system

Abstract

We provide the system. [Solution] A means of storing data obtained from data providers, A means for training a generative model based on the aforementioned data, A means of calculating the profits generated from the use of the aforementioned model and returning them to the provider, A means of recording the history of data usage and ensuring transparency, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] An object of the present invention is to solve problems related to securing a high-quality dataset required in training an AI model and designing an incentive for a data provider associated therewith. Specifically, it aims to establish an environment in which a data provider can provide data with confidence and a mechanism for appropriately distributing the benefits obtained by using the generated model. In addition, it is necessary to improve the transparency of data use and strengthen the protection of privacy and copyright.

Means for Solving the Problems

[0005] To solve the above problems, the present invention provides the following means: It includes means for securely storing data obtained from data providers and means for training generative models based on the data. It also provides means for calculating the benefits obtained from the use of the model and returning them to the data providers, thereby ensuring incentives. Furthermore, it incorporates means for recording the history of data usage and ensuring transparency, thereby building a mechanism to prevent misuse of data. It enables the management of usage restrictions by setting data usage permissions and implementing restrictions on use by third parties. In addition, it maximizes the value of the data by integrating data obtained from multiple data providers and utilizing it effectively for training models.

[0006] A "data provider" refers to an individual or legal entity that provides its data to the system.

[0007] "Data" refers to a collection of information used to train a generative model, and is recorded in electronic format.

[0008] A "generative model" refers to an AI model that is trained on data and can generate new information or solutions.

[0009] "Training methods" refer to the process and techniques for optimizing model parameters based on data.

[0010] "Profit" refers to the economic return obtained by using the generative model.

[0011] "Means of giving back" refers to a system and process for distributing a portion of the profits obtained to data providers.

[0012] "Means of ensuring transparency" refers to technologies and methods for recording information about data usage and making that history auditable.

[0013] "Permission to use" refers to the conditions set by the data provider regarding how specific data will be used.

[0014] "Means of integrating data from multiple data providers" refers to a system that aggregates data from multiple providers and combines it into a single training dataset. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a 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.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention is a system that manages a series of processes, from data collection from data providers to training generative models and the resulting profit distribution. The system is broadly composed of three elements: a server, terminals, and users.

[0037] The server receives and stores data sent from data providers. The server verifies the integrity of uploaded data and securely stores it in a database. It also maintains transparency by managing data usage conditions based on permissions set by data providers and recording usage history. The server's role also includes building training datasets and training data-driven generative AI models. Furthermore, it has a mechanism to calculate the revenue generated from the use of the generated models and return a portion of it to the data providers.

[0038] Terminals are devices used by data providers to upload data and by companies to download and use generated models. Data providers use their terminals to access the platform and upload data. Companies use their terminals to download generated models from the server and integrate them into their own systems for use.

[0039] Users can either provide data as data providers or utilize generative models as users. Interfaces are provided for each role, allowing data providers to check the status of their provided data and the benefits they receive. Meanwhile, companies using the models can select usage methods that suit the model's performance and purpose, thereby improving operational efficiency.

[0040] As a concrete example, consider a case where an educational institution provides student learning data, and an AI model is created based on that data. The educational institution uploads the data through the platform and restricts its use to the education sector. The server receives the data, runs a training process, and generates an AI model tailored to the educational institution's needs. The generated model is used by the educational institution in its own system, contributing to the optimization of learning and student support. Furthermore, the profits generated from the use of the model are returned to the educational institution, providing a return commensurate with the data provided. In this way, this invention offers a new form of data collection and utilization.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] Users log in to the platform using their own devices. To provide data, they enter summary information regarding the content and format of the data.

[0044] Step 2:

[0045] The device encrypts the data being uploaded and sends it to the server via secure communication.

[0046] Step 3:

[0047] The server verifies the received data and confirms its integrity. It calculates the hash value of the data to check for tampering.

[0048] Step 4:

[0049] The server stores data that has passed verification in storage. It generates metadata and records it in a database so that the data can be identified and tracked.

[0050] Step 5:

[0051] Users set the permission conditions for using the data they provide. They select options to specify the industry or company that will use the data and its specific purpose.

[0052] Step 6:

[0053] The server manages data usage restrictions based on user-defined permissions. It controls data access to ensure it is only used under permitted conditions.

[0054] Step 7:

[0055] The server builds a training dataset based on the obtained data. It preprocesses the data and converts it into a format suitable for model training.

[0056] Step 8:

[0057] The server trains the generative model using the training dataset. It searches for optimal parameters and improves the model's accuracy.

[0058] Step 9:

[0059] Users download the generated models on their devices and integrate them into their company's systems. The models are then used in business operations to improve efficiency.

[0060] Step 10:

[0061] The server tracks model usage and calculates revenue. It then initiates a process to return a portion of the profits based on model usage to data providers.

[0062] Step 11:

[0063] Users can review the benefits they receive from their accounts, verify that the benefits are being distributed correctly, and provide feedback.

[0064] (Example 1)

[0065] 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."

[0066] There is a lack of a mechanism to appropriately and transparently return the revenue generated from the use of AI models that are trained using information provided by data providers. Furthermore, when integrating information from multiple data providers, there are problems with verifying data integrity and managing usage permissions.

[0067] 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.

[0068] In this invention, the server includes means for storing information obtained from data providers, means for verifying the integrity of the information, and means for training a generative model based on the information. This makes it possible to securely and efficiently utilize the information of data providers and to return the revenue generated from the use of the generated model with transparency.

[0069] A "data provider" refers to an individual or organization that provides information to this system.

[0070] "Information" refers to various types of data and knowledge provided by data providers, and is the material used within the system.

[0071] A "generative model" refers to an artificial intelligence model built based on provided information, and is a program trained for a specific purpose.

[0072] "Revenue" refers to the economic benefits obtained by using the generative model.

[0073] "Information integrity" refers to a state in which the information provided is accurate and consistent, and is a requirement for ensuring the reliability of the data.

[0074] "Permission to use" refers to the authority to specify the terms and scope of use of information provided by the data provider.

[0075] "Transparency" refers to a state where processes such as information handling and revenue sharing are clear, and all parties involved can verify the details.

[0076] A description of the embodiment for carrying out the invention will be provided.

[0077] This system consists of three elements: servers, terminals, and users. It is primarily designed to automate and streamline the processes of data collection, verification, storage, model training, and revenue sharing.

[0078] Server Functions

[0079] After receiving information from a data provider, the server first verifies its integrity. Specifically, it uses integrity verification algorithms to check if the data quality is sufficient. Inconsistent or invalid data is notified to the data provider. After the integrity is confirmed, the data is securely stored in the database. A database management system (e.g., MySQL® or PostgreSQL) is used for data storage.

[0080] The server also records data usage history and manages access based on permissions to ensure transparency. This recording process is managed using blockchain technology, enabling transparent data management that is difficult to tamper with.

[0081] Most importantly, the server trains a generative AI model based on the collected information. This training is performed using a machine learning platform (e.g., TENSORFLOW® or PyTorch). Once the model is optimally trained, it becomes available for use by users in various applications. Revenue generated from the use of the model is automatically tracked and calculated using smart contracts and returned to the data providers.

[0082] Device functions

[0083] The terminal is a device used by data providers to upload information. From the terminal, users can access the platform and log in to their own dashboard. Here, they can upload data and set usage permissions.

[0084] On the other hand, corporate terminals download trained models from servers and integrate them into their own systems for use in business operations. Secure connections (e.g., HTTPS) are used throughout this process, ensuring data security.

[0085] User roles

[0086] Users can either provide information as data providers or utilize generative AI models as users. Data providers have an interface on the platform where they can check the status of their provided data and the revenue they receive. Meanwhile, companies using the models can apply generative AI models according to their needs to improve their operational efficiency.

[0087] Specific example

[0088] For example, consider a case where an educational institution provides student learning data to create a customized generative AI model. The institution accesses the platform using a terminal, uploads the learning data, and restricts its use to the education sector. The server receives this data and trains the model. The generated model is integrated into the institution's system and used to optimize learning. The benefits are returned to the institution according to their usage.

[0089] Example of a prompt

[0090] "Please describe the steps involved in building an AI model using student data from an educational institution, and the expected results."

[0091] This system makes it possible to effectively collect and utilize data and fairly distribute profits.

[0092] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0093] Step 1:

[0094] The user (data provider) uses their device to log in to the platform and access their dashboard. Next, they select the data they want to provide, such as training data, and upload it. The input is the data provided by the user, and the output is the transmission of that data to the server. During this process, the user also configures their data usage permissions.

[0095] Step 2:

[0096] The server verifies the data received from the user. A data integrity verification algorithm is used to confirm the validity of the data. The input is the data received from the terminal, and the output is the verified data. Data whose integrity has been confirmed is securely stored in the database.

[0097] Step 3:

[0098] The server uses validated data to build a training dataset. The dataset is preprocessed and converted into a format suitable for training the AI ​​model. The input is validated data, and the output is the AI ​​model training dataset. This dataset is saved for later processing.

[0099] Step 4:

[0100] The server uses the training dataset to train a generative AI model. A machine learning framework is used to optimize the model. The input is the training dataset, and the output is the trained generative AI model. The model's performance is evaluated using internal test data.

[0101] Step 5:

[0102] The terminal (corporate user) downloads a pre-trained generative AI model from the server. The downloaded model is integrated into the company's systems and used in its business processes. The input is the generative AI model provided by the server, and the output is the use of the model within the company. This process requires that the model is properly installed and configured.

[0103] Step 6:

[0104] The server records the usage of the generated AI model and calculates revenue. A smart contract is used to automate the distribution of revenue. The input is model usage data, and the output is revenue distribution information for data providers. This final step ensures that revenue is distributed fairly to data providers.

[0105] (Application Example 1)

[0106] 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."

[0107] In systems that recommend appropriate content to users based on viewing history and rating data, there is a need for a method that ensures fair compensation for the information provided by data providers, while also ensuring transparency and efficiently utilizing the data. However, conventional systems have shortcomings in terms of convenience and transparency.

[0108] 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.

[0109] In this invention, the server includes means for storing information obtained from data providers, means for training a generative model based on the information, means for calculating the profits generated from the use of the model and returning them to the provider, means for recording the history of information usage and ensuring transparency, means for generating recommended content tailored to individual users based on viewing history data obtained from providers, and means for returning value to users through content recommendations. This enables personalized content recommendations for users, fair profit sharing, and transparent data utilization.

[0110] A "data provider" is an entity that provides information to the system, and that information is used to train the generative model.

[0111] A "generative model" is a program or structure that is trained on information obtained from data providers and generates new outputs using a specific algorithm.

[0112] "Profit sharing" means distributing the economic or non-economic benefits obtained from the use of the system or the results of the model to the data providers who provided the information.

[0113] "Viewing history data" refers to information about content that a user has viewed in the past, and is used to understand the preferences and tendencies of individual users.

[0114] "Recommended content" refers to information or works that are selected based on an analysis of viewing history data, taking into account the individual user's interests and preferences, and are judged to be highly likely to be viewed next.

[0115] "Transparency" means that the processes for collecting, using, and sharing information are clear, and that all stakeholders can access and verify that information.

[0116] "Means of calculating profit" refers to a method or process for calculating the profit obtained from the use of generative models.

[0117] "Means of return" refers to methods or processes for returning the benefits to the information providers, thereby ensuring a fair distribution.

[0118] This invention realizes a system that trains a generative model using information obtained from data providers and fairly distributes the profits generated from the use of that model. A specific embodiment is shown below.

[0119] The server is responsible for acquiring and recording information from data providers. This information includes viewing history data and rating information, and is securely stored in a database using SQLite or PostgreSQL. The server automatically verifies data integrity and records usage history in detail to maintain transparency. It also trains generative AI models using TensorFlow or PyTorch to build personalized recommendation content optimized for each user.

[0120] The device provides an interface for users to input their viewing history data and presents them with recommended content generated from the server. In this process, programming languages ​​such as Python are used to present the recommendations in a visually easy-to-understand format.

