example, a possible prompt message might be, "Based on the scene that excited the user the most, select a recommended title to watch next."

[0557] In this way, personalized user experiences and fair revenue sharing are realized through emotional data analysis. This invention opens up new possibilities for content provision.

[0558] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0559] Step 1:

[0560] The user selects content using the device. The input includes the user's selected content ID and the video and audio data obtained at that moment. The device uses its built-in camera and microphone to capture the user's facial expressions and voice tone in real time from this data and analyze their emotions. The output is the analyzed emotion data.

[0561] Step 2:

[0562] The device sends the sentiment data acquired in Step 1 to the server. The inputs are user identification information and sentiment data. The server receives the data and uses a generative AI model to analyze it and determine which content will interest the user. The output is a list of optimal content candidates analyzed by the generative AI model.

[0563] Step 3:

[0564] The server selects content tailored to the user's interests and emotions based on the analysis results from a generative AI model. Inputs include user emotion data, content IDs, and usage history. The server uses an algorithm to optimize the user experience by listing personalized content. The output is a personalized content list.

[0565] Step 4:

[0566] The server sends optimized content to the terminal and provides it to the user. The input is the content list generated in step 3. The terminal displays recommended content to the user. Specifically, content that the user has shown interest in is displayed preferentially on the screen. The output is the provided content and the associated user reaction data.

[0567] Step 5:

[0568] The server calculates and distributes revenue based on the user's viewing behavior and the new sentiment data obtained in step 4. Inputs include the user's viewing history data, sentiment data, and usage data. The server uses a non-linear calculation algorithm to ensure fair revenue distribution to each content provider. The output is revenue distribution information for each content provider.

[0569] (Application Example 2)

[0570] 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."

[0571] Traditional content management systems have struggled to present content that takes user emotions into account, resulting in limited personalized experiences. Furthermore, revenue sharing has lacked fairness and transparency. Therefore, there is a need for a new system that analyzes user emotions in real time and improves the user experience by providing optimal content presentation methods accordingly.

[0572] 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.

[0573] In this invention, the server includes means for embedding invisible identification data in the generated data, means for tracking the number of times the generated data is generated and its usage, means for calculating and distributing profits based on contribution, means for analyzing user emotions and optimizing the content presentation method, and means for suggesting relevant content based on user emotion data. This enables the presentation of personalized content that responds to user emotions and fair revenue distribution.

[0574] "Generated data" refers to information or content that is automatically created using AI.

[0575] "Invisible identification data" refers to identifiers embedded in an invisible way, used to identify the source and rights holders of content and data.

[0576] "Contribution" refers to a numerical representation of the extent to which each participant contributed to the overall outcome.

[0577] "Profit sharing" is the process of allocating and distributing profits obtained based on generated data to multiple stakeholders according to their respective contributions.

[0578] "User emotion analysis" is a technology that uses sensors and algorithms to detect and analyze a user's emotions in real time.

[0579] "Means of optimizing content presentation methods" refer to processes or technologies for delivering content in the most appropriate way, based on user preferences and emotional states.

[0580] "Methods for suggesting relevant content" refer to methods for recommending content that is likely to be of interest to a user based on their past usage history and current emotional state.

[0581] To implement this invention, integration of a server, a user terminal, and an emotion analysis engine is required. The user accesses the generated content using a terminal equipped with a camera and microphone. The emotion engine installed in the terminal analyzes the user's facial expressions and voice tone in real time and generates emotion data.

[0582] The server receives this sentiment data and uses a generative AI model to determine the optimal way to present content based on the user's emotions. Specifically, it identifies points to emphasize in the content being viewed based on the sentiment data and selects relevant content to suggest next. The server also embeds invisible identification data into the generated information and tracks the number of times it is generated and its usage. Furthermore, it calculates and distributes profits fairly based on contribution.

[0583] For example, if a user shows signs of boredom while watching a movie, the emotion engine recognizes this situation, and the server suggests an action movie as the next recommended content. Another example of a prompt to the generative AI model for this process is, "Identify whether the user is enjoying themselves and suggest content that will enhance their entertainment experience." Such a system can make the user experience more personalized and improve user satisfaction.

