system

Abstract

We provide the system. [Solution] A means of storing information managed by the data owner in a secure computing environment, A means of identifying data usage through usage history and distributing rewards to data owners, When providing a model to a third party, there are means of providing the service without directly disclosing the contents of the model, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] With the spread of generative AI technology, there is a need to efficiently utilize the data possessed by individuals and organizations while safely managing it, but there are risks of data leakage and privacy breaches. In addition, the fairness of profit distribution associated with data utilization and the safe provision of AI models are difficult issues, which pose problems. It is necessary to solve these problems.

Means for Solving the Problems

[0005] This invention provides a system for storing information managed by data owners in a secure computing environment, identifying data usage based on usage history, and distributing rewards accordingly. It also includes means for providing models to third parties without disclosing their contents. Furthermore, by providing means for managing data billing settings and usage restrictions, and for securely providing model inference results, it enables the safe and efficient use of generative AI.

[0006] A "data owner" is an individual or entity that owns or controls information and has the right to use it.

[0007] A "secure computing environment" refers to a computer system that has security measures in place to prevent unauthorized access and information leaks from external sources during data storage and processing.

[0008] "Usage history" refers to information that maintains records of how data and models are used, showing which resources were used and how.

[0009] "Compensation" refers to the payment made to data owners or model providers for providing data or using models.

[0010] A "third party" refers to any individual or entity other than the data owner or user who directly manages the system.

[0011] "Billing settings" refers to the act or mechanism of defining the pricing structure that will be applied when using data or models.

[0012] "Usage restrictions" refer to the conditions or limitations that apply to the use of data or models, or the content of such restrictions.

[0013] "Inference result" refers to the conclusion or prediction that an AI model outputs after performing calculations based on the input data. [Brief explanation of the drawing]

[0014] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0015] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the 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, and the like.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention provides a system for securely handling data and models on a platform for utilizing generative AI. This system enables users to access a secure computing environment in the cloud and manage data and models there.

[0036] Users upload their datasets to the platform via their devices. The server receives this data and stores it in secure storage. Each dataset is assigned a unique ID, making it traceable. This ID is used to record the history of which models used which data.

[0037] The registration of generated AI models is done in a similar manner. Users send AI models from their terminals to the platform, and the server stores them. When a model is provided, the server is configured to keep the model's internal structure private from anyone other than the user, while only allowing external inference services. The server executes the model upon request and provides its output, but the internal details remain confidential.

[0038] Data owners can view their data usage and configure billing settings from their devices. The server compiles data on usage history, tracking how and which data was used, and distributes rewards to each user. The server calculates these rewards based on each user's chosen billing scheme and provides the corresponding rewards.

[0039] As a concrete example, a company can upload its specific dataset to the platform, use it to perform predictions with external AI models, and receive the results. Furthermore, the platform can constantly monitor how the data is used and manage it to ensure it benefits the company.

[0040] Thus, the present invention provides a safe and convenient service for users by simultaneously achieving secure data handling and efficient use of AI models.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user accesses the platform's login screen via their device and enters their authentication information. The server compares the entered username and password with the database to determine whether authentication is successful. If authentication is successful, the server starts a user session and returns the session ID to the device.

[0044] Step 2:

[0045] Users select their data files and upload them to the platform from their devices. The server stores the received data files in secure storage and generates a unique ID for each file. It also uses this ID to begin recording the data usage history.

[0046] Step 3:

[0047] Users register their AI models by submitting them to the platform from their devices. The server stores the uploaded models and assigns them unique IDs. Once the model registration is complete, users can configure settings for using the model.

[0048] Step 4:

[0049] When another user uses the model, they send an inference request from their terminal to the server. The server runs the model and performs inference based on the specified input data. It then sends the inference result back to the terminal of the user who made the request.

[0050] Step 5:

[0051] Users who own the data can check their data usage history from their device. The server collects usage information recorded in the database and displays it on the device in a visually viewable format.

[0052] Step 6:

[0053] The server periodically compiles usage history and calculates rewards based on data and model usage. Rewards are distributed to data owners and model providers according to the specified billing policy, and the details can be viewed on the user's device.

[0054] (Example 1)

[0055] 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."

[0056] To securely enable third parties to utilize digital data and mathematical models managed by data owners, an environment is needed that allows for accurate tracking of data usage while maintaining the confidentiality of the models and effectively utilizing their results. However, current technology makes it difficult to balance confidentiality and effective utilization when providing data and models externally. Furthermore, mechanisms for appropriately distributing compensation to data owners are insufficient. Solutions to these challenges are needed.

[0057] 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.

[0058] In this invention, the server includes means for storing digital data managed by the information owner within a secure information processing device, means for identifying the usage status of the digital data through operation history and distributing rewards to the information owner, and means for providing computational processing without directly disclosing the contents of the mathematical model when providing the mathematical model to a third party. This enables the secure management and effective utilization of digital data and mathematical models.

[0059] An "information owner" is an individual or legal entity that owns the digital data and mathematical models it manages and has the authority to grant permission for their use and set conditions for their use.

[0060] "Digital data" refers to all electronically stored information generated or acquired by the information owner, and specifically includes numerical data, text information, image data, etc.

[0061] "Information processing equipment" refers to all computing devices used for storing, managing, and processing digital data and mathematical models, and includes cloud systems equipped with security features.

[0062] "Operation history" refers to the history of access to and processing of digital data and mathematical models, recording which user performed what operation and when.

[0063] "Reward" refers to monetary or point-based compensation distributed to information owners for the use of digital data or mathematical models.

[0064] A "mathematical model" refers to a computational framework designed or provided by the user, which has the function of performing inference and prediction based on prompts.

[0065] "Computational processing" refers to a series of electronic processes that use mathematical models to perform calculations based on digital data and generate output.

[0066] This system is designed to efficiently manage digital data and mathematical models controlled by information owners within a secure information processing device, and to maintain confidentiality when providing them to third parties. Multiple hardware and software components are involved in its implementation.

[0067] The server provides a secure, cloud-based information processing environment. This environment includes storage and computing resources for storing and managing digital data and mathematical models. The server utilizes a database management system (e.g., PostgreSQL) to receive digital data and assign unique identifiers to it. It also encrypts the data and models and stores them in secure storage. This process uses encryption libraries (e.g., OpenSSL).

[0068] The terminal provides an interface for users to upload digital data and manipulate mathematical models. Through the terminal, users send digital data and models to the server and input necessary commands. This interface is built as an application that runs on a web browser (e.g., a web application using React or Angular).

[0069] Users manage their digital data and mathematical models using the interface provided on their device. They can check their digital data usage history and configure billing settings. Users perform specific analyses using generative AI models and utilize the results. In this process, they instruct the mathematical model's calculation process by entering prompts.

[0070] As a concrete example, consider a scenario where a company's data scientist uploads their company's commercially available statistical data to a platform and uses that data to make sales forecasts. In this case, an example of a prompt message might be, "Please make sales forecasts for the next quarter."

[0071] This system enables users to safely and effectively utilize digital data and generated AI models, ensuring confidentiality and proactive application.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] Users log in to the platform using their own devices. They enter their username and password on the login screen, and authentication is performed. Upon successful authentication, the device provides the functionality to upload digital data. The input is user information, and the output is the user's authentication session.

[0075] Step 2:

[0076] Users select digital data or generated AI models using the device's upload function. After selection, the information is entered into a form, and the data is uploaded to the platform by pressing the submit button. Input is digital data or AI model files, and output is the transmission of files to the server.

[0077] Step 3:

[0078] The server generates a unique identifier for the digital data received from the user. This identifier is assigned to the data and forms the basis of the operation history. The data is encrypted and stored in secure storage. In this case, the input is the data file sent by the user, and the output is the stored encrypted data.

[0079] Step 4:

[0080] The user inputs prompt messages into the generated AI model via their terminal. For example, they might input a command such as, "Please perform a sales forecast." The input is a prompt message, and the output is the prompt message information sent to the server.

[0081] Step 5:

[0082] The server executes the specified generative AI model based on the prompt statement. It performs calculations on the digital data necessary for the model to make predictions and generates results. In this process, the input is the prompt statement and data, and the output is the generated prediction result.

[0083] Step 6:

[0084] The server provides the user with the generated results. These results are displayed through an interface on the user's terminal. The user can view these results and perform other actions as needed. The input is the prediction results generated on the server side, and the output is the result display on the user's terminal.

[0085] Step 7:

[0086] The server manages the usage history of digital data and calculates rewards. Based on the operation history, it aggregates how and which data was used, and appropriately distributes rewards to the data owners. The inputs are the operation history and billing settings, and the output is the details of the reward distribution.

[0087] (Application Example 1)

[0088] 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."

[0089] In information processing, data managers are required to securely manage information, clearly document usage history, and fairly distribute compensation. Furthermore, when providing models, it is required that their contents be kept confidential and used as an analysis service for third parties. Additionally, there is a need for methods to quickly respond to the ever-changing situation when it comes to optimization proposals for streamlining labor in logistics and information distribution.

[0090] 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.

[0091] In this invention, the server includes means for securely storing information managed by the data administrator, means for identifying usage history and distributing rewards, means for providing analysis services without disclosing the contents of the model, and means for presenting proposals for optimizing information distribution labor. This enables secure information management, fair reward distribution, and efficient optimization of information distribution labor.

[0092] A "data controller" is an entity responsible for managing and protecting information.

[0093] An "information processing environment" is a secure technological foundation for storing, processing, and managing information.

[0094] "Usage history" refers to traceable data that records how information has been used.

[0095] An "analysis service" is a service that analyzes information and provides useful results and suggestions to external parties.

[0096] "Information distribution labor" refers to the entire system of work involved in the distribution and processing of information.

[0097] An "optimization proposal" is a specific improvement plan provided with the aim of increasing efficiency and improving performance.

[0098] The system for realizing this invention is a platform for securely managing data and AI models on the cloud. The server stores information received from data administrators in a secure information processing environment. It also has the functionality to track usage history using generating AI and to fairly distribute rewards.

[0099] Users register the information and AI models they manage on the platform via their devices. The server configures these models so that they can be used as an analysis service without directly exposing them. For example, to optimize information flow and labor in a logistics center, the server uses an AI model to analyze the information and provides the results as optimization suggestions.

[0100] Specifically, it is possible to optimize product shelf placement and picking routes based on operational data from the logistics center. Furthermore, an example of a prompt message for the generated AI model is: "Based on this week's operational data from the logistics center, please generate suggestions for optimizing inventory placement and picking routes."

[0101] This platform is developed using Python and cloud APIs, and can centrally handle secure data storage, usage history management, and model-based service delivery.

[0102] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0103] Step 1:

[0104] Users upload their datasets from their devices to a cloud-based platform. Once the device sends the data, the server receives it and stores it in a secure information processing environment. A unique ID is assigned to the data at this stage for later tracking. The input is a data file, and the output is a secure storage location within the platform.

[0105] Step 2:

[0106] The user registers the generated AI model from their device to the platform. The device sends the model data, and the server stores the received model. In addition, the server is configured to allow the provision of analysis services to external parties while keeping the internal structure of the model confidential. The input is the model data, and the output is the configured model on the server.

[0107] Step 3:

[0108] The user creates a prompt statement and submits a request for analysis. This prompt statement specifies the processing of logistics data that requires a particular optimization suggestion. The server performs the analysis using a generative AI model and generates optimization suggestions based on the prompt statement. The input is the prompt statement, and the output is the optimization suggestion.

[0109] Step 4:

[0110] The server provides the user with the optimization suggestions it has generated. The user then uses these suggestions to improve operations at the actual logistics center. The input is the analysis results from the server, and the output is the optimization suggestions provided to the user.

[0111] Step 5:

[0112] The server manages data usage history and compiles data on which users used how much data. Based on this usage history, it calculates reward distribution and distributes rewards appropriately to data administrators. The input is usage history data, and the output is a reward distribution plan.