[0121] Users can receive content optimized based on their individual viewing history and enjoy rewards commensurate with their viewing behavior. Specifically, these rewards include viewing credits and access to special content.

[0122] For example, if a user gives a high rating to a historical documentary, that information is stored on the server and used as training data for a generative model. As a result, the server selects content of similar interest and recommends it to the user through their device.

[0123] Within the program, an example of a prompt statement used when providing viewing history data to the generative AI model is the instruction, "Based on the user's viewing history data, recommend content that they are most likely to watch next."

[0124] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0125] Step 1:

[0126] The server receives viewing history data from data providers and checks its integrity. Viewing history data, as input, is validated through an error-checking algorithm before being stored in the database. This process ensures accurate and reliable data.

[0127] Step 2:

[0128] The server securely stores verified viewing history data in an SQLite or PostgreSQL database. The data provided as input is recorded along with a unique user identifier and organized for easy access in subsequent processing.

[0129] Step 3:

[0130] The server trains a generative AI model based on stored viewing history data. Using TensorFlow or PyTorch, the algorithm learns user preferences using the dataset as input. As a result, a personalized recommendation algorithm is output for each user.

[0131] Step 4:

[0132] The device uses a generative AI model obtained from the server to generate and present recommended content to the user. It receives a recommendation algorithm as input and displays the content visually and clearly through the user interface. This allows the user to easily select the next available content.

[0133] Step 5:

[0134] Users select and watch content from the recommended selections presented. Based on this, the viewing results are fed back to the system as input, initiating the next data provision cycle. This feedback is used as crucial data to further improve the accuracy of future recommendations.

[0135] Step 6:

[0136] The server calculates the profits generated from the use of the model and fairly distributes them to data providers. It enhances data provider satisfaction by managing a reward system based on the output of the generated AI model, including viewing credits and special access rights. Throughout this process, the profit-sharing status is recorded while maintaining transparency.

[0137] 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.

[0138] This invention is a system that comprehensively manages everything from data collection from data providers to training generative models and providing feedback using an emotion engine. The system consists of a server, terminals, users, and an emotion engine.

[0139] The server securely stores data received from data providers and manages data usage conditions based on access permissions. The server has the capability to build training datasets and train generative AI models. In addition, it dynamically adjusts the generative model's algorithms based on feedback data provided by the emotion engine to improve model accuracy. It also has a mechanism for calculating revenue and returning profits to data providers according to appropriate criteria.

[0140] The terminal serves as the primary interface for data providers and model users to access the system. Data providers can upload data through the terminal and also provide additional feedback using the sentiment engine. Model users can download AI models generated from the terminal and use them in their work to optimize business processes.

[0141] The emotion engine analyzes the user's emotional state in real time and provides feedback to other components of the system. For example, it can improve model performance by incorporating emotional data into the parameters of the training process. It also adjusts the criteria for reward distribution based on emotional data, striving to increase user satisfaction.

[0142] Users utilize the system as both data providers and model users. Data providers can manage the status of their uploaded data, set usage permissions, and receive profits. Meanwhile, companies, as model users, use the feedback obtained from the emotion engine to improve the efficiency of their operations.

[0143] A concrete example is a case where a medical facility provides patient feedback data to generate an AI model. The medical facility collects data, including the patient's emotional state, and uploads it to the system. The server analyzes the data and emotional feedback to train a generative AI model specifically tailored to the medical field. Through feedback from the emotional engine, the model contributes to improving patient care. In addition, the medical facility receives appropriate revenue based on the use of the model. This system makes it possible to build new AI models that utilize emotional data.

[0144] The following describes the processing flow.

[0145] Step 1:

[0146] Users log in to the platform through their own devices and enter the content and sentiment data they wish to provide. Users then set the necessary permissions to upload this data.

[0147] Step 2:

[0148] The device encrypts the provided data and emotional data and sends it to the server using a secure communication protocol.

[0149] Step 3:

[0150] The server authenticates and verifies the integrity of the received data. It then stores the data, along with necessary metadata, in secure storage.

[0151] Step 4:

[0152] The server manages the conditions for data usage permission based on the usage permissions set by the data provider. It also records data usage history to ensure transparency.

[0153] Step 5:

[0154] The server uses an emotion engine to analyze emotional data provided by users and generate the feedback necessary for training.

[0155] Step 6:

[0156] The server optimizes the dataset based on feedback from the sentiment engine and begins the generative model training process. The trained model is further refined with sentiment data.

[0157] Step 7:

[0158] On their devices, users download generated models from the server and integrate them into their own systems. These models are then used for specific business purposes.

[0159] Step 8:

[0160] The server calculates revenue based on model usage and sentimental title feedback. A portion of the calculated revenue is returned to the data provider, providing results that take user sentiment into account.

[0161] Step 9:

[0162] Users can check the status of their earnings distribution through their accounts and evaluate the feedback from the system. User feedback is used to further improve the system.

[0163] (Example 2)

[0164] 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".

[0165] This invention aims to solve the problem of efficiently utilizing a wide range of information obtained from data providers to improve the training accuracy of generative models, while also transparently and appropriately returning the profits obtained. Furthermore, it places emphasis on optimizing the user experience by reflecting users' emotional information in the model.

[0166] 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.

[0167] In this invention, the server includes means for storing information obtained from data providers, means for training a model generated based on the information, and means for calculating the results obtained from the use of the model and returning them to the provider. This enables efficient management and utilization of information.

[0168] A "data provider" refers to an entity that provides information to the system, and that information is used for training and analyzing models.

[0169] A "generated model" refers to a digital representation created by an algorithm based on acquired information, and possesses the ability to perform a variety of tasks.

[0170] "Calculating results" refers to the process of quantifying the gains and efficiency improvements obtained through the use of the model, and then providing those results back to the information provider.

[0171] "Recording the history of information usage" refers to a recording process that tracks how the provided information was used within the system, providing transparency and traceability.

[0172] "Emotional information" refers to data that indicates the emotional state of users, and is used for improving models and providing feedback.

[0173] "Dynamic adjustment" refers to the process of changing model parameters and algorithms in real time in response to changes in the environment and conditions.

[0174] To implement this invention, multiple components are used, including a server, a terminal, a user, and an emotion engine. The server is responsible for receiving and securely storing information obtained from data providers. This information is stored using a general-purpose database management system. The server also analyzes the information and builds the datasets necessary for training generative AI models.

[0175] The server trains models using existing machine learning frameworks such as TensorFlow and PyTorch, and maintains the results. The trained models are dynamically adjusted based on sentiment information provided by the sentiment engine to improve accuracy and utilization efficiency. The sentiment engine analyzes user feedback in real time and uses this data to adjust the model parameters.

[0176] The terminal serves as the primary interface for data providers and model users to access the system. Data providers can upload information via the terminal, and model users can download trained generative AI models via the terminal. Model users then integrate these models into their own business processes to streamline and optimize those processes.

[0177] A concrete example is a medical facility. Medical facilities provide patient feedback data to a server via terminals. An example of a prompt message could be: "Please tell me how to use the latest generative AI model to analyze patient feedback data and gain insights that will help improve patient care."

[0178] The operation of this entire system enables the effective use of information, continuous improvement of the model, and return of profits to providers, thereby contributing to increased user satisfaction.

[0179] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0180] Step 1: Data Collection

[0181] The server receives data uploaded by users from their devices. This data includes emotion-related information such as text, audio, and video. The input data is first pre-processed to standardize the format and remove unnecessary data before being securely stored in the database. This process ensures data consistency and security.

[0182] Step 2: Build the dataset

[0183] The server constructs a dataset suitable for model training from preprocessed data. This dataset construction includes assigning specific sentiment labels and categorizing the data. In this step, the raw input data is formatted for machine learning algorithms and outputted as a trainable dataset. This results in a high-quality dataset.

[0184] Step 3: Model Training

[0185] The server trains a generative AI model using the constructed dataset. This process utilizes frameworks such as TensorFlow and PyTorch, applying deep learning algorithms. The input is the dataset, and the output is the trained model. Specifically, feedback data from the emotion engine is used to dynamically adjust model parameters in order to improve prediction accuracy. This step completes a highly accurate model.

[0186] Step 4: Feedback Analysis

[0187] The terminal aggregates user feedback and analyzes it in conjunction with the emotion engine. Input includes ratings and comments indicating the user's emotional state. This feedback is analyzed and notified to the server, allowing for further optimization of the model. The output is feedback data as a result of the analysis, which is used as a new metric for model improvement.

[0188] Step 5: Delivering Results

[0189] Users download pre-trained generative AI models via their devices and utilize them in their work. The input is the model supplied from the server. The output is the efficiency improvements and optimizations achieved through the integration of the model into business processes. For example, a company can use the model to efficiently process customer emotional feedback in order to improve the quality of customer support.

[0190] Step 6: Profit Calculation and Distribution

[0191] The server calculates the revenue generated from model usage and distributes it appropriately to data providers. Inputs are model usage data and feedback evaluation data. Outputs are the calculated amounts distributed to each provider. This procedure helps build trust with data providers and promotes sustainable information sharing.

[0192] (Application Example 2)

[0193] 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."

[0194] Conventional generative AI models have not adequately improved model accuracy or considered user sentiment in their content recommendations. This has made it difficult to provide optimal content that enhances user satisfaction. Furthermore, there have been challenges in fully utilizing feedback data to provide benefits to data providers.

[0195] 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.

[0196] In this invention, the server includes means for storing data obtained from data providers, means for training a generative model based on the data, and means for analyzing emotional states and optimizing the generative model based on content recommendations. This enables the provision of personalized content that responds to the user's emotions.

[0197] A "data provider" is an entity that plays the role of supplying information to a data generation system.

[0198] A "generative model" is an algorithm that generates new information or content based on the information provided.

[0199] "Emotional state" refers to data that indicates the user's mental state and is analyzed through the emotion engine.

[0200] "Content recommendation" is the act of presenting the most relevant information based on the user's emotions and preferences.

[0201] "Means of calculating profits and returning them to providers" refers to a mechanism for appropriately distributing revenue generated through a production system.

[0202] "Means of recording data usage history and ensuring transparency" refers to methods of increasing the credibility of a system by accumulating a history of information usage.

[0203] A description of the embodiment for carrying out the invention will be provided.

[0204] In this system, the server securely stores information collected from data providers and uses it to train generative AI models. The data is protected by security protocols and centrally managed on the server. The generative models are optimized based on sentiment data provided by the sentiment engine and trained to generate personalized content for each user.

[0205] The terminal serves as the primary interface for data providers and model users to access the system. Data providers can use the terminal to upload data and provide additional feedback by inputting sentiment data, which can further improve the performance of generative models.

[0206] Users can download generative AI models obtained through their devices and apply them to their work and lifestyle. For example, they can listen to music to reduce daily stress using playlists suggested based on emotional data.

[0207] As a concrete example, when a user uses a music streaming app during their relaxation time after work, this system analyzes the user's emotional state and automatically selects and recommends music best suited for relaxation. An example of a prompt input to the generative AI model might be, "Use the latest emotion analysis technology to recommend the best meditation guide when the user's stress level is high." In this way, it is possible to improve the user experience.

[0208] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0209] Step 1:

[0210] The server receives data sent from data providers via terminals. This data includes user behavior logs and sentiment data. The server securely stores this data and saves it in a database. It takes data from data providers as input and outputs data saved to the database.

[0211] Step 2:

[0212] The server begins training a generative AI model based on the collected data. During this process, it analyzes emotional data and extracts data patterns corresponding to the user's emotional state. A training dataset is constructed and input into the generative model's algorithm, thereby optimizing the model. The input is the collected dataset, and the output is the optimized generative model.

[0213] Step 3:

[0214] The terminal provides an interface for model users to access the system. Model users download optimized generative AI models and use them for business or personal purposes. The terminal receives input from model users, retrieves models from the server, and deploys them to the terminal. The input is the model user's request, and the output is the distribution of the model to the terminal.