[0584] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0585] Step 1:

[0586] Users view content using a device equipped with a camera and microphone. During this stage, the device captures the user's facial expressions and voice. Inputs include camera images and audio data, while outputs include the user's facial expressions and voice tone. Sensor information is acquired in real time and sent to an emotion analysis engine.

[0587] Step 2:

[0588] The device uses an emotion analysis engine to analyze the user's emotions from acquired facial and voice data. Specifically, it identifies emotions such as joy, sadness, and surprise based on subtle facial movements and voice pitch. The input is the data acquired in step 1, and the output is the emotion data as a result of the analysis.

[0589] Step 3:

[0590] The server receives sentiment data sent from the terminal and uses a generative AI model to calculate the optimal way to present content based on the user's emotions. Here, the input is sentiment data and information about the content currently being viewed, and the output is the updated content presentation method. The generative AI model learns from past data and generates prompts to provide the user with more engaging content.

[0591] Step 4:

[0592] The server embeds invisible identification data into the content and tracks its generation count and usage. This allows it to record how often specific content is used. The input is the generated content, and the output is the content with the embedded identification data and usage statistics.

[0593] Step 5:

[0594] The server calculates revenue based on contribution and distributes it fairly to participants. This calculation is based on usage statistics and identification data. The input is tracked usage data, and the output is specific revenue information for each participant.

[0595] Step 6:

[0596] The system presents user-optimized content and suggests related new content. Input is optimized content information from the server, and output is the presentation and suggestion of new content to the user. The terminal displays recommended content based on the user's sentiment data.

[0597] 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.

[0598] 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.

[0599] 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.

[0600] [Fourth Embodiment]

[0601] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0602] 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.

[0603] 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).

[0604] 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.

[0605] 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.

[0606] 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).

[0607] 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.

[0608] 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.

[0609] 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.

[0610] 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.

[0611] 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.

[0612] 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.

[0613] 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".

[0614] An effective way to implement the invention is to construct a content generation AI management system. This system embeds invisible identification information into the generated information, protecting the rights of copyright holders while fairly distributing revenue.

[0615] System Configuration

[0616] User: The user inputs information through the interface of the generative AI service. The interface is typically provided via a web browser or a dedicated application. The user sends a request to the generative AI by specifying the prompts and detailed parameters required for content generation.

[0617] Server: The server receives requests from users and activates a generative AI model to generate content according to the specified parameters. During the generation process, the server embeds invisible identification information into the generated content. This identification information includes data to ensure traceability, such as copyright holder information and a unique ID based on the generation parameters.

[0618] Terminal: The terminal receives the results of processing user requests and displays the generated content. The terminal also provides functionality to allow users to review and edit the generated content.

[0619] Specific example

[0620] 1. Novel Generation: The server receives the novel plot from the user and generates chapters and sections based on the specified style and tone. The generated text is embedded with an invisible watermark containing copyright information. It is then displayed to the user via their terminal, where they can review the content and edit it if necessary.

[0621] 2. Music Content Production: Users input their preferred genre, tempo, and instruments to be used into the server. The server generates music tracks using a music generation AI model and embeds identification information. The generated music is delivered to the user via their device, and they can listen to it.

[0622] By implementing this system, copyright holders can ensure their creative works are properly managed, and users can enjoy a safe environment for using generated content. Revenue is accurately calculated based on contribution through the system's tracking mechanism, enabling a new business model powered by AI.

[0623] The following describes the processing flow.

[0624] Step 1:

[0625] The user inputs the necessary data (prompts, parameters, etc.) to provide to the generating AI and sends the request to the server through the interface on the terminal.

[0626] Step 2:

[0627] The terminal converts the data entered by the user into the appropriate format and sends it to the server according to the communication protocol.

[0628] Step 3:

[0629] The server analyzes the request received from the terminal and launches a generation AI model according to the specified conditions. The AI ​​model generates content based on the user's request.

