system

Abstract

Provide a system. [Solution] A means for updating the settings of an IoT device using generative artificial intelligence; A means of profiling data from IoT devices using blockchain technology; means for automatically collecting data from the IoT devices and generating a data report using generative artificial intelligence; A system including:

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] As IoT devices become increasingly decentralized, it becomes more difficult for them to communicate with each other, and device configuration changes and data collection and analysis are often performed manually, resulting in time-consuming and labor-intensive tasks. Furthermore, the lack of transparency in device operation history and data changes makes it difficult to accurately identify the cause of problems during troubleshooting. Maintaining consistent device configurations in an environment where numerous devices are interconnected is also a challenge. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means. A means is provided for automatically updating IoT device settings using generative artificial intelligence. This means allows users to easily change device settings. A means is also provided for profiling IoT device data using blockchain technology and managing the device's data change history in a transparent and consistent manner. Furthermore, a means is provided for automatically collecting data from the IoT devices and generating data reports using generative artificial intelligence, thereby improving the efficiency of data analysis. The above means improve the connectivity of IoT devices, increase infrastructure stability, and increase corporate value.

[0006] "Generative AI" is AI that has the ability to automatically generate new information and content based on user input information and existing data.

[0007] An "IoT device" is a sensor or piece of equipment that communicates with other devices and systems via the Internet and collects and transmits data.

[0008] The "means for updating settings" is a function for changing setting information to adjust the operation and performance of the device.

[0009] "Blockchain technology" is a distributed database system that is extremely difficult to tamper with by linking multiple data blocks in a series, and is a technology that ensures the transparency and reliability of information.

[0010] "Profiling" is a method of collecting and analyzing specific behavioral data of devices and users, and recording their characteristics and history.

[0011] A "data report" is a report that analyzes collected data and summarizes the results in an easy-to-understand format.

[0012] "Transparency" means that the origin of the data and its change history are clearly indicated, allowing third parties to easily verify its authenticity.

[0013] "Consistency" refers to a state in which data and configuration information are maintained without contradiction and continuity is maintained.

[0014] "Automation" is the ability of a system to operate autonomously without human intervention. [Brief explanation of the drawings]

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

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

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

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

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

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

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

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

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

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0032] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

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

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0036] This invention is a system that combines generative artificial intelligence and blockchain technology to automate configuration updates, data profiling, and data analysis of IoT devices, improving connectivity between devices.

[0037] System configuration

[0038] Generative artificial intelligence (AI)

[0039] Generative AI automatically generates new configuration information and data analysis results based on user-provided input information and existing device data. Specifically, the AI ​​engine periodically analyzes device data and proposes and implements configuration updates as needed.

[0040] Blockchain technology

[0041] Blockchain technology allows IoT device data to be registered in a distributed database, ensuring transparency and consistency. This technology ensures that device operation history and configuration change history are tamper-proof, facilitating troubleshooting and device management.

[0042] Program processing overview

[0043] Device Profiling

[0044] 1. A user enters the necessary information into the system to register a new IoT device.

[0045] 2. The server receives this information and uses blockchain technology to profile the device, specifically registering the device ID, registration timestamp, and other initial data on the blockchain.

[0046] Get Device Data

[0047] 1. The server uses a specific device ID to retrieve the latest data about that device from the blockchain.

[0048] 2. The acquired data is used to check the status and history of the device.

[0049] Update device settings

[0050] 1. The user enters new device settings.

[0051] 2. The device uses generative artificial intelligence to analyze the settings information entered and generate the optimal settings.

[0052] 3. The server registers the generated configuration information on the blockchain and updates the device configuration.

[0053] Automatic generation of data reports

[0054] 1. The server periodically retrieves the latest device data from the blockchain.

[0055] 2. The device uses generative artificial intelligence to analyze this data and automatically generate a data report.

[0056] 3. The data report is provided to the user or used for further analysis within the system.

[0057] Specific examples

[0058] Device Profiling Example

[0059] 1. The user registers a temperature sensor by entering information such as the device name, location, and type.

[0060] 2. The server registers this information on the blockchain and assigns a device ID.

[0061] Example of updating device settings

[0062] 1. The user enters new configuration information to set the temperature sensor threshold to 25 degrees.

[0063] 2. The device uses generative artificial intelligence to evaluate whether the settings are appropriate and generate optimal settings.

[0064] 3. The server registers this configuration information on the blockchain and updates the device settings.

[0065] Example of automatic data report generation

[0066] 1. The server retrieves the latest temperature sensor data from the blockchain.

[0067] 2. The device uses generative artificial intelligence to analyze this data and automatically generate a statistical report of temperature fluctuations.

[0068] 3. The generated report is provided to the user or used for further data analysis.

[0069] This system improves the connectivity of IoT devices, making it easier for users to manage devices and analyze data. In addition, the use of blockchain technology makes the device's operational history transparent, enabling highly reliable data management.

[0070] The processing flow will be explained below.

[0071] Device Profiling

[0072] Step 1:

[0073] The user enters registration information for a new IoT device (e.g., device name, location, type, etc.).

[0074] Step 2:

[0075] The server receives the entered information and generates a device ID.

[0076] Step 3:

[0077] The server creates a payload containing the generated device ID and registration information and sends an HTTP POST request to the Blockchain API.

[0078] Step 4:

[0079] The server receives the response from the Blockchain API and verifies whether the device was successfully registered.

[0080] Get Device Data

[0081] Step 1:

[0082] The server sends an HTTP GET request using a specific device ID to retrieve data about that device from the blockchain.

[0083] Step 2:

[0084] The server receives the response from the blockchain API and parses the data in JSON format.

[0085] Step 3:

[0086] The server analyzes the device data and provides it to applications and users.

[0087] Update device settings

[0088] Step 1:

[0089] The user inputs a new setting value (e.g., threshold value).

[0090] Step 2:

[0091] Based on the settings entered on the device, the device sends a request to a generative artificial intelligence API to generate the optimal settings.

[0092] Step 3:

[0093] The device receives the setting values ​​generated in response from the generative artificial intelligence API.

[0094] Step 4:

[0095] The server creates a payload containing the new configuration values ​​and sends an update device configuration request to the blockchain API.

[0096] Step 5:

[0097] The server receives the response from the Blockchain API and verifies whether the configuration update was successful.

[0098] Automatic generation of data reports

[0099] Step 1:

[0100] The server sends an HTTP GET request using the specific device ID to retrieve the latest device data from the blockchain.

[0101] Step 2:

[0102] The server receives the response from the blockchain API and parses the data in JSON format.

[0103] Step 3:

[0104] The device data analyzed by the terminal is sent to a generative artificial intelligence API, which then sends a request to generate a data report.

[0105] Step 4:

[0106] The device receives the data report generated in response from the generative artificial intelligence API.

[0107] Step 5:

[0108] The server provides the generated data report to the user or uses it for further data analysis within the system.

[0109] Specific examples

[0110] Device Profiling Example

[0111] Step 1:

[0112] The user enters the temperature sensor registration information (e.g., name, location, type).

[0113] Step 2:

[0114] The server receives the entered information and generates a device ID.

[0115] Step 3:

[0116] The server creates a payload containing the device ID and registration information and sends an HTTP POST request to the Blockchain API.

[0117] Step 4:

[0118] The server receives the response from the Blockchain API and verifies whether the device was successfully registered.

[0119] Example of updating device settings

[0120] Step 1:

[0121] The user enters a new threshold of 25 degrees for the temperature sensor.

[0122] Step 2:

[0123] The device sends a request to the artificial intelligence API for generating the input threshold value, and generates the optimal settings.

[0124] Step 3:

[0125] The device receives the setting values ​​generated in response from the generative artificial intelligence API.

[0126] Step 4:

[0127] The server creates a payload containing the new configuration values ​​and sends an update device configuration request to the blockchain API.

[0128] Step 5:

[0129] The server receives the response from the Blockchain API and verifies whether the configuration update was successful.

[0130] Example of automatic data report generation

[0131] Step 1:

[0132] The server sends an HTTP GET request using the temperature sensor ID to retrieve the latest device data from the blockchain.

[0133] Step 2:

[0134] The server receives the response from the blockchain API and parses the data in JSON format.

[0135] Step 3:

[0136] The device data analyzed by the terminal is sent to a generative artificial intelligence API, which then sends a request to generate a data report.

[0137] Step 4:

[0138] The device receives the data report generated in response from the generative artificial intelligence API.

[0139] Step 5:

[0140] The server provides the generated data report to the user or uses it for further data analysis within the system.

[0141] Example 1

[0142] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0143] In conventional IoT device management systems, device configuration updates, data profiling, and data analysis are performed manually, resulting in inefficiency and insufficient collaboration between devices. Furthermore, reliability is low because it is difficult to prevent data tampering and ensure consistency. Furthermore, there are limited ways to regularly check device status and data analysis results.

[0144] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0145] In this invention, the server includes: means for updating IoT device settings using generative artificial intelligence; means for profiling IoT device data using blockchain technology; means for automatically collecting IoT device data and generating data reports using generative artificial intelligence; means for a user to input device information, which the server receives and registers in the blockchain; means for the server to analyze data acquired from the device and generate optimal setting values; and means for the terminal to provide data analysis results using generative artificial intelligence. This automates IoT device setting updates, data profiling, and data analysis, improves collaboration between devices, prevents data tampering, and ensures consistency. Furthermore, it becomes possible to periodically grasp the device status and data analysis results.

[0146] "Generative AI" is an AI technology that automatically generates new configuration information and data analysis results based on input information provided by users and existing device data.

[0147] An "IoT device" is a physical device or sensor that is connected to the Internet and capable of sending and receiving data.

[0148] The "means for updating settings" is a method that uses generative artificial intelligence to analyze the setting information of an IoT device, generate optimal setting values ​​as needed, and automatically change the device settings.

[0149] "Blockchain technology" is a technology for recording data transaction history securely and in a decentralized manner, and has the advantage of preventing data tampering and unauthorized access.

[0150] "Means of data profiling" refers to a method of registering initial information about IoT devices using blockchain technology to identify and monitor the devices.

[0151] "Means for automatically collecting data" refers to a method for acquiring data from IoT devices periodically or in real time and sending it to a server.

[0152] The "means for generating data reports" refers to a method of analyzing collected data using generative artificial intelligence and automatically creating reports in a format that is easy for users to understand.

[0153] A "user" is a person or company that operates the system, inputs information, or receives reports.

[0154] The "server" is a central control unit that receives data from IoT devices, analyzes it, and registers it on the blockchain.

[0155] A "terminal" is a device operated by a user that uses generative artificial intelligence to analyze data and provide results.

[0156] "Profiling" is a procedure for compiling and managing unique information about IoT devices.

[0157] "Transparency" means that data is not tampered with and correct information can always be referenced.

[0158] "Means for managing data change history" refers to a method that uses blockchain technology to record the history of data changes and make them available for later review.

[0159] The "means of ensuring consistency" refers to a method of consistently managing the device's configuration status by registering configuration information generated using generative artificial intelligence on the blockchain.

[0160] "Data analysis results" are information and reports generated after analyzing acquired device data using generative artificial intelligence.

[0161] This invention is a system that combines generative artificial intelligence and blockchain technology to automate configuration updates, data profiling, and data analysis of IoT devices, improving connectivity between devices.

[0162] System configuration

[0163] 1. Generative Artificial Intelligence

[0164] Generative AI automatically generates new configuration information and data analysis results based on input information provided by users and existing device data. Specifically, the AI ​​engine periodically analyzes device data and proposes and implements configuration updates as needed. This AI engine applies natural language processing and machine learning technologies (e.g., TENSORFLOW (registered trademark) and PyTorch).

[0165] 2. Blockchain Technology

[0166] Blockchain technology registers IoT device data in a distributed database, ensuring transparency and consistency. This technology allows device operation history and configuration change history to be stored without tampering, making troubleshooting and device management easier. Blockchain technology uses existing distributed ledger technologies (e.g., Hyperledger Fabric and Ethereum).

[0167] Example of operation

[0168] Device Profiling

[0169] 1. The user enters the information required to register a new IoT device into the system. For example, if a temperature sensor is to be installed in the "living room," the user enters the device name, location, and type.

[0170] 2. The server receives this information and uses blockchain technology to profile the device, specifically registering the device ID, registration timestamp, and other initial data on the blockchain.

[0171] Update device settings

[0172] 1. A user enters a new device setting, for example, setting a temperature sensor threshold to 25 degrees.

[0173] 2. The device uses generative artificial intelligence to analyze the input setting information, evaluate whether the settings are appropriate, and generate optimal setting values.

[0174] 3. The server registers the generated configuration information on the blockchain and updates the device configuration.

[0175] Automatic generation of data reports

[0176] 1. The server retrieves the latest temperature sensor data from the blockchain. This process is performed periodically.

[0177] 2. The device uses generative artificial intelligence to analyze this data and automatically generate a statistical report of temperature fluctuations.

[0178] 3. The generated report is provided to the user or used for further analysis within the system.

[0179] Specific prompt examples

[0180] Prompt: "I want to update the temperature sensor settings on my IoT device. Set the new threshold to 25 degrees."

[0181] This system makes it easier for users to manage IoT devices and analyze data, improving the connectivity between devices. In addition, the use of blockchain technology ensures data transparency and consistency, enabling reliable data management.

[0182] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0183] Device profiling process steps

[0184] Step 1

[0185] The user enters registration information for a new IoT device (e.g., device name, location, device type).

[0186] Input: Device information (Device name: Temperature sensor, Location: Living room, Type: Temperature sensor)

[0187] What happens: A user enters information through a web interface or a mobile app.

[0188] Step 2

[0189] The server receives the entered information and generates a device ID.

[0190] Input: Device information entered by the user

[0191] Data processing: The server analyzes the device information and generates a unique device ID.

[0192] Output: Generated device ID

[0193] Step 3

[0194] The server registers the device ID, registration timestamp, and initial data on the blockchain.

[0195] Input: Device ID, registration timestamp, initial data (e.g., initial settings for a temperature sensor)

[0196] Data calculation: Calling blockchain API and recording device information on the distributed ledger

[0197] Output: Device profile registered on the blockchain

[0198] Steps for updating device settings

[0199] Step 1

[0200] The user enters a new device setting (e.g., a temperature sensor threshold).

[0201] Input: New device setting (e.g. threshold 25 degrees)

[0202] Specific operation: The user inputs a new threshold value on the setting input screen and presses the send button.

[0203] Step 2

[0204] The terminal receives the entered setting information and analyzes it.

[0205] Input: New device configuration information

[0206] Data processing: The device receives the setting information and converts it into the appropriate data format.

[0207] Output: Parsable configuration data

[0208] Step 3

[0209] The terminal uses generative artificial intelligence to generate optimal setting values ​​based on the input setting information.

[0210] Input: Parsable configuration data

[0211] Data calculation: Generative AI analyzes past data and current conditions to calculate optimal settings

[0212] Output: Recommended optimal settings

[0213] Step 4

[0214] The server registers the generated configuration information on the blockchain and updates the device configuration.

[0215] Input: Recommended optimal settings

[0216] Data calculation: Register new configuration information through the blockchain API

[0217] Output: The latest device configuration recorded on the blockchain

[0218] Processing steps for automatic generation of data reports

[0219] Step 1

[0220] The server retrieves the latest device data from the blockchain.

[0221] Input: Device ID

[0222] Data computation: Send a data retrieval request to the blockchain network

[0223] Output: Latest device data

[0224] Step 2

[0225] The device uses generative artificial intelligence to analyze the acquired data and automatically generate a data report.

[0226] Input: Latest device data

[0227] Data Calculation: Generative AI analyzes data and formats statistics and trends into reports

[0228] Output: Auto-generated data report

[0229] Step 3

[0230] The server provides the generated report to the user.

[0231] Input: Auto-generated data report

[0232] What happens: The server displays the report on a dashboard or emails it to the user.

[0233] Output: User-accessible data reports

[0234] (Application example 1)

[0235] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0236] Traditionally, configuring and optimizing factory robots required a lot of manual work and time, resulting in reduced productivity. In addition, it was difficult to ensure transparency and consistency in IoT device data management, leading to issues with data reliability. Furthermore, automating device configuration change history and behavior analysis required complex processes and human intervention, making troubleshooting and efficient operation difficult.

[0237] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0238] In this invention, the server includes means for updating IoT device settings using generative artificial intelligence, means for profiling IoT device data using blockchain technology, means for automatically collecting IoT device data and generating data reports using generative artificial intelligence, and means for configuring and optimizing factory robot operations. This enables automation of factory robot configuration and optimization, ensures transparency and consistency in IoT device data management, and enables efficient and reliable device operation.

[0239] "Generative AI" refers to AI that automatically generates new configuration information and data analysis results based on input information provided by the user and existing device data.

[0240] "IoT devices" are various devices that are interconnected via the Internet and are capable of collecting and communicating data.

[0241] "Means for updating settings" refers to means for optimizing the settings of IoT devices using generative artificial intelligence and automatically applying the latest settings.

[0242] "Blockchain technology" is a technology that builds a distributed database of data and ensures its transparency and consistency.

[0243] "Means for profiling data" refers to a means for using blockchain technology to perform detailed analysis of IoT device data and evaluate the device's condition and performance.

[0244] "Means for generating data reports" refers to means for analyzing data collected from IoT devices using generative artificial intelligence and outputting the results in the form of reports on a regular basis.

[0245] A "factory robot" is a robot designed to automatically perform various tasks in a factory.

[0246] "Means for setting and optimizing operation" refers to means for optimizing the operation settings of factory robots and adjusting them so that they can perform work efficiently and effectively.