[0113] 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.

[0114] This invention is a system that incorporates a function to recognize user emotions into a platform that securely manages data and enables the provision of generative AI models. Users can access the platform using a terminal and upload and manage their own data. The server stores this data in a secure computing environment and sets appropriate access permissions. It tracks how the data was used through data usage history and distributes rewards accordingly.

[0115] Furthermore, this system is equipped with an emotion engine. The terminal collects the user's biosignals and behavioral data and sends it to the server. The server analyzes this data using the emotion engine and recognizes the user's emotions in real time. The recognized emotion information is used to dynamically adjust the interface according to the user's emotional state. For example, if the user is feeling stressed, the information presentation speed can be slowed down and the information can be displayed in an easy-to-understand format.

[0116] Furthermore, the emotion engine can also provide feedback on the inference results in accordance with the user's emotions. For example, if the user expresses dissatisfaction, the server will re-evaluate the inference results and make adjustments as needed.

[0117] This system also includes a function to perform statistical analysis of emotional data. The server generates insights to improve the overall user experience of the system based on emotional data collected from multiple users.

[0118] As a concrete example, when a user requests a prediction from an AI model using a specific dataset, the server monitors the user's emotions during the process and adjusts the data presentation method and inference results to match the user's emotional state. In this way, the present invention can improve the user experience by simultaneously achieving the secure provision of data models and the optimization of emotion-based interactions.

[0119] The following describes the processing flow.

[0120] Step 1:

[0121] The user logs into the platform via their device and enters their authentication information. The server verifies this information, grants the user access, and initiates a session. Upon successful login, a session ID is sent to the user's device.

[0122] Step 2:

[0123] Users select a dataset from their device and upload it to the platform. The server stores the uploaded data in secure storage and assigns a unique ID to each piece of data. This enables data management.

[0124] Step 3:

[0125] The device collects the user's biometric signals and behavior as emotional data. This data is transmitted to the server in real time while the user is interacting with the device.

[0126] Step 4:

[0127] The server analyzes the received emotional data using an emotion engine to recognize the user's current emotional state. For example, it measures stress levels and concentration levels and uses this information to adjust the interface.

[0128] Step 5:

[0129] When a user performs inference using an AI model, the server executes the model and generates results. These results are then displayed in a format and content that is adjusted according to the recognized emotional state of the user, and then sent to the device.

[0130] Step 6:

[0131] The server records usage history based on the data used and user sentiment, and calculates rewards based on this. Based on the calculation results, it initiates a distribution process to data owners. This maximizes the use value of the data and models while providing sentiment-based feedback.

[0132] (Example 2)

[0133] 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".

[0134] Amidst the demand for secure and efficient information management, there is a need to protect and utilize data while ensuring a fair distribution of rewards based on its use. Furthermore, it is crucial to appropriately adjust system interfaces according to user emotions and provide a user-friendly environment. However, current technology makes it difficult to simultaneously achieve data security, usability, and optimized emotion-based interactions.

[0135] 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.

[0136] In this invention, the server includes means for recording data managed by the information holder in a protected information processing environment; means for identifying the usage status of the information through operation history and distributing rewards to the information holder; means for providing a computational model to others without directly disclosing the contents of the computational model; means for detecting the user's emotions using an emotion analysis device and dynamically adjusting the interface; means for adjusting the inference results produced in accordance with the user's recognized emotions; and means for analyzing emotion information collected from multiple users and generating insights to improve the overall user experience. This enables both data protection and convenience, optimization of emotion-based interactions, and improvement of the user experience.

[0137] An "information holder" refers to an individual or organization that owns the data or information and has the rights to manage and use it.

[0138] An "information processing environment" refers to a technical environment that includes computer systems and network infrastructure for storing, managing, processing, and protecting digital data.

[0139] "Operation history" refers to a collection of information that records the process of data usage and manipulation, and is used to track how data was used.

[0140] A "computational model" refers to an algorithm or analytical framework designed to perform calculations or inferences based on specific data.

[0141] An "emotion analysis device" is a sensor or software tool used to detect a user's emotional state, analyzing biosignals and behavioral data in real time.

[0142] An "interface" refers to the screen or method of operation that a user uses to interact with a computer system or application.

[0143] "Insight" refers to useful knowledge and understanding obtained as a result of data analysis, and is used for decision-making and system improvement.

[0144] This invention is a system aimed at securely managing information and optimizing emotion-based interactions. Embodiments of this system are described below.

[0145] Users first access the platform using a device and upload their data. This data may include health-related information and personal behavioral history. The device collects the user's biometric signals and behavioral data and sends it to a server for analysis. It is recommended to use protocols such as SSL / TLS during this process to enhance security.

[0146] The server stores data submitted by users in a secure information processing environment. This environment utilizes a data storage system (e.g., a common cloud storage service) and ensures data security through encryption. The server tracks the operation history of the stored data and records how the data was used. This allows for the distribution of appropriate rewards to users in exchange for providing data.

[0147] Furthermore, this system is equipped with an emotion analysis device. For example, when a user is experiencing stress, the system can adjust the interface colors and information presentation speed to provide a more relaxing environment. Emotion recognition is performed in real time by an AI algorithm, and the user experience is dynamically improved based on the results.

[0148] Furthermore, the server analyzes sentiment data collected from numerous users to generate insights for improving the overall user experience of the system. These insights are useful for improving the system's interface design and the services it provides. This process may involve utilizing statistical analysis tools and libraries (e.g., analysis libraries for Python or R).

[0149] For example, when a user requests a prediction from a generative AI model using a specific dataset, the server prompts the generative AI model with a question such as, "How should I present the data so that the model's predictions now maintain a relaxed state for the user?" In this way, the system can provide an interaction that is tailored to the user's emotions.

[0150] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0151] Step 1:

[0152] Users access the system via their devices and upload data. Input data includes personal health information and activity logs. The device uses an internet connection to send this data to the cloud and applies encryption protocols (e.g., SSL / TLS) to ensure data security. The output is the data files securely transmitted to the server.

[0153] Step 2:

[0154] The server receives uploaded data and stores it in a secure information processing environment. The input is encrypted data sent from the terminal. The server manages the data using a storage system (e.g., cloud storage) and controls access to the data by performing decryption. The output is a securely stored database entry.

[0155] Step 3:

[0156] The device continuously collects the user's biometric signals and behavioral data. Inputs include real-time heart rate and gesture data acquired from sensors. The device preprocesses this data, converts it to the required format, and sends it to the server. Outputs include biometric and behavioral data in a format suitable for analysis, which is then sent to the server.

[0157] Step 4:

[0158] The server analyzes the data received using an emotion analysis device to recognize the user's emotional state. Inputs include biosignals and behavioral data transmitted from the terminal. The server performs emotion recognition using an AI model and determines stress and relaxation levels using an algorithm. The output is information about the recognized emotional state.

[0159] Step 5:

[0160] The server dynamically adjusts the interface based on the recognized emotional state. The input is the emotional information obtained in step 4. The server generates commands to change the interface settings and sends them to the user's terminal. For example, it can adjust brightness or change display speed. The output is an interface design optimized for the user.

[0161] Step 6:

[0162] The server performs statistical analysis of sentiment data to generate insights that help improve the overall user experience. The input is sentiment data from multiple users. The server aggregates the data and uses data analysis tools to extract trends and patterns. The output generates analysis results and suggestions that help improve the system.

[0163] Step 7:

[0164] When generating prompts, the server incorporates content that takes into account the user's emotional state. The input is a question for the generating AI model. The server creates prompts in a format suitable for the generating AI model, achieving an interaction that matches the user's emotions. The output is an appropriate prompt to be supplied to the AI ​​model.

[0165] (Application Example 2)

[0166] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0167] The challenge lies in creating a system that can recognize the user's psychological state and reflect it in real time when providing machine learning models for secure data management and reward distribution, as well as offering machine learning models. Furthermore, this is required to improve the customer experience.

[0168] 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.

[0169] In this invention, the server includes means for storing information in a secure information processing device, means for identifying the usage status of information through usage history and distributing rewards to information managers, means for providing machine learning models to external parties without directly disclosing the contents of the models, means for recognizing the user's facial expressions and voice and analyzing their psychological state in real time, and means for dynamically adjusting the displayed content based on the psychological state and presenting it to the user. This not only enables the secure management and provision of data, but also enables the provision of services that are adapted to the user's emotions.

[0170] An "information manager" is an entity that owns information and is responsible for its management and use.

[0171] An "information processing device" is a device that includes a computing environment for collecting, storing, analyzing, and providing information.

[0172] "Usage history" refers to records of the use of information and services, including access time and details of operations performed.

[0173] "Compensation" refers to the profit paid to a provider as payment for the use of information or services.

[0174] A "machine learning model" is a set of algorithms that learn from data and then perform inferences on subsequent data.

[0175] "Means of recognizing facial expressions and voice" refers to technology that analyzes a user's face and voice to determine their psychological state.

[0176] "Means of analyzing psychological state" refers to techniques for analyzing and understanding a user's emotions and mental state.

[0177] "Dynamically adjusting the displayed content" means changing the way information is presented and the content of that information according to the user's psychological state.

[0178] This invention is a system that provides services based on the real-time psychological state of users while securely managing information owned by information managers. The server first stores the data provided by the information manager in a secure information processing device. At this time, the data usage history is recorded and used as a basis for reward distribution. Furthermore, when the server provides machine learning models to external parties, it ensures security by not directly disclosing the contents of the model, but by returning inference results based on requests from external parties.

[0179] The device is equipped with means to recognize the user's facial expressions and voice. This is used to analyze the user's psychological state in real time. This analysis includes facial expression analysis using OpenCV with a camera, and text conversion of voice collected through a microphone using the Google® Cloud Speech-to-Text API for emotion analysis. Based on the analysis results, the device's display content is dynamically adjusted to suit the user's psychological state.

[0180] As a concrete example, let's consider its use in a store. If the customer appears stressed during customer service, the terminal will adjust its display content and provide instructions to the provider such as, "They may be seeking understanding; please provide more details."

[0181] Examples of prompts for a generative AI model include the following:

[0182] "Generate insights from customer sentiment data to improve our services. In particular, focus your analysis on product categories with high levels of dissatisfaction and develop action plans based on those findings."

[0183] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0184] Step 1:

[0185] The device captures the user's facial expressions with its camera and acquires them as input data. Next, it uses OpenCV to analyze this facial data and outputs emotional information that indicates the user's psychological state. Specifically, it detects facial landmarks, extracts features, and performs emotion classification.

[0186] Step 2:

[0187] The device collects the user's voice using a microphone and receives the voice data as input. Using the Google Cloud Speech-to-Text API, this voice data is converted into text data, and then analyzed by an emotion analysis engine to output information indicating the user's psychological state. Specifically, it performs a process of converting the voice data into text and adding an emotion indicator.

[0188] Step 3:

[0189] The terminal integrates the emotional information acquired in Step 1 and Step 2. Based on this integrated data, it determines the user's overall psychological state and uses this as input data for dynamically adjusting the displayed content. The output information includes the selection of appropriate recommended services and the content to be displayed.

[0190] Step 4:

[0191] The server receives user psychological state data transmitted from the terminal and uses it as feedback to the provider within the bounds of secure information security. At this time, it outputs the final service content, adjusting the display speed and amount of information presented to match the user's psychological state while viewing the information. Specifically, it queries database information and runs an algorithm to support appropriate responses.

[0192] Step 5:

[0193] Based on server feedback, users optimize the services they provide using a generative AI model. In this process, they create and send prompt messages tailored to their psychological state and receive service improvement suggestions based on those prompts. Specifically, they use a prompt message generation engine to analyze various insights and receive responses.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] [Second Embodiment]

[0198] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0199] 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.

[0200] 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).

[0201] 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.

[0202] 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.

[0203] 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).

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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".