[0630] Step 4:

[0631] The server embeds invisible identifiers into the generated content using steganography techniques. This identifier includes copyright holder identification information and metadata about the generation process.

[0632] Step 5:

[0633] The server stores the generated content and related data (such as metadata and identification information used during generation) in a database.

[0634] Step 6:

[0635] The server sends the generated content to the terminal. The terminal receives this data and displays it in an appropriate format for user access.

[0636] Step 7:

[0637] The user reviews the displayed content and edits it as needed. These edits are saved on the device.

[0638] Step 8:

[0639] The server tracks content usage and analyzes the data for revenue sharing. It retains revenue calculated based on contribution and performs procedures to distribute it appropriately to copyright holders.

[0640] (Example 1)

[0641] 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".

[0642] The challenge lies in ensuring copyright protection and fair revenue sharing in content generation using generative AI. Specifically, it is necessary to add transparent identification information to the generated content, track its generation history and usage, and provide appropriate revenue sharing based on contribution. Furthermore, it is essential to ensure the convenience of users being able to review and edit the generated content.

[0643] 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.

[0644] In this invention, the server includes means for inputting information using an information processing device, means for generating content based on received prompts and parameters using a generation AI model, means for embedding invisible identification information into the generated content, means for displaying and making editable the generated content on a terminal device, means for tracking the number of times the generated content has been generated and its usage status, and means for calculating and distributing revenue based on contribution. This ensures traceability for the generated content and enables fair and efficient revenue distribution and improved convenience for content use.

[0645] An "information processing device" is a general term for electronic devices that have functions such as data input, calculation processing, and information output.

[0646] A "generative AI model" is an artificial intelligence system that incorporates an algorithm that automatically generates content based on given data and prompt text.

[0647] A "prompt" is input data that serves as hints and guidelines necessary for a generative AI model to generate content.

[0648] "Identification information" refers to information used to identify specific data or objects, and typically includes unique IDs and owner information.

[0649] "Terminal device" refers to a computer or electronic device used by a user to directly operate and input / output information.

[0650] "Tracking generation count and usage" is the process of recording and managing how many times generated content has been created and how it has been used.

[0651] "Revenue calculation based on contribution" is a method of fairly calculating revenue by evaluating the contribution of each involved element and participant.

[0652] A "nonlinear computation algorithm" refers to a computation method that uses complex computation techniques where the input and output are not linearly related, and which particularly takes into account the interactions and non-uniformity between elements.

[0653] This invention aims to efficiently create and manage content through a content generation AI management system.

[0654] Hardware and software to be used:

[0655] Users utilize information processing devices to generate content. These devices are typically personal computers, smartphones, or tablets, and they access the AI ​​service interface via a web browser or dedicated application.

[0656] The server processes requests and generates content using a generative AI model. This AI model utilizes natural language processing and machine learning techniques to automatically generate content based on diverse data.

[0657] Generative AI models operate in cloud computing or on-premises environments and perform complex data processing and calculations.

[0658] Data processing or data manipulation:

[0659] The server receives a prompt message from the user, parses its contents, and determines the necessary generation parameters.

[0660] The generated content automatically has an invisible watermark embedded by the server as identification information. This watermark contributes to maintaining traceability and protecting the rights of copyright holders.

[0661] The device provides users with the ability to view content and perform simple edits as needed.

[0662] Specific example:

[0663] A user wants to generate the first chapter of a novel and enters a prompt such as "a medieval fantasy adventure story." Based on this prompt, a server-side AI model operates and automatically generates text that reflects the specified style and tone. The resulting novel text is watermarked with identifying information.

[0664] This system allows users to quickly generate high-quality content while simultaneously achieving fair revenue sharing and copyright protection.

[0665] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0666] Step 1:

[0667] The user accesses the AI ​​generation service using their device. Through the user interface, they input a prompt (e.g., "A medieval fantasy adventure story") and set the detailed parameters required for generation (e.g., word count, style). This input data is sent to the server as a generation request.