[0247] This invention is a system that configures and optimizes the operation of factory robots and ensures transparency and consistency in data management of IoT devices. This system is composed of a combination of generative artificial intelligence and blockchain technology, and uses the following hardware and software:

[0248] Hardware and software used

[0249] Hardware: Various IoT sensors installed in the factory (temperature sensors, pressure sensors, speed sensors, etc.), factory robots that change settings

[0250] software:

[0251] Generative AI: Python AI libraries (e.g., TensorFlow)

[0252] Blockchain: A custom blockchain implemented in Python

[0253] Data analysis: A data processing system (e.g., Pandas, Numpy) for analyzing data streams from virtual sensors in real time.

[0254] System processing overview

[0255] The server automatically collects data from IoT sensors in the factory and uses generative artificial intelligence to generate optimal operating settings, which are then stored transparently using blockchain technology.

[0256] The server first collects sensor data and analyzes it in real time. Based on this data, generative artificial intelligence automatically generates optimal robot operation settings, enabling automated factory robot configuration and optimization.

[0257] The generated configuration information and analysis results are stored using blockchain technology, ensuring data transparency and consistency of change history. This data management method enables efficient and reliable device operation.

[0258] Specific examples

[0259] A system that optimizes the motor speed and grip force of a robot in a factory continuously collects data from sensors and uses that data to calculate the optimal settings using generative artificial intelligence. Specifically, the AI ​​takes the current sensor values ​​and generates the appropriate settings. This new setting is stored on the blockchain, ensuring the device's authenticity and transparency.

[0260] Example prompts to input to the generative AI model

[0261] "Calculate the optimal motor speed and grip force for this system based on the latest sensor data."

[0262] Specific prompt:

[0263] "Calculate the optimal motor speed and grip force for the robot based on the following sensor data:

[0264] {

[0265] 'current_speed': 1.0,

[0266] 'current_grip': 1.0

[0267] }"

[0268] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0269] Step 1:

[0270] The server collects data in real time from various IoT sensors in the factory. The input from the sensors is various types of data such as temperature, pressure, speed, etc. The server acquires these inputs and stores them in a database.

[0271] Step 2:

[0272] The server inputs the collected sensor data into a generative AI model, which analyzes the data and generates optimal motion settings. This generative AI calculates optimal settings for motor speed and grip force, for example.

[0273] Step 3:

[0274] The terminal receives the optimal operational settings output by the generative AI and transmits the results to the factory robots, where the input is the setting value output by the AI ​​model and the output is the new operational setting value applied to the robot.

[0275] Step 4:

[0276] The server registers the generated operational configuration and related sensor data on the blockchain. The input is the operational configuration and sensor data, and the output is the data stored on the blockchain. This process ensures data transparency and a change history.

[0277] Step 5:

[0278] The server periodically retrieves data from the blockchain and generates a data report, where the input is the data retrieved from the blockchain and the output is the data report provided to the user.

[0279] Step 6:

[0280] Users can refer to the generated data report to check the operation status of the factory robot and the optimization status of the settings, and can make any further adjustments necessary based on this.

[0281] These processing steps enable automated configuration and optimization of factory robots, and improve transparency and reliability of data management.

[0282] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0283] This invention relates to an IoT device management system that combines generative artificial intelligence, blockchain technology, and an emotion engine to automate the process of updating IoT device settings, data profiling, and automatic data report generation, and also enables device settings adjustment and report display based on the user's emotional state.

[0284] System configuration

[0285] Generative artificial intelligence (AI)

[0286] Generative AI automatically generates new configuration information and data analysis results based on user-provided input and existing device data. The AI ​​engine periodically analyzes device data and proposes and implements configuration updates as needed.

[0287] Blockchain technology

[0288] Blockchain technology allows IoT device data to be registered in a distributed database, ensuring transparency and consistency. This technology ensures that device operation history and configuration change history are tamper-proof, facilitating troubleshooting and device management.

[0289] Emotion Engine

[0290] The emotion engine recognizes the user's emotional state in real time and provides that information to the system, which can then dynamically adjust the settings of IoT devices and how data reports are displayed based on the user's emotional state.

[0291] Program processing overview

[0292] Device Profiling

[0293] 1. A user enters the necessary information to register a new IoT device into the system (e.g., device name, location, type, etc.).

[0294] 2. The server receives this information and generates a device ID.

[0295] 3. The server creates a payload containing the generated device ID and registration information and sends an HTTP POST request to the Blockchain API.

[0296] 4. The server receives the response from the blockchain API and verifies whether the device was successfully registered.

[0297] Get Device Data

[0298] 1. The server sends an HTTP GET request using a specific device ID to retrieve data about that device from the blockchain.

[0299] 2. The server receives the response from the blockchain API and parses the data in JSON format.

[0300] 3. The server provides the analyzed device data to applications and users.

[0301] Update device settings

[0302] 1. The user inputs a new setting value (e.g., threshold value).

[0303] 2. Based on the settings entered by the device, the device sends a request to the generative AI API to generate the optimal settings.

[0304] 3. The device receives the setting values ​​generated in response from the generative AI API.

[0305] 4. The server creates a payload containing the new configuration values ​​and sends an update device configuration request to the blockchain API.

[0306] 5. The server receives the response from the Blockchain API and verifies whether the configuration update was successful.

[0307] Automatic generation of data reports

[0308] 1. The server sends an HTTP GET request to retrieve the latest device data from the blockchain using the specific device ID.

[0309] 2. The server receives the response from the blockchain API and parses the data in JSON format.

[0310] 3. The device analyzes the device data and sends it to the generative AI API, which then sends a request to generate a data report.

[0311] 4. The device receives the generated data report in response from the generative AI API.

[0312] 5. The server provides the generated data report to the user or uses it for further data analysis within the system.

[0313] Use of emotion engine

[0314] 1. The emotion engine recognizes the user's emotional state in real time.

[0315] 2. The emotion engine sends the recognized emotion data to the system, which dynamically adjusts the device settings and how data reports are displayed.

[0316] 3. The server registers the emotion data on the blockchain to ensure transparency and consistency.

[0317] Specific examples

[0318] Device Profiling Example

[0319] 1. The user enters the temperature sensor registration information (e.g., name, location, type).

[0320] 2. The server receives the entered information and generates a device ID.

[0321] 3. The server creates a payload containing the device ID and registration information and sends an HTTP POST request to the Blockchain API.

[0322] 4. The server receives the response from the blockchain API and verifies whether the device was successfully registered.

[0323] Example of updating device settings

[0324] 1. The user enters a new threshold of 25 degrees for the temperature sensor.

[0325] 2. The device sends a request to the AI ​​API for the input thresholds to generate the optimal settings.

[0326] 3. The device receives the setting values ​​generated in response from the generative AI API.

[0327] 4. The server creates a payload containing the new configuration values ​​and sends an update device configuration request to the blockchain API.

[0328] 5. The server receives the response from the Blockchain API and verifies whether the configuration update was successful.

[0329] Example of automatic data report generation

[0330] 1. The server sends an HTTP GET request using the temperature sensor ID to retrieve the latest device data from the blockchain.

[0331] 2. The server receives the response from the blockchain API and parses the data in JSON format.

[0332] 3. The device analyzes the device data and sends it to the generative AI API, which then sends a request to generate a data report.

[0333] 4. The device receives the generated data report in response from the generative AI API.

[0334] 5. The server provides the generated data report to the user or uses it for further data analysis within the system.

[0335] Example of using the emotion engine

[0336] 1. The emotion engine recognizes the user's emotions in real time and sends the emotion data to the system.

[0337] 2. Your device will adjust device settings and how data reports are displayed based on emotion data.

[0338] 3. The server registers the emotion data on the blockchain to ensure transparency and consistency.

[0339] This system improves the connectivity of IoT devices, making it easier for users to manage devices and analyze data. Furthermore, the use of an emotion engine enables optimal operation based on the user's emotional state, improving the user experience. Furthermore, the use of blockchain technology makes the device's operational history transparent, enabling reliable data management.

[0340] The processing flow will be explained below.

[0341] Device Profiling

[0342] Step 1:

[0343] The user enters the registration information for a new IoT device (e.g., device name, location, type, etc.) into the system's input form.

[0344] Step 2:

[0345] The server takes the information provided by the user and generates a new device ID.

[0346] Step 3:

[0347] The server creates a payload containing the device ID and the registration information provided by the user and sends an HTTP POST request to the Blockchain API.

[0348] Step 4:

[0349] The server receives the response sent from the Blockchain API and verifies whether the device was successfully registered.

[0350] Get Device Data

[0351] Step 1:

[0352] The server sends an HTTP GET request using a specific device ID to retrieve data about that device from the blockchain.

[0353] Step 2:

[0354] The server receives the response sent from the blockchain API and parses the data in JSON format.

[0355] Step 3:

[0356] The server converts the parsed device data into a format for presentation to the application user.

[0357] Step 4:

[0358] Access, view, and manipulate device data provided by the user.

[0359] Update device settings

[0360] Step 1:

[0361] The user enters new device setting values ​​(e.g., thresholds) into the system's input form.

[0362] Step 2:

[0363] Based on the settings entered by the user, the device sends a request to a generative artificial intelligence API to generate the optimal settings.

[0364] Step 3:

[0365] The device receives the setting values ​​generated in the response sent from the generative artificial intelligence API.

[0366] Step 4:

[0367] The server creates a payload containing the new configuration values ​​and sends an update device configuration request to the blockchain API.

[0368] Step 5:

[0369] The server receives the response sent from the Blockchain API and checks whether the configuration update was completed successfully.

[0370] Automatic generation of data reports

[0371] Step 1:

[0372] The server sends an HTTP GET request using the specific device ID to retrieve the latest device data from the blockchain.

[0373] Step 2:

[0374] The server receives the response sent from the blockchain API and parses the data in JSON format.

[0375] Step 3:

[0376] The device data analyzed by the terminal is sent to a generative artificial intelligence API, which then sends a request to generate a data report.

[0377] Step 4:

[0378] The device receives the data report generated in response from the generative artificial intelligence API.

[0379] Step 5:

[0380] The server converts the generated data report into a format for presentation to the user.

[0381] Use of emotion engine

[0382] Step 1:

[0383] The emotion engine analyzes the user's facial expressions, voice, etc. to recognize the user's emotional state in real time.

[0384] Step 2:

[0385] The emotion engine transmits the recognized emotion data to the server.

[0386] Step 3:

[0387] The server uses the received emotion data to dynamically adjust device settings and how data reports are displayed accordingly.

[0388] Step 4:

[0389] The server registers the emotion data on the blockchain, ensuring transparency and consistency.

[0390] Specific examples

[0391] Device Profiling Example

[0392] Step 1:

[0393] The user enters the temperature sensor registration information (e.g., name, location, and type) into the system's input form.

[0394] Step 2:

[0395] The server takes the information provided by the user and generates a new device ID.

[0396] Step 3:

[0397] The server creates a payload containing the device ID and registration information and sends an HTTP POST request to the Blockchain API.

[0398] Step 4:

[0399] The server receives the response sent from the Blockchain API and verifies whether the device was successfully registered.

[0400] Example of updating device settings

[0401] Step 1:

[0402] The user enters the new temperature sensor threshold of 25 degrees into the system's input form.

[0403] Step 2:

[0404] The device sends a request to the artificial intelligence API for generating the input threshold value, and generates the optimal settings.

[0405] Step 3:

[0406] The device receives the setting values ​​generated in the response sent from the generative artificial intelligence API.

[0407] Step 4:

[0408] The server creates a payload containing the new configuration values ​​and sends an update device configuration request to the blockchain API.

[0409] Step 5:

[0410] The server receives the response sent from the Blockchain API and checks whether the configuration update was completed successfully.

[0411] Example of automatic data report generation

[0412] Step 1:

[0413] The server sends an HTTP GET request using the temperature sensor ID to retrieve the latest device data from the blockchain.

[0414] Step 2:

[0415] The server receives the response sent from the blockchain API and parses the data in JSON format.

[0416] Step 3:

[0417] The device data analyzed by the terminal is sent to a generative artificial intelligence API, which then sends a request to generate a data report.

[0418] Step 4:

[0419] The device receives the data report generated in response from the generative artificial intelligence API.

[0420] Step 5:

[0421] The server converts the generated data report into a format for presentation to the user.

[0422] Example of using the emotion engine

[0423] Step 1:

[0424] The emotion engine recognizes the user's emotions in real time and transmits the emotion data to the server.

[0425] Step 2:

[0426] Your device will adjust device settings and how data reports are displayed based on your emotional data.

[0427] Step 3:

[0428] The server registers the emotion data on the blockchain, ensuring transparency and consistency.

[0429] Example 2

[0430] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0431] As the number of IoT devices increases, managing them and updating their settings becomes more complex. Furthermore, there is an increasing demand for device behavior and display methods that take into account the user's emotional state. Furthermore, while it is important to ensure the transparency and integrity of device data, no effective system exists to address these challenges. To solve these problems, an innovative system combining generative artificial intelligence, blockchain technology, and an emotion engine is needed.

[0432] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0433] In this invention, the server includes means for updating IoT device settings using generative artificial intelligence, means for profiling IoT device data using blockchain technology, means for automatically collecting IoT device data and generating data reports using generative artificial intelligence, means for recognizing a user's emotional state in real time using an emotion engine and dynamically adjusting the IoT device settings and display of the data report, and means for dynamically adjusting the display method of the IoT device settings and data report based on the user's emotional state. This enables optimal device settings and data report display according to the user's emotional state, while ensuring the transparency and consistency of device data.

[0434] "Generative AI" is an AI technology that automatically generates new configuration information and data analysis results based on input information provided by users and existing device data.

[0435] "Blockchain technology" is a technology that registers data in a distributed database and keeps operation history and setting change history untampered in order to ensure data transparency and consistency.

[0436] The "emotion engine" is a technology that recognizes a user's emotional state in real time and provides that information to the system, dynamically adjusting device settings and the way data reports are displayed based on the user's emotional state.

[0437] An "IoT device" is a physical device that can communicate with other devices and systems over the Internet.

[0438] "Profiling" is the process of collecting data about a specific target (in this case, an IoT device) and analyzing its characteristics and behavioral patterns.

[0439] A "data report" is a report generated based on collected and analyzed data, often including visual graphs and statistics.

[0440] "Payload" refers to the actual portion of data exchanged between systems, that is, the information actually sent from the sender to the receiver.

[0441] "Unique Device ID" means a unique identifier used to distinguish a particular device from all other devices.

[0442] An "HTTP request" is a request sent from a client to a server when accessing a resource on the web, and includes methods such as GET and POST.

[0443] MODE FOR CARRYING OUT THE INVENTION

[0444] This invention relates to an IoT device management system that combines generative artificial intelligence, blockchain technology, and an emotion engine to automate the process of updating IoT device settings, data profiling, and automatic data report generation, and also enables device settings adjustment and report display based on the user's emotional state.

[0445] Hardware and software used

[0446] The server is the hardware that handles the main processing of the system. It runs multiple services, profiling device data, updating settings, and generating data reports.

[0447] A terminal is a device (e.g., PC, smartphone, tablet) through which a user interacts with the system. The terminal receives input from the user and sends requests to the server or the generative AI API based on that input.

[0448] Generative AI is software that automatically generates new configuration information and data analysis results based on input information provided by the user and existing device data.

[0449] Blockchain technology is used to ensure transparency and consistency of devices by registering device data and operation history in a distributed database.

[0450] An emotion engine is software that recognizes a user's emotional state in real time and provides that information to a system that can dynamically adjust the settings of IoT devices and the way data reports are displayed based on the user's emotional state.

[0451] System processing overview and specific examples

[0452] Device profiling examples

[0453] The user enters the temperature sensor's registration information (e.g., name, location, and type). The server receives the entered information and generates a unique device ID. The server then creates a payload containing the generated device ID and registration information and sends an HTTP POST request to the blockchain API. The server then receives a response from the blockchain API and confirms whether the device has been successfully registered.

[0454] Example of updating device settings

[0455] The user inputs a new threshold value for the temperature sensor (e.g., 25 degrees). The device sends a request to the generative AI API based on the input threshold value to generate optimal settings. The device receives the generated setting value in the response from the generative AI API and sends it to the server. The server creates a payload containing the new setting value and sends a device setting update request to the blockchain API. Finally, the server receives the response from the blockchain API and confirms that the setting update was completed successfully.

[0456] Example of automatic generation of data reports

[0457] The server uses the temperature sensor ID to send an HTTP GET request to retrieve the latest device data from the blockchain. The server receives the response from the blockchain API and parses the data in JSON format. The device then sends the parsed device data to the generative AI API and sends a request to generate a data report. The device then receives the generated data report in the response from the generative AI API and provides it to the server. The server then provides the generated data report to the user or uses it for further data analysis within the system.

[0458] Examples of using the emotion engine

[0459] The emotion engine recognizes users' emotions in real time and transmits the emotion data to the system. The device adjusts device settings and data report display methods based on the emotion data. The server registers the emotion data on the blockchain to ensure transparency and consistency.

[0460] Prompt Sentence Examples

[0461] "Analyze the following device data and generate a data report: Temperature sensor ID: 123, Temperature: 25°C, Humidity: 60%"

[0462] This invention allows users to easily manage IoT devices and analyze data. Furthermore, the use of an emotion engine enables optimal operation according to the user's emotional state, improving the user experience. Furthermore, the use of blockchain technology makes the device's operational history transparent, enabling highly reliable data management.

[0463] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0464] Device Profiling

[0465] Step 1:

[0466] The user accesses the system's UI and enters information about the new IoT device (e.g., device name, location, and type).

[0467] Input: Device information such as device name, location, and type

[0468] Output: User input information

[0469] What happens: A user navigates through a form, filling in the fields and finally clicking the submit button.

[0470] Step 2:

[0471] The server receives input information from the user.

[0472] Input: User input information

[0473] Output: A set of information to generate a device ID

[0474] What it does: The server stores the received information in a temporary database, then generates a unique device ID based on the entered data.