[0210] This invention provides a system for securely handling data and models on a platform for utilizing generative AI. This system enables users to access a secure computing environment in the cloud and manage data and models there.

[0211] Users upload their datasets to the platform via their devices. The server receives this data and stores it in secure storage. Each dataset is assigned a unique ID, making it traceable. This ID is used to record the history of which models used which data.

[0212] The registration of generated AI models is done in a similar manner. Users send AI models from their terminals to the platform, and the server stores them. When a model is provided, the server is configured to keep the model's internal structure private from anyone other than the user, while only allowing external inference services. The server executes the model upon request and provides its output, but the internal details remain confidential.

[0213] Data owners can view their data usage and configure billing settings from their devices. The server compiles data on usage history, tracking how and which data was used, and distributes rewards to each user. The server calculates these rewards based on each user's chosen billing scheme and provides the corresponding rewards.

[0214] As a concrete example, a company can upload its specific dataset to the platform, use it to perform predictions with external AI models, and receive the results. Furthermore, the platform can constantly monitor how the data is used and manage it to ensure it benefits the company.

[0215] Thus, the present invention provides a safe and convenient service for users by simultaneously achieving secure data handling and efficient use of AI models.

[0216] The following describes the processing flow.

[0217] Step 1:

[0218] The user accesses the platform's login screen via their device and enters their authentication information. The server compares the entered username and password with the database to determine whether authentication is successful. If authentication is successful, the server starts a user session and returns the session ID to the device.

[0219] Step 2:

[0220] Users select their data files and upload them to the platform from their devices. The server stores the received data files in secure storage and generates a unique ID for each file. It also uses this ID to begin recording the data usage history.

[0221] Step 3:

[0222] Users register their AI models by submitting them to the platform from their devices. The server stores the uploaded models and assigns them unique IDs. Once the model registration is complete, users can configure settings for using the model.

[0223] Step 4:

[0224] When another user uses the model, they send an inference request from their terminal to the server. The server runs the model and performs inference based on the specified input data. It then sends the inference result back to the terminal of the user who made the request.

[0225] Step 5:

[0226] Users who own the data can check their data usage history from their device. The server collects usage information recorded in the database and displays it on the device in a visually viewable format.

[0227] Step 6:

[0228] The server periodically compiles usage history and calculates rewards based on data and model usage. Rewards are distributed to data owners and model providers according to the specified billing policy, and the details can be viewed on the user's device.

[0229] (Example 1)

[0230] 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."

[0231] To securely enable third parties to utilize digital data and mathematical models managed by data owners, an environment is needed that allows for accurate tracking of data usage while maintaining the confidentiality of the models and effectively utilizing their results. However, current technology makes it difficult to balance confidentiality and effective utilization when providing data and models externally. Furthermore, mechanisms for appropriately distributing compensation to data owners are insufficient. Solutions to these challenges are needed.

[0232] 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.

[0233] In this invention, the server includes means for storing digital data managed by the information owner within a secure information processing device, means for identifying the usage status of the digital data through operation history and distributing rewards to the information owner, and means for providing computational processing without directly disclosing the contents of the mathematical model when providing the mathematical model to a third party. This enables the secure management and effective utilization of digital data and mathematical models.

[0234] An "information owner" is an individual or legal entity that owns the digital data and mathematical models it manages and has the authority to grant permission for their use and set conditions for their use.

[0235] "Digital data" refers to all electronically stored information generated or acquired by the information owner, and specifically includes numerical data, text information, image data, etc.

[0236] "Information processing equipment" refers to all computing devices used for storing, managing, and processing digital data and mathematical models, and includes cloud systems equipped with security features.

[0237] "Operation history" refers to the history of access to and processing of digital data and mathematical models, recording which user performed what operation and when.

[0238] "Reward" refers to monetary or point-based compensation distributed to information owners for the use of digital data or mathematical models.

[0239] A "mathematical model" refers to a computational framework designed or provided by the user, which has the function of performing inference and prediction based on prompts.

[0240] "Computational processing" refers to a series of electronic processes that use mathematical models to perform calculations based on digital data and generate output.

[0241] This system is designed to efficiently manage digital data and mathematical models controlled by information owners within a secure information processing device, and to maintain confidentiality when providing them to third parties. Multiple hardware and software components are involved in its implementation.

[0242] The server provides a secure, cloud-based information processing environment. This environment includes storage and computing resources for storing and managing digital data and mathematical models. The server utilizes a database management system (e.g., PostgreSQL) to receive digital data and assign unique identifiers to it. It also encrypts the data and models and stores them in secure storage. This process uses encryption libraries (e.g., OpenSSL).

[0243] The terminal provides an interface for users to upload digital data and manipulate mathematical models. Through the terminal, users send digital data and models to the server and input necessary commands. This interface is built as an application that runs on a web browser (e.g., a web application using React or Angular).

[0244] Users manage their digital data and mathematical models using the interface provided on their device. They can check their digital data usage history and configure billing settings. Users perform specific analyses using generative AI models and utilize the results. In this process, they instruct the mathematical model's calculation process by entering prompts.

[0245] As a concrete example, consider a scenario where a company's data scientist uploads their company's commercially available statistical data to a platform and uses that data to make sales forecasts. In this case, an example of a prompt message might be, "Please make sales forecasts for the next quarter."

[0246] This system enables users to safely and effectively utilize digital data and generated AI models, ensuring confidentiality and proactive application.

[0247] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0248] Step 1:

[0249] Users log in to the platform using their own devices. They enter their username and password on the login screen, and authentication is performed. Upon successful authentication, the device provides the functionality to upload digital data. The input is user information, and the output is the user's authentication session.

[0250] Step 2:

[0251] Users select digital data or generated AI models using the device's upload function. After selection, the information is entered into a form, and the data is uploaded to the platform by pressing the submit button. Input is digital data or AI model files, and output is the transmission of files to the server.

[0252] Step 3:

[0253] The server generates a unique identifier for the digital data received from the user. This identifier is assigned to the data and forms the basis of the operation history. The data is encrypted and stored in secure storage. In this case, the input is the data file sent by the user, and the output is the stored encrypted data.

[0254] Step 4:

[0255] The user inputs prompt messages into the generated AI model via their terminal. For example, they might input a command such as, "Please perform a sales forecast." The input is a prompt message, and the output is the prompt message information sent to the server.

[0256] Step 5:

[0257] The server executes the specified generative AI model based on the prompt statement. It performs calculations on the digital data necessary for the model to make predictions and generates results. In this process, the input is the prompt statement and data, and the output is the generated prediction result.

[0258] Step 6:

[0259] The server provides the user with the generated results. These results are displayed through an interface on the user's terminal. The user can view these results and perform other actions as needed. The input is the prediction results generated on the server side, and the output is the result display on the user's terminal.

[0260] Step 7:

[0261] The server manages the usage history of digital data and calculates rewards. Based on the operation history, it aggregates how and which data was used, and appropriately distributes rewards to the data owners. The inputs are the operation history and billing settings, and the output is the details of the reward distribution.

[0262] (Application Example 1)

[0263] 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."

[0264] In information processing, data managers are required to securely manage information, clearly document usage history, and fairly distribute compensation. Furthermore, when providing models, it is required that their contents be kept confidential and used as an analysis service for third parties. Additionally, there is a need for methods to quickly respond to the ever-changing situation when it comes to optimization proposals for streamlining labor in logistics and information distribution.

[0265] 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.

[0266] In this invention, the server includes means for securely storing information managed by the data administrator, means for identifying usage history and distributing rewards, means for providing analysis services without disclosing the contents of the model, and means for presenting proposals for optimizing information distribution labor. This enables secure information management, fair reward distribution, and efficient optimization of information distribution labor.

[0267] A "data controller" is an entity responsible for managing and protecting information.

[0268] An "information processing environment" is a secure technological foundation for storing, processing, and managing information.

[0269] "Usage history" refers to traceable data that records how information has been used.

[0270] An "analysis service" is a service that analyzes information and provides useful results and suggestions to external parties.

[0271] "Information distribution labor" refers to the entire system of work involved in the distribution and processing of information.

[0272] An "optimization proposal" is a specific improvement plan provided with the aim of increasing efficiency and improving performance.

[0273] The system for realizing this invention is a platform for securely managing data and AI models on the cloud. The server stores information received from data administrators in a secure information processing environment. It also has the functionality to track usage history using generating AI and to fairly distribute rewards.

[0274] Users register the information and AI models they manage on the platform via their devices. The server configures these models so that they can be used as an analysis service without directly exposing them. For example, to optimize information flow and labor in a logistics center, the server uses an AI model to analyze the information and provides the results as optimization suggestions.

[0275] Specifically, it is possible to optimize product shelf placement and picking routes based on operational data from the logistics center. Furthermore, an example of a prompt message for the generated AI model is: "Based on this week's operational data from the logistics center, please generate suggestions for optimizing inventory placement and picking routes."

[0276] This platform is developed using Python and cloud APIs, and can centrally handle secure data storage, usage history management, and model-based service delivery.

[0277] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0278] Step 1:

[0279] The user uploads their own dataset from the terminal to the platform on the cloud. When the terminal sends the data, the server receives the data and stores it in a secure information processing environment. At this time, a unique ID is assigned to the data and used for subsequent tracking. The input is the data file, and the output is a secure storage location within the platform.

[0280] Step 2:

[0281] The user registers the generated AI model from the terminal to the platform. The terminal sends the model data, and the server stores the received model. In addition, the server makes settings to allow the provision of external analysis services while keeping the internal structure of the model non-public. The input is the model data, and the output is the configured model in the server.

[0282] Step 3:

[0283] The user creates a prompt sentence and makes a request for analysis. This prompt sentence specifies the processing of logistics data that requires specific optimization suggestions. The server performs analysis using the generated AI model and generates optimization suggestions based on the prompt sentence. The input is the prompt sentence, and the output is the optimization suggestion.

[0284] Step 4:

[0285] The server provides the generated optimization suggestions to the user. The user attempts to improve the operations at the actual logistics center based on the suggestions. The input is the analysis result from the server, and the output is the optimization suggestion provided to the user.

[0286] Step 5:

[0287] The server manages the usage history of the data and aggregates how much data each user has used. Based on this, the calculation of reward distribution is performed, and the rewards are appropriately distributed to the data administrators. The input is the usage history data, and the output is the reward distribution plan.

[0288] 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.

[0289] This invention is a system that incorporates a function to recognize user emotions into a platform that securely manages data and enables the provision of generative AI models. Users can access the platform using a terminal and upload and manage their own data. The server stores this data in a secure computing environment and sets appropriate access permissions. It tracks how the data was used through data usage history and distributes rewards accordingly.

[0290] Furthermore, this system is equipped with an emotion engine. The terminal collects the user's biosignals and behavioral data and sends it to the server. The server analyzes this data using the emotion engine and recognizes the user's emotions in real time. The recognized emotion information is used to dynamically adjust the interface according to the user's emotional state. For example, if the user is feeling stressed, the information presentation speed can be slowed down and the information can be displayed in an easy-to-understand format.

[0291] Furthermore, the emotion engine can also provide feedback on the inference results in accordance with the user's emotions. For example, if the user expresses dissatisfaction, the server will re-evaluate the inference results and make adjustments as needed.

[0292] This system also includes a function to perform statistical analysis of emotional data. The server generates insights to improve the overall user experience of the system based on emotional data collected from multiple users.

[0293] As a concrete example, when a user requests a prediction from an AI model using a specific dataset, the server monitors the user's emotions during the process and adjusts the data presentation method and inference results to match the user's emotional state. In this way, the present invention can improve the user experience by simultaneously achieving the secure provision of data models and the optimization of emotion-based interactions.

[0294] The following describes the processing flow.

[0295] Step 1:

[0296] The user logs into the platform via their device and enters their authentication information. The server verifies this information, grants the user access, and initiates a session. Upon successful login, a session ID is sent to the user's device.