[0668] Step 2:

[0669] The server parses the prompt and parameters received from the user. This analysis extracts the information necessary to generate the requested content and prepares it for the generation AI model. The server configures the model appropriately based on the input data and then invokes the generation AI model.

[0670] Step 3:

[0671] The server activates a generative AI model and generates content using the prompt text and extracted parameters. During this process, the AI ​​performs natural language processing and automatically constructs content that matches the specified style and tone through data calculations. The generated content is obtained as output data.

[0672] Step 4:

[0673] The server embeds invisible identifying information into the generated content. This is implemented as a watermark containing copyright holder information and generation date and time, based on a specified algorithm. This process adds an identifying feature to the generated data.

[0674] Step 5:

[0675] The server sends content with embedded identification information to the device. After the data transfer is complete, the user can visually confirm the generated content through the user interface on the device.

[0676] Step 6:

[0677] Users can review the generated content displayed on their device and edit it as needed, such as correcting typos or adjusting sentences. After reviewing, users can save or export the edited content.

[0678] (Application Example 1)

[0679] 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".

[0680] In the creation and distribution of digital content, it is difficult to ensure copyright protection and fair distribution of profits. Furthermore, there is a need to ensure effective traceability to prevent the misuse of generated content. Under these circumstances, it is necessary to develop an environment where users can safely verify and widely share the content they generate.

[0681] 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.

[0682] In this invention, the server includes means for embedding invisible identification data in the generated digital information, means for tracking the number of times the generated digital information has been generated and its usage, and means for calculating and distributing rewards based on contribution. This enables copyright protection and fair distribution of profits.

[0683] "Digital information" refers to content and data created in electronic format, including text, music, and video generated by generative AI models.

[0684] "Identification data" refers to invisible data embedded in generated digital information, which allows for the identification of copyright holders and generation parameters.

[0685] "Traceability" refers to tracking the history of digital information from its creation to its use, and clarifying its usage and copyright ownership.

[0686] "Rewards" refer to monetary compensation distributed to users and copyright holders based on their contribution to the digital information created using the generative AI model.

[0687] A "smart device" refers to a portable, high-functioning information terminal, such as a smartphone, that is used by users to view and share digitally generated information.

[0688] In the system for realizing this invention, a server plays a central role. The user accesses the generative AI service interface via a smart device and inputs prompts and detailed parameters. An example of a prompt is "jazz, instrumentation: piano, saxophone, 5 minutes, energetic." Based on this input, the server activates the generative AI model and generates digital content that matches the specified parameters.

[0689] The server embeds invisible identification data into the generated content, clearly identifying copyright holders and generation conditions. This identification data ensures the traceability of the generated content and prevents misuse. The server also tracks the number of generations and usage, calculating and distributing rewards based on contribution. This ensures fair revenue sharing.

[0690] Users can review digital content they create using smart devices and edit or share it as needed. For example, they can listen to a music track they've created, review the result, and then send it to friends or family. Smart devices such as smartphones and tablets leverage their computing power to provide an interface for users to instantly access and utilize the digital information they generate.

[0691] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0692] Step 1:

[0693] The user inputs prompts and detailed parameters for the AI ​​generation model through the smart device interface. These prompts might be phrases like "Jazz, Instrumentation: Piano, Saxophone, 5 mins, Energetic." This sends a specific music generation request to the server.

[0694] Step 2:

[0695] The server analyzes the received prompt message and activates an AI model based on the specified style and tone. Using the prompt message in the input data, the generating AI model calculates appropriate digital content. The output is a music track in the style desired by the user. The server internally uses the AI ​​model to generate content suitable for the prompt.

[0696] Step 3:

[0697] The server embeds invisible identification data into the generated digital content. This identification data includes copyright holder information and generation conditions, forming the basis for traceability. The output of the server at this stage is content with the identification data embedded.