[0215] Step 4:

[0216] Users utilize content generated by an AI model using their device. This content is optimized according to the user's emotional state, allowing them to experience something that matches their mood. For example, when a user wants to relax, the device suggests a music playlist accordingly. The input is the user's emotional state, and the output is the customized content provided to the user.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] 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.

[0223] 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).

[0224] 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.

[0225] 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.

[0226] 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).

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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".

[0233] This invention is a system that manages a series of processes, from data collection from data providers to training generative models and the resulting profit distribution. The system is broadly composed of three elements: a server, terminals, and users.

[0234] The server receives and stores data sent from data providers. The server verifies the integrity of uploaded data and securely stores it in a database. It also maintains transparency by managing data usage conditions based on permissions set by data providers and recording usage history. The server's role also includes building training datasets and training data-driven generative AI models. Furthermore, it has a mechanism to calculate the revenue generated from the use of the generated models and return a portion of it to the data providers.

[0235] Terminals are devices used by data providers to upload data and by companies to download and use generated models. Data providers use their terminals to access the platform and upload data. Companies use their terminals to download generated models from the server and integrate them into their own systems for use.

[0236] Users can either provide data as data providers or utilize generative models as users. Interfaces are provided for each role, allowing data providers to check the status of their provided data and the benefits they receive. Meanwhile, companies using the models can select usage methods that suit the model's performance and purpose, thereby improving operational efficiency.

[0237] As a concrete example, consider a case where an educational institution provides student learning data, and an AI model is created based on that data. The educational institution uploads the data through the platform and restricts its use to the education sector. The server receives the data, runs a training process, and generates an AI model tailored to the educational institution's needs. The generated model is used by the educational institution in its own system, contributing to the optimization of learning and student support. Furthermore, the profits generated from the use of the model are returned to the educational institution, providing a return commensurate with the data provided. In this way, this invention offers a new form of data collection and utilization.

[0238] The following describes the processing flow.

[0239] Step 1:

[0240] Users log in to the platform using their own devices. To provide data, they enter summary information regarding the content and format of the data.

[0241] Step 2:

[0242] The device encrypts the data being uploaded and sends it to the server via secure communication.

[0243] Step 3:

[0244] The server verifies the received data and confirms its integrity. It calculates the hash value of the data to check for tampering.

[0245] Step 4:

[0246] The server stores data that has passed verification in storage. It generates metadata and records it in a database so that the data can be identified and tracked.

[0247] Step 5:

[0248] Users set the permission conditions for using the data they provide. They select options to specify the industry or company that will use the data and its specific purpose.

[0249] Step 6:

[0250] The server manages data usage restrictions based on user-defined permissions. It controls data access to ensure it is only used under permitted conditions.

[0251] Step 7:

[0252] The server builds a training dataset based on the obtained data. It preprocesses the data and converts it into a format suitable for model training.

[0253] Step 8:

[0254] The server trains the generative model using the training dataset. It searches for optimal parameters and improves the model's accuracy.

[0255] Step 9:

[0256] Users download the generated models on their devices and integrate them into their company's systems. The models are then used in business operations to improve efficiency.

[0257] Step 10:

[0258] The server tracks model usage and calculates revenue. It then initiates a process to return a portion of the profits based on model usage to data providers.

[0259] Step 11:

[0260] Users can review the benefits they receive from their accounts, verify that the benefits are being distributed correctly, and provide feedback.

[0261] (Example 1)

[0262] 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."

[0263] There is a lack of a mechanism to appropriately and transparently return the revenue generated from the use of AI models that are trained using information provided by data providers. Furthermore, when integrating information from multiple data providers, there are problems with verifying data integrity and managing usage permissions.

[0264] 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.

[0265] In this invention, the server includes means for storing information obtained from data providers, means for verifying the integrity of the information, and means for training a generative model based on the information. This makes it possible to securely and efficiently utilize the information of data providers and to return the revenue generated from the use of the generated model with transparency.

[0266] A "data provider" refers to an individual or organization that provides information to this system.

[0267] "Information" refers to various types of data and knowledge provided by data providers, and is the material used within the system.

[0268] A "generative model" refers to an artificial intelligence model built based on provided information, and is a program trained for a specific purpose.

[0269] "Revenue" refers to the economic benefits obtained by using the generative model.

[0270] "Information integrity" refers to a state in which the information provided is accurate and consistent, and is a requirement for ensuring the reliability of the data.

[0271] "Permission to use" refers to the authority to specify the terms and scope of use of information provided by the data provider.

[0272] "Transparency" refers to a state where processes such as information handling and revenue sharing are clear, and all parties involved can verify the details.

[0273] A description of the embodiment for carrying out the invention will be provided.

[0274] This system consists of three elements: servers, terminals, and users. It is primarily designed to automate and streamline the processes of data collection, verification, storage, model training, and revenue sharing.

[0275] Server Functions

[0276] After receiving information from a data provider, the server first verifies its integrity. Specifically, it uses integrity verification algorithms to check if the data quality is sufficient. Inconsistent or invalid data is notified to the data provider. After the integrity is confirmed, the data is securely stored in the database. A database management system (e.g., MySQL or PostgreSQL) is used for data storage.

[0277] The server also records data usage history and manages access based on permissions to ensure transparency. This recording process is managed using blockchain technology, enabling transparent data management that is difficult to tamper with.

[0278] Most importantly, the server trains a generative AI model based on the collected information. This training is performed using a machine learning platform (e.g., TensorFlow or PyTorch). Once the model is optimally trained, it becomes available for use by users in various applications. Revenue generated from the use of the model is automatically tracked and calculated using smart contracts and returned to the data providers.

[0279] Device functions

[0280] The terminal is a device used by data providers to upload information. From the terminal, users can access the platform and log in to their own dashboard. Here, they can upload data and set usage permissions.

[0281] On the one hand, the enterprise terminal downloads the trained model from the server, integrates it into its own system, and uses it for business. In this process, a secure connection (e.g., HTTPS) is used to ensure data security.

[0282] Role of the user

[0283] Users provide information as data providers or utilize the model as users of the generative AI model. Data provider users have an interface to check the status of their provided data and the revenue to be returned on the platform. On the other hand, enterprise model users apply the generative AI model according to their own needs to improve business efficiency.

[0284] Specific example

[0285] For example, consider a case where an educational institution provides students' learning data to create a customized generative AI model. The educational institution uses a terminal to access the platform, uploads the learning data, and limits the usage permission to the education field. The server receives this, trains the model. The generated model is incorporated into the educational institution's system and used to optimize learning. The profit is returned to the educational institution according to its usage.

[0286] Example of prompt text

[0287] "Please show the construction procedure and expected results of the AI model using the student data of the educational institution."

[0288] This system enables the effective accumulation and utilization of data and the fair distribution of revenue.

[0289] The flow of the specific process in Example 1 will be described using FIG. 11.

[0290] Step 1:

[0291] The user (data provider) uses their device to log in to the platform and access their dashboard. Next, they select the data they want to provide, such as training data, and upload it. The input is the data provided by the user, and the output is the transmission of that data to the server. During this process, the user also configures their data usage permissions.

[0292] Step 2:

[0293] The server verifies the data received from the user. A data integrity verification algorithm is used to confirm the validity of the data. The input is the data received from the terminal, and the output is the verified data. Data whose integrity has been confirmed is securely stored in the database.

[0294] Step 3:

[0295] The server uses validated data to build a training dataset. The dataset is preprocessed and converted into a format suitable for training the AI ​​model. The input is validated data, and the output is the AI ​​model training dataset. This dataset is saved for later processing.

[0296] Step 4:

[0297] The server uses the training dataset to train a generative AI model. A machine learning framework is used to optimize the model. The input is the training dataset, and the output is the trained generative AI model. The model's performance is evaluated using internal test data.

[0298] Step 5:

[0299] The terminal (corporate user) downloads a pre-trained generative AI model from the server. The downloaded model is integrated into the company's systems and used in its business processes. The input is the generative AI model provided by the server, and the output is the use of the model within the company. This process requires that the model is properly installed and configured.

[0300] Step 6:

[0301] The server records the usage of the generated AI model and calculates revenue. A smart contract is used to automate the distribution of revenue. The input is model usage data, and the output is revenue distribution information for data providers. This final step ensures that revenue is distributed fairly to data providers.

[0302] (Application Example 1)

[0303] 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."

[0304] In systems that recommend appropriate content to users based on viewing history and rating data, there is a need for a method that ensures fair compensation for the information provided by data providers, while also ensuring transparency and efficiently utilizing the data. However, conventional systems have shortcomings in terms of convenience and transparency.

[0305] 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.

[0306] In this invention, the server includes means for storing information acquired from a data provider, means for training a generation model based on the information, means for calculating the profit generated from the use of the model and returning it to the provider, means for recording the history of information use and ensuring transparency, means for generating recommended content tailored to individual users based on viewing history data acquired from the provider, and means for returning to the user through content recommendation. Thereby, it becomes possible to realize content recommendations individualized for users, and to achieve fair profit distribution and transparent data utilization.

[0307] The "data provider" is the entity that provides information to the system, and the information is used for training the generation model.

[0308] The "generation model" is a program or structure trained based on information acquired from a data provider and generates new outputs by a specific algorithm.

[0309] "Profit return" means distributing the economic or non-economic profit obtained from the use of the system or the results of the model to the data provider who provided the information.

[0310] The "viewing history data" is information on the content that a user has viewed in the past and is used to grasp the preferences and tendencies of individual users.

[0311] The "recommended content" is information or works selected based on the analysis of viewing history data and judged to be highly likely to be viewed next based on the interests and hobbies of individual users.

[0312] "Transparency" means that the processes related to information collection, use, and profit distribution are clear, and all relevant parties can confirm that information.

[0313] The "means for calculating profit" is a method or process for calculating the profit obtained from the use of the generation model.

[0314] "Means of return" refers to methods or processes for returning the benefits to the information providers, thereby ensuring a fair distribution.

[0315] This invention realizes a system that trains a generative model using information obtained from data providers and fairly distributes the profits generated from the use of that model. A specific embodiment is shown below.

[0316] The server is responsible for acquiring and recording information from data providers. This information includes viewing history data and rating information, and is securely stored in a database using SQLite or PostgreSQL. The server automatically verifies data integrity and records usage history in detail to maintain transparency. It also trains generative AI models using TensorFlow or PyTorch to build personalized recommendation content optimized for each user.

[0317] The device provides an interface for users to input their viewing history data and presents them with recommended content generated from the server. In this process, programming languages ​​such as Python are used to present the recommendations in a visually easy-to-understand format.

[0318] Users can receive content optimized based on their individual viewing history and enjoy rewards commensurate with their viewing behavior. Specifically, these rewards include viewing credits and access to special content.

[0319] For example, if a user gives a high rating to a historical documentary, that information is stored on the server and used as training data for a generative model. As a result, the server selects content of similar interest and recommends it to the user through their device.

[0320] Within the program, an example of a prompt statement used when providing viewing history data to the generative AI model is the instruction, "Based on the user's viewing history data, recommend content that they are most likely to watch next."

[0321] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0322] Step 1:

[0323] The server receives viewing history data from data providers and checks its integrity. Viewing history data, as input, is validated through an error-checking algorithm before being stored in the database. This process ensures accurate and reliable data.

[0324] Step 2:

[0325] The server securely stores verified viewing history data in an SQLite or PostgreSQL database. The data provided as input is recorded along with a unique user identifier and organized for easy access in subsequent processing.

[0326] Step 3:

[0327] The server trains a generative AI model based on stored viewing history data. Using TensorFlow or PyTorch, the algorithm learns user preferences using the dataset as input. As a result, a personalized recommendation algorithm is output for each user.

[0328] Step 4:

[0329] The device uses a generative AI model obtained from the server to generate and present recommended content to the user. It receives a recommendation algorithm as input and displays the content visually and clearly through the user interface. This allows the user to easily select the next available content.