[0297] Step 2:

[0298] Users select a dataset from their device and upload it to the platform. The server stores the uploaded data in secure storage and assigns a unique ID to each piece of data. This enables data management.

[0299] Step 3:

[0300] The device collects the user's biometric signals and behavior as emotional data. This data is transmitted to the server in real time while the user is interacting with the device.

[0301] Step 4:

[0302] The server analyzes the received emotional data using an emotion engine to recognize the user's current emotional state. For example, it measures stress levels and concentration levels and uses this information to adjust the interface.

[0303] Step 5:

[0304] When the user performs an inference using the AI model, the server executes the model to generate a result. This result is adjusted in terms of display format and content according to the recognized emotional state of the user, and then transmitted to the terminal.

[0305] Step 6:

[0306] The server records the used data and the usage history based on the user's emotions, and calculates the reward based on this. Based on the calculation result, it starts the distribution process for the data owner. Thereby, while maximizing the usage value of the data and the model, it provides feedback based on emotions.

[0307] (Example 2)

[0308] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0309] In the context where secure and efficient management of information is required, it is necessary to utilize data while protecting it and to fairly distribute the rewards based on such utilization. Also, it is important to appropriately adjust the system interface according to the emotions of the user and provide an environment with less stress for the user. However, with the current technology, it is difficult to simultaneously achieve data security, convenience, and optimization of emotion-based interactions.

[0310] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following respective means.

[0311] In this invention, the server includes means for recording data managed by the information holder in a protected information processing environment; means for identifying the usage status of the information through operation history and distributing rewards to the information holder; means for providing a computational model to others without directly disclosing the contents of the computational model; means for detecting the user's emotions using an emotion analysis device and dynamically adjusting the interface; means for adjusting the inference results produced in accordance with the user's recognized emotions; and means for analyzing emotion information collected from multiple users and generating insights to improve the overall user experience. This enables both data protection and convenience, optimization of emotion-based interactions, and improvement of the user experience.

[0312] An "information holder" refers to an individual or organization that owns the data or information and has the rights to manage and use it.

[0313] An "information processing environment" refers to a technical environment that includes computer systems and network infrastructure for storing, managing, processing, and protecting digital data.

[0314] "Operation history" refers to a collection of information that records the process of data usage and manipulation, and is used to track how data was used.

[0315] A "computational model" refers to an algorithm or analytical framework designed to perform calculations or inferences based on specific data.

[0316] An "emotion analysis device" is a sensor or software tool used to detect a user's emotional state, analyzing biosignals and behavioral data in real time.

[0317] An "interface" refers to the screen or method of operation that a user uses to interact with a computer system or application.

[0318] "Insight" refers to useful knowledge and understanding obtained as a result of data analysis, and is used for decision-making and system improvement.

[0319] This invention is a system aimed at securely managing information and optimizing emotion-based interactions. Embodiments of this system are described below.

[0320] Users first access the platform using a device and upload their data. This data may include health-related information and personal behavioral history. The device collects the user's biometric signals and behavioral data and sends it to a server for analysis. It is recommended to use protocols such as SSL / TLS during this process to enhance security.

[0321] The server stores data submitted by users in a secure information processing environment. This environment utilizes a data storage system (e.g., a common cloud storage service) and ensures data security through encryption. The server tracks the operation history of the stored data and records how the data was used. This allows for the distribution of appropriate rewards to users in exchange for providing data.

[0322] Furthermore, this system is equipped with an emotion analysis device. For example, when a user is experiencing stress, the system can adjust the interface colors and information presentation speed to provide a more relaxing environment. Emotion recognition is performed in real time by an AI algorithm, and the user experience is dynamically improved based on the results.

[0323] Furthermore, the server analyzes sentiment data collected from numerous users to generate insights for improving the overall user experience of the system. These insights are useful for improving the system's interface design and the services it provides. This process may involve utilizing statistical analysis tools and libraries (e.g., analysis libraries for Python or R).

[0324] For example, when a user requests a prediction from a generative AI model using a specific dataset, the server prompts the generative AI model with a question such as, "How should I present the data so that the model's predictions now maintain a relaxed state for the user?" In this way, the system can provide an interaction that is tailored to the user's emotions.

[0325] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0326] Step 1:

[0327] Users access the system via their devices and upload data. Input data includes personal health information and activity logs. The device uses an internet connection to send this data to the cloud and applies encryption protocols (e.g., SSL / TLS) to ensure data security. The output is the data files securely transmitted to the server.

[0328] Step 2:

[0329] The server receives uploaded data and stores it in a secure information processing environment. The input is encrypted data sent from the terminal. The server manages the data using a storage system (e.g., cloud storage) and controls access to the data by performing decryption. The output is a securely stored database entry.

[0330] Step 3:

[0331] The device continuously collects the user's biometric signals and behavioral data. Inputs include real-time heart rate and gesture data acquired from sensors. The device preprocesses this data, converts it to the required format, and sends it to the server. Outputs include biometric and behavioral data in a format suitable for analysis, which is then sent to the server.

[0332] Step 4:

[0333] The server analyzes the data received using an emotion analysis device to recognize the user's emotional state. Inputs include biosignals and behavioral data transmitted from the terminal. The server performs emotion recognition using an AI model and determines stress and relaxation levels using an algorithm. The output is information about the recognized emotional state.

[0334] Step 5:

[0335] The server dynamically adjusts the interface based on the recognized emotional state. The input is the emotional information obtained in step 4. The server generates commands to change the interface settings and sends them to the user's terminal. For example, it can adjust brightness or change display speed. The output is an interface design optimized for the user.

[0336] Step 6:

[0337] The server performs statistical analysis of sentiment data to generate insights that help improve the overall user experience. The input is sentiment data from multiple users. The server aggregates the data and uses data analysis tools to extract trends and patterns. The output generates analysis results and suggestions that help improve the system.

[0338] Step 7:

[0339] When generating prompts, the server incorporates content that takes into account the user's emotional state. The input is a question for the generating AI model. The server creates prompts in a format suitable for the generating AI model, achieving an interaction that matches the user's emotions. The output is an appropriate prompt to be supplied to the AI ​​model.

[0340] (Application Example 2)

[0341] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0342] The challenge lies in creating a system that can recognize the user's psychological state and reflect it in real time when providing machine learning models for secure data management and reward distribution, as well as offering machine learning models. Furthermore, this is required to improve the customer experience.

[0343] 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.

[0344] In this invention, the server includes means for storing information in a secure information processing device, means for identifying the usage status of information through usage history and distributing rewards to information managers, means for providing machine learning models to external parties without directly disclosing the contents of the models, means for recognizing the user's facial expressions and voice and analyzing their psychological state in real time, and means for dynamically adjusting the displayed content based on the psychological state and presenting it to the user. This not only enables the secure management and provision of data, but also enables the provision of services that are adapted to the user's emotions.

[0345] An "information manager" is an entity that owns information and is responsible for its management and use.

[0346] An "information processing device" is a device that includes a computing environment for collecting, storing, analyzing, and providing information.

[0347] "Usage history" refers to records of the use of information and services, including access time and details of operations performed.

[0348] "Compensation" refers to the profit paid to a provider as payment for the use of information or services.

[0349] A "machine learning model" is a set of algorithms that learn from data and then perform inferences on subsequent data.

[0350] "Means of recognizing facial expressions and voice" refers to technology that analyzes a user's face and voice to determine their psychological state.

[0351] "Means of analyzing psychological state" refers to techniques for analyzing and understanding a user's emotions and mental state.

[0352] "Dynamically adjusting the displayed content" means changing the way information is presented and the content of that information according to the user's psychological state.

[0353] This invention is a system that provides services based on the real-time psychological state of users while securely managing information owned by information managers. The server first stores the data provided by the information manager in a secure information processing device. At this time, the data usage history is recorded and used as a basis for reward distribution. Furthermore, when the server provides machine learning models to external parties, it ensures security by not directly disclosing the contents of the model, but by returning inference results based on requests from external parties.

[0354] The device is equipped with means to recognize the user's facial expressions and voice. This is used to analyze the user's psychological state in real time. This analysis includes facial expression analysis using OpenCV with the camera, and text conversion of voice collected through the microphone using the Google Cloud Speech-to-Text API for emotion analysis. Based on the analysis results, the device's display content is dynamically adjusted to suit the user's psychological state.

[0355] As a concrete example, let's consider its use in a store. If the customer appears stressed during customer service, the terminal will adjust its display content and provide instructions to the provider such as, "They may be seeking understanding; please provide more details."

[0356] Examples of prompts for a generative AI model include the following:

[0357] "Generate insights from customer sentiment data to improve our services. In particular, focus your analysis on product categories with high levels of dissatisfaction and develop action plans based on those findings."

[0358] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0359] Step 1:

[0360] The device captures the user's facial expressions with its camera and acquires them as input data. Next, it uses OpenCV to analyze this facial data and outputs emotional information that indicates the user's psychological state. Specifically, it detects facial landmarks, extracts features, and performs emotion classification.

[0361] Step 2:

[0362] The device collects the user's voice using a microphone and receives the voice data as input. Using the Google Cloud Speech-to-Text API, this voice data is converted into text data, and then analyzed by an emotion analysis engine to output information indicating the user's psychological state. Specifically, it performs a process of converting the voice data into text and adding an emotion indicator.

[0363] Step 3:

[0364] The terminal integrates the emotional information acquired in Step 1 and Step 2. Based on this integrated data, it determines the user's overall psychological state and uses this as input data for dynamically adjusting the displayed content. The output information includes the selection of appropriate recommended services and the content to be displayed.

[0365] Step 4:

[0366] The server receives user psychological state data transmitted from the terminal and uses it as feedback to the provider within the bounds of secure information security. At this time, it outputs the final service content, adjusting the display speed and amount of information presented to match the user's psychological state while viewing the information. Specifically, it queries database information and runs an algorithm to support appropriate responses.

[0367] Step 5:

[0368] Based on server feedback, users optimize the services they provide using a generative AI model. In this process, they create and send prompt messages tailored to their psychological state and receive service improvement suggestions based on those prompts. Specifically, they use a prompt message generation engine to analyze various insights and receive responses.

[0369] 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.

[0370] 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.

[0371] 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.

[0372] [Third Embodiment]

[0373] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0374] 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.

[0375] 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).

[0376] 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.

[0377] 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.

[0378] 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).

[0379] 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.

[0380] 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.

[0381] 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.

[0382] 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.

[0383] 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.

[0384] 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".

[0385] This invention provides a system for securely handling data and models on a platform for utilizing generative AI. This system enables users to access a secure computing environment in the cloud and manage data and models there.

[0386] Users upload their datasets to the platform via their devices. The server receives this data and stores it in secure storage. Each dataset is assigned a unique ID, making it traceable. This ID is used to record the history of which models used which data.

[0387] The registration of generated AI models is done in a similar manner. Users send AI models from their terminals to the platform, and the server stores them. When a model is provided, the server is configured to keep the model's internal structure private from anyone other than the user, while only allowing external inference services. The server executes the model upon request and provides its output, but the internal details remain confidential.

[0388] Data owners can view their data usage and configure billing settings from their devices. The server compiles data on usage history, tracking how and which data was used, and distributes rewards to each user. The server calculates these rewards based on each user's chosen billing scheme and provides the corresponding rewards.

[0389] As a concrete example, a company can upload its specific dataset to the platform, use it to perform predictions with external AI models, and receive the results. Furthermore, the platform can constantly monitor how the data is used and manage it to ensure it benefits the company.

[0390] Thus, the present invention provides a safe and convenient service for users by simultaneously achieving secure data handling and efficient use of AI models.

[0391] The following describes the processing flow.

[0392] Step 1:

[0393] The user accesses the platform's login screen via their device and enters their authentication information. The server compares the entered username and password with the database to determine whether authentication is successful. If authentication is successful, the server starts a user session and returns the session ID to the device.