[0475] Step 3:

[0476] The server generates a unique device ID.

[0477] Input: User input information

[0478] Output: Unique device ID

[0479] What happens: The server uses a UUID (Universal Unique Identifier) ​​generator to generate a unique ID.

[0480] Step 4:

[0481] The server combines the generated device ID and the user's input information into a payload and sends an HTTP POST request to the blockchain API.

[0482] Input: Device ID, user input information

[0483] Output: A request to the Blockchain API

[0484] What happens: The server creates a new payload and sends an HTTP POST request to the blockchain API.

[0485] Step 5:

[0486] The server receives the response from the Blockchain API and confirms that the device was successfully registered.

[0487] Input: Response from the Blockchain API

[0488] Output: Confirmation of registration result

[0489] Specific operation: The server checks the response status code to see if the registration was successful. If successful, it saves the device information in the database.

[0490] Update device settings

[0491] Step 1:

[0492] The user inputs new device setting values ​​(e.g., thresholds) on the system setting screen.

[0493] Input: New setting value (e.g. threshold)

[0494] Output: User configuration information

[0495] Specific behavior: The user enters a new threshold in the settings input form and clicks the save button.

[0496] Step 2:

[0497] The device sends a request to the generative artificial intelligence API based on the input settings.

[0498] Input: New setting value

[0499] Output: A request to the generative AI API

[0500] Specific operation: The device creates a request payload containing the new setting values ​​and sends an HTTP POST request to the generative AI API.

[0501] Step 3:

[0502] The device receives the setting values ​​generated in response from the generative artificial intelligence API.

[0503] Input: Response from the generative AI API

[0504] Output: Optimal setting

[0505] Specific operation: Analyze the response data received by the device and extract the optimal setting values.

[0506] Step 4:

[0507] The server creates a payload containing the new configuration values ​​and sends an update device configuration request to the blockchain API.

[0508] Input: Optimal setting

[0509] Output: An update request to the blockchain API

[0510] What happens: The server creates the update payload and sends an HTTP POST request to the blockchain API.

[0511] Step 5:

[0512] The server receives the response from the Blockchain API and confirms that the configuration update was successful.

[0513] Input: Response from the Blockchain API

[0514] Output: Confirmation of update results

[0515] Specific behavior: The server checks the response status code to confirm that the settings were updated successfully. If successful, it saves the setting change history to the database.

[0516] Automatic generation of data reports

[0517] Step 1:

[0518] The server sends an HTTP GET request using the specific device ID to retrieve the latest device data from the blockchain.

[0519] Input: Device ID

[0520] Output: A get request to the Blockchain API

[0521] Specific operation: The server creates an HTTP GET request based on the device ID and sends it to the blockchain API.

[0522] Step 2:

[0523] The server receives the response from the blockchain API and parses the data in JSON format.

[0524] Input: Response from the Blockchain API

[0525] Output: Parsed device data

[0526] Specific operation: The response data received by the server is analyzed using a JSON parser.

[0527] Step 3:

[0528] The device data analyzed by the terminal is sent to a generative artificial intelligence API, which then sends a request to generate a data report.

[0529] Input: Parsed device data

[0530] Output: A generated request to the generative AI API

[0531] Specific operation: The device creates a request payload for generating a data report and sends an HTTP POST request to the generative artificial intelligence API.

[0532] Step 4:

[0533] The device receives the data report generated in response from the generative artificial intelligence API.

[0534] Input: Response from the generative AI API

[0535] Output: Data report

[0536] Specific operations: Analyze the response data received by the terminal and extract the generated data report.

[0537] Step 5:

[0538] The server provides the generated data report to the user or uses it for further data analysis within the system.

[0539] Input: Data Report

[0540] Output: Report display to user or internal data analysis

[0541] Specific Actions: The server displays the data report through the user interface and notifies the user, or stores it in a database for further analysis within the system.

[0542] Use of emotion engine

[0543] Step 1:

[0544] The emotion engine recognizes the user's emotions in real time and transmits the emotion data to the system.

[0545] Input: User's emotional state

[0546] Output: Emotion data

[0547] Specific operation: The emotion engine analyzes camera and sensor data to recognize the user's emotional state, and sends the recognized emotional data to the system.

[0548] Step 2:

[0549] Your device will adjust device settings and how data reports are displayed based on your emotion data.

[0550] Input: Emotion data

[0551] Output: How to view adjusted device settings and data reports

[0552] Specific behavior: Based on the emotion data received by the device, logic is applied to optimize settings and display methods, and actual device settings and display methods are changed.

[0553] Step 3:

[0554] The server registers the emotion data on the blockchain, ensuring transparency and consistency.

[0555] Input: Emotion data

[0556] Output: Record on the blockchain

[0557] Specific operation: The server creates a payload containing emotion data and sends an HTTP POST request to the blockchain API. After sending, it receives a response and confirms that the emotion data was successfully recorded.

[0558] (Application example 2)

[0559] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0560] In modern virtual stores, it is important to dynamically manage and adjust the environment using IoT devices to maximize the user experience. However, conventional systems do not adjust settings in real time based on the user's emotional state, making it difficult to optimize operations based on the user's emotions and behavior. Furthermore, there is a lack of means to ensure data transparency and consistency. Therefore, there is a need for a system that can appropriately adjust devices according to the user's emotional state and manage data in a secure and transparent manner.

[0561] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0562] In this invention, the server includes: means for updating IoT device settings using generative artificial intelligence; means for profiling IoT device data using blockchain technology; means for automatically collecting IoT device data and generating data reports using generative artificial intelligence; means for recognizing a user's emotional state in real time using an emotion engine and dynamically adjusting IoT device settings based on the state; and means for registering the emotion data obtained by the emotion engine in the blockchain to ensure transparency and consistency. This enables optimal adjustment of IoT device settings based on the user's emotional state, thereby realizing safe and transparent data management.

[0563] "Generative AI" is an AI technology that automatically generates new settings and analysis results based on input information provided by the user and existing data.

[0564] An "IoT device" is a physical device that communicates with each other over the Internet and collects and controls data.

[0565] "Blockchain technology" is a technology that uses a distributed database to ensure data transparency and consistency. This technology prevents data tampering and enables highly reliable data management.

[0566] "Data profiling" is the process of collecting data related to a particular device and analyzing its characteristics and patterns.

[0567] A "data report" is a report that analyzes collected data and summarizes it visually or in writing, making it easy to understand trends and problems in the data.

[0568] An "emotion engine" is a technology that recognizes a user's emotional state in real time and integrates that information with other systems.

[0569] "Transparency" refers to a state in which data and processes can be easily verified from the outside and are highly reliable.

[0570] "Consistency" refers to a state in which data is managed according to certain rules and standards, and its quality is always maintained.

[0571] "Real-time" refers to responding immediately to an event or process as it occurs.

[0572] This invention relates to an IoT device management system that combines generative artificial intelligence, blockchain technology, and an emotion engine. The system aims to improve the user experience in virtual stores using devices such as smart glasses.

[0573] The system configuration is as follows:

[0574] 1. Generative Artificial Intelligence

[0575] The server uses generative artificial intelligence (AI) to automatically generate new configuration information and data analysis results based on user-provided input information and existing device data. The AI ​​periodically analyzes device data and generates appropriate configuration updates and data reports.

[0576] 2. Blockchain Technology

[0577] The server uses blockchain technology to register IoT device data and configuration change history in a distributed database, ensuring transparency and consistency and preventing tampering with device operation history and data.

[0578] 3. Emotion Engine

[0579] The emotion engine recognizes the user's emotional state in real time and provides that data to the server, which then dynamically adjusts the settings of the IoT device and how data reports are displayed. The emotion data is also registered on the blockchain to ensure transparency and consistency.

[0580] Example of a system

[0581] An example of a virtual store assistant might be implemented as follows:

[0582] Adjusting lighting based on user's emotional state

[0583] As a user wearing smart glasses walks around a virtual store, the emotion engine recognizes the user's emotional state in real time. For example, if the user is "having fun," the generative AI will brighten the smart lighting in the store and set the color temperature to a warmer setting.

[0584] Device registration and configuration updates

[0585] Device information is entered from the terminal through the smart glasses, and the server registers it on the blockchain. Device settings are updated by generating optimal settings based on user input using generative artificial intelligence, which then registers the settings on the blockchain again.

[0586] Hardware and software used

[0587] 1. Hardware

[0588] Smart glasses (AR / VR headsets)

[0589] Emotion Recognition Camera

[0590] 2. Software

[0591] Emotion Engine API

[0592] AI Generator API

[0593] Blockchain API

[0594] Examples of prompt statements

[0595] An example of a prompt to input to a generative AI model is as follows:

[0596] Use the latest customer sentiment data to suggest optimal settings for smart lighting in your virtual store: for example, if the customer is "having fun," set the brightness to 100% and the color temperature to cool white.

[0597] This invention enables IoT devices in virtual stores to work together and optimize operations based on the user's emotional state. Blockchain technology also makes the device's operational history transparent, enabling highly reliable data management.

[0598] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0599] Step 1:

[0600] Registering devices

[0601] The user puts on the smart glasses and inputs information to register a new IoT device (e.g., device name, installation location, type) into the terminal via the smart glasses. The terminal receives this information and sends it to the server. The server generates a device ID based on this information and sends an HTTP POST request to register the device information on the blockchain. The response confirms that the device has been successfully registered.

[0602] Input: Device information (device name, location, type)

[0603] Output: Device ID, result registered on the blockchain

[0604] How it works: The user enters device information through the smart glasses, the device sends the information to the server, and the server registers it on the blockchain.

[0605] Step 2:

[0606] Update device settings

[0607] The user inputs new settings (e.g., thresholds) through the smart glasses. The device requests the input settings from the generative AI API, which generates optimal settings. The server registers the generated settings in the blockchain and responds by confirming whether the update was successful.

[0608] Input: New setting value (e.g. threshold)

[0609] Output: Generated optimal settings, results registered on the blockchain

[0610] How it works: The user inputs new settings through the smart glasses, the device sends them to the generative AI API, and the server registers the generated settings on the blockchain.

[0611] Step 3:

[0612] Automatic generation of data reports

[0613] The server sends an HTTP GET request using a specific device ID to retrieve the latest device data from the blockchain. The server sends the data retrieved in response to the generative AI API and requests it to generate a data report. The generated data report is then received and provided to the user.

[0614] Input: Device ID, data from blockchain

[0615] Output: Generated data report

[0616] How it works: The server retrieves device data from the blockchain, sends it to a generative AI API, and provides the generated data report to the user.

[0617] Step 4:

[0618] Use of emotion engine

[0619] The emotion engine recognizes the user's emotional state in real time, sends the recognized emotional data to the server, which registers the data on the blockchain, and dynamically adjusts the device settings based on the emotional data.

[0620] Input: Emotion data

[0621] Output: Dynamically adjusted device settings, emotion data registered on the blockchain

[0622] How it works: The emotion engine recognizes the user's emotional state in real time, the server registers the emotional data on the blockchain, and the device adjusts the device settings.

[0623] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0624] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0625] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0626] [Second embodiment]

[0627] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0628] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0629] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0630] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0631] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0632] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0633] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0634] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0635] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0636] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0637] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0638] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0639] This invention is a system that combines generative artificial intelligence and blockchain technology to automate configuration updates, data profiling, and data analysis of IoT devices, improving connectivity between devices.

[0640] System configuration

[0641] Generative artificial intelligence (AI)

[0642] Generative AI automatically generates new configuration information and data analysis results based on user-provided input information and existing device data. Specifically, the AI ​​engine periodically analyzes device data and proposes and implements configuration updates as needed.

[0643] Blockchain technology

[0644] Blockchain technology allows IoT device data to be registered in a distributed database, ensuring transparency and consistency. This technology ensures that device operation history and configuration change history are tamper-proof, facilitating troubleshooting and device management.

[0645] Program processing overview

[0646] Device Profiling

[0647] 1. A user enters the necessary information into the system to register a new IoT device.

[0648] 2. The server receives this information and uses blockchain technology to profile the device, specifically registering the device ID, registration timestamp, and other initial data on the blockchain.

[0649] Get Device Data

[0650] 1. The server uses a specific device ID to retrieve the latest data about that device from the blockchain.

[0651] 2. The acquired data is used to check the status and history of the device.

[0652] Update device settings

[0653] 1. The user enters new device settings.

[0654] 2. The device uses generative artificial intelligence to analyze the settings information entered and generate the optimal settings.

[0655] 3. The server registers the generated configuration information on the blockchain and updates the device configuration.

[0656] Automatic generation of data reports

[0657] 1. The server periodically retrieves the latest device data from the blockchain.

[0658] 2. The device uses generative artificial intelligence to analyze this data and automatically generate a data report.

[0659] 3. The data report is provided to the user or used for further analysis within the system.

[0660] Specific examples

[0661] Device Profiling Example

[0662] 1. The user registers a temperature sensor by entering information such as the device name, location, and type.

[0663] 2. The server registers this information on the blockchain and assigns a device ID.

[0664] Example of updating device settings

[0665] 1. The user enters new configuration information to set the temperature sensor threshold to 25 degrees.

[0666] 2. The device uses generative artificial intelligence to evaluate whether the settings are appropriate and generate optimal settings.

[0667] 3. The server registers this configuration information on the blockchain and updates the device settings.

[0668] Example of automatic data report generation

[0669] 1. The server retrieves the latest temperature sensor data from the blockchain.

[0670] 2. The device uses generative artificial intelligence to analyze this data and automatically generate a statistical report of temperature fluctuations.

[0671] 3. The generated report is provided to the user or used for further data analysis.

[0672] This system improves the connectivity of IoT devices, making it easier for users to manage devices and analyze data. In addition, the use of blockchain technology makes the device's operational history transparent, enabling highly reliable data management.

[0673] The processing flow will be explained below.

[0674] Device Profiling

[0675] Step 1:

[0676] The user enters registration information for a new IoT device (e.g., device name, location, type, etc.).

[0677] Step 2:

[0678] The server receives the entered information and generates a device ID.

[0679] Step 3:

[0680] The server creates a payload containing the generated device ID and registration information and sends an HTTP POST request to the Blockchain API.

[0681] Step 4:

[0682] The server receives the response from the Blockchain API and verifies whether the device was successfully registered.

[0683] Get Device Data

[0684] Step 1:

[0685] The server sends an HTTP GET request using a specific device ID to retrieve data about that device from the blockchain.

[0686] Step 2:

[0687] The server receives the response from the blockchain API and parses the data in JSON format.

[0688] Step 3:

[0689] The server analyzes the device data and provides it to applications and users.

[0690] Update device settings

[0691] Step 1:

[0692] The user inputs a new setting value (e.g., threshold value).

[0693] Step 2:

[0694] Based on the settings entered on the device, the device sends a request to a generative artificial intelligence API to generate the optimal settings.

[0695] Step 3:

[0696] The device receives the setting values ​​generated in response from the generative artificial intelligence API.

[0697] Step 4:

[0698] The server creates a payload containing the new configuration values ​​and sends an update device configuration request to the blockchain API.

[0699] Step 5:

[0700] The server receives the response from the Blockchain API and verifies whether the configuration update was successful.

[0701] Automatic generation of data reports

[0702] Step 1:

[0703] The server sends an HTTP GET request using the specific device ID to retrieve the latest device data from the blockchain.

[0704] Step 2:

[0705] The server receives the response from the blockchain API and parses the data in JSON format.

[0706] Step 3:

[0707] The device data analyzed by the terminal is sent to a generative artificial intelligence API, which then sends a request to generate a data report.

[0708] Step 4:

[0709] The device receives the data report generated in response from the generative artificial intelligence API.

[0710] Step 5:

[0711] The server provides the generated data report to the user or uses it for further data analysis within the system.

[0712] Specific examples

[0713] Device Profiling Example

[0714] Step 1:

[0715] The user enters the temperature sensor registration information (e.g., name, location, type).

[0716] Step 2:

[0717] The server receives the entered information and generates a device ID.

[0718] Step 3:

[0719] The server creates a payload containing the device ID and registration information and sends an HTTP POST request to the Blockchain API.

[0720] Step 4:

[0721] The server receives the response from the Blockchain API and verifies whether the device was successfully registered.

[0722] Example of updating device settings

[0723] Step 1:

[0724] The user enters a new threshold of 25 degrees for the temperature sensor.

[0725] Step 2:

[0726] The device sends a request to the artificial intelligence API for generating the input threshold value, and generates the optimal settings.

[0727] Step 3:

[0728] The device receives the setting values ​​generated in response from the generative artificial intelligence API.

[0729] Step 4:

[0730] The server creates a payload containing the new configuration values ​​and sends an update device configuration request to the blockchain API.

[0731] Step 5:

[0732] The server receives the response from the Blockchain API and verifies whether the configuration update was successful.

[0733] Example of automatic data report generation

[0734] Step 1:

[0735] The server sends an HTTP GET request using the temperature sensor ID to retrieve the latest device data from the blockchain.

[0736] Step 2:

[0737] The server receives the response from the blockchain API and parses the data in JSON format.

[0738] Step 3:

[0739] The device data analyzed by the terminal is sent to a generative artificial intelligence API, which then sends a request to generate a data report.

[0740] Step 4:

[0741] The device receives the data report generated in response from the generative artificial intelligence API.

[0742] Step 5:

[0743] The server provides the generated data report to the user or uses it for further data analysis within the system.

[0744] Example 1

[0745] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0746] In conventional IoT device management systems, device configuration updates, data profiling, and data analysis are performed manually, resulting in inefficiency and insufficient collaboration between devices. Furthermore, reliability is low because it is difficult to prevent data tampering and ensure consistency. Furthermore, there are limited ways to regularly check device status and data analysis results.