[0698] Step 4:

[0699] The server records the number of times generated content is created and its usage, and uses this information to calculate rewards based on contribution. The server collects this information and prepares the basic data for appropriate reward distribution. The output is the calculated reward information.

[0700] Step 5:

[0701] The terminal receives digital content, including identification data provided by the user, and provides an interface that allows the user to view, edit, and share it. Using a smart device, the user can review the generated music track and modify or share it as needed. The output is user-manipulable digital content.

[0702] 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.

[0703] This invention improves the accuracy of content presentation and revenue distribution by incorporating an emotion engine that recognizes user emotions into a content generation AI management system. This system has the functionality to embed invisible identifying information into generated information and to track the number of times information is generated and its usage. Furthermore, it has a means for calculating and distributing revenue based on contribution, and uses the emotion engine to enable user-responsive interaction.

[0704] System Configuration

[0705] User: Users access the system using a device that includes an interface for recognizing emotions. User reactions and feedback are continuously collected.

[0706] Device: The device is equipped with an emotion engine that uses sensors such as cameras and microphones to analyze the user's emotions (e.g., joy, sadness, surprise, etc.) in real time. The user's emotion data is used to adjust how the generated information is delivered.

[0707] Server: The server optimizes the display of generated content based on sentiment data sent from the terminal. It manages features to make the user experience more personalized based on identification information and generated content. It also combines sentiment data with other usage data to perform more sophisticated usage analysis and revenue sharing.

[0708] Specific example

[0709] 1. In the case of educational content: When a user uses an AI-generated educational program, the emotion engine analyzes in real time whether the user is interested in the content. Based on this information, the server adjusts the learning pace and difficulty level to provide an environment in which the user can continue learning with enthusiasm.

[0710] 2. Applications in the entertainment sector: Analyze the emotional responses of users who use movie and music content, highlighting points of interest and suggesting next recommended content. This improves the viewing experience and extracts usage patterns to maximize revenue.

[0711] As described above, by incorporating an emotion engine, the AI-generated content management system can improve the user experience and achieve efficient and fair revenue distribution. This embodiment of the invention presents a new methodology for the generation and provision of digital content.

[0712] The following describes the processing flow.

[0713] Step 1:

[0714] The user inputs and sends a request to the generating AI through the terminal's interface. The terminal converts this request into the appropriate format and prepares it for transmission to the server.

[0715] Step 2:

[0716] The server analyzes the request data received from the terminal and activates the generative AI model according to the specified conditions. The generative AI model generates content based on the user's input.

[0717] Step 3:

[0718] The server embeds invisible identifying information into the generated content using steganography techniques. This information includes identifying information related to the copyright holder and generation parameters.

[0719] Step 4:

[0720] The server sends the generated content back to the terminal and saves it to the database. Initial setup for tracking user activity is also performed at this stage.

[0721] Step 5:

[0722] The device activates an emotion engine and monitors the user's emotions in real time through its camera and microphone. The device analyzes the collected emotion data and sends it to a server.

[0723] Step 6:

[0724] The server adjusts how generated content is displayed based on sentiment data sent from the device. For example, if the user shows no interest, it displays options to suggest other relevant content.

[0725] Step 7:

[0726] Users view or use the provided content and obtain an experience that matches their emotions. If necessary, users can send feedback to the server via their device.

[0727] Step 8:

[0728] The server combines collected sentiment data with other usage data to calculate revenue sharing based on contribution. Each time revenue is generated, the management system is updated to ensure it is distributed appropriately to copyright holders.

[0729] (Example 2)

[0730] 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".

[0731] Traditional content management systems have struggled to optimize individual user experiences while considering user emotions and to ensure fair revenue sharing. In particular, dynamic interaction and channel optimization, which combine generated information with user emotion data, has been difficult to achieve due to technical limitations.

[0732] 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.

[0733] In this invention, the server includes means for embedding invisible identification information into the generated information, means for tracking the number of times the generated information is generated and its usage, and means for calculating and distributing revenue based on contribution. This enables the optimization of individual experiences based on user sentiment data and fair revenue distribution.