[0330] Step 5:

[0331] Users select and watch content from the recommended selections presented. Based on this, the viewing results are fed back to the system as input, initiating the next data provision cycle. This feedback is used as crucial data to further improve the accuracy of future recommendations.

[0332] Step 6:

[0333] The server calculates the profits generated from the use of the model and fairly distributes them to data providers. It enhances data provider satisfaction by managing a reward system based on the output of the generated AI model, including viewing credits and special access rights. Throughout this process, the profit-sharing status is recorded while maintaining transparency.

[0334] 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.

[0335] This invention is a system that comprehensively manages everything from data collection from data providers to training generative models and providing feedback using an emotion engine. The system consists of a server, terminals, users, and an emotion engine.

[0336] The server securely stores data received from data providers and manages data usage conditions based on access permissions. The server has the capability to build training datasets and train generative AI models. In addition, it dynamically adjusts the generative model's algorithms based on feedback data provided by the emotion engine to improve model accuracy. It also has a mechanism for calculating revenue and returning profits to data providers according to appropriate criteria.

[0337] The terminal serves as the primary interface for data providers and model users to access the system. Data providers can upload data through the terminal and also provide additional feedback using the sentiment engine. Model users can download AI models generated from the terminal and use them in their work to optimize business processes.

[0338] The emotion engine analyzes the user's emotional state in real time and provides feedback to other components of the system. For example, it can improve model performance by incorporating emotional data into the parameters of the training process. It also adjusts the criteria for reward distribution based on emotional data, striving to increase user satisfaction.

[0339] Users utilize the system as both data providers and model users. Data providers can manage the status of their uploaded data, set usage permissions, and receive profits. Meanwhile, companies, as model users, use the feedback obtained from the emotion engine to improve the efficiency of their operations.

[0340] A concrete example is a case where a medical facility provides patient feedback data to generate an AI model. The medical facility collects data, including the patient's emotional state, and uploads it to the system. The server analyzes the data and emotional feedback to train a generative AI model specifically tailored to the medical field. Through feedback from the emotional engine, the model contributes to improving patient care. In addition, the medical facility receives appropriate revenue based on the use of the model. This system makes it possible to build new AI models that utilize emotional data.

[0341] The following describes the processing flow.

[0342] Step 1:

[0343] Users log in to the platform through their own devices and enter the content and sentiment data they wish to provide. Users then set the necessary permissions to upload this data.

[0344] Step 2:

[0345] The device encrypts the provided data and emotional data and sends it to the server using a secure communication protocol.

[0346] Step 3:

[0347] The server authenticates and verifies the integrity of the received data. It then stores the data, along with necessary metadata, in secure storage.

[0348] Step 4:

[0349] The server manages the conditions for data usage permission based on the usage permissions set by the data provider. It also records data usage history to ensure transparency.

[0350] Step 5:

[0351] The server uses an emotion engine to analyze emotional data provided by users and generate the feedback necessary for training.

[0352] Step 6:

[0353] The server optimizes the dataset based on feedback from the sentiment engine and begins the generative model training process. The trained model is further refined with sentiment data.

[0354] Step 7:

[0355] On their devices, users download generated models from the server and integrate them into their own systems. These models are then used for specific business purposes.

[0356] Step 8:

[0357] The server calculates revenue based on model usage and sentimental title feedback. A portion of the calculated revenue is returned to the data provider, providing results that take user sentiment into account.

[0358] Step 9:

[0359] Users can check the status of their earnings distribution through their accounts and evaluate the feedback from the system. User feedback is used to further improve the system.

[0360] (Example 2)

[0361] 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".

[0362] This invention aims to solve the problem of efficiently utilizing a wide range of information obtained from data providers to improve the training accuracy of generative models, while also transparently and appropriately returning the profits obtained. Furthermore, it places emphasis on optimizing the user experience by reflecting users' emotional information in the model.

[0363] 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.

[0364] In this invention, the server includes means for storing information obtained from data providers, means for training a model generated based on the information, and means for calculating the results obtained from the use of the model and returning them to the provider. This enables efficient management and utilization of information.

[0365] A "data provider" refers to an entity that provides information to the system, and that information is used for training and analyzing models.

[0366] A "generated model" refers to a digital representation created by an algorithm based on acquired information, and possesses the ability to perform a variety of tasks.

[0367] "Calculating results" refers to the process of quantifying the gains and efficiency improvements obtained through the use of the model, and then providing those results back to the information provider.

[0368] "Recording the history of information usage" refers to a recording process that tracks how the provided information was used within the system, providing transparency and traceability.

[0369] "Emotional information" refers to data that indicates the emotional state of users, and is used for improving models and providing feedback.

[0370] "Dynamic adjustment" refers to the process of changing model parameters and algorithms in real time in response to changes in the environment and conditions.

[0371] To implement this invention, multiple components are used, including a server, a terminal, a user, and an emotion engine. The server is responsible for receiving and securely storing information obtained from data providers. This information is stored using a general-purpose database management system. The server also analyzes the information and builds the datasets necessary for training generative AI models.

[0372] The server trains models using existing machine learning frameworks such as TensorFlow and PyTorch, and maintains the results. The trained models are dynamically adjusted based on sentiment information provided by the sentiment engine to improve accuracy and utilization efficiency. The sentiment engine analyzes user feedback in real time and uses this data to adjust the model parameters.

[0373] The terminal serves as the primary interface for data providers and model users to access the system. Data providers can upload information via the terminal, and model users can download trained generative AI models via the terminal. Model users then integrate these models into their own business processes to streamline and optimize those processes.

[0374] A concrete example is a medical facility. Medical facilities provide patient feedback data to a server via terminals. An example of a prompt message could be: "Please tell me how to use the latest generative AI model to analyze patient feedback data and gain insights that will help improve patient care."

[0375] The operation of this entire system enables the effective use of information, continuous improvement of the model, and return of profits to providers, thereby contributing to increased user satisfaction.

[0376] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0377] Step 1: Data Collection

[0378] The server receives data uploaded by users from their devices. This data includes emotion-related information such as text, audio, and video. The input data is first pre-processed to standardize the format and remove unnecessary data before being securely stored in the database. This process ensures data consistency and security.

[0379] Step 2: Build the dataset

[0380] The server constructs a dataset suitable for model training from preprocessed data. This dataset construction includes assigning specific sentiment labels and categorizing the data. In this step, the raw input data is formatted for machine learning algorithms and outputted as a trainable dataset. This results in a high-quality dataset.

[0381] Step 3: Model Training

[0382] The server trains a generative AI model using the constructed dataset. This process utilizes frameworks such as TensorFlow and PyTorch, applying deep learning algorithms. The input is the dataset, and the output is the trained model. Specifically, feedback data from the emotion engine is used to dynamically adjust model parameters in order to improve prediction accuracy. This step completes a highly accurate model.

[0383] Step 4: Feedback Analysis

[0384] The terminal aggregates user feedback and analyzes it in conjunction with the emotion engine. Input includes ratings and comments indicating the user's emotional state. This feedback is analyzed and notified to the server, allowing for further optimization of the model. The output is feedback data as a result of the analysis, which is used as a new metric for model improvement.

[0385] Step 5: Delivering Results

[0386] Users download pre-trained generative AI models via their devices and utilize them in their work. The input is the model supplied from the server. The output is the efficiency improvements and optimizations achieved through the integration of the model into business processes. For example, a company can use the model to efficiently process customer emotional feedback in order to improve the quality of customer support.

[0387] Step 6: Profit Calculation and Distribution

[0388] The server calculates the revenue generated from model usage and distributes it appropriately to data providers. Inputs are model usage data and feedback evaluation data. Outputs are the calculated amounts distributed to each provider. This procedure helps build trust with data providers and promotes sustainable information sharing.

[0389] (Application Example 2)

[0390] 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".

[0391] Conventional generative AI models have not adequately improved model accuracy or considered user sentiment in their content recommendations. This has made it difficult to provide optimal content that enhances user satisfaction. Furthermore, there have been challenges in fully utilizing feedback data to provide benefits to data providers.

[0392] 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.

[0393] In this invention, the server includes means for storing data obtained from data providers, means for training a generative model based on the data, and means for analyzing emotional states and optimizing the generative model based on content recommendations. This enables the provision of personalized content that responds to the user's emotions.

[0394] A "data provider" is an entity that plays the role of supplying information to a data generation system.

[0395] A "generative model" is an algorithm that generates new information or content based on the information provided.

[0396] "Emotional state" refers to data that indicates the user's mental state and is analyzed through the emotion engine.

[0397] "Content recommendation" is the act of presenting the most relevant information based on the user's emotions and preferences.

[0398] "Means of calculating profits and returning them to providers" refers to a mechanism for appropriately distributing revenue generated through a production system.

[0399] "Means of recording data usage history and ensuring transparency" refers to methods of increasing the credibility of a system by accumulating a history of information usage.

[0400] A description of the embodiment for carrying out the invention will be provided.

[0401] In this system, the server securely stores information collected from data providers and uses it to train generative AI models. The data is protected by security protocols and centrally managed on the server. The generative models are optimized based on sentiment data provided by the sentiment engine and trained to generate personalized content for each user.

[0402] The terminal serves as the primary interface for data providers and model users to access the system. Data providers can use the terminal to upload data and provide additional feedback by inputting sentiment data, which can further improve the performance of generative models.

[0403] Users can download generative AI models obtained through their devices and apply them to their work and lifestyle. For example, they can listen to music to reduce daily stress using playlists suggested based on emotional data.

[0404] As a concrete example, when a user uses a music streaming app during their relaxation time after work, this system analyzes the user's emotional state and automatically selects and recommends music best suited for relaxation. An example of a prompt input to the generative AI model might be, "Use the latest emotion analysis technology to recommend the best meditation guide when the user's stress level is high." In this way, it is possible to improve the user experience.

[0405] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0406] Step 1:

[0407] The server receives data sent from data providers via terminals. This data includes user behavior logs and sentiment data. The server securely stores this data and saves it in a database. It takes data from data providers as input and outputs data saved to the database.

[0408] Step 2:

[0409] The server begins training a generative AI model based on the collected data. During this process, it analyzes emotional data and extracts data patterns corresponding to the user's emotional state. A training dataset is constructed and input into the generative model's algorithm, thereby optimizing the model. The input is the collected dataset, and the output is the optimized generative model.

[0410] Step 3:

[0411] The terminal provides an interface for model users to access the system. Model users download optimized generative AI models and use them for business or personal purposes. The terminal receives input from model users, retrieves models from the server, and deploys them to the terminal. The input is the model user's request, and the output is the distribution of the model to the terminal.

[0412] Step 4:

[0413] Users utilize content generated by an AI model using their device. This content is optimized according to the user's emotional state, allowing them to experience something that matches their mood. For example, when a user wants to relax, the device suggests a music playlist accordingly. The input is the user's emotional state, and the output is the customized content provided to the user.

[0414] 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.

[0415] 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.

[0416] 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.

[0417] [Third Embodiment]

[0418] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0419] 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.

[0420] 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).

[0421] 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.

[0422] 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.

[0423] 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).

[0424] 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.

[0425] 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.

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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".

[0430] This invention is a system that manages a series of processes, from data collection from data providers to training generative models and the resulting profit distribution. The system is broadly composed of three elements: a server, terminals, and users.

[0431] The server receives and stores data sent from data providers. The server verifies the integrity of uploaded data and securely stores it in a database. It also maintains transparency by managing data usage conditions based on permissions set by data providers and recording usage history. The server's role also includes building training datasets and training data-driven generative AI models. Furthermore, it has a mechanism to calculate the revenue generated from the use of the generated models and return a portion of it to the data providers.

[0432] Terminals are devices used by data providers to upload data and by companies to download and use generated models. Data providers use their terminals to access the platform and upload data. Companies use their terminals to download generated models from the server and integrate them into their own systems for use.

[0433] Users can either provide data as data providers or utilize generative models as users. Interfaces are provided for each role, allowing data providers to check the status of their provided data and the benefits they receive. Meanwhile, companies using the models can select usage methods that suit the model's performance and purpose, thereby improving operational efficiency.