[0747] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0748] In this invention, the server includes: means for updating IoT device settings using generative artificial intelligence; means for profiling IoT device data using blockchain technology; means for automatically collecting IoT device data and generating data reports using generative artificial intelligence; means for a user to input device information, which the server receives and registers in the blockchain; means for the server to analyze data acquired from the device and generate optimal setting values; and means for the terminal to provide data analysis results using generative artificial intelligence. This automates IoT device setting updates, data profiling, and data analysis, improves collaboration between devices, prevents data tampering, and ensures consistency. Furthermore, it becomes possible to periodically grasp the device status and data analysis results.

[0749] "Generative AI" is an AI technology that automatically generates new configuration information and data analysis results based on input information provided by users and existing device data.

[0750] An "IoT device" is a physical device or sensor that is connected to the Internet and capable of sending and receiving data.

[0751] The "means for updating settings" is a method that uses generative artificial intelligence to analyze the setting information of an IoT device, generate optimal setting values ​​as needed, and automatically change the device settings.

[0752] "Blockchain technology" is a technology for recording data transaction history securely and in a decentralized manner, and has the advantage of preventing data tampering and unauthorized access.

[0753] "Means of data profiling" refers to a method of registering initial information about IoT devices using blockchain technology to identify and monitor the devices.

[0754] "Means for automatically collecting data" refers to a method for acquiring data from IoT devices periodically or in real time and sending it to a server.

[0755] The "means for generating data reports" refers to a method of analyzing collected data using generative artificial intelligence and automatically creating reports in a format that is easy for users to understand.

[0756] A "user" is a person or company that operates the system, inputs information, or receives reports.

[0757] The "server" is a central control unit that receives data from IoT devices, analyzes it, and registers it on the blockchain.

[0758] A "terminal" is a device operated by a user that uses generative artificial intelligence to analyze data and provide results.

[0759] "Profiling" is a procedure for compiling and managing unique information about IoT devices.

[0760] "Transparency" means that data is not tampered with and correct information can always be referenced.

[0761] "Means for managing data change history" refers to a method that uses blockchain technology to record the history of data changes and make them available for later review.

[0762] The "means of ensuring consistency" refers to a method of consistently managing the device's configuration status by registering configuration information generated using generative artificial intelligence on the blockchain.

[0763] "Data analysis results" are information and reports generated after analyzing acquired device data using generative artificial intelligence.

[0764] This invention is a system that combines generative artificial intelligence and blockchain technology to automate configuration updates, data profiling, and data analysis of IoT devices, improving connectivity between devices.

[0765] System configuration

[0766] 1. Generative Artificial Intelligence

[0767] Generative AI automatically generates new configuration information and data analysis results based on user-provided input information and existing device data. Specifically, the AI ​​engine periodically analyzes device data and proposes and implements configuration updates as needed. This AI engine applies natural language processing and machine learning technologies (e.g., TensorFlow and PyTorch).

[0768] 2. Blockchain Technology

[0769] Blockchain technology registers IoT device data in a distributed database, ensuring transparency and consistency. This technology allows device operation history and configuration change history to be stored without tampering, making troubleshooting and device management easier. Blockchain technology uses existing distributed ledger technologies (e.g., Hyperledger Fabric and Ethereum).

[0770] Example of operation

[0771] Device Profiling

[0772] 1. The user enters the information required to register a new IoT device into the system. For example, if a temperature sensor is to be installed in the "living room," the user enters the device name, location, and type.

[0773] 2. The server receives this information and uses blockchain technology to profile the device, specifically registering the device ID, registration timestamp, and other initial data on the blockchain.

[0774] Update device settings

[0775] 1. A user enters a new device setting, for example, setting a temperature sensor threshold to 25 degrees.

[0776] 2. The device uses generative artificial intelligence to analyze the input setting information, evaluate whether the settings are appropriate, and generate optimal setting values.

[0777] 3. The server registers the generated configuration information on the blockchain and updates the device configuration.

[0778] Automatic generation of data reports

[0779] 1. The server retrieves the latest temperature sensor data from the blockchain. This process is performed periodically.

[0780] 2. The device uses generative artificial intelligence to analyze this data and automatically generate a statistical report of temperature fluctuations.

[0781] 3. The generated report is provided to the user or used for further analysis within the system.

[0782] Specific prompt examples

[0783] Prompt: "I want to update the temperature sensor settings on my IoT device. Set the new threshold to 25 degrees."

[0784] This system makes it easier for users to manage IoT devices and analyze data, improving the connectivity between devices. In addition, the use of blockchain technology ensures data transparency and consistency, enabling reliable data management.

[0785] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0786] Device profiling process steps

[0787] Step 1

[0788] The user enters registration information for a new IoT device (e.g., device name, location, device type).

[0789] Input: Device information (Device name: Temperature sensor, Location: Living room, Type: Temperature sensor)

[0790] What happens: A user enters information through a web interface or a mobile app.

[0791] Step 2

[0792] The server receives the entered information and generates a device ID.

[0793] Input: Device information entered by the user

[0794] Data processing: The server analyzes the device information and generates a unique device ID.

[0795] Output: Generated device ID

[0796] Step 3

[0797] The server registers the device ID, registration timestamp, and initial data on the blockchain.

[0798] Input: Device ID, registration timestamp, initial data (e.g., initial settings for a temperature sensor)

[0799] Data calculation: Calling blockchain API and recording device information on the distributed ledger

[0800] Output: Device profile registered on the blockchain

[0801] Steps for updating device settings

[0802] Step 1

[0803] The user enters a new device setting (e.g., a temperature sensor threshold).

[0804] Input: New device setting (e.g. threshold 25 degrees)

[0805] Specific operation: The user inputs a new threshold value on the setting input screen and presses the send button.

[0806] Step 2

[0807] The terminal receives the entered setting information and analyzes it.

[0808] Input: New device configuration information

[0809] Data processing: The device receives the setting information and converts it into the appropriate data format.

[0810] Output: Parsable configuration data

[0811] Step 3

[0812] The terminal uses generative artificial intelligence to generate optimal setting values ​​based on the input setting information.

[0813] Input: Parsable configuration data

[0814] Data calculation: Generative AI analyzes past data and current conditions to calculate optimal settings

[0815] Output: Recommended optimal settings

[0816] Step 4

[0817] The server registers the generated configuration information on the blockchain and updates the device configuration.

[0818] Input: Recommended optimal settings

[0819] Data calculation: Register new configuration information through the blockchain API

[0820] Output: The latest device configuration recorded on the blockchain

[0821] Processing steps for automatic generation of data reports

[0822] Step 1

[0823] The server retrieves the latest device data from the blockchain.

[0824] Input: Device ID

[0825] Data computation: Send a data retrieval request to the blockchain network

[0826] Output: Latest device data

[0827] Step 2

[0828] The device uses generative artificial intelligence to analyze the acquired data and automatically generate a data report.

[0829] Input: Latest device data

[0830] Data Calculation: Generative AI analyzes data and formats statistics and trends into reports

[0831] Output: Auto-generated data report

[0832] Step 3

[0833] The server provides the generated report to the user.

[0834] Input: Auto-generated data report

[0835] What happens: The server displays the report on a dashboard or emails it to the user.

[0836] Output: User-accessible data reports

[0837] (Application example 1)

[0838] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0839] Traditionally, configuring and optimizing factory robots required a lot of manual work and time, resulting in reduced productivity. In addition, it was difficult to ensure transparency and consistency in IoT device data management, leading to issues with data reliability. Furthermore, automating device configuration change history and behavior analysis required complex processes and human intervention, making troubleshooting and efficient operation difficult.

[0840] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0841] In this invention, the server includes means for updating IoT device settings using generative artificial intelligence, means for profiling IoT device data using blockchain technology, means for automatically collecting IoT device data and generating data reports using generative artificial intelligence, and means for configuring and optimizing factory robot operations. This enables automation of factory robot configuration and optimization, ensures transparency and consistency in IoT device data management, and enables efficient and reliable device operation.

[0842] "Generative AI" refers to AI that automatically generates new configuration information and data analysis results based on input information provided by the user and existing device data.

[0843] "IoT devices" are various devices that are interconnected via the Internet and are capable of collecting and communicating data.

[0844] "Means for updating settings" refers to means for optimizing the settings of IoT devices using generative artificial intelligence and automatically applying the latest settings.

[0845] "Blockchain technology" is a technology that builds a distributed database of data and ensures its transparency and consistency.

[0846] "Means for profiling data" refers to a means for using blockchain technology to perform detailed analysis of IoT device data and evaluate the device's condition and performance.

[0847] "Means for generating data reports" refers to means for analyzing data collected from IoT devices using generative artificial intelligence and outputting the results in the form of reports on a regular basis.

[0848] A "factory robot" is a robot designed to automatically perform various tasks in a factory.

[0849] "Means for setting and optimizing operation" refers to means for optimizing the operation settings of factory robots and adjusting them so that they can perform work efficiently and effectively.

[0850] This invention is a system that configures and optimizes the operation of factory robots and ensures transparency and consistency in data management of IoT devices. This system is composed of a combination of generative artificial intelligence and blockchain technology, and uses the following hardware and software:

[0851] Hardware and software used

[0852] Hardware: Various IoT sensors installed in the factory (temperature sensors, pressure sensors, speed sensors, etc.), factory robots that change settings

[0853] software:

[0854] Generative AI: Python AI libraries (e.g., TensorFlow)

[0855] Blockchain: A custom blockchain implemented in Python

[0856] Data analysis: A data processing system (e.g., Pandas, Numpy) for analyzing data streams from virtual sensors in real time.

[0857] System processing overview

[0858] The server automatically collects data from IoT sensors in the factory and uses generative artificial intelligence to generate optimal operating settings, which are then stored transparently using blockchain technology.

[0859] The server first collects sensor data and analyzes it in real time. Based on this data, generative artificial intelligence automatically generates optimal robot operation settings, enabling automated factory robot configuration and optimization.

[0860] The generated configuration information and analysis results are stored using blockchain technology, ensuring data transparency and consistency of change history. This data management method enables efficient and reliable device operation.

[0861] Specific examples

[0862] A system that optimizes the motor speed and grip force of a robot in a factory continuously collects data from sensors and uses that data to calculate the optimal settings using generative artificial intelligence. Specifically, the AI ​​takes the current sensor values ​​and generates the appropriate settings. This new setting is stored on the blockchain, ensuring the device's authenticity and transparency.

[0863] Example prompts to input to the generative AI model

[0864] "Calculate the optimal motor speed and grip force for this system based on the latest sensor data."

[0865] Specific prompt:

[0866] "Calculate the optimal motor speed and grip force for the robot based on the following sensor data:

[0867] {

[0868] 'current_speed': 1.0,

[0869] 'current_grip': 1.0

[0870] }"

[0871] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0872] Step 1:

[0873] The server collects data in real time from various IoT sensors in the factory. The input from the sensors is various types of data such as temperature, pressure, speed, etc. The server acquires these inputs and stores them in a database.

[0874] Step 2:

[0875] The server inputs the collected sensor data into a generative AI model, which analyzes the data and generates optimal motion settings. This generative AI calculates optimal settings for motor speed and grip force, for example.

[0876] Step 3:

[0877] The terminal receives the optimal operational settings output by the generative AI and transmits the results to the factory robots, where the input is the setting value output by the AI ​​model and the output is the new operational setting value applied to the robot.

[0878] Step 4:

[0879] The server registers the generated operational configuration and related sensor data on the blockchain. The input is the operational configuration and sensor data, and the output is the data stored on the blockchain. This process ensures data transparency and a change history.

[0880] Step 5:

[0881] The server periodically retrieves data from the blockchain and generates a data report, where the input is the data retrieved from the blockchain and the output is the data report provided to the user.

[0882] Step 6:

[0883] Users can refer to the generated data report to check the operation status of the factory robot and the optimization status of the settings, and can make any further adjustments necessary based on this.

[0884] These processing steps enable automated configuration and optimization of factory robots, and improve transparency and reliability of data management.

[0885] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0886] This invention relates to an IoT device management system that combines generative artificial intelligence, blockchain technology, and an emotion engine to automate the process of updating IoT device settings, data profiling, and automatic data report generation, and also enables device settings adjustment and report display based on the user's emotional state.

[0887] System configuration

[0888] Generative artificial intelligence (AI)

[0889] Generative AI automatically generates new configuration information and data analysis results based on user-provided input and existing device data. The AI ​​engine periodically analyzes device data and proposes and implements configuration updates as needed.

[0890] Blockchain technology

[0891] Blockchain technology allows IoT device data to be registered in a distributed database, ensuring transparency and consistency. This technology ensures that device operation history and configuration change history are tamper-proof, facilitating troubleshooting and device management.

[0892] Emotion Engine

[0893] The emotion engine recognizes the user's emotional state in real time and provides that information to the system, which can then dynamically adjust the settings of IoT devices and how data reports are displayed based on the user's emotional state.

[0894] Program processing overview

[0895] Device Profiling

[0896] 1. A user enters the necessary information to register a new IoT device into the system (e.g., device name, location, type, etc.).

[0897] 2. The server receives this information and generates a device ID.

[0898] 3. The server creates a payload containing the generated device ID and registration information and sends an HTTP POST request to the Blockchain API.

[0899] 4. The server receives the response from the blockchain API and verifies whether the device was successfully registered.

[0900] Get Device Data

[0901] 1. The server sends an HTTP GET request using a specific device ID to retrieve data about that device from the blockchain.

[0902] 2. The server receives the response from the blockchain API and parses the data in JSON format.

[0903] 3. The server provides the analyzed device data to applications and users.

[0904] Update device settings

[0905] 1. The user inputs a new setting value (e.g., threshold value).

[0906] 2. Based on the settings entered by the device, the device sends a request to the generative AI API to generate the optimal settings.

[0907] 3. The device receives the setting values ​​generated in response from the generative AI API.

[0908] 4. The server creates a payload containing the new configuration values ​​and sends an update device configuration request to the blockchain API.

[0909] 5. The server receives the response from the Blockchain API and verifies whether the configuration update was successful.

[0910] Automatic generation of data reports

[0911] 1. The server sends an HTTP GET request to retrieve the latest device data from the blockchain using the specific device ID.

[0912] 2. The server receives the response from the blockchain API and parses the data in JSON format.

[0913] 3. The device analyzes the device data and sends it to the generative AI API, which then sends a request to generate a data report.

[0914] 4. The device receives the generated data report in response from the generative AI API.

[0915] 5. The server provides the generated data report to the user or uses it for further data analysis within the system.

[0916] Use of emotion engine

[0917] 1. The emotion engine recognizes the user's emotional state in real time.

[0918] 2. The emotion engine sends the recognized emotion data to the system, which dynamically adjusts the device settings and how data reports are displayed.

[0919] 3. The server registers the emotion data on the blockchain to ensure transparency and consistency.

[0920] Specific examples

[0921] Device Profiling Example

[0922] 1. The user enters the temperature sensor registration information (e.g., name, location, type).

[0923] 2. The server receives the entered information and generates a device ID.

[0924] 3. The server creates a payload containing the device ID and registration information and sends an HTTP POST request to the Blockchain API.

[0925] 4. The server receives the response from the blockchain API and verifies whether the device was successfully registered.

[0926] Example of updating device settings

[0927] 1. The user enters a new threshold of 25 degrees for the temperature sensor.

[0928] 2. The device sends a request to the AI ​​API for the input thresholds to generate the optimal settings.

[0929] 3. The device receives the setting values ​​generated in response from the generative AI API.

[0930] 4. The server creates a payload containing the new configuration values ​​and sends an update device configuration request to the blockchain API.

[0931] 5. The server receives the response from the Blockchain API and verifies whether the configuration update was successful.

[0932] Example of automatic data report generation

[0933] 1. The server sends an HTTP GET request using the temperature sensor ID to retrieve the latest device data from the blockchain.

[0934] 2. The server receives the response from the blockchain API and parses the data in JSON format.

[0935] 3. The device analyzes the device data and sends it to the generative AI API, which then sends a request to generate a data report.

[0936] 4. The device receives the generated data report in response from the generative AI API.

[0937] 5. The server provides the generated data report to the user or uses it for further data analysis within the system.

[0938] Example of using the emotion engine

[0939] 1. The emotion engine recognizes the user's emotions in real time and sends the emotion data to the system.

[0940] 2. Your device will adjust device settings and how data reports are displayed based on emotion data.

[0941] 3. The server registers the emotion data on the blockchain to ensure transparency and consistency.

[0942] This system improves the connectivity of IoT devices, making it easier for users to manage devices and analyze data. Furthermore, the use of an emotion engine enables optimal operation based on the user's emotional state, improving the user experience. Furthermore, the use of blockchain technology makes the device's operational history transparent, enabling reliable data management.

[0943] The processing flow will be explained below.

[0944] Device Profiling

[0945] Step 1:

[0946] The user enters the registration information for a new IoT device (e.g., device name, location, type, etc.) into the system's input form.

[0947] Step 2:

[0948] The server takes the information provided by the user and generates a new device ID.

[0949] Step 3:

[0950] The server creates a payload containing the device ID and the registration information provided by the user and sends an HTTP POST request to the Blockchain API.

[0951] Step 4:

[0952] The server receives the response sent from the Blockchain API and verifies whether the device was successfully registered.

[0953] Get Device Data

[0954] Step 1:

[0955] The server sends an HTTP GET request using a specific device ID to retrieve data about that device from the blockchain.

[0956] Step 2:

[0957] The server receives the response sent from the blockchain API and parses the data in JSON format.

[0958] Step 3:

[0959] The server converts the parsed device data into a format for presentation to the application user.

[0960] Step 4:

[0961] Access, view, and manipulate device data provided by the user.

[0962] Update device settings

[0963] Step 1:

[0964] The user enters new device setting values ​​(e.g., thresholds) into the system's input form.