[0734] "Generated information" refers to digital content and data generated by artificial intelligence models.

[0735] "Invisible identification information" refers to information that enables tracking of specific data or information embedded in a form that is not directly displayed to the user.

[0736] "Generation count" refers to a metric used to track how many times a particular piece of information or content is generated.

[0737] "Usage status" refers to data that shows how and to what extent information and content generated within the system are being used.

[0738] "Contribution" refers to an indicator that shows how much each user or content creator contributed to revenue.

[0739] "Real-time analysis" refers to the process of immediately processing collected data and deriving results.

[0740] "Optimizing individual experiences" refers to the process of delivering more specific and personalized services and content based on each user's profile and behavior.

[0741] "Dynamic adjustment" refers to a process that automatically changes settings and conditions according to the current situation.

[0742] This invention is a system based on a generative AI content management system that provides a personalized content experience using user emotion data. Specifically, it uses a terminal equipped with an emotion engine to collect user reactions and feedback in real time and send them to a server. The server analyzes the user's emotion data using a generative AI model and dynamically provides optimized content.

[0743] Hardware and software

[0744] The device is equipped with sensors such as a camera and microphone, and has an emotion engine built into it. This allows it to analyze the user's facial expressions and voice in real time. The emotion engine installed in the device has the function of classifying the user's emotions into categories such as joy, sadness, and surprise, and generating data.

[0745] The server integrates generated sentiment data and content identification information, and uses algorithms to identify and provide the most suitable content. The server runs a generative AI model, which generates customized content for each user based on past usage and sentiment data.

[0746] Specific example

[0747] 1. In the case of educational content:

[0748] The user is using an educational application equipped with an emotion engine. The server analyzes whether the user is interested in the problem and adjusts the difficulty level of the learning content based on that information. For example, if the user looks bored, the server will provide a more challenging problem. A specific example of a prompt might be, "What should be provided next when this user is feeling bored?"

[0749] 2. Applications in the entertainment field:

[0750] For users of movie and music streaming services, the device analyzes the user's emotions. If surprise or excitement is detected, this information is sent to the server and used to recommend the next content to watch. For example, a possible prompt message might be, "Based on the scene that excited the user the most, select a recommended title to watch next."

[0751] In this way, personalized user experiences and fair revenue sharing are realized through emotional data analysis. This invention opens up new possibilities for content provision.

[0752] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0753] Step 1:

[0754] The user selects content using the device. The input includes the user's selected content ID and the video and audio data obtained at that moment. The device uses its built-in camera and microphone to capture the user's facial expressions and voice tone in real time from this data and analyze their emotions. The output is the analyzed emotion data.

[0755] Step 2:

[0756] The device sends the sentiment data acquired in Step 1 to the server. The inputs are user identification information and sentiment data. The server receives the data and uses a generative AI model to analyze it and determine which content will interest the user. The output is a list of optimal content candidates analyzed by the generative AI model.

[0757] Step 3:

[0758] The server selects content tailored to the user's interests and emotions based on the analysis results from a generative AI model. Inputs include user emotion data, content IDs, and usage history. The server uses an algorithm to optimize the user experience by listing personalized content. The output is a personalized content list.

[0759] Step 4:

[0760] The server sends optimized content to the terminal and provides it to the user. The input is the content list generated in step 3. The terminal displays recommended content to the user. Specifically, content that the user has shown interest in is displayed preferentially on the screen. The output is the provided content and the associated user reaction data.

[0761] Step 5:

[0762] The server calculates and distributes revenue based on the user's viewing behavior and the new sentiment data obtained in step 4. Inputs include the user's viewing history data, sentiment data, and usage data. The server uses a non-linear calculation algorithm to ensure fair revenue distribution to each content provider. The output is revenue distribution information for each content provider.

[0763] (Application Example 2)

[0764] 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".

[0765] Traditional content management systems have struggled to present content that takes user emotions into account, resulting in limited personalized experiences. Furthermore, revenue sharing has lacked fairness and transparency. Therefore, there is a need for a new system that analyzes user emotions in real time and improves the user experience by providing optimal content presentation methods accordingly.