[0434] As a concrete example, consider a case where an educational institution provides student learning data, and an AI model is created based on that data. The educational institution uploads the data through the platform and restricts its use to the education sector. The server receives the data, runs a training process, and generates an AI model tailored to the educational institution's needs. The generated model is used by the educational institution in its own system, contributing to the optimization of learning and student support. Furthermore, the profits generated from the use of the model are returned to the educational institution, providing a return commensurate with the data provided. In this way, this invention offers a new form of data collection and utilization.

[0435] The following describes the processing flow.

[0436] Step 1:

[0437] Users log in to the platform using their own devices. To provide data, they enter summary information regarding the content and format of the data.

[0438] Step 2:

[0439] The device encrypts the data being uploaded and sends it to the server via secure communication.

[0440] Step 3:

[0441] The server verifies the received data and confirms its integrity. It calculates the hash value of the data to check for tampering.

[0442] Step 4:

[0443] The server stores data that has passed verification in storage. It generates metadata and records it in a database so that the data can be identified and tracked.

[0444] Step 5:

[0445] Users set the permission conditions for using the data they provide. They select options to specify the industry or company that will use the data and its specific purpose.

[0446] Step 6:

[0447] The server manages data usage restrictions based on user-defined permissions. It controls data access to ensure it is only used under permitted conditions.

[0448] Step 7:

[0449] The server builds a training dataset based on the obtained data. It preprocesses the data and converts it into a format suitable for model training.

[0450] Step 8:

[0451] The server trains the generative model using the training dataset. It searches for optimal parameters and improves the model's accuracy.

[0452] Step 9:

[0453] Users download the generated models on their devices and integrate them into their company's systems. The models are then used in business operations to improve efficiency.

[0454] Step 10:

[0455] The server tracks model usage and calculates revenue. It then initiates a process to return a portion of the profits based on model usage to data providers.

[0456] Step 11:

[0457] Users can review the benefits they receive from their accounts, verify that the benefits are being distributed correctly, and provide feedback.

[0458] (Example 1)

[0459] 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."

[0460] There is a lack of a mechanism to appropriately and transparently return the revenue generated from the use of AI models that are trained using information provided by data providers. Furthermore, when integrating information from multiple data providers, there are problems with verifying data integrity and managing usage permissions.

[0461] 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.

[0462] In this invention, the server includes means for storing information obtained from data providers, means for verifying the integrity of the information, and means for training a generative model based on the information. This makes it possible to securely and efficiently utilize the information of data providers and to return the revenue generated from the use of the generated model with transparency.

[0463] A "data provider" refers to an individual or organization that provides information to this system.

[0464] "Information" refers to various types of data and knowledge provided by data providers, and is the material used within the system.

[0465] A "generative model" refers to an artificial intelligence model built based on provided information, and is a program trained for a specific purpose.

[0466] "Revenue" refers to the economic benefits obtained by using the generative model.

[0467] "Information integrity" refers to a state in which the information provided is accurate and consistent, and is a requirement for ensuring the reliability of the data.

[0468] "Permission to use" refers to the authority to specify the terms and scope of use of information provided by the data provider.

[0469] "Transparency" refers to a state where processes such as information handling and revenue sharing are clear, and all parties involved can verify the details.

[0470] A description of the embodiment for carrying out the invention will be provided.

[0471] This system consists of three elements: servers, terminals, and users. It is primarily designed to automate and streamline the processes of data collection, verification, storage, model training, and revenue sharing.

[0472] Server Functions

[0473] After receiving information from a data provider, the server first verifies its integrity. Specifically, it uses integrity verification algorithms to check if the data quality is sufficient. Inconsistent or invalid data is notified to the data provider. After the integrity is confirmed, the data is securely stored in the database. A database management system (e.g., MySQL or PostgreSQL) is used for data storage.

[0474] The server also records data usage history and manages access based on permissions to ensure transparency. This recording process is managed using blockchain technology, enabling transparent data management that is difficult to tamper with.

[0475] Most importantly, the server trains a generative AI model based on the collected information. This training is performed using a machine learning platform (e.g., TensorFlow or PyTorch). Once the model is optimally trained, it becomes available for use by users in various applications. Revenue generated from the use of the model is automatically tracked and calculated using smart contracts and returned to the data providers.

[0476] Device functions

[0477] The terminal is a device used by data providers to upload information. From the terminal, users can access the platform and log in to their own dashboard. Here, they can upload data and set usage permissions.

[0478] On the other hand, corporate terminals download trained models from servers and integrate them into their own systems for use in business operations. Secure connections (e.g., HTTPS) are used throughout this process, ensuring data security.

[0479] User roles

[0480] Users can either provide information as data providers or utilize generative AI models as users. Data providers have an interface on the platform where they can check the status of their provided data and the revenue they receive. Meanwhile, companies using the models can apply generative AI models according to their needs to improve their operational efficiency.

[0481] Specific example

[0482] For example, consider a case where an educational institution provides student learning data to create a customized generative AI model. The institution accesses the platform using a terminal, uploads the learning data, and restricts its use to the education sector. The server receives this data and trains the model. The generated model is integrated into the institution's system and used to optimize learning. The benefits are returned to the institution according to their usage.

[0483] Example of a prompt

[0484] "Please describe the steps involved in building an AI model using student data from an educational institution, and the expected results."

[0485] This system makes it possible to effectively collect and utilize data and fairly distribute profits.

[0486] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0487] Step 1:

[0488] The user (data provider) uses their device to log in to the platform and access their dashboard. Next, they select the data they want to provide, such as training data, and upload it. The input is the data provided by the user, and the output is the transmission of that data to the server. During this process, the user also configures their data usage permissions.

[0489] Step 2:

[0490] The server verifies the data received from the user. A data integrity verification algorithm is used to confirm the validity of the data. The input is the data received from the terminal, and the output is the verified data. Data whose integrity has been confirmed is securely stored in the database.

[0491] Step 3:

[0492] The server uses validated data to build a training dataset. The dataset is preprocessed and converted into a format suitable for training the AI ​​model. The input is validated data, and the output is the AI ​​model training dataset. This dataset is saved for later processing.

[0493] Step 4:

[0494] The server uses the training dataset to train a generative AI model. A machine learning framework is used to optimize the model. The input is the training dataset, and the output is the trained generative AI model. The model's performance is evaluated using internal test data.

[0495] Step 5:

[0496] The terminal (corporate user) downloads a pre-trained generative AI model from the server. The downloaded model is integrated into the company's systems and used in its business processes. The input is the generative AI model provided by the server, and the output is the use of the model within the company. This process requires that the model is properly installed and configured.

[0497] Step 6:

[0498] The server records the usage of the generated AI model and calculates revenue. A smart contract is used to automate the distribution of revenue. The input is model usage data, and the output is revenue distribution information for data providers. This final step ensures that revenue is distributed fairly to data providers.

[0499] (Application Example 1)

[0500] 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."

[0501] In systems that recommend appropriate content to users based on viewing history and rating data, there is a need for a method that ensures fair compensation for the information provided by data providers, while also ensuring transparency and efficiently utilizing the data. However, conventional systems have shortcomings in terms of convenience and transparency.

[0502] 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.

[0503] In this invention, the server includes means for storing information obtained from data providers, means for training a generative model based on the information, means for calculating the profits generated from the use of the model and returning them to the provider, means for recording the history of information usage and ensuring transparency, means for generating recommended content tailored to individual users based on viewing history data obtained from providers, and means for returning value to users through content recommendations. This enables personalized content recommendations for users, fair profit sharing, and transparent data utilization.

[0504] A "data provider" is an entity that provides information to the system, and that information is used to train the generative model.

[0505] A "generative model" is a program or structure that is trained on information obtained from data providers and generates new outputs using a specific algorithm.

[0506] "Profit sharing" means distributing the economic or non-economic benefits obtained from the use of the system or the results of the model to the data providers who provided the information.

[0507] "Viewing history data" refers to information about content that a user has viewed in the past, and is used to understand the preferences and tendencies of individual users.

[0508] "Recommended content" refers to information or works that are selected based on an analysis of viewing history data, taking into account the individual user's interests and preferences, and are judged to be highly likely to be viewed next.

[0509] "Transparency" means that the processes for collecting, using, and sharing information are clear, and that all stakeholders can access and verify that information.

[0510] "Means of calculating profit" refers to a method or process for calculating the profit obtained from the use of generative models.

[0511] "Means of return" refers to methods or processes for returning the benefits to the information providers, thereby ensuring a fair distribution.

[0512] This invention realizes a system that trains a generative model using information obtained from data providers and fairly distributes the profits generated from the use of that model. A specific embodiment is shown below.

[0513] The server is responsible for acquiring and recording information from data providers. This information includes viewing history data and rating information, and is securely stored in a database using SQLite or PostgreSQL. The server automatically verifies data integrity and records usage history in detail to maintain transparency. It also trains generative AI models using TensorFlow or PyTorch to build personalized recommendation content optimized for each user.

[0514] The device provides an interface for users to input their viewing history data and presents them with recommended content generated from the server. In this process, programming languages ​​such as Python are used to present the recommendations in a visually easy-to-understand format.

[0515] Users can receive content optimized based on their individual viewing history and enjoy rewards commensurate with their viewing behavior. Specifically, these rewards include viewing credits and access to special content.

[0516] For example, if a user gives a high rating to a historical documentary, that information is stored on the server and used as training data for a generative model. As a result, the server selects content of similar interest and recommends it to the user through their device.

[0517] Within the program, an example of a prompt statement used when providing viewing history data to the generative AI model is the instruction, "Based on the user's viewing history data, recommend content that they are most likely to watch next."

[0518] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0519] Step 1:

[0520] The server receives viewing history data from data providers and checks its integrity. Viewing history data, as input, is validated through an error-checking algorithm before being stored in the database. This process ensures accurate and reliable data.

[0521] Step 2:

[0522] The server securely stores verified viewing history data in an SQLite or PostgreSQL database. The data provided as input is recorded along with a unique user identifier and organized for easy access in subsequent processing.

[0523] Step 3:

[0524] The server trains a generative AI model based on stored viewing history data. Using TensorFlow or PyTorch, the algorithm learns user preferences using the dataset as input. As a result, a personalized recommendation algorithm is output for each user.

[0525] Step 4:

[0526] The device uses a generative AI model obtained from the server to generate and present recommended content to the user. It receives a recommendation algorithm as input and displays the content visually and clearly through the user interface. This allows the user to easily select the next available content.

[0527] Step 5:

[0528] Users select and watch content from the recommended selections presented. Based on this, the viewing results are fed back to the system as input, initiating the next data provision cycle. This feedback is used as crucial data to further improve the accuracy of future recommendations.

[0529] Step 6:

[0530] The server calculates the profits generated from the use of the model and fairly distributes them to data providers. It enhances data provider satisfaction by managing a reward system based on the output of the generated AI model, including viewing credits and special access rights. Throughout this process, the profit-sharing status is recorded while maintaining transparency.

[0531] 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.

[0532] This invention is a system that comprehensively manages everything from data collection from data providers to training generative models and providing feedback using an emotion engine. The system consists of a server, terminals, users, and an emotion engine.

[0533] The server securely stores data received from data providers and manages data usage conditions based on access permissions. The server has the capability to build training datasets and train generative AI models. In addition, it dynamically adjusts the generative model's algorithms based on feedback data provided by the emotion engine to improve model accuracy. It also has a mechanism for calculating revenue and returning profits to data providers according to appropriate criteria.

[0534] The terminal serves as the primary interface for data providers and model users to access the system. Data providers can upload data through the terminal and also provide additional feedback using the sentiment engine. Model users can download AI models generated from the terminal and use them in their work to optimize business processes.

[0535] The emotion engine analyzes the user's emotional state in real time and provides feedback to other components of the system. For example, it can improve model performance by incorporating emotional data into the parameters of the training process. It also adjusts the criteria for reward distribution based on emotional data, striving to increase user satisfaction.