[0394] Step 2:

[0395] Users select their data files and upload them to the platform from their devices. The server stores the received data files in secure storage and generates a unique ID for each file. It also uses this ID to begin recording the data usage history.

[0396] Step 3:

[0397] Users register their AI models by submitting them to the platform from their devices. The server stores the uploaded models and assigns them unique IDs. Once the model registration is complete, users can configure settings for using the model.

[0398] Step 4:

[0399] When another user uses the model, they send an inference request from their terminal to the server. The server runs the model and performs inference based on the specified input data. It then sends the inference result back to the terminal of the user who made the request.

[0400] Step 5:

[0401] Users who own the data can check their data usage history from their device. The server collects usage information recorded in the database and displays it on the device in a visually viewable format.

[0402] Step 6:

[0403] The server periodically compiles usage history and calculates rewards based on data and model usage. Rewards are distributed to data owners and model providers according to the specified billing policy, and the details can be viewed on the user's device.

[0404] (Example 1)

[0405] 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."

[0406] To securely enable third parties to utilize digital data and mathematical models managed by data owners, an environment is needed that allows for accurate tracking of data usage while maintaining the confidentiality of the models and effectively utilizing their results. However, current technology makes it difficult to balance confidentiality and effective utilization when providing data and models externally. Furthermore, mechanisms for appropriately distributing compensation to data owners are insufficient. Solutions to these challenges are needed.

[0407] 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.

[0408] In this invention, the server includes means for storing digital data managed by the information owner within a secure information processing device, means for identifying the usage status of the digital data through operation history and distributing rewards to the information owner, and means for providing computational processing without directly disclosing the contents of the mathematical model when providing the mathematical model to a third party. This enables the secure management and effective utilization of digital data and mathematical models.

[0409] An "information owner" is an individual or legal entity that owns the digital data and mathematical models it manages and has the authority to grant permission for their use and set conditions for their use.

[0410] "Digital data" refers to all electronically stored information generated or acquired by the information owner, and specifically includes numerical data, text information, image data, etc.

[0411] "Information processing equipment" refers to all computing devices used for storing, managing, and processing digital data and mathematical models, and includes cloud systems equipped with security features.

[0412] "Operation history" refers to the history of access to and processing of digital data and mathematical models, recording which user performed what operation and when.

[0413] "Reward" refers to monetary or point-based compensation distributed to information owners for the use of digital data or mathematical models.

[0414] A "mathematical model" refers to a computational framework designed or provided by the user, which has the function of performing inference and prediction based on prompts.

[0415] "Computational processing" refers to a series of electronic processes that use mathematical models to perform calculations based on digital data and generate output.

[0416] This system is designed to efficiently manage digital data and mathematical models controlled by information owners within a secure information processing device, and to maintain confidentiality when providing them to third parties. Multiple hardware and software components are involved in its implementation.

[0417] The server provides a secure, cloud-based information processing environment. This environment includes storage and computing resources for storing and managing digital data and mathematical models. The server utilizes a database management system (e.g., PostgreSQL) to receive digital data and assign unique identifiers to it. It also encrypts the data and models and stores them in secure storage. This process uses encryption libraries (e.g., OpenSSL).

[0418] The terminal provides an interface for users to upload digital data and manipulate mathematical models. Through the terminal, users send digital data and models to the server and input necessary commands. This interface is built as an application that runs on a web browser (e.g., a web application using React or Angular).

[0419] Users manage their digital data and mathematical models using the interface provided on their device. They can check their digital data usage history and configure billing settings. Users perform specific analyses using generative AI models and utilize the results. In this process, they instruct the mathematical model's calculation process by entering prompts.

[0420] As a concrete example, consider a scenario where a company's data scientist uploads their company's commercially available statistical data to a platform and uses that data to make sales forecasts. In this case, an example of a prompt message might be, "Please make sales forecasts for the next quarter."

[0421] This system enables users to safely and effectively utilize digital data and generated AI models, ensuring confidentiality and proactive application.

[0422] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0423] Step 1:

[0424] Users log in to the platform using their own devices. They enter their username and password on the login screen, and authentication is performed. Upon successful authentication, the device provides the functionality to upload digital data. The input is user information, and the output is the user's authentication session.

[0425] Step 2:

[0426] Users select digital data or generated AI models using the device's upload function. After selection, the information is entered into a form, and the data is uploaded to the platform by pressing the submit button. Input is digital data or AI model files, and output is the transmission of files to the server.

[0427] Step 3:

[0428] The server generates a unique identifier for the digital data received from the user. This identifier is assigned to the data and forms the basis of the operation history. The data is encrypted and stored in secure storage. In this case, the input is the data file sent by the user, and the output is the stored encrypted data.

[0429] Step 4:

[0430] The user inputs prompt messages into the generated AI model via their terminal. For example, they might input a command such as, "Please perform a sales forecast." The input is a prompt message, and the output is the prompt message information sent to the server.

[0431] Step 5:

[0432] The server executes the specified generative AI model based on the prompt statement. It performs calculations on the digital data necessary for the model to make predictions and generates results. In this process, the input is the prompt statement and data, and the output is the generated prediction result.

[0433] Step 6:

[0434] The server provides the user with the generated results. These results are displayed through an interface on the user's terminal. The user can view these results and perform other actions as needed. The input is the prediction results generated on the server side, and the output is the result display on the user's terminal.

[0435] Step 7:

[0436] The server manages the usage history of digital data and calculates rewards. Based on the operation history, it aggregates how and which data was used, and appropriately distributes rewards to the data owners. The inputs are the operation history and billing settings, and the output is the details of the reward distribution.

[0437] (Application Example 1)

[0438] 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."

[0439] In information processing, data managers are required to securely manage information, clearly document usage history, and fairly distribute compensation. Furthermore, when providing models, it is required that their contents be kept confidential and used as an analysis service for third parties. Additionally, there is a need for methods to quickly respond to the ever-changing situation when it comes to optimization proposals for streamlining labor in logistics and information distribution.

[0440] 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.

[0441] In this invention, the server includes means for securely storing information managed by the data administrator, means for identifying usage history and distributing rewards, means for providing analysis services without disclosing the contents of the model, and means for presenting proposals for optimizing information distribution labor. This enables secure information management, fair reward distribution, and efficient optimization of information distribution labor.

[0442] A "data controller" is an entity responsible for managing and protecting information.

[0443] An "information processing environment" is a secure technological foundation for storing, processing, and managing information.

[0444] "Usage history" refers to traceable data that records how information has been used.

[0445] An "analysis service" is a service that analyzes information and provides useful results and suggestions to external parties.

[0446] "Information distribution labor" refers to the entire system of work involved in the distribution and processing of information.

[0447] An "optimization proposal" is a specific improvement plan provided with the aim of increasing efficiency and improving performance.

[0448] The system for realizing this invention is a platform for securely managing data and AI models on the cloud. The server stores information received from data administrators in a secure information processing environment. It also has the functionality to track usage history using generating AI and to fairly distribute rewards.

[0449] Users register the information and AI models they manage on the platform via their devices. The server configures these models so that they can be used as an analysis service without directly exposing them. For example, to optimize information flow and labor in a logistics center, the server uses an AI model to analyze the information and provides the results as optimization suggestions.

[0450] Specifically, it is possible to optimize product shelf placement and picking routes based on operational data from the logistics center. Furthermore, an example of a prompt message for the generated AI model is: "Based on this week's operational data from the logistics center, please generate suggestions for optimizing inventory placement and picking routes."

[0451] This platform is developed using Python and cloud APIs, and can centrally handle secure data storage, usage history management, and model-based service delivery.

[0452] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0453] Step 1:

[0454] Users upload their datasets from their devices to a cloud-based platform. Once the device sends the data, the server receives it and stores it in a secure information processing environment. A unique ID is assigned to the data at this stage for later tracking. The input is a data file, and the output is a secure storage location within the platform.

[0455] Step 2:

[0456] The user registers the generated AI model from their device to the platform. The device sends the model data, and the server stores the received model. In addition, the server is configured to allow the provision of analysis services to external parties while keeping the internal structure of the model confidential. The input is the model data, and the output is the configured model on the server.

[0457] Step 3:

[0458] The user creates a prompt statement and submits a request for analysis. This prompt statement specifies the processing of logistics data that requires a particular optimization suggestion. The server performs the analysis using a generative AI model and generates optimization suggestions based on the prompt statement. The input is the prompt statement, and the output is the optimization suggestion.

[0459] Step 4:

[0460] The server provides the user with the optimization suggestions it has generated. The user then uses these suggestions to improve operations at the actual logistics center. The input is the analysis results from the server, and the output is the optimization suggestions provided to the user.

[0461] Step 5:

[0462] The server manages data usage history and compiles data on which users used how much data. Based on this usage history, it calculates reward distribution and distributes rewards appropriately to data administrators. The input is usage history data, and the output is a reward distribution plan.

[0463] 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.

[0464] This invention is a system that incorporates a function to recognize user emotions into a platform that securely manages data and enables the provision of generative AI models. Users can access the platform using a terminal and upload and manage their own data. The server stores this data in a secure computing environment and sets appropriate access permissions. It tracks how the data was used through data usage history and distributes rewards accordingly.

[0465] Furthermore, this system is equipped with an emotion engine. The terminal collects the user's biosignals and behavioral data and sends it to the server. The server analyzes this data using the emotion engine and recognizes the user's emotions in real time. The recognized emotion information is used to dynamically adjust the interface according to the user's emotional state. For example, if the user is feeling stressed, the information presentation speed can be slowed down and the information can be displayed in an easy-to-understand format.

[0466] Furthermore, the emotion engine can also provide feedback on the inference results in accordance with the user's emotions. For example, if the user expresses dissatisfaction, the server will re-evaluate the inference results and make adjustments as needed.

[0467] This system also includes a function to perform statistical analysis of emotional data. The server generates insights to improve the overall user experience of the system based on emotional data collected from multiple users.

[0468] As a concrete example, when a user requests a prediction from an AI model using a specific dataset, the server monitors the user's emotions during the process and adjusts the data presentation method and inference results to match the user's emotional state. In this way, the present invention can improve the user experience by simultaneously achieving the secure provision of data models and the optimization of emotion-based interactions.

[0469] The following describes the processing flow.

[0470] Step 1:

[0471] The user logs into the platform via their device and enters their authentication information. The server verifies this information, grants the user access, and initiates a session. Upon successful login, a session ID is sent to the user's device.

[0472] Step 2:

[0473] Users select a dataset from their device and upload it to the platform. The server stores the uploaded data in secure storage and assigns a unique ID to each piece of data. This enables data management.

[0474] Step 3:

[0475] The device collects the user's biometric signals and behavior as emotional data. This data is transmitted to the server in real time while the user is interacting with the device.

[0476] Step 4:

[0477] The server analyzes the received emotional data using an emotion engine to recognize the user's current emotional state. For example, it measures stress levels and concentration levels and uses this information to adjust the interface.

[0478] Step 5:

[0479] When a user performs inference using an AI model, the server executes the model and generates results. These results are then displayed in a format and content that is adjusted according to the recognized emotional state of the user, and then sent to the device.

[0480] Step 6:

[0481] The server records usage history based on the data used and user sentiment, and calculates rewards based on this. Based on the calculation results, it initiates a distribution process to data owners. This maximizes the use value of the data and models while providing sentiment-based feedback.

[0482] (Example 2)

[0483] 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."

[0484] Amidst the demand for secure and efficient information management, there is a need to protect and utilize data while ensuring a fair distribution of rewards based on its use. Furthermore, it is crucial to appropriately adjust system interfaces according to user emotions and provide a user-friendly environment. However, current technology makes it difficult to simultaneously achieve data security, usability, and optimized emotion-based interactions.

[0485] 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.