[0766] 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.

[0767] In this invention, the server includes means for embedding invisible identification data in the generated data, means for tracking the number of times the generated data is generated and its usage, means for calculating and distributing profits based on contribution, means for analyzing user emotions and optimizing the content presentation method, and means for suggesting relevant content based on user emotion data. This enables the presentation of personalized content that responds to user emotions and fair revenue distribution.

[0768] "Generated data" refers to information or content that is automatically created using AI.

[0769] "Invisible identification data" refers to identifiers embedded in an invisible way, used to identify the source and rights holders of content and data.

[0770] "Contribution" refers to a numerical representation of the extent to which each participant contributed to the overall outcome.

[0771] "Profit sharing" is the process of allocating and distributing profits obtained based on generated data to multiple stakeholders according to their respective contributions.

[0772] "User emotion analysis" is a technology that uses sensors and algorithms to detect and analyze a user's emotions in real time.

[0773] "Means of optimizing content presentation methods" refer to processes or technologies for delivering content in the most appropriate way, based on user preferences and emotional states.

[0774] "Methods for suggesting relevant content" refer to methods for recommending content that is likely to be of interest to a user based on their past usage history and current emotional state.

[0775] To implement this invention, integration of a server, a user terminal, and an emotion analysis engine is required. The user accesses the generated content using a terminal equipped with a camera and microphone. The emotion engine installed in the terminal analyzes the user's facial expressions and voice tone in real time and generates emotion data.

[0776] The server receives this sentiment data and uses a generative AI model to determine the optimal way to present content based on the user's emotions. Specifically, it identifies points to emphasize in the content being viewed based on the sentiment data and selects relevant content to suggest next. The server also embeds invisible identification data into the generated information and tracks the number of times it is generated and its usage. Furthermore, it calculates and distributes profits fairly based on contribution.

[0777] For example, if a user shows signs of boredom while watching a movie, the emotion engine recognizes this situation, and the server suggests an action movie as the next recommended content. Another example of a prompt to the generative AI model for this process is, "Identify whether the user is enjoying themselves and suggest content that will enhance their entertainment experience." Such a system can make the user experience more personalized and improve user satisfaction.

[0778] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0779] Step 1:

[0780] Users view content using a device equipped with a camera and microphone. During this stage, the device captures the user's facial expressions and voice. Inputs include camera images and audio data, while outputs include the user's facial expressions and voice tone. Sensor information is acquired in real time and sent to an emotion analysis engine.

[0781] Step 2:

[0782] The device uses an emotion analysis engine to analyze the user's emotions from acquired facial and voice data. Specifically, it identifies emotions such as joy, sadness, and surprise based on subtle facial movements and voice pitch. The input is the data acquired in step 1, and the output is the emotion data as a result of the analysis.

[0783] Step 3:

[0784] The server receives sentiment data sent from the terminal and uses a generative AI model to calculate the optimal way to present content based on the user's emotions. Here, the input is sentiment data and information about the content currently being viewed, and the output is the updated content presentation method. The generative AI model learns from past data and generates prompts to provide the user with more engaging content.

[0785] Step 4:

[0786] The server embeds invisible identification data into the content and tracks its generation count and usage. This allows it to record how often specific content is used. The input is the generated content, and the output is the content with the embedded identification data and usage statistics.

[0787] Step 5:

[0788] The server calculates revenue based on contribution and distributes it fairly to participants. This calculation is based on usage statistics and identification data. The input is tracked usage data, and the output is specific revenue information for each participant.

[0789] Step 6:

[0790] The system presents user-optimized content and suggests related new content. Input is optimized content information from the server, and output is the presentation and suggestion of new content to the user. The terminal displays recommended content based on the user's sentiment data.

[0791] 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.

[0792] 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.

[0793] 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 robot 414.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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."

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0812] The following is further disclosed regarding the embodiments described above.