[0965] Step 2:

[0966] Based on the settings entered by the user, the device sends a request to a generative artificial intelligence API to generate the optimal settings.

[0967] Step 3:

[0968] The device receives the setting values ​​generated in the response sent from the generative artificial intelligence API.

[0969] Step 4:

[0970] The server creates a payload containing the new configuration values ​​and sends an update device configuration request to the blockchain API.

[0971] Step 5:

[0972] The server receives the response sent from the Blockchain API and checks whether the configuration update was completed successfully.

[0973] Automatic generation of data reports

[0974] Step 1:

[0975] The server sends an HTTP GET request using the specific device ID to retrieve the latest device data from the blockchain.

[0976] Step 2:

[0977] The server receives the response sent from the blockchain API and parses the data in JSON format.

[0978] Step 3:

[0979] The device data analyzed by the terminal is sent to a generative artificial intelligence API, which then sends a request to generate a data report.

[0980] Step 4:

[0981] The device receives the data report generated in response from the generative artificial intelligence API.

[0982] Step 5:

[0983] The server converts the generated data report into a format for presentation to the user.

[0984] Use of emotion engine

[0985] Step 1:

[0986] The emotion engine analyzes the user's facial expressions, voice, etc. to recognize the user's emotional state in real time.

[0987] Step 2:

[0988] The emotion engine transmits the recognized emotion data to the server.

[0989] Step 3:

[0990] The server uses the received emotion data to dynamically adjust device settings and how data reports are displayed accordingly.

[0991] Step 4:

[0992] The server registers the emotion data on the blockchain, ensuring transparency and consistency.

[0993] Specific examples

[0994] Device Profiling Example

[0995] Step 1:

[0996] The user enters the temperature sensor registration information (e.g., name, location, and type) into the system's input form.

[0997] Step 2:

[0998] The server takes the information provided by the user and generates a new device ID.

[0999] Step 3:

[1000] The server creates a payload containing the device ID and registration information and sends an HTTP POST request to the Blockchain API.

[1001] Step 4:

[1002] The server receives the response sent from the Blockchain API and verifies whether the device was successfully registered.

[1003] Example of updating device settings

[1004] Step 1:

[1005] The user enters the new temperature sensor threshold of 25 degrees into the system's input form.

[1006] Step 2:

[1007] The device sends a request to the artificial intelligence API for generating the input threshold value, and generates the optimal settings.

[1008] Step 3:

[1009] The device receives the setting values ​​generated in the response sent from the generative artificial intelligence API.

[1010] Step 4:

[1011] The server creates a payload containing the new configuration values ​​and sends an update device configuration request to the blockchain API.

[1012] Step 5:

[1013] The server receives the response sent from the Blockchain API and checks whether the configuration update was completed successfully.

[1014] Example of automatic data report generation

[1015] Step 1:

[1016] The server sends an HTTP GET request using the temperature sensor ID to retrieve the latest device data from the blockchain.

[1017] Step 2:

[1018] The server receives the response sent from the blockchain API and parses the data in JSON format.

[1019] Step 3:

[1020] The device data analyzed by the terminal is sent to a generative artificial intelligence API, which then sends a request to generate a data report.

[1021] Step 4:

[1022] The device receives the data report generated in response from the generative artificial intelligence API.

[1023] Step 5:

[1024] The server converts the generated data report into a format for presentation to the user.

[1025] Example of using the emotion engine

[1026] Step 1:

[1027] The emotion engine recognizes the user's emotions in real time and transmits the emotion data to the server.

[1028] Step 2:

[1029] Your device will adjust device settings and how data reports are displayed based on your emotional data.

[1030] Step 3:

[1031] The server registers the emotion data on the blockchain, ensuring transparency and consistency.

[1032] Example 2

[1033] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1034] As the number of IoT devices increases, managing them and updating their settings becomes more complex. Furthermore, there is an increasing demand for device behavior and display methods that take into account the user's emotional state. Furthermore, while it is important to ensure the transparency and integrity of device data, no effective system exists to address these challenges. To solve these problems, an innovative system combining generative artificial intelligence, blockchain technology, and an emotion engine is needed.

[1035] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1036] In this invention, the server includes means for updating IoT device settings using generative artificial intelligence, means for profiling IoT device data using blockchain technology, means for automatically collecting IoT device data and generating data reports using generative artificial intelligence, means for recognizing a user's emotional state in real time using an emotion engine and dynamically adjusting the IoT device settings and display of the data report, and means for dynamically adjusting the display method of the IoT device settings and data report based on the user's emotional state. This enables optimal device settings and data report display according to the user's emotional state, while ensuring the transparency and consistency of device data.

[1037] "Generative AI" is an AI technology that automatically generates new configuration information and data analysis results based on input information provided by users and existing device data.

[1038] "Blockchain technology" is a technology that registers data in a distributed database and keeps operation history and setting change history untampered in order to ensure data transparency and consistency.

[1039] The "emotion engine" is a technology that recognizes a user's emotional state in real time and provides that information to the system, dynamically adjusting device settings and the way data reports are displayed based on the user's emotional state.

[1040] An "IoT device" is a physical device that can communicate with other devices and systems over the Internet.

[1041] "Profiling" is the process of collecting data about a specific target (in this case, an IoT device) and analyzing its characteristics and behavioral patterns.

[1042] A "data report" is a report generated based on collected and analyzed data, often including visual graphs and statistics.

[1043] "Payload" refers to the actual portion of data exchanged between systems, that is, the information actually sent from the sender to the receiver.

[1044] "Unique Device ID" means a unique identifier used to distinguish a particular device from all other devices.

[1045] An "HTTP request" is a request sent from a client to a server when accessing a resource on the web, and includes methods such as GET and POST.

[1046] MODE FOR CARRYING OUT THE INVENTION

[1047] This invention relates to an IoT device management system that combines generative artificial intelligence, blockchain technology, and an emotion engine to automate the process of updating IoT device settings, data profiling, and automatic data report generation, and also enables device settings adjustment and report display based on the user's emotional state.

[1048] Hardware and software used

[1049] The server is the hardware that handles the main processing of the system. It runs multiple services, profiling device data, updating settings, and generating data reports.

[1050] A terminal is a device (e.g., PC, smartphone, tablet) through which a user interacts with the system. The terminal receives input from the user and sends requests to the server or the generative AI API based on that input.

[1051] Generative AI is software that automatically generates new configuration information and data analysis results based on input information provided by the user and existing device data.

[1052] Blockchain technology is used to ensure transparency and consistency of devices by registering device data and operation history in a distributed database.

[1053] An emotion engine is software that recognizes a user's emotional state in real time and provides that information to a system that can dynamically adjust the settings of IoT devices and the way data reports are displayed based on the user's emotional state.

[1054] System processing overview and specific examples

[1055] Device profiling examples

[1056] The user enters the temperature sensor's registration information (e.g., name, location, and type). The server receives the entered information and generates a unique device ID. The server then creates a payload containing the generated device ID and registration information and sends an HTTP POST request to the blockchain API. The server then receives a response from the blockchain API and confirms whether the device has been successfully registered.

[1057] Example of updating device settings

[1058] The user inputs a new threshold value for the temperature sensor (e.g., 25 degrees). The device sends a request to the generative AI API based on the input threshold value to generate optimal settings. The device receives the generated setting value in the response from the generative AI API and sends it to the server. The server creates a payload containing the new setting value and sends a device setting update request to the blockchain API. Finally, the server receives the response from the blockchain API and confirms that the setting update was completed successfully.

[1059] Example of automatic generation of data reports

[1060] The server uses the temperature sensor ID to send an HTTP GET request to retrieve the latest device data from the blockchain. The server receives the response from the blockchain API and parses the data in JSON format. The device then sends the parsed device data to the generative AI API and sends a request to generate a data report. The device then receives the generated data report in the response from the generative AI API and provides it to the server. The server then provides the generated data report to the user or uses it for further data analysis within the system.

[1061] Examples of using the emotion engine

[1062] The emotion engine recognizes users' emotions in real time and transmits the emotion data to the system. The device adjusts device settings and data report display methods based on the emotion data. The server registers the emotion data on the blockchain to ensure transparency and consistency.

[1063] Prompt Sentence Examples

[1064] "Analyze the following device data and generate a data report: Temperature sensor ID: 123, Temperature: 25°C, Humidity: 60%"

[1065] This invention allows users to easily manage IoT devices and analyze data. Furthermore, the use of an emotion engine enables optimal operation according to the user's emotional state, improving the user experience. Furthermore, the use of blockchain technology makes the device's operational history transparent, enabling highly reliable data management.

[1066] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1067] Device Profiling

[1068] Step 1:

[1069] The user accesses the system's UI and enters information about the new IoT device (e.g., device name, location, and type).

[1070] Input: Device information such as device name, location, and type

[1071] Output: User input information

[1072] What happens: A user navigates through a form, filling in the fields and finally clicking the submit button.

[1073] Step 2:

[1074] The server receives input information from the user.

[1075] Input: User input information

[1076] Output: A set of information to generate a device ID

[1077] What it does: The server stores the received information in a temporary database, then generates a unique device ID based on the entered data.

[1078] Step 3:

[1079] The server generates a unique device ID.

[1080] Input: User input information

[1081] Output: Unique device ID

[1082] What happens: The server uses a UUID (Universal Unique Identifier) ​​generator to generate a unique ID.

[1083] Step 4:

[1084] The server combines the generated device ID and the user's input information into a payload and sends an HTTP POST request to the blockchain API.

[1085] Input: Device ID, user input information

[1086] Output: A request to the Blockchain API

[1087] What happens: The server creates a new payload and sends an HTTP POST request to the blockchain API.

[1088] Step 5:

[1089] The server receives the response from the Blockchain API and confirms that the device was successfully registered.

[1090] Input: Response from the Blockchain API

[1091] Output: Confirmation of registration result

[1092] Specific operation: The server checks the response status code to see if the registration was successful. If successful, it saves the device information in the database.

[1093] Update device settings

[1094] Step 1:

[1095] The user inputs new device setting values ​​(e.g., thresholds) on the system setting screen.

[1096] Input: New setting value (e.g. threshold)

[1097] Output: User configuration information

[1098] Specific behavior: The user enters a new threshold in the settings input form and clicks the save button.

[1099] Step 2:

[1100] The device sends a request to the generative artificial intelligence API based on the input settings.

[1101] Input: New setting value

[1102] Output: A request to the generative AI API

[1103] Specific operation: The device creates a request payload containing the new setting values ​​and sends an HTTP POST request to the generative AI API.

[1104] Step 3:

[1105] The device receives the setting values ​​generated in response from the generative artificial intelligence API.

[1106] Input: Response from the generative AI API

[1107] Output: Optimal setting

[1108] Specific operation: Analyze the response data received by the device and extract the optimal setting values.

[1109] Step 4:

[1110] The server creates a payload containing the new configuration values ​​and sends an update device configuration request to the blockchain API.

[1111] Input: Optimal setting

[1112] Output: An update request to the blockchain API

[1113] What happens: The server creates the update payload and sends an HTTP POST request to the blockchain API.

[1114] Step 5:

[1115] The server receives the response from the Blockchain API and confirms that the configuration update was successful.

[1116] Input: Response from the Blockchain API

[1117] Output: Confirmation of update results

[1118] Specific behavior: The server checks the response status code to confirm that the settings were updated successfully. If successful, it saves the setting change history to the database.

[1119] Automatic generation of data reports

[1120] Step 1:

[1121] The server sends an HTTP GET request using the specific device ID to retrieve the latest device data from the blockchain.

[1122] Input: Device ID

[1123] Output: A get request to the Blockchain API

[1124] Specific operation: The server creates an HTTP GET request based on the device ID and sends it to the blockchain API.

[1125] Step 2:

[1126] The server receives the response from the blockchain API and parses the data in JSON format.

[1127] Input: Response from the Blockchain API

[1128] Output: Parsed device data

[1129] Specific operation: The response data received by the server is analyzed using a JSON parser.

[1130] Step 3:

[1131] The device data analyzed by the terminal is sent to a generative artificial intelligence API, which then sends a request to generate a data report.

[1132] Input: Parsed device data

[1133] Output: A generated request to the generative AI API

[1134] Specific operation: The device creates a request payload for generating a data report and sends an HTTP POST request to the generative artificial intelligence API.

[1135] Step 4:

[1136] The device receives the data report generated in response from the generative artificial intelligence API.

[1137] Input: Response from the generative AI API

[1138] Output: Data report

[1139] Specific operations: Analyze the response data received by the terminal and extract the generated data report.

[1140] Step 5:

[1141] The server provides the generated data report to the user or uses it for further data analysis within the system.

[1142] Input: Data Report

[1143] Output: Report display to user or internal data analysis

[1144] Specific Actions: The server displays the data report through the user interface and notifies the user, or stores it in a database for further analysis within the system.

[1145] Use of emotion engine

[1146] Step 1:

[1147] The emotion engine recognizes the user's emotions in real time and transmits the emotion data to the system.

[1148] Input: User's emotional state

[1149] Output: Emotion data

[1150] Specific operation: The emotion engine analyzes camera and sensor data to recognize the user's emotional state, and sends the recognized emotional data to the system.

[1151] Step 2:

[1152] Your device will adjust device settings and how data reports are displayed based on your emotion data.

[1153] Input: Emotion data

[1154] Output: How to view adjusted device settings and data reports

[1155] Specific behavior: Based on the emotion data received by the device, logic is applied to optimize settings and display methods, and actual device settings and display methods are changed.

[1156] Step 3:

[1157] The server registers the emotion data on the blockchain, ensuring transparency and consistency.

[1158] Input: Emotion data

[1159] Output: Record on the blockchain

[1160] Specific operation: The server creates a payload containing emotion data and sends an HTTP POST request to the blockchain API. After sending, it receives a response and confirms that the emotion data was successfully recorded.

[1161] (Application example 2)

[1162] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1163] In modern virtual stores, it is important to dynamically manage and adjust the environment using IoT devices to maximize the user experience. However, conventional systems do not adjust settings in real time based on the user's emotional state, making it difficult to optimize operations based on the user's emotions and behavior. Furthermore, there is a lack of means to ensure data transparency and consistency. Therefore, there is a need for a system that can appropriately adjust devices according to the user's emotional state and manage data in a secure and transparent manner.

[1164] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1165] In this invention, the server includes: means for updating IoT device settings using generative artificial intelligence; means for profiling IoT device data using blockchain technology; means for automatically collecting IoT device data and generating data reports using generative artificial intelligence; means for recognizing a user's emotional state in real time using an emotion engine and dynamically adjusting IoT device settings based on the state; and means for registering the emotion data obtained by the emotion engine in the blockchain to ensure transparency and consistency. This enables optimal adjustment of IoT device settings based on the user's emotional state, thereby realizing safe and transparent data management.

[1166] "Generative AI" is an AI technology that automatically generates new settings and analysis results based on input information provided by the user and existing data.

[1167] An "IoT device" is a physical device that communicates with each other over the Internet and collects and controls data.

[1168] "Blockchain technology" is a technology that uses a distributed database to ensure data transparency and consistency. This technology prevents data tampering and enables highly reliable data management.

[1169] "Data profiling" is the process of collecting data related to a particular device and analyzing its characteristics and patterns.

[1170] A "data report" is a report that analyzes collected data and summarizes it visually or in writing, making it easy to understand trends and problems in the data.

[1171] An "emotion engine" is a technology that recognizes a user's emotional state in real time and integrates that information with other systems.

[1172] "Transparency" refers to a state in which data and processes can be easily verified from the outside and are highly reliable.

[1173] "Consistency" refers to a state in which data is managed according to certain rules and standards, and its quality is always maintained.

[1174] "Real-time" refers to responding immediately to an event or process as it occurs.

[1175] This invention relates to an IoT device management system that combines generative artificial intelligence, blockchain technology, and an emotion engine. The system aims to improve the user experience in virtual stores using devices such as smart glasses.

[1176] The system configuration is as follows:

[1177] 1. Generative Artificial Intelligence

[1178] The server uses generative artificial intelligence (AI) to automatically generate new configuration information and data analysis results based on user-provided input information and existing device data. The AI ​​periodically analyzes device data and generates appropriate configuration updates and data reports.

[1179] 2. Blockchain Technology

[1180] The server uses blockchain technology to register IoT device data and configuration change history in a distributed database, ensuring transparency and consistency and preventing tampering with device operation history and data.

[1181] 3. Emotion Engine

[1182] The emotion engine recognizes the user's emotional state in real time and provides that data to the server, which then dynamically adjusts the settings of the IoT device and how data reports are displayed. The emotion data is also registered on the blockchain to ensure transparency and consistency.

[1183] Example of a system

[1184] An example of a virtual store assistant might be implemented as follows:

[1185] Adjusting lighting based on user's emotional state

[1186] As a user wearing smart glasses walks around a virtual store, the emotion engine recognizes the user's emotional state in real time. For example, if the user is "having fun," the generative AI will brighten the smart lighting in the store and set the color temperature to a warmer setting.

[1187] Device registration and configuration updates

[1188] Device information is entered from the terminal through the smart glasses, and the server registers it on the blockchain. Device settings are updated by generating optimal settings based on user input using generative artificial intelligence, which then registers the settings on the blockchain again.

[1189] Hardware and software used

[1190] 1. Hardware

[1191] Smart glasses (AR / VR headsets)

[1192] Emotion Recognition Camera

[1193] 2. Software

[1194] Emotion Engine API

[1195] AI Generator API

[1196] Blockchain API

[1197] Examples of prompt statements

[1198] An example of a prompt to input to a generative AI model is as follows:

[1199] Use the latest customer sentiment data to suggest optimal settings for smart lighting in your virtual store: for example, if the customer is "having fun," set the brightness to 100% and the color temperature to cool white.