[0536] Users utilize the system as both data providers and model users. Data providers can manage the status of their uploaded data, set usage permissions, and receive profits. Meanwhile, companies, as model users, use the feedback obtained from the emotion engine to improve the efficiency of their operations.

[0537] A concrete example is a case where a medical facility provides patient feedback data to generate an AI model. The medical facility collects data, including the patient's emotional state, and uploads it to the system. The server analyzes the data and emotional feedback to train a generative AI model specifically tailored to the medical field. Through feedback from the emotional engine, the model contributes to improving patient care. In addition, the medical facility receives appropriate revenue based on the use of the model. This system makes it possible to build new AI models that utilize emotional data.

[0538] The following describes the processing flow.

[0539] Step 1:

[0540] Users log in to the platform through their own devices and enter the content and sentiment data they wish to provide. Users then set the necessary permissions to upload this data.

[0541] Step 2:

[0542] The device encrypts the provided data and emotional data and sends it to the server using a secure communication protocol.

[0543] Step 3:

[0544] The server authenticates and verifies the integrity of the received data. It then stores the data, along with necessary metadata, in secure storage.

[0545] Step 4:

[0546] The server manages the conditions for data usage permission based on the usage permissions set by the data provider. It also records data usage history to ensure transparency.

[0547] Step 5:

[0548] The server uses an emotion engine to analyze emotional data provided by users and generate the feedback necessary for training.

[0549] Step 6:

[0550] The server optimizes the dataset based on feedback from the sentiment engine and begins the generative model training process. The trained model is further refined with sentiment data.

[0551] Step 7:

[0552] On their devices, users download generated models from the server and integrate them into their own systems. These models are then used for specific business purposes.

[0553] Step 8:

[0554] The server calculates revenue based on model usage and sentimental title feedback. A portion of the calculated revenue is returned to the data provider, providing results that take user sentiment into account.

[0555] Step 9:

[0556] Users can check the status of their earnings distribution through their accounts and evaluate the feedback from the system. User feedback is used to further improve the system.

[0557] (Example 2)

[0558] 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."

[0559] This invention aims to solve the problem of efficiently utilizing a wide range of information obtained from data providers to improve the training accuracy of generative models, while also transparently and appropriately returning the profits obtained. Furthermore, it places emphasis on optimizing the user experience by reflecting users' emotional information in the model.

[0560] 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.

[0561] In this invention, the server includes means for storing information obtained from data providers, means for training a model generated based on the information, and means for calculating the results obtained from the use of the model and returning them to the provider. This enables efficient management and utilization of information.

[0562] A "data provider" refers to an entity that provides information to the system, and that information is used for training and analyzing models.

[0563] A "generated model" refers to a digital representation created by an algorithm based on acquired information, and possesses the ability to perform a variety of tasks.

[0564] "Calculating results" refers to the process of quantifying the gains and efficiency improvements obtained through the use of the model, and then providing those results back to the information provider.

[0565] "Recording the history of information usage" refers to a recording process that tracks how the provided information was used within the system, providing transparency and traceability.

[0566] "Emotional information" refers to data that indicates the emotional state of users, and is used for improving models and providing feedback.

[0567] "Dynamic adjustment" refers to the process of changing model parameters and algorithms in real time in response to changes in the environment and conditions.

[0568] To implement this invention, multiple components are used, including a server, a terminal, a user, and an emotion engine. The server is responsible for receiving and securely storing information obtained from data providers. This information is stored using a general-purpose database management system. The server also analyzes the information and builds the datasets necessary for training generative AI models.

[0569] The server trains models using existing machine learning frameworks such as TensorFlow and PyTorch, and maintains the results. The trained models are dynamically adjusted based on sentiment information provided by the sentiment engine to improve accuracy and utilization efficiency. The sentiment engine analyzes user feedback in real time and uses this data to adjust the model parameters.

[0570] The terminal serves as the primary interface for data providers and model users to access the system. Data providers can upload information via the terminal, and model users can download trained generative AI models via the terminal. Model users then integrate these models into their own business processes to streamline and optimize those processes.

[0571] A concrete example is a medical facility. Medical facilities provide patient feedback data to a server via terminals. An example of a prompt message could be: "Please tell me how to use the latest generative AI model to analyze patient feedback data and gain insights that will help improve patient care."

[0572] The operation of this entire system enables the effective use of information, continuous improvement of the model, and return of profits to providers, thereby contributing to increased user satisfaction.

[0573] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0574] Step 1: Data Collection

[0575] The server receives data uploaded by users from their devices. This data includes emotion-related information such as text, audio, and video. The input data is first pre-processed to standardize the format and remove unnecessary data before being securely stored in the database. This process ensures data consistency and security.

[0576] Step 2: Build the dataset

[0577] The server constructs a dataset suitable for model training from preprocessed data. This dataset construction includes assigning specific sentiment labels and categorizing the data. In this step, the raw input data is formatted for machine learning algorithms and outputted as a trainable dataset. This results in a high-quality dataset.

[0578] Step 3: Model Training

[0579] The server trains a generative AI model using the constructed dataset. This process utilizes frameworks such as TensorFlow and PyTorch, applying deep learning algorithms. The input is the dataset, and the output is the trained model. Specifically, feedback data from the emotion engine is used to dynamically adjust model parameters in order to improve prediction accuracy. This step completes a highly accurate model.

[0580] Step 4: Feedback Analysis

[0581] The terminal aggregates user feedback and analyzes it in conjunction with the emotion engine. Input includes ratings and comments indicating the user's emotional state. This feedback is analyzed and notified to the server, allowing for further optimization of the model. The output is feedback data as a result of the analysis, which is used as a new metric for model improvement.

[0582] Step 5: Delivering Results

[0583] Users download pre-trained generative AI models via their devices and utilize them in their work. The input is the model supplied from the server. The output is the efficiency improvements and optimizations achieved through the integration of the model into business processes. For example, a company can use the model to efficiently process customer emotional feedback in order to improve the quality of customer support.

[0584] Step 6: Profit Calculation and Distribution

[0585] The server calculates the revenue generated from model usage and distributes it appropriately to data providers. Inputs are model usage data and feedback evaluation data. Outputs are the calculated amounts distributed to each provider. This procedure helps build trust with data providers and promotes sustainable information sharing.

[0586] (Application Example 2)

[0587] 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."

[0588] Conventional generative AI models have not adequately improved model accuracy or considered user sentiment in their content recommendations. This has made it difficult to provide optimal content that enhances user satisfaction. Furthermore, there have been challenges in fully utilizing feedback data to provide benefits to data providers.

[0589] 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.

[0590] In this invention, the server includes means for storing data obtained from data providers, means for training a generative model based on the data, and means for analyzing emotional states and optimizing the generative model based on content recommendations. This enables the provision of personalized content that responds to the user's emotions.

[0591] A "data provider" is an entity that plays the role of supplying information to a data generation system.

[0592] A "generative model" is an algorithm that generates new information or content based on the information provided.

[0593] "Emotional state" refers to data that indicates the user's mental state and is analyzed through the emotion engine.

[0594] "Content recommendation" is the act of presenting the most relevant information based on the user's emotions and preferences.

[0595] "Means of calculating profits and returning them to providers" refers to a mechanism for appropriately distributing revenue generated through a production system.

[0596] "Means of recording data usage history and ensuring transparency" refers to methods of increasing the credibility of a system by accumulating a history of information usage.

[0597] A description of the embodiment for carrying out the invention will be provided.

[0598] In this system, the server securely stores information collected from data providers and uses it to train generative AI models. The data is protected by security protocols and centrally managed on the server. The generative models are optimized based on sentiment data provided by the sentiment engine and trained to generate personalized content for each user.

[0599] The terminal serves as the primary interface for data providers and model users to access the system. Data providers can use the terminal to upload data and provide additional feedback by inputting sentiment data, which can further improve the performance of generative models.

[0600] Users can download generative AI models obtained through their devices and apply them to their work and lifestyle. For example, they can listen to music to reduce daily stress using playlists suggested based on emotional data.

[0601] As a concrete example, when a user uses a music streaming app during their relaxation time after work, this system analyzes the user's emotional state and automatically selects and recommends music best suited for relaxation. An example of a prompt input to the generative AI model might be, "Use the latest emotion analysis technology to recommend the best meditation guide when the user's stress level is high." In this way, it is possible to improve the user experience.

[0602] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0603] Step 1:

[0604] The server receives data sent from data providers via terminals. This data includes user behavior logs and sentiment data. The server securely stores this data and saves it in a database. It takes data from data providers as input and outputs data saved to the database.

[0605] Step 2:

[0606] The server begins training a generative AI model based on the collected data. During this process, it analyzes emotional data and extracts data patterns corresponding to the user's emotional state. A training dataset is constructed and input into the generative model's algorithm, thereby optimizing the model. The input is the collected dataset, and the output is the optimized generative model.

[0607] Step 3:

[0608] The terminal provides an interface for model users to access the system. Model users download optimized generative AI models and use them for business or personal purposes. The terminal receives input from model users, retrieves models from the server, and deploys them to the terminal. The input is the model user's request, and the output is the distribution of the model to the terminal.

[0609] Step 4:

[0610] Users utilize content generated by an AI model using their device. This content is optimized according to the user's emotional state, allowing them to experience something that matches their mood. For example, when a user wants to relax, the device suggests a music playlist accordingly. The input is the user's emotional state, and the output is the customized content provided to the user.

[0611] 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.

[0612] 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.

[0613] 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.

[0614] [Fourth Embodiment]

[0615] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0616] 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.

[0617] 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).

[0618] 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.

[0619] 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.

[0620] 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).

[0621] 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.

[0622] 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.

[0623] 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.

[0624] 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.

[0625] 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.

[0626] 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.

[0627] 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".

[0628] This invention is a system that manages a series of processes, from data collection from data providers to training generative models and the resulting profit distribution. The system is broadly composed of three elements: a server, terminals, and users.

[0629] The server receives and stores data sent from data providers. The server verifies the integrity of uploaded data and securely stores it in a database. It also maintains transparency by managing data usage conditions based on permissions set by data providers and recording usage history. The server's role also includes building training datasets and training data-driven generative AI models. Furthermore, it has a mechanism to calculate the revenue generated from the use of the generated models and return a portion of it to the data providers.

[0630] Terminals are devices used by data providers to upload data and by companies to download and use generated models. Data providers use their terminals to access the platform and upload data. Companies use their terminals to download generated models from the server and integrate them into their own systems for use.

[0631] Users can either provide data as data providers or utilize generative models as users. Interfaces are provided for each role, allowing data providers to check the status of their provided data and the benefits they receive. Meanwhile, companies using the models can select usage methods that suit the model's performance and purpose, thereby improving operational efficiency.

[0632] As a concrete example, consider a case where an educational institution provides student learning data, and an AI model is created based on that data. The educational institution uploads the data through the platform and restricts its use to the education sector. The server receives the data, runs a training process, and generates an AI model tailored to the educational institution's needs. The generated model is used by the educational institution in its own system, contributing to the optimization of learning and student support. Furthermore, the profits generated from the use of the model are returned to the educational institution, providing a return commensurate with the data provided. In this way, this invention offers a new form of data collection and utilization.

[0633] The following describes the processing flow.

[0634] Step 1:

[0635] Users log in to the platform using their own devices. To provide data, they enter summary information regarding the content and format of the data.

[0636] Step 2:

[0637] The device encrypts the data being uploaded and sends it to the server via secure communication.

[0638] Step 3:

[0639] The server verifies the received data and confirms its integrity. It calculates the hash value of the data to check for tampering.

[0640] Step 4:

[0641] The server stores data that has passed verification in storage. It generates metadata and records it in a database so that the data can be identified and tracked.

[0642] Step 5:

[0643] Users set the permission conditions for using the data they provide. They select options to specify the industry or company that will use the data and its specific purpose.