[0813] (Claim 1)

[0814] A means of embedding invisible identification information into the generated information,

[0815] A means for tracking the number of times generated information is generated and its usage,

[0816] A means of calculating and distributing profits based on contribution,

[0817] Means for providing the generated information to the user,

[0818] A system that includes this.

[0819] (Claim 2)

[0820] The system according to claim 1, wherein the invisible identification information includes the identification information of the copyright holder.

[0821] (Claim 3)

[0822] The system according to claim 1, wherein the distribution of the aforementioned revenue is performed based on a nonlinear calculation algorithm.

[0823] "Example 1"

[0824] (Claim 1)

[0825] A means of inputting information using an information processing device,

[0826] A means for generating content based on received prompts and parameters using a generative AI model,

[0827] A means of embedding invisible identification information into the generated content,

[0828] A means of displaying and making editable the generated content on a terminal device,

[0829] A means for tracking the number of times generated content has been generated and its usage status,

[0830] A means of calculating and distributing profits based on contribution,

[0831] Means for providing the generated information to the user,

[0832] A system that includes this.

[0833] (Claim 2)

[0834] The system according to claim 1, wherein the invisible identification information includes the identification information of the copyright holder.

[0835] (Claim 3)

[0836] The system according to claim 1, wherein the distribution of the aforementioned revenue is performed based on a nonlinear calculation algorithm.

[0837] "Application Example 1"

[0838] (Claim 1)

[0839] A means of embedding invisible identification data into generated digital information,

[0840] A means for tracking the number of times generated digital information is generated and its usage status,

[0841] A means of calculating and distributing rewards based on contribution,

[0842] Means for providing the generated digital information to users,

[0843] A means for users to view and share digital information they have generated using smart devices,

[0844] A means of ensuring traceability based on identification data that enables the protection of generated digital information,

[0845] A system that includes this.

[0846] (Claim 2)

[0847] The system according to claim 1, wherein the invisible identification data includes the identification information of the copyright holder.

[0848] (Claim 3)

[0849] The system according to claim 1, wherein the distribution of the aforementioned reward is performed based on a nonlinear computation algorithm.

[0850] "Example 2 of combining an emotion engine"

[0851] (Claim 1)

[0852] A means of embedding invisible identification information into the generated information,

[0853] A means for tracking the number of times generated information is generated and its usage,

[0854] A means of calculating and distributing profits based on contribution,

[0855] A means of analyzing the user's emotions in real time based on the generated information and optimizing the individual experience,

[0856] A means of dynamically adjusting the way content is presented based on emotional data,

[0857] A system that includes this.

[0858] (Claim 2)

[0859] The system according to claim 1, wherein the invisible identification information includes the identification information of the copyright holder.

[0860] (Claim 3)

[0861] The system according to claim 1, wherein the distribution of the aforementioned revenue is performed based on a nonlinear calculation algorithm.

[0862] "Application example 2 of combining emotional engines"

[0863] (Claim 1)

[0864] A means of embedding invisible identification data into the generated data,

[0865] A means for tracking the number of times generated data is generated and its usage,

[0866] A means of calculating and distributing profits based on contribution,

[0867] A means to analyze user emotions and optimize how content is presented,

[0868] A method for suggesting relevant content based on user sentiment data,

[0869] A system that includes this.

[0870] (Claim 2)

[0871] The system according to claim 1, wherein the invisible identification data includes the identification information of the rights holder.

[0872] (Claim 3)

[0873] The system according to claim 1, wherein the distribution of the aforementioned profits is carried out based on a nonlinear algorithm. [Explanation of Symbols]

[0874] 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 embedding invisible identification information into the generated information, A means for tracking the number of times generated information is generated and its usage, A means of calculating and distributing profits based on contribution, Means for providing the generated information to the user, A system that includes this.

2. The system according to claim 1, wherein the invisible identification information includes the identification information of the copyright holder.

3. The system according to claim 1, wherein the distribution of the aforementioned revenue is performed based on a nonlinear calculation algorithm.

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