[0486] In this invention, the server includes means for recording data managed by the information holder in a protected information processing environment; means for identifying the usage status of the information through operation history and distributing rewards to the information holder; means for providing a computational model to others without directly disclosing the contents of the computational model; means for detecting the user's emotions using an emotion analysis device and dynamically adjusting the interface; means for adjusting the inference results produced in accordance with the user's recognized emotions; and means for analyzing emotion information collected from multiple users and generating insights to improve the overall user experience. This enables both data protection and convenience, optimization of emotion-based interactions, and improvement of the user experience.

[0487] An "information holder" refers to an individual or organization that owns the data or information and has the rights to manage and use it.

[0488] An "information processing environment" refers to a technical environment that includes computer systems and network infrastructure for storing, managing, processing, and protecting digital data.

[0489] "Operation history" refers to a collection of information that records the process of data usage and manipulation, and is used to track how data was used.

[0490] A "computational model" refers to an algorithm or analytical framework designed to perform calculations or inferences based on specific data.

[0491] An "emotion analysis device" is a sensor or software tool used to detect a user's emotional state, analyzing biosignals and behavioral data in real time.

[0492] An "interface" refers to the screen or method of operation that a user uses to interact with a computer system or application.

[0493] "Insight" refers to useful knowledge and understanding obtained as a result of data analysis, and is used for decision-making and system improvement.

[0494] This invention is a system aimed at securely managing information and optimizing emotion-based interactions. Embodiments of this system are described below.

[0495] Users first access the platform using a device and upload their data. This data may include health-related information and personal behavioral history. The device collects the user's biometric signals and behavioral data and sends it to a server for analysis. It is recommended to use protocols such as SSL / TLS during this process to enhance security.

[0496] The server stores data submitted by users in a secure information processing environment. This environment utilizes a data storage system (e.g., a common cloud storage service) and ensures data security through encryption. The server tracks the operation history of the stored data and records how the data was used. This allows for the distribution of appropriate rewards to users in exchange for providing data.

[0497] Furthermore, this system is equipped with an emotion analysis device. For example, when a user is experiencing stress, the system can adjust the interface colors and information presentation speed to provide a more relaxing environment. Emotion recognition is performed in real time by an AI algorithm, and the user experience is dynamically improved based on the results.

[0498] Furthermore, the server analyzes sentiment data collected from numerous users to generate insights for improving the overall user experience of the system. These insights are useful for improving the system's interface design and the services it provides. This process may involve utilizing statistical analysis tools and libraries (e.g., analysis libraries for Python or R).

[0499] For example, when a user requests a prediction from a generative AI model using a specific dataset, the server prompts the generative AI model with a question such as, "How should I present the data so that the model's predictions now maintain a relaxed state for the user?" In this way, the system can provide an interaction that is tailored to the user's emotions.

[0500] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0501] Step 1:

[0502] Users access the system via their devices and upload data. Input data includes personal health information and activity logs. The device uses an internet connection to send this data to the cloud and applies encryption protocols (e.g., SSL / TLS) to ensure data security. The output is the data files securely transmitted to the server.

[0503] Step 2:

[0504] The server receives uploaded data and stores it in a secure information processing environment. The input is encrypted data sent from the terminal. The server manages the data using a storage system (e.g., cloud storage) and controls access to the data by performing decryption. The output is a securely stored database entry.

[0505] Step 3:

[0506] The device continuously collects the user's biometric signals and behavioral data. Inputs include real-time heart rate and gesture data acquired from sensors. The device preprocesses this data, converts it to the required format, and sends it to the server. Outputs include biometric and behavioral data in a format suitable for analysis, which is then sent to the server.

[0507] Step 4:

[0508] The server analyzes the data received using an emotion analysis device to recognize the user's emotional state. Inputs include biosignals and behavioral data transmitted from the terminal. The server performs emotion recognition using an AI model and determines stress and relaxation levels using an algorithm. The output is information about the recognized emotional state.

[0509] Step 5:

[0510] The server dynamically adjusts the interface based on the recognized emotional state. The input is the emotional information obtained in step 4. The server generates commands to change the interface settings and sends them to the user's terminal. For example, it can adjust brightness or change display speed. The output is an interface design optimized for the user.

[0511] Step 6:

[0512] The server performs statistical analysis of sentiment data to generate insights that help improve the overall user experience. The input is sentiment data from multiple users. The server aggregates the data and uses data analysis tools to extract trends and patterns. The output generates analysis results and suggestions that help improve the system.

[0513] Step 7:

[0514] When generating prompts, the server incorporates content that takes into account the user's emotional state. The input is a question for the generating AI model. The server creates prompts in a format suitable for the generating AI model, achieving an interaction that matches the user's emotions. The output is an appropriate prompt to be supplied to the AI ​​model.

[0515] (Application Example 2)

[0516] 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."

[0517] The challenge lies in creating a system that can recognize the user's psychological state and reflect it in real time when providing machine learning models for secure data management and reward distribution, as well as offering machine learning models. Furthermore, this is required to improve the customer experience.

[0518] 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.

[0519] In this invention, the server includes means for storing information in a secure information processing device, means for identifying the usage status of information through usage history and distributing rewards to information managers, means for providing machine learning models to external parties without directly disclosing the contents of the models, means for recognizing the user's facial expressions and voice and analyzing their psychological state in real time, and means for dynamically adjusting the displayed content based on the psychological state and presenting it to the user. This not only enables the secure management and provision of data, but also enables the provision of services that are adapted to the user's emotions.

[0520] An "information manager" is an entity that owns information and is responsible for its management and use.

[0521] An "information processing device" is a device that includes a computing environment for collecting, storing, analyzing, and providing information.

[0522] "Usage history" refers to records of the use of information and services, including access time and details of operations performed.

[0523] "Compensation" refers to the profit paid to a provider as payment for the use of information or services.

[0524] A "machine learning model" is a set of algorithms that learn from data and then perform inferences on subsequent data.

[0525] "Means of recognizing facial expressions and voice" refers to technology that analyzes a user's face and voice to determine their psychological state.

[0526] "Means of analyzing psychological state" refers to techniques for analyzing and understanding a user's emotions and mental state.

[0527] "Dynamically adjusting the displayed content" means changing the way information is presented and the content of that information according to the user's psychological state.

[0528] This invention is a system that provides services based on the real-time psychological state of users while securely managing information owned by information managers. The server first stores the data provided by the information manager in a secure information processing device. At this time, the data usage history is recorded and used as a basis for reward distribution. Furthermore, when the server provides machine learning models to external parties, it ensures security by not directly disclosing the contents of the model, but by returning inference results based on requests from external parties.

[0529] The device is equipped with means to recognize the user's facial expressions and voice. This is used to analyze the user's psychological state in real time. This analysis includes facial expression analysis using OpenCV with the camera, and text conversion of voice collected through the microphone using the Google Cloud Speech-to-Text API for emotion analysis. Based on the analysis results, the device's display content is dynamically adjusted to suit the user's psychological state.

[0530] As a concrete example, let's consider its use in a store. If the customer appears stressed during customer service, the terminal will adjust its display content and provide instructions to the provider such as, "They may be seeking understanding; please provide more details."

[0531] Examples of prompts for a generative AI model include the following:

[0532] "Generate insights from customer sentiment data to improve our services. In particular, focus your analysis on product categories with high levels of dissatisfaction and develop action plans based on those findings."

[0533] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0534] Step 1:

[0535] The device captures the user's facial expressions with its camera and acquires them as input data. Next, it uses OpenCV to analyze this facial data and outputs emotional information that indicates the user's psychological state. Specifically, it detects facial landmarks, extracts features, and performs emotion classification.

[0536] Step 2:

[0537] The device collects the user's voice using a microphone and receives the voice data as input. Using the Google Cloud Speech-to-Text API, this voice data is converted into text data, and then analyzed by an emotion analysis engine to output information indicating the user's psychological state. Specifically, it performs a process of converting the voice data into text and adding an emotion indicator.

[0538] Step 3:

[0539] The terminal integrates the emotional information acquired in Step 1 and Step 2. Based on this integrated data, it determines the user's overall psychological state and uses this as input data for dynamically adjusting the displayed content. The output information includes the selection of appropriate recommended services and the content to be displayed.

[0540] Step 4:

[0541] The server receives user psychological state data transmitted from the terminal and uses it as feedback to the provider within the bounds of secure information security. At this time, it outputs the final service content, adjusting the display speed and amount of information presented to match the user's psychological state while viewing the information. Specifically, it queries database information and runs an algorithm to support appropriate responses.

[0542] Step 5:

[0543] Based on server feedback, users optimize the services they provide using a generative AI model. In this process, they create and send prompt messages tailored to their psychological state and receive service improvement suggestions based on those prompts. Specifically, they use a prompt message generation engine to analyze various insights and receive responses.

[0544] 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.

[0545] 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.

[0546] 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.

[0547] [Fourth Embodiment]

[0548] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0549] 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.

[0550] 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).

[0551] 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.

[0552] 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.

[0553] 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).

[0554] 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.

[0555] 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.

[0556] 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.

[0557] 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.

[0558] 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.

[0559] 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.

[0560] 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".

[0561] This invention provides a system for securely handling data and models on a platform for utilizing generative AI. This system enables users to access a secure computing environment in the cloud and manage data and models there.

[0562] Users upload their datasets to the platform via their devices. The server receives this data and stores it in secure storage. Each dataset is assigned a unique ID, making it traceable. This ID is used to record the history of which models used which data.

[0563] The registration of generated AI models is done in a similar manner. Users send AI models from their terminals to the platform, and the server stores them. When a model is provided, the server is configured to keep the model's internal structure private from anyone other than the user, while only allowing external inference services. The server executes the model upon request and provides its output, but the internal details remain confidential.

[0564] Data owners can view their data usage and configure billing settings from their devices. The server compiles data on usage history, tracking how and which data was used, and distributes rewards to each user. The server calculates these rewards based on each user's chosen billing scheme and provides the corresponding rewards.

[0565] As a concrete example, a company can upload its specific dataset to the platform, use it to perform predictions with external AI models, and receive the results. Furthermore, the platform can constantly monitor how the data is used and manage it to ensure it benefits the company.

[0566] Thus, the present invention provides a safe and convenient service for users by simultaneously achieving secure data handling and efficient use of AI models.

[0567] The following describes the processing flow.

[0568] Step 1:

[0569] The user accesses the platform's login screen via their device and enters their authentication information. The server compares the entered username and password with the database to determine whether authentication is successful. If authentication is successful, the server starts a user session and returns the session ID to the device.

[0570] Step 2:

[0571] Users select their data files and upload them to the platform from their devices. The server stores the received data files in secure storage and generates a unique ID for each file. It also uses this ID to begin recording the data usage history.

[0572] Step 3:

[0573] Users register their AI models by submitting them to the platform from their devices. The server stores the uploaded models and assigns them unique IDs. Once the model registration is complete, users can configure settings for using the model.

[0574] Step 4:

[0575] When another user uses the model, they send an inference request from their terminal to the server. The server runs the model and performs inference based on the specified input data. It then sends the inference result back to the terminal of the user who made the request.

[0576] Step 5:

[0577] Users who own the data can check their data usage history from their device. The server collects usage information recorded in the database and displays it on the device in a visually viewable format.

[0578] Step 6:

[0579] The server periodically compiles usage history and calculates rewards based on data and model usage. Rewards are distributed to data owners and model providers according to the specified billing policy, and the details can be viewed on the user's device.

[0580] (Example 1)

[0581] 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".

[0582] To securely enable third parties to utilize digital data and mathematical models managed by data owners, an environment is needed that allows for accurate tracking of data usage while maintaining the confidentiality of the models and effectively utilizing their results. However, current technology makes it difficult to balance confidentiality and effective utilization when providing data and models externally. Furthermore, mechanisms for appropriately distributing compensation to data owners are insufficient. Solutions to these challenges are needed.

[0583] 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.