[1200] This invention enables IoT devices in virtual stores to work together and optimize operations based on the user's emotional state. Blockchain technology also makes the device's operational history transparent, enabling highly reliable data management.

[1201] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1202] Step 1:

[1203] Registering devices

[1204] The user puts on the smart glasses and inputs information to register a new IoT device (e.g., device name, installation location, type) into the terminal via the smart glasses. The terminal receives this information and sends it to the server. The server generates a device ID based on this information and sends an HTTP POST request to register the device information on the blockchain. The response confirms that the device has been successfully registered.

[1205] Input: Device information (device name, location, type)

[1206] Output: Device ID, result registered on the blockchain

[1207] How it works: The user enters device information through the smart glasses, the device sends the information to the server, and the server registers it on the blockchain.

[1208] Step 2:

[1209] Update device settings

[1210] The user inputs new settings (e.g., thresholds) through the smart glasses. The device requests the input settings from the generative AI API, which generates optimal settings. The server registers the generated settings in the blockchain and responds by confirming whether the update was successful.

[1211] Input: New setting value (e.g. threshold)

[1212] Output: Generated optimal settings, results registered on the blockchain

[1213] How it works: The user inputs new settings through the smart glasses, the device sends them to the generative AI API, and the server registers the generated settings on the blockchain.

[1214] Step 3:

[1215] Automatic generation of data reports

[1216] The server sends an HTTP GET request using a specific device ID to retrieve the latest device data from the blockchain. The server sends the data retrieved in response to the generative AI API and requests it to generate a data report. The generated data report is then received and provided to the user.

[1217] Input: Device ID, data from blockchain

[1218] Output: Generated data report

[1219] How it works: The server retrieves device data from the blockchain, sends it to a generative AI API, and provides the generated data report to the user.

[1220] Step 4:

[1221] Use of emotion engine

[1222] The emotion engine recognizes the user's emotional state in real time, sends the recognized emotional data to the server, which registers the data on the blockchain, and dynamically adjusts the device settings based on the emotional data.

[1223] Input: Emotion data

[1224] Output: Dynamically adjusted device settings, emotion data registered on the blockchain

[1225] How it works: The emotion engine recognizes the user's emotional state in real time, the server registers the emotional data on the blockchain, and the device adjusts the device settings.

[1226] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1227] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1228] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1229] [Third embodiment]

[1230] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1231] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1232] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1233] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1234] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1235] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1236] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1237] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1238] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1239] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1240] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1241] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[1242] This invention is a system that combines generative artificial intelligence and blockchain technology to automate configuration updates, data profiling, and data analysis of IoT devices, improving connectivity between devices.

[1243] System configuration

[1244] Generative artificial intelligence (AI)

[1245] Generative AI automatically generates new configuration information and data analysis results based on user-provided input information and existing device data. Specifically, the AI ​​engine periodically analyzes device data and proposes and implements configuration updates as needed.

[1246] Blockchain technology

[1247] Blockchain technology allows IoT device data to be registered in a distributed database, ensuring transparency and consistency. This technology ensures that device operation history and configuration change history are tamper-proof, facilitating troubleshooting and device management.

[1248] Program processing overview

[1249] Device Profiling

[1250] 1. A user enters the necessary information into the system to register a new IoT device.

[1251] 2. The server receives this information and uses blockchain technology to profile the device, specifically registering the device ID, registration timestamp, and other initial data on the blockchain.

[1252] Get Device Data

[1253] 1. The server uses a specific device ID to retrieve the latest data about that device from the blockchain.

[1254] 2. The acquired data is used to check the status and history of the device.

[1255] Update device settings

[1256] 1. The user enters new device settings.

[1257] 2. The device uses generative artificial intelligence to analyze the settings information entered and generate the optimal settings.

[1258] 3. The server registers the generated configuration information on the blockchain and updates the device configuration.

[1259] Automatic generation of data reports

[1260] 1. The server periodically retrieves the latest device data from the blockchain.

[1261] 2. The device uses generative artificial intelligence to analyze this data and automatically generate a data report.

[1262] 3. The data report is provided to the user or used for further analysis within the system.

[1263] Specific examples

[1264] Device Profiling Example

[1265] 1. The user registers a temperature sensor by entering information such as the device name, location, and type.

[1266] 2. The server registers this information on the blockchain and assigns a device ID.

[1267] Example of updating device settings

[1268] 1. The user enters new configuration information to set the temperature sensor threshold to 25 degrees.

[1269] 2. The device uses generative artificial intelligence to evaluate whether the settings are appropriate and generate optimal settings.

[1270] 3. The server registers this configuration information on the blockchain and updates the device settings.

[1271] Example of automatic data report generation

[1272] 1. The server retrieves the latest temperature sensor data from the blockchain.

[1273] 2. The device uses generative artificial intelligence to analyze this data and automatically generate a statistical report of temperature fluctuations.

[1274] 3. The generated report is provided to the user or used for further data analysis.

[1275] This system improves the connectivity of IoT devices, making it easier for users to manage devices and analyze data. In addition, the use of blockchain technology makes the device's operational history transparent, enabling highly reliable data management.

[1276] The processing flow will be explained below.

[1277] Device Profiling

[1278] Step 1:

[1279] The user enters registration information for a new IoT device (e.g., device name, location, type, etc.).

[1280] Step 2:

[1281] The server receives the entered information and generates a device ID.

[1282] Step 3:

[1283] The server creates a payload containing the generated device ID and registration information and sends an HTTP POST request to the Blockchain API.

[1284] Step 4:

[1285] The server receives the response from the Blockchain API and verifies whether the device was successfully registered.

[1286] Get Device Data

[1287] Step 1:

[1288] The server sends an HTTP GET request using a specific device ID to retrieve data about that device from the blockchain.

[1289] Step 2:

[1290] The server receives the response from the blockchain API and parses the data in JSON format.

[1291] Step 3:

[1292] The server analyzes the device data and provides it to applications and users.

[1293] Update device settings

[1294] Step 1:

[1295] The user inputs a new setting value (e.g., threshold value).

[1296] Step 2:

[1297] Based on the settings entered on the device, the device sends a request to a generative artificial intelligence API to generate the optimal settings.

[1298] Step 3:

[1299] The device receives the setting values ​​generated in response from the generative artificial intelligence API.

[1300] Step 4:

[1301] The server creates a payload containing the new configuration values ​​and sends an update device configuration request to the blockchain API.

[1302] Step 5:

[1303] The server receives the response from the Blockchain API and verifies whether the configuration update was successful.

[1304] Automatic generation of data reports

[1305] Step 1:

[1306] The server sends an HTTP GET request using the specific device ID to retrieve the latest device data from the blockchain.

[1307] Step 2:

[1308] The server receives the response from the blockchain API and parses the data in JSON format.

[1309] Step 3:

[1310] The device data analyzed by the terminal is sent to a generative artificial intelligence API, which then sends a request to generate a data report.

[1311] Step 4:

[1312] The device receives the data report generated in response from the generative artificial intelligence API.

[1313] Step 5:

[1314] The server provides the generated data report to the user or uses it for further data analysis within the system.

[1315] Specific examples

[1316] Device Profiling Example

[1317] Step 1:

[1318] The user enters the temperature sensor registration information (e.g., name, location, type).

[1319] Step 2:

[1320] The server receives the entered information and generates a device ID.

[1321] Step 3:

[1322] The server creates a payload containing the device ID and registration information and sends an HTTP POST request to the Blockchain API.

[1323] Step 4:

[1324] The server receives the response from the Blockchain API and verifies whether the device was successfully registered.

[1325] Example of updating device settings

[1326] Step 1:

[1327] The user enters a new threshold of 25 degrees for the temperature sensor.

[1328] Step 2:

[1329] The device sends a request to the artificial intelligence API for generating the input threshold value, and generates the optimal settings.

[1330] Step 3:

[1331] The device receives the setting values ​​generated in response from the generative artificial intelligence API.

[1332] Step 4:

[1333] The server creates a payload containing the new configuration values ​​and sends an update device configuration request to the blockchain API.

[1334] Step 5:

[1335] The server receives the response from the Blockchain API and verifies whether the configuration update was successful.

[1336] Example of automatic data report generation

[1337] Step 1:

[1338] The server sends an HTTP GET request using the temperature sensor ID to retrieve the latest device data from the blockchain.

[1339] Step 2:

[1340] The server receives the response from the blockchain API and parses the data in JSON format.

[1341] Step 3:

[1342] The device data analyzed by the terminal is sent to a generative artificial intelligence API, which then sends a request to generate a data report.

[1343] Step 4:

[1344] The device receives the data report generated in response from the generative artificial intelligence API.

[1345] Step 5:

[1346] The server provides the generated data report to the user or uses it for further data analysis within the system.

[1347] Example 1

[1348] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1349] In conventional IoT device management systems, device configuration updates, data profiling, and data analysis are performed manually, resulting in inefficiency and insufficient collaboration between devices. Furthermore, reliability is low because it is difficult to prevent data tampering and ensure consistency. Furthermore, there are limited ways to regularly check device status and data analysis results.

[1350] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1351] In this invention, the server includes: means for updating IoT device settings using generative artificial intelligence; means for profiling IoT device data using blockchain technology; means for automatically collecting IoT device data and generating data reports using generative artificial intelligence; means for a user to input device information, which the server receives and registers in the blockchain; means for the server to analyze data acquired from the device and generate optimal setting values; and means for the terminal to provide data analysis results using generative artificial intelligence. This automates IoT device setting updates, data profiling, and data analysis, improves collaboration between devices, prevents data tampering, and ensures consistency. Furthermore, it becomes possible to periodically grasp the device status and data analysis results.

[1352] "Generative AI" is an AI technology that automatically generates new configuration information and data analysis results based on input information provided by users and existing device data.

[1353] An "IoT device" is a physical device or sensor that is connected to the Internet and capable of sending and receiving data.

[1354] The "means for updating settings" is a method that uses generative artificial intelligence to analyze the setting information of an IoT device, generate optimal setting values ​​as needed, and automatically change the device settings.

[1355] "Blockchain technology" is a technology for recording data transaction history securely and in a decentralized manner, and has the advantage of preventing data tampering and unauthorized access.

[1356] "Means of data profiling" refers to a method of registering initial information about IoT devices using blockchain technology to identify and monitor the devices.

[1357] "Means for automatically collecting data" refers to a method for acquiring data from IoT devices periodically or in real time and sending it to a server.

[1358] The "means for generating data reports" refers to a method of analyzing collected data using generative artificial intelligence and automatically creating reports in a format that is easy for users to understand.

[1359] A "user" is a person or company that operates the system, inputs information, or receives reports.

[1360] The "server" is a central control unit that receives data from IoT devices, analyzes it, and registers it on the blockchain.

[1361] A "terminal" is a device operated by a user that uses generative artificial intelligence to analyze data and provide results.

[1362] "Profiling" is a procedure for compiling and managing unique information about IoT devices.

[1363] "Transparency" means that data is not tampered with and correct information can always be referenced.

[1364] "Means for managing data change history" refers to a method that uses blockchain technology to record the history of data changes and make them available for later review.

[1365] The "means of ensuring consistency" refers to a method of consistently managing the device's configuration status by registering configuration information generated using generative artificial intelligence on the blockchain.

[1366] "Data analysis results" are information and reports generated after analyzing acquired device data using generative artificial intelligence.

[1367] This invention is a system that combines generative artificial intelligence and blockchain technology to automate configuration updates, data profiling, and data analysis of IoT devices, improving connectivity between devices.

[1368] System configuration

[1369] 1. Generative Artificial Intelligence

[1370] Generative AI automatically generates new configuration information and data analysis results based on user-provided input information and existing device data. Specifically, the AI ​​engine periodically analyzes device data and proposes and implements configuration updates as needed. This AI engine applies natural language processing and machine learning technologies (e.g., TensorFlow and PyTorch).

[1371] 2. Blockchain Technology

[1372] Blockchain technology registers IoT device data in a distributed database, ensuring transparency and consistency. This technology allows device operation history and configuration change history to be stored without tampering, making troubleshooting and device management easier. Blockchain technology uses existing distributed ledger technologies (e.g., Hyperledger Fabric and Ethereum).

[1373] Example of operation

[1374] Device Profiling

[1375] 1. The user enters the information required to register a new IoT device into the system. For example, if a temperature sensor is to be installed in the "living room," the user enters the device name, location, and type.

[1376] 2. The server receives this information and uses blockchain technology to profile the device, specifically registering the device ID, registration timestamp, and other initial data on the blockchain.

[1377] Update device settings

[1378] 1. A user enters a new device setting, for example, setting a temperature sensor threshold to 25 degrees.

[1379] 2. The device uses generative artificial intelligence to analyze the input setting information, evaluate whether the settings are appropriate, and generate optimal setting values.

[1380] 3. The server registers the generated configuration information on the blockchain and updates the device configuration.

[1381] Automatic generation of data reports

[1382] 1. The server retrieves the latest temperature sensor data from the blockchain. This process is performed periodically.

[1383] 2. The device uses generative artificial intelligence to analyze this data and automatically generate a statistical report of temperature fluctuations.

[1384] 3. The generated report is provided to the user or used for further analysis within the system.

[1385] Specific prompt examples

[1386] Prompt: "I want to update the temperature sensor settings on my IoT device. Set the new threshold to 25 degrees."

[1387] This system makes it easier for users to manage IoT devices and analyze data, improving the connectivity between devices. In addition, the use of blockchain technology ensures data transparency and consistency, enabling reliable data management.

[1388] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1389] Device profiling process steps

[1390] Step 1

[1391] The user enters registration information for a new IoT device (e.g., device name, location, device type).

[1392] Input: Device information (Device name: Temperature sensor, Location: Living room, Type: Temperature sensor)

[1393] What happens: A user enters information through a web interface or a mobile app.

[1394] Step 2

[1395] The server receives the entered information and generates a device ID.

[1396] Input: Device information entered by the user

[1397] Data processing: The server analyzes the device information and generates a unique device ID.

[1398] Output: Generated device ID

[1399] Step 3

[1400] The server registers the device ID, registration timestamp, and initial data on the blockchain.

[1401] Input: Device ID, registration timestamp, initial data (e.g., initial settings for a temperature sensor)

[1402] Data calculation: Calling blockchain API and recording device information on the distributed ledger

[1403] Output: Device profile registered on the blockchain

[1404] Steps for updating device settings

[1405] Step 1

[1406] The user enters a new device setting (e.g., a temperature sensor threshold).

[1407] Input: New device setting (e.g. threshold 25 degrees)

[1408] Specific operation: The user inputs a new threshold value on the setting input screen and presses the send button.

[1409] Step 2

[1410] The terminal receives the entered setting information and analyzes it.

[1411] Input: New device configuration information

[1412] Data processing: The device receives the setting information and converts it into the appropriate data format.

[1413] Output: Parsable configuration data

[1414] Step 3

[1415] The terminal uses generative artificial intelligence to generate optimal setting values ​​based on the input setting information.

[1416] Input: Parsable configuration data

[1417] Data calculation: Generative AI analyzes past data and current conditions to calculate optimal settings

[1418] Output: Recommended optimal settings

[1419] Step 4

[1420] The server registers the generated configuration information on the blockchain and updates the device configuration.

[1421] Input: Recommended optimal settings

[1422] Data calculation: Register new configuration information through the blockchain API

[1423] Output: The latest device configuration recorded on the blockchain

[1424] Processing steps for automatic generation of data reports

[1425] Step 1

[1426] The server retrieves the latest device data from the blockchain.

[1427] Input: Device ID

[1428] Data computation: Send a data retrieval request to the blockchain network

[1429] Output: Latest device data

[1430] Step 2

[1431] The device uses generative artificial intelligence to analyze the acquired data and automatically generate a data report.

[1432] Input: Latest device data

[1433] Data Calculation: Generative AI analyzes data and formats statistics and trends into reports

[1434] Output: Auto-generated data report

[1435] Step 3

[1436] The server provides the generated report to the user.

[1437] Input: Auto-generated data report

[1438] What happens: The server displays the report on a dashboard or emails it to the user.

[1439] Output: User-accessible data reports

[1440] (Application example 1)

[1441] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1442] Traditionally, configuring and optimizing factory robots required a lot of manual work and time, resulting in reduced productivity. In addition, it was difficult to ensure transparency and consistency in IoT device data management, leading to issues with data reliability. Furthermore, automating device configuration change history and behavior analysis required complex processes and human intervention, making troubleshooting and efficient operation difficult.

[1443] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1444] In this invention, the server includes means for updating IoT device settings using generative artificial intelligence, means for profiling IoT device data using blockchain technology, means for automatically collecting IoT device data and generating data reports using generative artificial intelligence, and means for configuring and optimizing factory robot operations. This enables automation of factory robot configuration and optimization, ensures transparency and consistency in IoT device data management, and enables efficient and reliable device operation.

[1445] "Generative AI" refers to AI that automatically generates new configuration information and data analysis results based on input information provided by the user and existing device data.

[1446] "IoT devices" are various devices that are interconnected via the Internet and are capable of collecting and communicating data.

[1447] "Means for updating settings" refers to means for optimizing the settings of IoT devices using generative artificial intelligence and automatically applying the latest settings.

[1448] "Blockchain technology" is a technology that builds a distributed database of data and ensures its transparency and consistency.

[1449] "Means for profiling data" refers to a means for using blockchain technology to perform detailed analysis of IoT device data and evaluate the device's condition and performance.

[1450] "Means for generating data reports" refers to means for analyzing data collected from IoT devices using generative artificial intelligence and outputting the results in the form of reports on a regular basis.

[1451] A "factory robot" is a robot designed to automatically perform various tasks in a factory.

[1452] "Means for setting and optimizing operation" refers to means for optimizing the operation settings of factory robots and adjusting them so that they can perform work efficiently and effectively.