[0644] Step 6:

[0645] The server manages data usage restrictions based on user-defined permissions. It controls data access to ensure it is only used under permitted conditions.

[0646] Step 7:

[0647] The server builds a training dataset based on the obtained data. It preprocesses the data and converts it into a format suitable for model training.

[0648] Step 8:

[0649] The server trains the generative model using the training dataset. It searches for optimal parameters and improves the model's accuracy.

[0650] Step 9:

[0651] Users download the generated models on their devices and integrate them into their company's systems. The models are then used in business operations to improve efficiency.

[0652] Step 10:

[0653] The server tracks model usage and calculates revenue. It then initiates a process to return a portion of the profits based on model usage to data providers.

[0654] Step 11:

[0655] Users can review the benefits they receive from their accounts, verify that the benefits are being distributed correctly, and provide feedback.

[0656] (Example 1)

[0657] 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".

[0658] There is a lack of a mechanism to appropriately and transparently return the revenue generated from the use of AI models that are trained using information provided by data providers. Furthermore, when integrating information from multiple data providers, there are problems with verifying data integrity and managing usage permissions.

[0659] 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.

[0660] In this invention, the server includes means for storing information obtained from data providers, means for verifying the integrity of the information, and means for training a generative model based on the information. This makes it possible to securely and efficiently utilize the information of data providers and to return the revenue generated from the use of the generated model with transparency.

[0661] A "data provider" refers to an individual or organization that provides information to this system.

[0662] "Information" refers to various types of data and knowledge provided by data providers, and is the material used within the system.

[0663] A "generative model" refers to an artificial intelligence model built based on provided information, and is a program trained for a specific purpose.

[0664] "Revenue" refers to the economic benefits obtained by using the generative model.

[0665] "Information integrity" refers to a state in which the information provided is accurate and consistent, and is a requirement for ensuring the reliability of the data.

[0666] "Permission to use" refers to the authority to specify the terms and scope of use of information provided by the data provider.

[0667] "Transparency" refers to a state where processes such as information handling and revenue sharing are clear, and all parties involved can verify the details.

[0668] A description of the embodiment for carrying out the invention will be provided.

[0669] This system consists of three elements: servers, terminals, and users. It is primarily designed to automate and streamline the processes of data collection, verification, storage, model training, and revenue sharing.

[0670] Server Functions

[0671] After receiving information from a data provider, the server first verifies its integrity. Specifically, it uses integrity verification algorithms to check if the data quality is sufficient. Inconsistent or invalid data is notified to the data provider. After the integrity is confirmed, the data is securely stored in the database. A database management system (e.g., MySQL or PostgreSQL) is used for data storage.

[0672] The server also records data usage history and manages access based on permissions to ensure transparency. This recording process is managed using blockchain technology, enabling transparent data management that is difficult to tamper with.

[0673] Most importantly, the server trains a generative AI model based on the collected information. This training is performed using a machine learning platform (e.g., TensorFlow or PyTorch). Once the model is optimally trained, it becomes available for use by users in various applications. Revenue generated from the use of the model is automatically tracked and calculated using smart contracts and returned to the data providers.

[0674] Device functions

[0675] The terminal is a device used by data providers to upload information. From the terminal, users can access the platform and log in to their own dashboard. Here, they can upload data and set usage permissions.

[0676] On the other hand, corporate terminals download trained models from servers and integrate them into their own systems for use in business operations. Secure connections (e.g., HTTPS) are used throughout this process, ensuring data security.

[0677] User roles

[0678] Users can either provide information as data providers or utilize generative AI models as users. Data providers have an interface on the platform where they can check the status of their provided data and the revenue they receive. Meanwhile, companies using the models can apply generative AI models according to their needs to improve their operational efficiency.

[0679] Specific example

[0680] For example, consider a case where an educational institution provides student learning data to create a customized generative AI model. The institution accesses the platform using a terminal, uploads the learning data, and restricts its use to the education sector. The server receives this data and trains the model. The generated model is integrated into the institution's system and used to optimize learning. The benefits are returned to the institution according to their usage.

[0681] Example of a prompt

[0682] "Please describe the steps involved in building an AI model using student data from an educational institution, and the expected results."

[0683] This system makes it possible to effectively collect and utilize data and fairly distribute profits.

[0684] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0685] Step 1:

[0686] The user (data provider) uses their device to log in to the platform and access their dashboard. Next, they select the data they want to provide, such as training data, and upload it. The input is the data provided by the user, and the output is the transmission of that data to the server. During this process, the user also configures their data usage permissions.

[0687] Step 2:

[0688] The server verifies the data received from the user. A data integrity verification algorithm is used to confirm the validity of the data. The input is the data received from the terminal, and the output is the verified data. Data whose integrity has been confirmed is securely stored in the database.

[0689] Step 3:

[0690] The server uses validated data to build a training dataset. The dataset is preprocessed and converted into a format suitable for training the AI ​​model. The input is validated data, and the output is the AI ​​model training dataset. This dataset is saved for later processing.

[0691] Step 4:

[0692] The server uses the training dataset to train a generative AI model. A machine learning framework is used to optimize the model. The input is the training dataset, and the output is the trained generative AI model. The model's performance is evaluated using internal test data.

[0693] Step 5:

[0694] The terminal (corporate user) downloads a pre-trained generative AI model from the server. The downloaded model is integrated into the company's systems and used in its business processes. The input is the generative AI model provided by the server, and the output is the use of the model within the company. This process requires that the model is properly installed and configured.

[0695] Step 6:

[0696] The server records the usage of the generated AI model and calculates revenue. A smart contract is used to automate the distribution of revenue. The input is model usage data, and the output is revenue distribution information for data providers. This final step ensures that revenue is distributed fairly to data providers.

[0697] (Application Example 1)

[0698] 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".

[0699] In systems that recommend appropriate content to users based on viewing history and rating data, there is a need for a method that ensures fair compensation for the information provided by data providers, while also ensuring transparency and efficiently utilizing the data. However, conventional systems have shortcomings in terms of convenience and transparency.

[0700] 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.

[0701] In this invention, the server includes means for storing information obtained from data providers, means for training a generative model based on the information, means for calculating the profits generated from the use of the model and returning them to the provider, means for recording the history of information usage and ensuring transparency, means for generating recommended content tailored to individual users based on viewing history data obtained from providers, and means for returning value to users through content recommendations. This enables personalized content recommendations for users, fair profit sharing, and transparent data utilization.

[0702] A "data provider" is an entity that provides information to the system, and that information is used to train the generative model.

[0703] A "generative model" is a program or structure that is trained on information obtained from data providers and generates new outputs using a specific algorithm.

[0704] "Profit sharing" means distributing the economic or non-economic benefits obtained from the use of the system or the results of the model to the data providers who provided the information.

[0705] "Viewing history data" refers to information about content that a user has viewed in the past, and is used to understand the preferences and tendencies of individual users.

[0706] "Recommended content" refers to information or works that are selected based on an analysis of viewing history data, taking into account the individual user's interests and preferences, and are judged to be highly likely to be viewed next.

[0707] "Transparency" means that the processes for collecting, using, and sharing information are clear, and that all stakeholders can access and verify that information.

[0708] "Means of calculating profit" refers to a method or process for calculating the profit obtained from the use of generative models.

[0709] "Means of return" refers to methods or processes for returning the benefits to the information providers, thereby ensuring a fair distribution.

[0710] This invention realizes a system that trains a generative model using information obtained from data providers and fairly distributes the profits generated from the use of that model. A specific embodiment is shown below.

[0711] The server is responsible for acquiring and recording information from data providers. This information includes viewing history data and rating information, and is securely stored in a database using SQLite or PostgreSQL. The server automatically verifies data integrity and records usage history in detail to maintain transparency. It also trains generative AI models using TensorFlow or PyTorch to build personalized recommendation content optimized for each user.

[0712] The device provides an interface for users to input their viewing history data and presents them with recommended content generated from the server. In this process, programming languages ​​such as Python are used to present the recommendations in a visually easy-to-understand format.

[0713] Users can receive content optimized based on their individual viewing history and enjoy rewards commensurate with their viewing behavior. Specifically, these rewards include viewing credits and access to special content.

[0714] For example, if a user gives a high rating to a historical documentary, that information is stored on the server and used as training data for a generative model. As a result, the server selects content of similar interest and recommends it to the user through their device.

[0715] Within the program, an example of a prompt statement used when providing viewing history data to the generative AI model is the instruction, "Based on the user's viewing history data, recommend content that they are most likely to watch next."

[0716] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0717] Step 1:

[0718] The server receives viewing history data from data providers and checks its integrity. Viewing history data, as input, is validated through an error-checking algorithm before being stored in the database. This process ensures accurate and reliable data.

[0719] Step 2:

[0720] The server securely stores verified viewing history data in an SQLite or PostgreSQL database. The data provided as input is recorded along with a unique user identifier and organized for easy access in subsequent processing.

[0721] Step 3:

[0722] The server trains a generative AI model based on stored viewing history data. Using TensorFlow or PyTorch, the algorithm learns user preferences using the dataset as input. As a result, a personalized recommendation algorithm is output for each user.

[0723] Step 4:

[0724] The device uses a generative AI model obtained from the server to generate and present recommended content to the user. It receives a recommendation algorithm as input and displays the content visually and clearly through the user interface. This allows the user to easily select the next available content.

[0725] Step 5:

[0726] Users select and watch content from the recommended selections presented. Based on this, the viewing results are fed back to the system as input, initiating the next data provision cycle. This feedback is used as crucial data to further improve the accuracy of future recommendations.

[0727] Step 6:

[0728] The server calculates the profits generated from the use of the model and fairly distributes them to data providers. It enhances data provider satisfaction by managing a reward system based on the output of the generated AI model, including viewing credits and special access rights. Throughout this process, the profit-sharing status is recorded while maintaining transparency.

[0729] 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.

[0730] This invention is a system that comprehensively manages everything from data collection from data providers to training generative models and providing feedback using an emotion engine. The system consists of a server, terminals, users, and an emotion engine.

[0731] The server securely stores data received from data providers and manages data usage conditions based on access permissions. The server has the capability to build training datasets and train generative AI models. In addition, it dynamically adjusts the generative model's algorithms based on feedback data provided by the emotion engine to improve model accuracy. It also has a mechanism for calculating revenue and returning profits to data providers according to appropriate criteria.

[0732] The terminal serves as the primary interface for data providers and model users to access the system. Data providers can upload data through the terminal and also provide additional feedback using the sentiment engine. Model users can download AI models generated from the terminal and use them in their work to optimize business processes.

[0733] The emotion engine analyzes the user's emotional state in real time and provides feedback to other components of the system. For example, it can improve model performance by incorporating emotional data into the parameters of the training process. It also adjusts the criteria for reward distribution based on emotional data, striving to increase user satisfaction.

[0734] Users utilize the system as both data providers and model users. Data providers can manage the status of their uploaded data, set usage permissions, and receive profits. Meanwhile, companies, as model users, use the feedback obtained from the emotion engine to improve the efficiency of their operations.

[0735] A concrete example is a case where a medical facility provides patient feedback data to generate an AI model. The medical facility collects data, including the patient's emotional state, and uploads it to the system. The server analyzes the data and emotional feedback to train a generative AI model specifically tailored to the medical field. Through feedback from the emotional engine, the model contributes to improving patient care. In addition, the medical facility receives appropriate revenue based on the use of the model. This system makes it possible to build new AI models that utilize emotional data.

[0736] The following describes the processing flow.

[0737] Step 1:

[0738] Users log in to the platform through their own devices and enter the content and sentiment data they wish to provide. Users then set the necessary permissions to upload this data.

[0739] Step 2:

[0740] The device encrypts the provided data and emotional data and sends it to the server using a secure communication protocol.

[0741] Step 3:

[0742] The server authenticates and verifies the integrity of the received data. It then stores the data, along with necessary metadata, in secure storage.

[0743] Step 4:

[0744] The server manages the conditions for data usage permission based on the usage permissions set by the data provider. It also records data usage history to ensure transparency.