[0584] In this invention, the server includes means for storing digital data managed by the information owner within a secure information processing device, means for identifying the usage status of the digital data through operation history and distributing rewards to the information owner, and means for providing computational processing without directly disclosing the contents of the mathematical model when providing the mathematical model to a third party. This enables the secure management and effective utilization of digital data and mathematical models.

[0585] An "information owner" is an individual or legal entity that owns the digital data and mathematical models it manages and has the authority to grant permission for their use and set conditions for their use.

[0586] "Digital data" refers to all electronically stored information generated or acquired by the information owner, and specifically includes numerical data, text information, image data, etc.

[0587] "Information processing equipment" refers to all computing devices used for storing, managing, and processing digital data and mathematical models, and includes cloud systems equipped with security features.

[0588] "Operation history" refers to the history of access to and processing of digital data and mathematical models, recording which user performed what operation and when.

[0589] "Reward" refers to monetary or point-based compensation distributed to information owners for the use of digital data or mathematical models.

[0590] A "mathematical model" refers to a computational framework designed or provided by the user, which has the function of performing inference and prediction based on prompts.

[0591] "Computational processing" refers to a series of electronic processes that use mathematical models to perform calculations based on digital data and generate output.

[0592] This system is designed to efficiently manage digital data and mathematical models controlled by information owners within a secure information processing device, and to maintain confidentiality when providing them to third parties. Multiple hardware and software components are involved in its implementation.

[0593] The server provides a secure, cloud-based information processing environment. This environment includes storage and computing resources for storing and managing digital data and mathematical models. The server utilizes a database management system (e.g., PostgreSQL) to receive digital data and assign unique identifiers to it. It also encrypts the data and models and stores them in secure storage. This process uses encryption libraries (e.g., OpenSSL).

[0594] The terminal provides an interface for users to upload digital data and manipulate mathematical models. Through the terminal, users send digital data and models to the server and input necessary commands. This interface is built as an application that runs on a web browser (e.g., a web application using React or Angular).

[0595] Users manage their digital data and mathematical models using the interface provided on their device. They can check their digital data usage history and configure billing settings. Users perform specific analyses using generative AI models and utilize the results. In this process, they instruct the mathematical model's calculation process by entering prompts.

[0596] As a concrete example, consider a scenario where a company's data scientist uploads their company's commercially available statistical data to a platform and uses that data to make sales forecasts. In this case, an example of a prompt message might be, "Please make sales forecasts for the next quarter."

[0597] This system enables users to safely and effectively utilize digital data and generated AI models, ensuring confidentiality and proactive application.

[0598] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0599] Step 1:

[0600] Users log in to the platform using their own devices. They enter their username and password on the login screen, and authentication is performed. Upon successful authentication, the device provides the functionality to upload digital data. The input is user information, and the output is the user's authentication session.

[0601] Step 2:

[0602] Users select digital data or generated AI models using the device's upload function. After selection, the information is entered into a form, and the data is uploaded to the platform by pressing the submit button. Input is digital data or AI model files, and output is the transmission of files to the server.

[0603] Step 3:

[0604] The server generates a unique identifier for the digital data received from the user. This identifier is assigned to the data and forms the basis of the operation history. The data is encrypted and stored in secure storage. In this case, the input is the data file sent by the user, and the output is the stored encrypted data.

[0605] Step 4:

[0606] The user inputs prompt messages into the generated AI model via their terminal. For example, they might input a command such as, "Please perform a sales forecast." The input is a prompt message, and the output is the prompt message information sent to the server.

[0607] Step 5:

[0608] The server executes the specified generative AI model based on the prompt statement. It performs calculations on the digital data necessary for the model to make predictions and generates results. In this process, the input is the prompt statement and data, and the output is the generated prediction result.

[0609] Step 6:

[0610] The server provides the user with the generated results. These results are displayed through an interface on the user's terminal. The user can view these results and perform other actions as needed. The input is the prediction results generated on the server side, and the output is the result display on the user's terminal.

[0611] Step 7:

[0612] The server manages the usage history of digital data and calculates rewards. Based on the operation history, it aggregates how and which data was used, and appropriately distributes rewards to the data owners. The inputs are the operation history and billing settings, and the output is the details of the reward distribution.

[0613] (Application Example 1)

[0614] 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".

[0615] In information processing, data managers are required to securely manage information, clearly document usage history, and fairly distribute compensation. Furthermore, when providing models, it is required that their contents be kept confidential and used as an analysis service for third parties. Additionally, there is a need for methods to quickly respond to the ever-changing situation when it comes to optimization proposals for streamlining labor in logistics and information distribution.

[0616] 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.

[0617] In this invention, the server includes means for securely storing information managed by the data administrator, means for identifying usage history and distributing rewards, means for providing analysis services without disclosing the contents of the model, and means for presenting proposals for optimizing information distribution labor. This enables secure information management, fair reward distribution, and efficient optimization of information distribution labor.

[0618] A "data controller" is an entity responsible for managing and protecting information.

[0619] An "information processing environment" is a secure technological foundation for storing, processing, and managing information.

[0620] "Usage history" refers to traceable data that records how information has been used.

[0621] An "analysis service" is a service that analyzes information and provides useful results and suggestions to external parties.

[0622] "Information distribution labor" refers to the entire system of work involved in the distribution and processing of information.

[0623] An "optimization proposal" is a specific improvement plan provided with the aim of increasing efficiency and improving performance.

[0624] The system for realizing this invention is a platform for securely managing data and AI models on the cloud. The server stores information received from data administrators in a secure information processing environment. It also has the functionality to track usage history using generating AI and to fairly distribute rewards.

[0625] Users register the information and AI models they manage on the platform via their devices. The server configures these models so that they can be used as an analysis service without directly exposing them. For example, to optimize information flow and labor in a logistics center, the server uses an AI model to analyze the information and provides the results as optimization suggestions.

[0626] Specifically, it is possible to optimize product shelf placement and picking routes based on operational data from the logistics center. Furthermore, an example of a prompt message for the generated AI model is: "Based on this week's operational data from the logistics center, please generate suggestions for optimizing inventory placement and picking routes."

[0627] This platform is developed using Python and cloud APIs, and can centrally handle secure data storage, usage history management, and model-based service delivery.

[0628] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0629] Step 1:

[0630] Users upload their datasets from their devices to a cloud-based platform. Once the device sends the data, the server receives it and stores it in a secure information processing environment. A unique ID is assigned to the data at this stage for later tracking. The input is a data file, and the output is a secure storage location within the platform.

[0631] Step 2:

[0632] The user registers the generated AI model from their device to the platform. The device sends the model data, and the server stores the received model. In addition, the server is configured to allow the provision of analysis services to external parties while keeping the internal structure of the model confidential. The input is the model data, and the output is the configured model on the server.

[0633] Step 3:

[0634] The user creates a prompt statement and submits a request for analysis. This prompt statement specifies the processing of logistics data that requires a particular optimization suggestion. The server performs the analysis using a generative AI model and generates optimization suggestions based on the prompt statement. The input is the prompt statement, and the output is the optimization suggestion.

[0635] Step 4:

[0636] The server provides the user with the optimization suggestions it has generated. The user then uses these suggestions to improve operations at the actual logistics center. The input is the analysis results from the server, and the output is the optimization suggestions provided to the user.

[0637] Step 5:

[0638] The server manages data usage history and compiles data on which users used how much data. Based on this usage history, it calculates reward distribution and distributes rewards appropriately to data administrators. The input is usage history data, and the output is a reward distribution plan.

[0639] 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.

[0640] This invention is a system that incorporates a function to recognize user emotions into a platform that securely manages data and enables the provision of generative AI models. Users can access the platform using a terminal and upload and manage their own data. The server stores this data in a secure computing environment and sets appropriate access permissions. It tracks how the data was used through data usage history and distributes rewards accordingly.

[0641] Furthermore, this system is equipped with an emotion engine. The terminal collects the user's biosignals and behavioral data and sends it to the server. The server analyzes this data using the emotion engine and recognizes the user's emotions in real time. The recognized emotion information is used to dynamically adjust the interface according to the user's emotional state. For example, if the user is feeling stressed, the information presentation speed can be slowed down and the information can be displayed in an easy-to-understand format.

[0642] Furthermore, the emotion engine can also provide feedback on the inference results in accordance with the user's emotions. For example, if the user expresses dissatisfaction, the server will re-evaluate the inference results and make adjustments as needed.

[0643] This system also includes a function to perform statistical analysis of emotional data. The server generates insights to improve the overall user experience of the system based on emotional data collected from multiple users.

[0644] As a concrete example, when a user requests a prediction from an AI model using a specific dataset, the server monitors the user's emotions during the process and adjusts the data presentation method and inference results to match the user's emotional state. In this way, the present invention can improve the user experience by simultaneously achieving the secure provision of data models and the optimization of emotion-based interactions.

[0645] The following describes the processing flow.

[0646] Step 1:

[0647] The user logs into the platform via their device and enters their authentication information. The server verifies this information, grants the user access, and initiates a session. Upon successful login, a session ID is sent to the user's device.

[0648] Step 2:

[0649] Users select a dataset from their device and upload it to the platform. The server stores the uploaded data in secure storage and assigns a unique ID to each piece of data. This enables data management.

[0650] Step 3:

[0651] The device collects the user's biometric signals and behavior as emotional data. This data is transmitted to the server in real time while the user is interacting with the device.

[0652] Step 4:

[0653] The server analyzes the received emotional data using an emotion engine to recognize the user's current emotional state. For example, it measures stress levels and concentration levels and uses this information to adjust the interface.

[0654] Step 5:

[0655] When a user performs inference using an AI model, the server executes the model and generates results. These results are then displayed in a format and content that is adjusted according to the recognized emotional state of the user, and then sent to the device.

[0656] Step 6:

[0657] The server records usage history based on the data used and user sentiment, and calculates rewards based on this. Based on the calculation results, it initiates a distribution process to data owners. This maximizes the use value of the data and models while providing sentiment-based feedback.

[0658] (Example 2)

[0659] 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".

[0660] Amidst the demand for secure and efficient information management, there is a need to protect and utilize data while ensuring a fair distribution of rewards based on its use. Furthermore, it is crucial to appropriately adjust system interfaces according to user emotions and provide a user-friendly environment. However, current technology makes it difficult to simultaneously achieve data security, usability, and optimized emotion-based interactions.

[0661] 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.

[0662] In this invention, the server includes means for recording data managed by the information holder in a protected information processing environment; means for identifying the usage status of the information through operation history and distributing rewards to the information holder; means for providing a computational model to others without directly disclosing the contents of the computational model; means for detecting the user's emotions using an emotion analysis device and dynamically adjusting the interface; means for adjusting the inference results produced in accordance with the user's recognized emotions; and means for analyzing emotion information collected from multiple users and generating insights to improve the overall user experience. This enables both data protection and convenience, optimization of emotion-based interactions, and improvement of the user experience.

[0663] An "information holder" refers to an individual or organization that owns the data or information and has the rights to manage and use it.

[0664] An "information processing environment" refers to a technical environment that includes computer systems and network infrastructure for storing, managing, processing, and protecting digital data.

[0665] "Operation history" refers to a collection of information that records the process of data usage and manipulation, and is used to track how data was used.

[0666] A "computational model" refers to an algorithm or analytical framework designed to perform calculations or inferences based on specific data.

[0667] An "emotion analysis device" is a sensor or software tool used to detect a user's emotional state, analyzing biosignals and behavioral data in real time.

[0668] An "interface" refers to the screen or method of operation that a user uses to interact with a computer system or application.

[0669] "Insight" refers to useful knowledge and understanding obtained as a result of data analysis, and is used for decision-making and system improvement.

[0670] This invention is a system aimed at securely managing information and optimizing emotion-based interactions. Embodiments of this system are described below.

[0671] Users first access the platform using a device and upload their data. This data may include health-related information and personal behavioral history. The device collects the user's biometric signals and behavioral data and sends it to a server for analysis. It is recommended to use protocols such as SSL / TLS during this process to enhance security.