[1453] This invention is a system that configures and optimizes the operation of factory robots and ensures transparency and consistency in data management of IoT devices. This system is composed of a combination of generative artificial intelligence and blockchain technology, and uses the following hardware and software:

[1454] Hardware and software used

[1455] Hardware: Various IoT sensors installed in the factory (temperature sensors, pressure sensors, speed sensors, etc.), factory robots that change settings

[1456] software:

[1457] Generative AI: Python AI libraries (e.g., TensorFlow)

[1458] Blockchain: A custom blockchain implemented in Python

[1459] Data analysis: A data processing system (e.g., Pandas, Numpy) for analyzing data streams from virtual sensors in real time.

[1460] System processing overview

[1461] The server automatically collects data from IoT sensors in the factory and uses generative artificial intelligence to generate optimal operating settings, which are then stored transparently using blockchain technology.

[1462] The server first collects sensor data and analyzes it in real time. Based on this data, generative artificial intelligence automatically generates optimal robot operation settings, enabling automated factory robot configuration and optimization.

[1463] The generated configuration information and analysis results are stored using blockchain technology, ensuring data transparency and consistency of change history. This data management method enables efficient and reliable device operation.

[1464] Specific examples

[1465] A system that optimizes the motor speed and grip force of a robot in a factory continuously collects data from sensors and uses that data to calculate the optimal settings using generative artificial intelligence. Specifically, the AI ​​takes the current sensor values ​​and generates the appropriate settings. This new setting is stored on the blockchain, ensuring the device's authenticity and transparency.

[1466] Example prompts to input to the generative AI model

[1467] "Calculate the optimal motor speed and grip force for this system based on the latest sensor data."

[1468] Specific prompt:

[1469] "Calculate the optimal motor speed and grip force for the robot based on the following sensor data:

[1470] {

[1471] 'current_speed': 1.0,

[1472] 'current_grip': 1.0

[1473] }"

[1474] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1475] Step 1:

[1476] The server collects data in real time from various IoT sensors in the factory. The input from the sensors is various types of data such as temperature, pressure, speed, etc. The server acquires these inputs and stores them in a database.

[1477] Step 2:

[1478] The server inputs the collected sensor data into a generative AI model, which analyzes the data and generates optimal motion settings. This generative AI calculates optimal settings for motor speed and grip force, for example.

[1479] Step 3:

[1480] The terminal receives the optimal operational settings output by the generative AI and transmits the results to the factory robots, where the input is the setting value output by the AI ​​model and the output is the new operational setting value applied to the robot.

[1481] Step 4:

[1482] The server registers the generated operational configuration and related sensor data on the blockchain. The input is the operational configuration and sensor data, and the output is the data stored on the blockchain. This process ensures data transparency and a change history.

[1483] Step 5:

[1484] The server periodically retrieves data from the blockchain and generates a data report, where the input is the data retrieved from the blockchain and the output is the data report provided to the user.

[1485] Step 6:

[1486] Users can refer to the generated data report to check the operation status of the factory robot and the optimization status of the settings, and can make any further adjustments necessary based on this.

[1487] These processing steps enable automated configuration and optimization of factory robots, and improve transparency and reliability of data management.

[1488] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1489] This invention relates to an IoT device management system that combines generative artificial intelligence, blockchain technology, and an emotion engine to automate the process of updating IoT device settings, data profiling, and automatic data report generation, and also enables device settings adjustment and report display based on the user's emotional state.

[1490] System configuration

[1491] Generative artificial intelligence (AI)

[1492] Generative AI automatically generates new configuration information and data analysis results based on user-provided input and existing device data. The AI ​​engine periodically analyzes device data and proposes and implements configuration updates as needed.

[1493] Blockchain technology

[1494] Blockchain technology allows IoT device data to be registered in a distributed database, ensuring transparency and consistency. This technology ensures that device operation history and configuration change history are tamper-proof, facilitating troubleshooting and device management.

[1495] Emotion Engine

[1496] The emotion engine recognizes the user's emotional state in real time and provides that information to the system, which can then dynamically adjust the settings of IoT devices and how data reports are displayed based on the user's emotional state.

[1497] Program processing overview

[1498] Device Profiling

[1499] 1. A user enters the necessary information to register a new IoT device into the system (e.g., device name, location, type, etc.).

[1500] 2. The server receives this information and generates a device ID.

[1501] 3. The server creates a payload containing the generated device ID and registration information and sends an HTTP POST request to the Blockchain API.

[1502] 4. The server receives the response from the blockchain API and verifies whether the device was successfully registered.

[1503] Get Device Data

[1504] 1. The server sends an HTTP GET request using a specific device ID to retrieve data about that device from the blockchain.

[1505] 2. The server receives the response from the blockchain API and parses the data in JSON format.

[1506] 3. The server provides the analyzed device data to applications and users.

[1507] Update device settings

[1508] 1. The user inputs a new setting value (e.g., threshold value).

[1509] 2. Based on the settings entered by the device, the device sends a request to the generative AI API to generate the optimal settings.

[1510] 3. The device receives the setting values ​​generated in response from the generative AI API.

[1511] 4. The server creates a payload containing the new configuration values ​​and sends an update device configuration request to the blockchain API.

[1512] 5. The server receives the response from the Blockchain API and verifies whether the configuration update was successful.

[1513] Automatic generation of data reports

[1514] 1. The server sends an HTTP GET request to retrieve the latest device data from the blockchain using the specific device ID.

[1515] 2. The server receives the response from the blockchain API and parses the data in JSON format.

[1516] 3. The device analyzes the device data and sends it to the generative AI API, which then sends a request to generate a data report.

[1517] 4. The device receives the generated data report in response from the generative AI API.

[1518] 5. The server provides the generated data report to the user or uses it for further data analysis within the system.

[1519] Use of emotion engine

[1520] 1. The emotion engine recognizes the user's emotional state in real time.

[1521] 2. The emotion engine sends the recognized emotion data to the system, which dynamically adjusts the device settings and how data reports are displayed.

[1522] 3. The server registers the emotion data on the blockchain to ensure transparency and consistency.

[1523] Specific examples

[1524] Device Profiling Example

[1525] 1. The user enters the temperature sensor registration information (e.g., name, location, type).

[1526] 2. The server receives the entered information and generates a device ID.

[1527] 3. The server creates a payload containing the device ID and registration information and sends an HTTP POST request to the Blockchain API.

[1528] 4. The server receives the response from the blockchain API and verifies whether the device was successfully registered.

[1529] Example of updating device settings

[1530] 1. The user enters a new threshold of 25 degrees for the temperature sensor.

[1531] 2. The device sends a request to the AI ​​API for the input thresholds to generate the optimal settings.

[1532] 3. The device receives the setting values ​​generated in response from the generative AI API.

[1533] 4. The server creates a payload containing the new configuration values ​​and sends an update device configuration request to the blockchain API.

[1534] 5. The server receives the response from the Blockchain API and verifies whether the configuration update was successful.

[1535] Example of automatic data report generation

[1536] 1. The server sends an HTTP GET request using the temperature sensor ID to retrieve the latest device data from the blockchain.

[1537] 2. The server receives the response from the blockchain API and parses the data in JSON format.

[1538] 3. The device analyzes the device data and sends it to the generative AI API, which then sends a request to generate a data report.

[1539] 4. The device receives the generated data report in response from the generative AI API.

[1540] 5. The server provides the generated data report to the user or uses it for further data analysis within the system.

[1541] Example of using the emotion engine

[1542] 1. The emotion engine recognizes the user's emotions in real time and sends the emotion data to the system.

[1543] 2. Your device will adjust device settings and how data reports are displayed based on emotion data.

[1544] 3. The server registers the emotion data on the blockchain to ensure transparency and consistency.

[1545] This system improves the connectivity of IoT devices, making it easier for users to manage devices and analyze data. Furthermore, the use of an emotion engine enables optimal operation based on the user's emotional state, improving the user experience. Furthermore, the use of blockchain technology makes the device's operational history transparent, enabling reliable data management.

[1546] The processing flow will be explained below.

[1547] Device Profiling

[1548] Step 1:

[1549] The user enters the registration information for a new IoT device (e.g., device name, location, type, etc.) into the system's input form.

[1550] Step 2:

[1551] The server takes the information provided by the user and generates a new device ID.

[1552] Step 3:

[1553] The server creates a payload containing the device ID and the registration information provided by the user and sends an HTTP POST request to the Blockchain API.

[1554] Step 4:

[1555] The server receives the response sent from the Blockchain API and verifies whether the device was successfully registered.

[1556] Get Device Data

[1557] Step 1:

[1558] The server sends an HTTP GET request using a specific device ID to retrieve data about that device from the blockchain.

[1559] Step 2:

[1560] The server receives the response sent from the blockchain API and parses the data in JSON format.

[1561] Step 3:

[1562] The server converts the parsed device data into a format for presentation to the application user.

[1563] Step 4:

[1564] Access, view, and manipulate device data provided by the user.

[1565] Update device settings

[1566] Step 1:

[1567] The user enters new device setting values ​​(e.g., thresholds) into the system's input form.

[1568] Step 2:

[1569] Based on the settings entered by the user, the device sends a request to a generative artificial intelligence API to generate the optimal settings.

[1570] Step 3:

[1571] The device receives the setting values ​​generated in the response sent from the generative artificial intelligence API.

[1572] Step 4:

[1573] The server creates a payload containing the new configuration values ​​and sends an update device configuration request to the blockchain API.

[1574] Step 5:

[1575] The server receives the response sent from the Blockchain API and checks whether the configuration update was completed successfully.

[1576] Automatic generation of data reports

[1577] Step 1:

[1578] The server sends an HTTP GET request using the specific device ID to retrieve the latest device data from the blockchain.

[1579] Step 2:

[1580] The server receives the response sent from the blockchain API and parses the data in JSON format.

[1581] Step 3:

[1582] The device data analyzed by the terminal is sent to a generative artificial intelligence API, which then sends a request to generate a data report.

[1583] Step 4:

[1584] The device receives the data report generated in response from the generative artificial intelligence API.

[1585] Step 5:

[1586] The server converts the generated data report into a format for presentation to the user.

[1587] Use of emotion engine

[1588] Step 1:

[1589] The emotion engine analyzes the user's facial expressions, voice, etc. to recognize the user's emotional state in real time.

[1590] Step 2:

[1591] The emotion engine transmits the recognized emotion data to the server.

[1592] Step 3:

[1593] The server uses the received emotion data to dynamically adjust device settings and how data reports are displayed accordingly.

[1594] Step 4:

[1595] The server registers the emotion data on the blockchain, ensuring transparency and consistency.

[1596] Specific examples

[1597] Device Profiling Example

[1598] Step 1:

[1599] The user enters the temperature sensor registration information (e.g., name, location, and type) into the system's input form.

[1600] Step 2:

[1601] The server takes the information provided by the user and generates a new device ID.

[1602] Step 3:

[1603] The server creates a payload containing the device ID and registration information and sends an HTTP POST request to the Blockchain API.

[1604] Step 4:

[1605] The server receives the response sent from the Blockchain API and verifies whether the device was successfully registered.

[1606] Example of updating device settings

[1607] Step 1:

[1608] The user enters the new temperature sensor threshold of 25 degrees into the system's input form.

[1609] Step 2:

[1610] The device sends a request to the artificial intelligence API for generating the input threshold value, and generates the optimal settings.

[1611] Step 3:

[1612] The device receives the setting values ​​generated in the response sent from the generative artificial intelligence API.

[1613] Step 4:

[1614] The server creates a payload containing the new configuration values ​​and sends an update device configuration request to the blockchain API.

[1615] Step 5:

[1616] The server receives the response sent from the Blockchain API and checks whether the configuration update was completed successfully.

[1617] Example of automatic data report generation

[1618] Step 1:

[1619] The server sends an HTTP GET request using the temperature sensor ID to retrieve the latest device data from the blockchain.

[1620] Step 2:

[1621] The server receives the response sent from the blockchain API and parses the data in JSON format.

[1622] Step 3:

[1623] The device data analyzed by the terminal is sent to a generative artificial intelligence API, which then sends a request to generate a data report.

[1624] Step 4:

[1625] The device receives the data report generated in response from the generative artificial intelligence API.

[1626] Step 5:

[1627] The server converts the generated data report into a format for presentation to the user.

[1628] Example of using the emotion engine

[1629] Step 1:

[1630] The emotion engine recognizes the user's emotions in real time and transmits the emotion data to the server.

[1631] Step 2:

[1632] Your device will adjust device settings and how data reports are displayed based on your emotional data.

[1633] Step 3:

[1634] The server registers the emotion data on the blockchain, ensuring transparency and consistency.

[1635] Example 2

[1636] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1637] As the number of IoT devices increases, managing them and updating their settings becomes more complex. Furthermore, there is an increasing demand for device behavior and display methods that take into account the user's emotional state. Furthermore, while it is important to ensure the transparency and integrity of device data, no effective system exists to address these challenges. To solve these problems, an innovative system combining generative artificial intelligence, blockchain technology, and an emotion engine is needed.

[1638] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1639] In this invention, the server includes means for updating IoT device settings using generative artificial intelligence, means for profiling IoT device data using blockchain technology, means for automatically collecting IoT device data and generating data reports using generative artificial intelligence, means for recognizing a user's emotional state in real time using an emotion engine and dynamically adjusting the IoT device settings and display of the data report, and means for dynamically adjusting the display method of the IoT device settings and data report based on the user's emotional state. This enables optimal device settings and data report display according to the user's emotional state, while ensuring the transparency and consistency of device data.

[1640] "Generative AI" is an AI technology that automatically generates new configuration information and data analysis results based on input information provided by users and existing device data.

[1641] "Blockchain technology" is a technology that registers data in a distributed database and keeps operation history and setting change history untampered in order to ensure data transparency and consistency.

[1642] The "emotion engine" is a technology that recognizes a user's emotional state in real time and provides that information to the system, dynamically adjusting device settings and the way data reports are displayed based on the user's emotional state.

[1643] An "IoT device" is a physical device that can communicate with other devices and systems over the Internet.

[1644] "Profiling" is the process of collecting data about a specific target (in this case, an IoT device) and analyzing its characteristics and behavioral patterns.

[1645] A "data report" is a report generated based on collected and analyzed data, often including visual graphs and statistics.

[1646] "Payload" refers to the actual portion of data exchanged between systems, that is, the information actually sent from the sender to the receiver.

[1647] "Unique Device ID" means a unique identifier used to distinguish a particular device from all other devices.

[1648] An "HTTP request" is a request sent from a client to a server when accessing a resource on the web, and includes methods such as GET and POST.

[1649] MODE FOR CARRYING OUT THE INVENTION

[1650] This invention relates to an IoT device management system that combines generative artificial intelligence, blockchain technology, and an emotion engine to automate the process of updating IoT device settings, data profiling, and automatic data report generation, and also enables device settings adjustment and report display based on the user's emotional state.

[1651] Hardware and software used

[1652] The server is the hardware that handles the main processing of the system. It runs multiple services, profiling device data, updating settings, and generating data reports.

[1653] A terminal is a device (e.g., PC, smartphone, tablet) through which a user interacts with the system. The terminal receives input from the user and sends requests to the server or the generative AI API based on that input.

[1654] Generative AI is software that automatically generates new configuration information and data analysis results based on input information provided by the user and existing device data.

[1655] Blockchain technology is used to ensure transparency and consistency of devices by registering device data and operation history in a distributed database.

[1656] An emotion engine is software that recognizes a user's emotional state in real time and provides that information to a system that can dynamically adjust the settings of IoT devices and the way data reports are displayed based on the user's emotional state.

[1657] System processing overview and specific examples

[1658] Device profiling examples

[1659] The user enters the temperature sensor's registration information (e.g., name, location, and type). The server receives the entered information and generates a unique device ID. The server then creates a payload containing the generated device ID and registration information and sends an HTTP POST request to the blockchain API. The server then receives a response from the blockchain API and confirms whether the device has been successfully registered.

[1660] Example of updating device settings

[1661] The user inputs a new threshold value for the temperature sensor (e.g., 25 degrees). The device sends a request to the generative AI API based on the input threshold value to generate optimal settings. The device receives the generated setting value in the response from the generative AI API and sends it to the server. The server creates a payload containing the new setting value and sends a device setting update request to the blockchain API. Finally, the server receives the response from the blockchain API and confirms that the setting update was completed successfully.

[1662] Example of automatic generation of data reports

[1663] The server uses the temperature sensor ID to send an HTTP GET request to retrieve the latest device data from the blockchain. The server receives the response from the blockchain API and parses the data in JSON format. The device then sends the parsed device data to the generative AI API and sends a request to generate a data report. The device then receives the generated data report in the response from the generative AI API and provides it to the server. The server then provides the generated data report to the user or uses it for further data analysis within the system.

[1664] Examples of using the emotion engine

[1665] The emotion engine recognizes users' emotions in real time and transmits the emotion data to the system. The device adjusts device settings and data report display methods based on the emotion data. The server registers the emotion data on the blockchain to ensure transparency and consistency.

[1666] Prompt Sentence Examples

[1667] "Analyze the following device data and generate a data report: Temperature sensor ID: 123, Temperature: 25°C, Humidity: 60%"

[1668] This invention allows users to easily manage IoT devices and analyze data. Furthermore, the use of an emotion engine enables optimal operation according to the user's emotional state, improving the user experience. Furthermore, the use of blockchain technology makes the device's operational history transparent, enabling highly reliable data management.

[1669] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1670] Device Profiling

[1671] Step 1:

[1672] The user accesses the system's UI and enters information about the new IoT device (e.g., device name, location, and type).

[1673] Input: Device information such as device name, location, and type

[1674] Output: User input information

[1675] What happens: A user navigates through a form, filling in the fields and finally clicking the submit button.

[1676] Step 2:

[1677] The server receives input information from the user.