[0745] Step 5:

[0746] The server uses an emotion engine to analyze emotional data provided by users and generate the feedback necessary for training.

[0747] Step 6:

[0748] The server optimizes the dataset based on feedback from the sentiment engine and begins the generative model training process. The trained model is further refined with sentiment data.

[0749] Step 7:

[0750] On their devices, users download generated models from the server and integrate them into their own systems. These models are then used for specific business purposes.

[0751] Step 8:

[0752] The server calculates revenue based on model usage and sentimental title feedback. A portion of the calculated revenue is returned to the data provider, providing results that take user sentiment into account.

[0753] Step 9:

[0754] Users can check the status of their earnings distribution through their accounts and evaluate the feedback from the system. User feedback is used to further improve the system.

[0755] (Example 2)

[0756] 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".

[0757] This invention aims to solve the problem of efficiently utilizing a wide range of information obtained from data providers to improve the training accuracy of generative models, while also transparently and appropriately returning the profits obtained. Furthermore, it places emphasis on optimizing the user experience by reflecting users' emotional information in the model.

[0758] 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.

[0759] In this invention, the server includes means for storing information obtained from data providers, means for training a model generated based on the information, and means for calculating the results obtained from the use of the model and returning them to the provider. This enables efficient management and utilization of information.

[0760] A "data provider" refers to an entity that provides information to the system, and that information is used for training and analyzing models.

[0761] A "generated model" refers to a digital representation created by an algorithm based on acquired information, and possesses the ability to perform a variety of tasks.

[0762] "Calculating results" refers to the process of quantifying the gains and efficiency improvements obtained through the use of the model, and then providing those results back to the information provider.

[0763] "Recording the history of information usage" refers to a recording process that tracks how the provided information was used within the system, providing transparency and traceability.

[0764] "Emotional information" refers to data that indicates the emotional state of users, and is used for improving models and providing feedback.

[0765] "Dynamic adjustment" refers to the process of changing model parameters and algorithms in real time in response to changes in the environment and conditions.

[0766] To implement this invention, multiple components are used, including a server, a terminal, a user, and an emotion engine. The server is responsible for receiving and securely storing information obtained from data providers. This information is stored using a general-purpose database management system. The server also analyzes the information and builds the datasets necessary for training generative AI models.

[0767] The server trains models using existing machine learning frameworks such as TensorFlow and PyTorch, and maintains the results. The trained models are dynamically adjusted based on sentiment information provided by the sentiment engine to improve accuracy and utilization efficiency. The sentiment engine analyzes user feedback in real time and uses this data to adjust the model parameters.

[0768] The terminal serves as the primary interface for data providers and model users to access the system. Data providers can upload information via the terminal, and model users can download trained generative AI models via the terminal. Model users then integrate these models into their own business processes to streamline and optimize those processes.

[0769] A concrete example is a medical facility. Medical facilities provide patient feedback data to a server via terminals. An example of a prompt message could be: "Please tell me how to use the latest generative AI model to analyze patient feedback data and gain insights that will help improve patient care."

[0770] The operation of this entire system enables the effective use of information, continuous improvement of the model, and return of profits to providers, thereby contributing to increased user satisfaction.

[0771] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0772] Step 1: Data Collection

[0773] The server receives data uploaded by users from their devices. This data includes emotion-related information such as text, audio, and video. The input data is first pre-processed to standardize the format and remove unnecessary data before being securely stored in the database. This process ensures data consistency and security.

[0774] Step 2: Build the dataset

[0775] The server constructs a dataset suitable for model training from preprocessed data. This dataset construction includes assigning specific sentiment labels and categorizing the data. In this step, the raw input data is formatted for machine learning algorithms and outputted as a trainable dataset. This results in a high-quality dataset.

[0776] Step 3: Model Training

[0777] The server trains a generative AI model using the constructed dataset. This process utilizes frameworks such as TensorFlow and PyTorch, applying deep learning algorithms. The input is the dataset, and the output is the trained model. Specifically, feedback data from the emotion engine is used to dynamically adjust model parameters in order to improve prediction accuracy. This step completes a highly accurate model.

[0778] Step 4: Feedback Analysis

[0779] The terminal aggregates user feedback and analyzes it in conjunction with the emotion engine. Input includes ratings and comments indicating the user's emotional state. This feedback is analyzed and notified to the server, allowing for further optimization of the model. The output is feedback data as a result of the analysis, which is used as a new metric for model improvement.

[0780] Step 5: Delivering Results

[0781] Users download pre-trained generative AI models via their devices and utilize them in their work. The input is the model supplied from the server. The output is the efficiency improvements and optimizations achieved through the integration of the model into business processes. For example, a company can use the model to efficiently process customer emotional feedback in order to improve the quality of customer support.

[0782] Step 6: Profit Calculation and Distribution

[0783] The server calculates the revenue generated from model usage and distributes it appropriately to data providers. Inputs are model usage data and feedback evaluation data. Outputs are the calculated amounts distributed to each provider. This procedure helps build trust with data providers and promotes sustainable information sharing.

[0784] (Application Example 2)

[0785] 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".

[0786] Conventional generative AI models have not adequately improved model accuracy or considered user sentiment in their content recommendations. This has made it difficult to provide optimal content that enhances user satisfaction. Furthermore, there have been challenges in fully utilizing feedback data to provide benefits to data providers.

[0787] 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.

[0788] In this invention, the server includes means for storing data obtained from data providers, means for training a generative model based on the data, and means for analyzing emotional states and optimizing the generative model based on content recommendations. This enables the provision of personalized content that responds to the user's emotions.

[0789] A "data provider" is an entity that plays the role of supplying information to a data generation system.

[0790] A "generative model" is an algorithm that generates new information or content based on the information provided.

[0791] "Emotional state" refers to data that indicates the user's mental state and is analyzed through the emotion engine.

[0792] "Content recommendation" is the act of presenting the most relevant information based on the user's emotions and preferences.

[0793] "Means of calculating profits and returning them to providers" refers to a mechanism for appropriately distributing revenue generated through a production system.

[0794] "Means of recording data usage history and ensuring transparency" refers to methods of increasing the credibility of a system by accumulating a history of information usage.

[0795] A description of the embodiment for carrying out the invention will be provided.

[0796] In this system, the server securely stores information collected from data providers and uses it to train generative AI models. The data is protected by security protocols and centrally managed on the server. The generative models are optimized based on sentiment data provided by the sentiment engine and trained to generate personalized content for each user.

[0797] The terminal serves as the primary interface for data providers and model users to access the system. Data providers can use the terminal to upload data and provide additional feedback by inputting sentiment data, which can further improve the performance of generative models.

[0798] Users can download generative AI models obtained through their devices and apply them to their work and lifestyle. For example, they can listen to music to reduce daily stress using playlists suggested based on emotional data.

[0799] As a concrete example, when a user uses a music streaming app during their relaxation time after work, this system analyzes the user's emotional state and automatically selects and recommends music best suited for relaxation. An example of a prompt input to the generative AI model might be, "Use the latest emotion analysis technology to recommend the best meditation guide when the user's stress level is high." In this way, it is possible to improve the user experience.

[0800] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0801] Step 1:

[0802] The server receives data sent from data providers via terminals. This data includes user behavior logs and sentiment data. The server securely stores this data and saves it in a database. It takes data from data providers as input and outputs data saved to the database.

[0803] Step 2:

[0804] The server begins training a generative AI model based on the collected data. During this process, it analyzes emotional data and extracts data patterns corresponding to the user's emotional state. A training dataset is constructed and input into the generative model's algorithm, thereby optimizing the model. The input is the collected dataset, and the output is the optimized generative model.

[0805] Step 3:

[0806] The terminal provides an interface for model users to access the system. Model users download optimized generative AI models and use them for business or personal purposes. The terminal receives input from model users, retrieves models from the server, and deploys them to the terminal. The input is the model user's request, and the output is the distribution of the model to the terminal.

[0807] Step 4:

[0808] Users utilize content generated by an AI model using their device. This content is optimized according to the user's emotional state, allowing them to experience something that matches their mood. For example, when a user wants to relax, the device suggests a music playlist accordingly. The input is the user's emotional state, and the output is the customized content provided to the user.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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."

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] The following is further disclosed regarding the embodiments described above.

[0831] (Claim 1)

[0832] A means of storing data obtained from data providers,

[0833] A means for training a generative model based on the aforementioned data,

[0834] A means of calculating the profits generated from the use of the aforementioned model and returning them to the provider,

[0835] A means of recording the history of data usage and ensuring transparency,

[0836] A system that includes this.

[0837] (Claim 2)

[0838] The system according to claim 1, further comprising means for setting permission to use the aforementioned data and for implementing restrictions on its use.

[0839] (Claim 3)

[0840] The system according to claim 1, further comprising means for integrating data from multiple data providers and using it to train a model.

[0841] "Example 1"

[0842] (Claim 1)

[0843] A means of storing information obtained from data providers,

[0844] Means for verifying the consistency of the aforementioned information,

[0845] A means for training a generative model based on the aforementioned information,

[0846] A means of calculating the revenue generated from the use of the aforementioned model and returning it to the information provider,

[0847] A means of recording the history of information usage and ensuring transparency,

[0848] A system that includes this.

[0849] (Claim 2)

[0850] The system according to claim 1, further comprising means for setting permission to use the aforementioned information and for implementing restrictions on its use.

[0851] (Claim 3)

[0852] The system according to claim 1, further comprising means for integrating information from multiple information providers and using it to train a model.

[0853] "Application Example 1"

[0854] (Claim 1)

[0855] A means for storing information obtained from data suppliers,

[0856] A means for training a generative model based on the aforementioned information,

[0857] A means of calculating the profits generated from the use of the aforementioned model and returning them to the supplier,

[0858] Means to record the history of information usage and ensure transparency,

[0859] A means of generating personalized recommended content for individual users based on viewing history data obtained from suppliers,

[0860] A means of giving back to users through content recommendations,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, further comprising means for setting permission to use the aforementioned information and for implementing restrictions on its use.

[0864] (Claim 3)

[0865] The system according to claim 1, further comprising means for integrating information from multiple data providers and using it to train a model.

[0866] "Example 2 of combining an emotion engine"

[0867] (Claim 1)

[0868] Means for holding information obtained from data providers,

[0869] A means for training a model generated based on the aforementioned information,

[0870] A means of calculating the results obtained from using the model and returning them to the provider,

[0871] Means for recording the history of information usage and maintaining transparency,

[0872] A means of analyzing user emotional information and providing it as feedback to other components,

[0873] A means of dynamically adjusting model parameters based on emotional information,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, further comprising means for setting and restricting the use of the aforementioned information.

[0877] (Claim 3)

[0878] The system according to claim 1, further comprising means for combining information from multiple information providers and using it to train a model.

[0879] "Application example 2 of combining emotional engines"

[0880] (Claim 1)

[0881] A means of storing data obtained from data providers,

[0882] A means for training a generative model based on the aforementioned data,

[0883] A means of analyzing emotional states and optimizing generative models based on content recommendations,

[0884] A means of calculating the profits generated from the use of the aforementioned model and returning them to the provider,

[0885] A means of recording the history of data usage and ensuring transparency,

[0886] A system that includes this.

[0887] (Claim 2)

[0888] The system according to claim 1, further comprising means for setting permission to use the aforementioned data and for implementing restrictions on its use.

[0889] (Claim 3)

[0890] The system according to claim 1, further comprising means for integrating data from multiple data providers and using it to train a model. [Explanation of Symbols]

[0891] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of storing data obtained from data providers, A means for training a generative model based on the aforementioned data, A means of calculating the profits generated from the use of the aforementioned model and returning them to the provider, A means of recording the history of data usage and ensuring transparency, A system that includes this.

2. The system according to claim 1, further comprising means for setting permission to use the aforementioned data and implementing restrictions on its use.

3. The system according to claim 1, further comprising means for integrating data from multiple data providers and using it to train a model.

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