[0672] The server stores data submitted by users in a secure information processing environment. This environment utilizes a data storage system (e.g., a common cloud storage service) and ensures data security through encryption. The server tracks the operation history of the stored data and records how the data was used. This allows for the distribution of appropriate rewards to users in exchange for providing data.

[0673] Furthermore, this system is equipped with an emotion analysis device. For example, when a user is experiencing stress, the system can adjust the interface colors and information presentation speed to provide a more relaxing environment. Emotion recognition is performed in real time by an AI algorithm, and the user experience is dynamically improved based on the results.

[0674] Furthermore, the server analyzes sentiment data collected from numerous users to generate insights for improving the overall user experience of the system. These insights are useful for improving the system's interface design and the services it provides. This process may involve utilizing statistical analysis tools and libraries (e.g., analysis libraries for Python or R).

[0675] For example, when a user requests a prediction from a generative AI model using a specific dataset, the server prompts the generative AI model with a question such as, "How should I present the data so that the model's predictions now maintain a relaxed state for the user?" In this way, the system can provide an interaction that is tailored to the user's emotions.

[0676] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0677] Step 1:

[0678] Users access the system via their devices and upload data. Input data includes personal health information and activity logs. The device uses an internet connection to send this data to the cloud and applies encryption protocols (e.g., SSL / TLS) to ensure data security. The output is the data files securely transmitted to the server.

[0679] Step 2:

[0680] The server receives uploaded data and stores it in a secure information processing environment. The input is encrypted data sent from the terminal. The server manages the data using a storage system (e.g., cloud storage) and controls access to the data by performing decryption. The output is a securely stored database entry.

[0681] Step 3:

[0682] The device continuously collects the user's biometric signals and behavioral data. Inputs include real-time heart rate and gesture data acquired from sensors. The device preprocesses this data, converts it to the required format, and sends it to the server. Outputs include biometric and behavioral data in a format suitable for analysis, which is then sent to the server.

[0683] Step 4:

[0684] The server analyzes the data received using an emotion analysis device to recognize the user's emotional state. Inputs include biosignals and behavioral data transmitted from the terminal. The server performs emotion recognition using an AI model and determines stress and relaxation levels using an algorithm. The output is information about the recognized emotional state.

[0685] Step 5:

[0686] The server dynamically adjusts the interface based on the recognized emotional state. The input is the emotional information obtained in step 4. The server generates commands to change the interface settings and sends them to the user's terminal. For example, it can adjust brightness or change display speed. The output is an interface design optimized for the user.

[0687] Step 6:

[0688] The server performs statistical analysis of sentiment data to generate insights that help improve the overall user experience. The input is sentiment data from multiple users. The server aggregates the data and uses data analysis tools to extract trends and patterns. The output generates analysis results and suggestions that help improve the system.

[0689] Step 7:

[0690] When generating prompts, the server incorporates content that takes into account the user's emotional state. The input is a question for the generating AI model. The server creates prompts in a format suitable for the generating AI model, achieving an interaction that matches the user's emotions. The output is an appropriate prompt to be supplied to the AI ​​model.

[0691] (Application Example 2)

[0692] 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".

[0693] The challenge lies in creating a system that can recognize the user's psychological state and reflect it in real time when providing machine learning models for secure data management and reward distribution, as well as offering machine learning models. Furthermore, this is required to improve the customer experience.

[0694] 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.

[0695] In this invention, the server includes means for storing information in a secure information processing device, means for identifying the usage status of information through usage history and distributing rewards to information managers, means for providing machine learning models to external parties without directly disclosing the contents of the models, means for recognizing the user's facial expressions and voice and analyzing their psychological state in real time, and means for dynamically adjusting the displayed content based on the psychological state and presenting it to the user. This not only enables the secure management and provision of data, but also enables the provision of services that are adapted to the user's emotions.

[0696] An "information manager" is an entity that owns information and is responsible for its management and use.

[0697] An "information processing device" is a device that includes a computing environment for collecting, storing, analyzing, and providing information.

[0698] "Usage history" refers to records of the use of information and services, including access time and details of operations performed.

[0699] "Compensation" refers to the profit paid to a provider as payment for the use of information or services.

[0700] A "machine learning model" is a set of algorithms that learn from data and then perform inferences on subsequent data.

[0701] "Means of recognizing facial expressions and voice" refers to technology that analyzes a user's face and voice to determine their psychological state.

[0702] "Means of analyzing psychological state" refers to techniques for analyzing and understanding a user's emotions and mental state.

[0703] "Dynamically adjusting the displayed content" means changing the way information is presented and the content of that information according to the user's psychological state.

[0704] This invention is a system that provides services based on the real-time psychological state of users while securely managing information owned by information managers. The server first stores the data provided by the information manager in a secure information processing device. At this time, the data usage history is recorded and used as a basis for reward distribution. Furthermore, when the server provides machine learning models to external parties, it ensures security by not directly disclosing the contents of the model, but by returning inference results based on requests from external parties.

[0705] The device is equipped with means to recognize the user's facial expressions and voice. This is used to analyze the user's psychological state in real time. This analysis includes facial expression analysis using OpenCV with the camera, and text conversion of voice collected through the microphone using the Google Cloud Speech-to-Text API for emotion analysis. Based on the analysis results, the device's display content is dynamically adjusted to suit the user's psychological state.

[0706] As a concrete example, let's consider its use in a store. If the customer appears stressed during customer service, the terminal will adjust its display content and provide instructions to the provider such as, "They may be seeking understanding; please provide more details."

[0707] Examples of prompts for a generative AI model include the following:

[0708] "Generate insights from customer sentiment data to improve our services. In particular, focus your analysis on product categories with high levels of dissatisfaction and develop action plans based on those findings."

[0709] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0710] Step 1:

[0711] The device captures the user's facial expressions with its camera and acquires them as input data. Next, it uses OpenCV to analyze this facial data and outputs emotional information that indicates the user's psychological state. Specifically, it detects facial landmarks, extracts features, and performs emotion classification.

[0712] Step 2:

[0713] The device collects the user's voice using a microphone and receives the voice data as input. Using the Google Cloud Speech-to-Text API, this voice data is converted into text data, and then analyzed by an emotion analysis engine to output information indicating the user's psychological state. Specifically, it performs a process of converting the voice data into text and adding an emotion indicator.

[0714] Step 3:

[0715] The terminal integrates the emotional information acquired in Step 1 and Step 2. Based on this integrated data, it determines the user's overall psychological state and uses this as input data for dynamically adjusting the displayed content. The output information includes the selection of appropriate recommended services and the content to be displayed.

[0716] Step 4:

[0717] The server receives user psychological state data transmitted from the terminal and uses it as feedback to the provider within the bounds of secure information security. At this time, it outputs the final service content, adjusting the display speed and amount of information presented to match the user's psychological state while viewing the information. Specifically, it queries database information and runs an algorithm to support appropriate responses.

[0718] Step 5:

[0719] Based on server feedback, users optimize the services they provide using a generative AI model. In this process, they create and send prompt messages tailored to their psychological state and receive service improvement suggestions based on those prompts. Specifically, they use a prompt message generation engine to analyze various insights and receive responses.

[0720] 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.

[0721] 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.

[0722] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0723] 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.

[0724] 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.

[0725] 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.

[0726] 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.

[0727] 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.

[0728] 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."

[0729] 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.

[0730] 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.

[0731] 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.

[0732] 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.

[0733] 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.

[0734] 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.

[0735] 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.

[0736] 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.

[0737] 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.

[0738] 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.

[0739] 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.

[0740] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0741] The following is further disclosed regarding the embodiments described above.

[0742] (Claim 1)

[0743] A means of storing information managed by the data owner in a secure computing environment,

[0744] A means of identifying data usage through usage history and distributing rewards to data owners,

[0745] When providing a model to a third party, there are means of providing the service without directly disclosing the contents of the model,

[0746] A system that includes this.

[0747] (Claim 2)

[0748] The system according to claim 1, further comprising means for enabling the data owner to manage billing settings and usage restrictions for the data.

[0749] (Claim 3)

[0750] The system according to claim 1, comprising means for a user to perform inference processing using their own model and securely provide the results to other users.

[0751] "Example 1"

[0752] (Claim 1)

[0753] A means of storing digital data managed by the information owner within a secure information processing device,

[0754] A means of identifying the usage of digital data through operation history and distributing rewards to information owners,

[0755] When providing a mathematical model to a third party, there are means of providing computational processing without directly disclosing the contents of the mathematical model,

[0756] A means to enable data owners to review digital data usage history and manage billing settings and usage restrictions,

[0757] A means by which the user inputs a command to execute a mathematical model and supplies the result,

[0758] A system that includes this.

[0759] (Claim 2)

[0760] The system according to claim 1, characterized in that digital data is assigned a unique identifier and becomes traceable.

[0761] (Claim 3)

[0762] The system according to claim 1, comprising means for a user to perform inference processing using their own mathematical model and securely provide the results to other users.

[0763] "Application Example 1"

[0764] (Claim 1)

[0765] A means of storing information managed by data administrators within a secure information processing environment,

[0766] A means of identifying how information is being used through usage history and distributing rewards to data administrators,

[0767] When providing a model to a third party, there are means of supplying analysis services without directly disclosing the contents of the model,

[0768] A means of analyzing information using an information processing device and presenting proposals for optimizing information distribution and labor,

[0769] ...

[0770] A system that includes this.

[0771] (Claim 2)

[0772] The system according to claim 1, comprising means that enable an information provider to manage pricing and usage restrictions for information.

[0773] (Claim 3)

[0774] The system according to claim 1, comprising means for a user to perform analytical processing using their own model and securely provide the results to other users.

[0775] "Example 2 of combining an emotion engine"

[0776] (Claim 1)

[0777] A means of recording data managed by the information holder within a protected information processing environment,

[0778] A means of identifying the usage status of information through operation history and distributing rewards to the information holders,

[0779] When providing a computational model to others, there are means of providing the service without directly disclosing the contents of the computational model,

[0780] A means for detecting the user's emotions using an emotion analysis device and dynamically adjusting the interface,

[0781] A means of adjusting the inference results produced in accordance with the emotions perceived by the user,

[0782] A means of analyzing emotional information collected from multiple users and generating insights to improve the overall user experience,

[0783] A system that includes this.

[0784] (Claim 2)

[0785] The system according to claim 1, comprising means that the information holder can manage pricing and usage restrictions on the data.

[0786] (Claim 3)

[0787] The system according to claim 1, comprising means for a user to perform prediction processing using their own computational model and to provide the results to other users in a protected manner.

[0788] "Application example 2 when combining with an emotional engine"

[0789] (Claim 1)

[0790] A means of storing information managed by a data administrator within a secure information processing device,

[0791] A means of identifying information usage through usage history and distributing rewards to information managers,

[0792] When providing machine learning models to external parties, there are methods for providing the service without directly disclosing the model's contents, and

[0793] A means of recognizing the user's facial expressions and voice and analyzing their psychological state in real time,

[0794] A means of dynamically adjusting and presenting display content to the user based on their psychological state,

[0795] A system that includes this.

[0796] (Claim 2)

[0797] The system according to claim 1, further comprising means for an information administrator to manage billing settings and usage restrictions for information.

[0798] (Claim 3)

[0799] The system according to claim 1, comprising means for a user to perform inference processing using their own machine learning model and securely provide the results to other users. [Explanation of Symbols]

[0800] 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 information managed by the data owner in a secure computing environment, A means of identifying data usage through usage history and distributing rewards to data owners, When providing a model to a third party, there are means of providing the service without directly disclosing the contents of the model, A system that includes this.

2. The system according to claim 1, further comprising means that enable the data owner to manage billing settings and usage restrictions for the data.

3. The system according to claim 1, comprising means for a user to perform inference processing using their own model and securely provide the results to other users.

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