[1678] Input: User input information

[1679] Output: A set of information to generate a device ID

[1680] What it does: The server stores the received information in a temporary database, then generates a unique device ID based on the entered data.

[1681] Step 3:

[1682] The server generates a unique device ID.

[1683] Input: User input information

[1684] Output: Unique device ID

[1685] What happens: The server uses a UUID (Universal Unique Identifier) ​​generator to generate a unique ID.

[1686] Step 4:

[1687] The server combines the generated device ID and the user's input information into a payload and sends an HTTP POST request to the blockchain API.

[1688] Input: Device ID, user input information

[1689] Output: A request to the Blockchain API

[1690] What happens: The server creates a new payload and sends an HTTP POST request to the blockchain API.

[1691] Step 5:

[1692] The server receives the response from the Blockchain API and confirms that the device was successfully registered.

[1693] Input: Response from the Blockchain API

[1694] Output: Confirmation of registration result

[1695] Specific operation: The server checks the response status code to see if the registration was successful. If successful, it saves the device information in the database.

[1696] Update device settings

[1697] Step 1:

[1698] The user inputs new device setting values ​​(e.g., thresholds) on the system setting screen.

[1699] Input: New setting value (e.g. threshold)

[1700] Output: User configuration information

[1701] Specific behavior: The user enters a new threshold in the settings input form and clicks the save button.

[1702] Step 2:

[1703] The device sends a request to the generative artificial intelligence API based on the input settings.

[1704] Input: New setting value

[1705] Output: A request to the generative AI API

[1706] Specific operation: The device creates a request payload containing the new setting values ​​and sends an HTTP POST request to the generative AI API.

[1707] Step 3:

[1708] The device receives the setting values ​​generated in response from the generative artificial intelligence API.

[1709] Input: Response from the generative AI API

[1710] Output: Optimal setting

[1711] Specific operation: Analyze the response data received by the device and extract the optimal setting values.

[1712] Step 4:

[1713] The server creates a payload containing the new configuration values ​​and sends an update device configuration request to the blockchain API.

[1714] Input: Optimal setting

[1715] Output: An update request to the blockchain API

[1716] What happens: The server creates the update payload and sends an HTTP POST request to the blockchain API.

[1717] Step 5:

[1718] The server receives the response from the Blockchain API and confirms that the configuration update was successful.

[1719] Input: Response from the Blockchain API

[1720] Output: Confirmation of update results

[1721] Specific behavior: The server checks the response status code to confirm that the settings were updated successfully. If successful, it saves the setting change history to the database.

[1722] Automatic generation of data reports

[1723] Step 1:

[1724] The server sends an HTTP GET request using the specific device ID to retrieve the latest device data from the blockchain.

[1725] Input: Device ID

[1726] Output: A get request to the Blockchain API

[1727] Specific operation: The server creates an HTTP GET request based on the device ID and sends it to the blockchain API.

[1728] Step 2:

[1729] The server receives the response from the blockchain API and parses the data in JSON format.

[1730] Input: Response from the Blockchain API

[1731] Output: Parsed device data

[1732] Specific operation: The response data received by the server is analyzed using a JSON parser.

[1733] Step 3:

[1734] The device data analyzed by the terminal is sent to a generative artificial intelligence API, which then sends a request to generate a data report.

[1735] Input: Parsed device data

[1736] Output: A generated request to the generative AI API

[1737] Specific operation: The device creates a request payload for generating a data report and sends an HTTP POST request to the generative artificial intelligence API.

[1738] Step 4:

[1739] The device receives the data report generated in response from the generative artificial intelligence API.

[1740] Input: Response from the generative AI API

[1741] Output: Data report

[1742] Specific operations: Analyze the response data received by the terminal and extract the generated data report.

[1743] Step 5:

[1744] The server provides the generated data report to the user or uses it for further data analysis within the system.

[1745] Input: Data Report

[1746] Output: Report display to user or internal data analysis

[1747] Specific Actions: The server displays the data report through the user interface and notifies the user, or stores it in a database for further analysis within the system.

[1748] Use of emotion engine

[1749] Step 1:

[1750] The emotion engine recognizes the user's emotions in real time and transmits the emotion data to the system.

[1751] Input: User's emotional state

[1752] Output: Emotion data

[1753] Specific operation: The emotion engine analyzes camera and sensor data to recognize the user's emotional state, and sends the recognized emotional data to the system.

[1754] Step 2:

[1755] Your device will adjust device settings and how data reports are displayed based on your emotion data.

[1756] Input: Emotion data

[1757] Output: How to view adjusted device settings and data reports

[1758] Specific behavior: Based on the emotion data received by the device, logic is applied to optimize settings and display methods, and actual device settings and display methods are changed.

[1759] Step 3:

[1760] The server registers the emotion data on the blockchain, ensuring transparency and consistency.

[1761] Input: Emotion data

[1762] Output: Record on the blockchain

[1763] Specific operation: The server creates a payload containing emotion data and sends an HTTP POST request to the blockchain API. After sending, it receives a response and confirms that the emotion data was successfully recorded.

[1764] (Application example 2)

[1765] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1766] In modern virtual stores, it is important to dynamically manage and adjust the environment using IoT devices to maximize the user experience. However, conventional systems do not adjust settings in real time based on the user's emotional state, making it difficult to optimize operations based on the user's emotions and behavior. Furthermore, there is a lack of means to ensure data transparency and consistency. Therefore, there is a need for a system that can appropriately adjust devices according to the user's emotional state and manage data in a secure and transparent manner.

[1767] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1768] In this invention, the server includes: means for updating IoT device settings using generative artificial intelligence; means for profiling IoT device data using blockchain technology; means for automatically collecting IoT device data and generating data reports using generative artificial intelligence; means for recognizing a user's emotional state in real time using an emotion engine and dynamically adjusting IoT device settings based on the state; and means for registering the emotion data obtained by the emotion engine in the blockchain to ensure transparency and consistency. This enables optimal adjustment of IoT device settings based on the user's emotional state, thereby realizing safe and transparent data management.

[1769] "Generative AI" is an AI technology that automatically generates new settings and analysis results based on input information provided by the user and existing data.

[1770] An "IoT device" is a physical device that communicates with each other over the Internet and collects and controls data.

[1771] "Blockchain technology" is a technology that uses a distributed database to ensure data transparency and consistency. This technology prevents data tampering and enables highly reliable data management.

[1772] "Data profiling" is the process of collecting data related to a particular device and analyzing its characteristics and patterns.

[1773] A "data report" is a report that analyzes collected data and summarizes it visually or in writing, making it easy to understand trends and problems in the data.

[1774] An "emotion engine" is a technology that recognizes a user's emotional state in real time and integrates that information with other systems.

[1775] "Transparency" refers to a state in which data and processes can be easily verified from the outside and are highly reliable.

[1776] "Consistency" refers to a state in which data is managed according to certain rules and standards, and its quality is always maintained.

[1777] "Real-time" refers to responding immediately to an event or process as it occurs.

[1778] This invention relates to an IoT device management system that combines generative artificial intelligence, blockchain technology, and an emotion engine. The system aims to improve the user experience in virtual stores using devices such as smart glasses.

[1779] The system configuration is as follows:

[1780] 1. Generative Artificial Intelligence

[1781] The server uses generative artificial intelligence (AI) to automatically generate new configuration information and data analysis results based on user-provided input information and existing device data. The AI ​​periodically analyzes device data and generates appropriate configuration updates and data reports.

[1782] 2. Blockchain Technology

[1783] The server uses blockchain technology to register IoT device data and configuration change history in a distributed database, ensuring transparency and consistency and preventing tampering with device operation history and data.

[1784] 3. Emotion Engine

[1785] The emotion engine recognizes the user's emotional state in real time and provides that data to the server, which then dynamically adjusts the settings of the IoT device and how data reports are displayed. The emotion data is also registered on the blockchain to ensure transparency and consistency.

[1786] Example of a system

[1787] An example of a virtual store assistant might be implemented as follows:

[1788] Adjusting lighting based on user's emotional state

[1789] As a user wearing smart glasses walks around a virtual store, the emotion engine recognizes the user's emotional state in real time. For example, if the user is "having fun," the generative AI will brighten the smart lighting in the store and set the color temperature to a warmer setting.

[1790] Device registration and configuration updates

[1791] Device information is entered from the terminal through the smart glasses, and the server registers it on the blockchain. Device settings are updated by generating optimal settings based on user input using generative artificial intelligence, which then registers the settings on the blockchain again.

[1792] Hardware and software used

[1793] 1. Hardware

[1794] Smart glasses (AR / VR headsets)

[1795] Emotion Recognition Camera

[1796] 2. Software

[1797] Emotion Engine API

[1798] AI Generator API

[1799] Blockchain API

[1800] Examples of prompt statements

[1801] An example of a prompt to input to a generative AI model is as follows:

[1802] Use the latest customer sentiment data to suggest optimal settings for smart lighting in your virtual store: for example, if the customer is "having fun," set the brightness to 100% and the color temperature to cool white.

[1803] This invention enables IoT devices in virtual stores to work together and optimize operations based on the user's emotional state. Blockchain technology also makes the device's operational history transparent, enabling highly reliable data management.

[1804] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1805] Step 1:

[1806] Registering devices

[1807] The user puts on the smart glasses and inputs information to register a new IoT device (e.g., device name, installation location, type) into the terminal via the smart glasses. The terminal receives this information and sends it to the server. The server generates a device ID based on this information and sends an HTTP POST request to register the device information on the blockchain. The response confirms that the device has been successfully registered.

[1808] Input: Device information (device name, location, type)

[1809] Output: Device ID, result registered on the blockchain

[1810] How it works: The user enters device information through the smart glasses, the device sends the information to the server, and the server registers it on the blockchain.

[1811] Step 2:

[1812] Update device settings

[1813] The user inputs new settings (e.g., thresholds) through the smart glasses. The device requests the input settings from the generative AI API, which generates optimal settings. The server registers the generated settings in the blockchain and responds by confirming whether the update was successful.

[1814] Input: New setting value (e.g. threshold)

[1815] Output: Generated optimal settings, results registered on the blockchain

[1816] How it works: The user inputs new settings through the smart glasses, the device sends them to the generative AI API, and the server registers the generated settings on the blockchain.

[1817] Step 3:

[1818] Automatic generation of data reports

[1819] The server sends an HTTP GET request using a specific device ID to retrieve the latest device data from the blockchain. The server sends the data retrieved in response to the generative AI API and requests it to generate a data report. The generated data report is then received and provided to the user.

[1820] Input: Device ID, data from blockchain

[1821] Output: Generated data report

[1822] How it works: The server retrieves device data from the blockchain, sends it to a generative AI API, and provides the generated data report to the user.

[1823] Step 4:

[1824] Use of emotion engine

[1825] The emotion engine recognizes the user's emotional state in real time, sends the recognized emotional data to the server, which registers the data on the blockchain, and dynamically adjusts the device settings based on the emotional data.

[1826] Input: Emotion data

[1827] Output: Dynamically adjusted device settings, emotion data registered on the blockchain

[1828] How it works: The emotion engine recognizes the user's emotional state in real time, the server registers the emotional data on the blockchain, and the device adjusts the device settings.

[1829] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1830] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1831] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1832] [Fourth embodiment]

[1833] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1834] 7, a 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.

[1835] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1836] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1837] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1838] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1839] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1840] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1841] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1842] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1843] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1844] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1845] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1846] This invention is a system that combines generative artificial intelligence and blockchain technology to automate configuration updates, data profiling, and data analysis of IoT devices, improving connectivity between devices.

[1847] System configuration

[1848] Generative artificial intelligence (AI)

[1849] Generative AI automatically generates new configuration information and data analysis results based on user-provided input information and existing device data. Specifically, the AI ​​engine periodically analyzes device data and proposes and implements configuration updates as needed.

[1850] Blockchain technology

[1851] Blockchain technology allows IoT device data to be registered in a distributed database, ensuring transparency and consistency. This technology ensures that device operation history and configuration change history are tamper-proof, facilitating troubleshooting and device management.

[1852] Program processing overview

[1853] Device Profiling

[1854] 1. A user enters the necessary information into the system to register a new IoT device.

[1855] 2. The server receives this information and uses blockchain technology to profile the device, specifically registering the device ID, registration timestamp, and other initial data on the blockchain.

[1856] Get Device Data

[1857] 1. The server uses a specific device ID to retrieve the latest data about that device from the blockchain.

[1858] 2. The acquired data is used to check the status and history of the device.

[1859] Update device settings

[1860] 1. The user enters new device settings.

[1861] 2. The device uses generative artificial intelligence to analyze the settings information entered and generate the optimal settings.

[1862] 3. The server registers the generated configuration information on the blockchain and updates the device configuration.

[1863] Automatic generation of data reports

[1864] 1. The server periodically retrieves the latest device data from the blockchain.

[1865] 2. The device uses generative artificial intelligence to analyze this data and automatically generate a data report.

[1866] 3. The data report is provided to the user or used for further analysis within the system.

[1867] Specific examples

[1868] Device Profiling Example

[1869] 1. The user registers a temperature sensor by entering information such as the device name, location, and type.

[1870] 2. The server registers this information on the blockchain and assigns a device ID.

[1871] Example of updating device settings

[1872] 1. The user enters new configuration information to set the temperature sensor threshold to 25 degrees.

[1873] 2. The device uses generative artificial intelligence to evaluate whether the settings are appropriate and generate optimal settings.

[1874] 3. The server registers this configuration information on the blockchain and updates the device settings.

[1875] Example of automatic data report generation

[1876] 1. The server retrieves the latest temperature sensor data from the blockchain.

[1877] 2. The device uses generative artificial intelligence to analyze this data and automatically generate a statistical report of temperature fluctuations.

[1878] 3. The generated report is provided to the user or used for further data analysis.

[1879] This system improves the connectivity of IoT devices, making it easier for users to manage devices and analyze data. In addition, the use of blockchain technology makes the device's operational history transparent, enabling highly reliable data management.

[1880] The processing flow will be explained below.

[1881] Device Profiling

[1882] Step 1:

[1883] The user enters registration information for a new IoT device (e.g., device name, location, type, etc.).

[1884] Step 2:

[1885] The server receives the entered information and generates a device ID.

[1886] Step 3:

[1887] The server creates a payload containing the generated device ID and registration information and sends an HTTP POST request to the Blockchain API.

[1888] Step 4:

[1889] The server receives the response from the Blockchain API and verifies whether the device was successfully registered.

[1890] Get Device Data

[1891] Step 1:

[1892] The server sends an HTTP GET request using a specific device ID to retrieve data about that device from the blockchain.

[1893] Step 2:

[1894] The server receives the response from the blockchain API and parses the data in JSON format.

[1895] Step 3:

[1896] The server analyzes the device data and provides it to applications and users.

[1897] Update device settings

[1898] Step 1:

[1899] The user inputs a new setting value (e.g., threshold value).

[1900] Step 2:

[1901] Based on the settings entered on the device, the device sends a request to a generative artificial intelligence API to generate the optimal settings.

[1902] Step 3:

[1903] The device receives the setting values ​​generated in response from the generative artificial intelligence API.

[1904] Step 4:

[1905] The server creates a payload containing the new configuration values ​​and sends an update device configuration request to the blockchain API.

[1906] Step 5:

[1907] The server receives the response from the Blockchain API and verifies whether the configuration update was successful.

[1908] Automatic generation of data reports

[1909] Step 1:

[1910] The server sends an HTTP GET request using the specific device ID to retrieve the latest device data from the blockchain.

[1911] Step 2:

[1912] The server receives the response from the blockchain API and parses the data in JSON format.

[1913] Step 3:

[1914] The device data analyzed by the terminal is sent to a generative artificial intelligence API, which then sends a request to generate a data report.

[1915] Step 4:

[1916] The device receives the data report generated in response from the generative artificial intelligence API.

[1917] Step 5:

[1918] The server provides the generated data report to the user or uses it for further data analysis within the system.

[1919] Specific examples

[1920] Device Profiling Example

[1921] Step 1:

[1922] The user enters the temperature sensor registration information (e.g., name, location, type).

[1923] Step 2:

[1924] The server receives the entered information and generates a device ID.

[1925] Step 3:

[1926] The server creates a payload containing the device ID and registration information and sends an HTTP POST request to the Blockchain API.

[1927] Step 4:

[1928] The server receives the response from the Blockchain API and verifies whether the device was successfully registered.

[1929] Example of updating device settings

[1930] Step 1:

[1931] The user enters a new threshold of 25 degrees for the temperature sensor.

[1932] Step 2:

[1933] The device sends a request to the artificial intelligence API for generating the input threshold value, and generates the optimal settings.

[1934] Step 3:

[1935] The device receives the setting values ​​generated in response from the generative artificial intelligence API.

[1936] Step 4:

[1937] The server creates a payload containing the new configuration values ​​and sends an update device configuration request to the blockchain API.

[1938] Step 5:

[1939] The server receives the response from the Blockchain API and verifies whether the configuration update was successful.

[1940] Example of automatic data report generation

[1941] Step 1:

[1942] The server sends an HTTP GET request using the temperature sensor ID to retrieve the latest device data from the blockchain.

[1943] Step 2:

[1944] The server receives the response from the blockchain API and parses the data in JSON format.

[1945] Step 3:

[1946] The device data analyzed by the terminal is sent to a generative artificial intelligence API, which then sends a request to generate a data report.

[1947] Step 4:

[1948] The device receives the data report generated in response from the generative artificial intelligence API.

[1949] Step 5:

[1950] The server provides the generated data report to the user or uses it for further data analysis within the system.

[1951] Example 1

[1952] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1953] In conventional IoT device management systems, device configuration updates, data profiling, and data analysis are performed manually, resulting in inefficiency and insufficient collaboration between devices. Furthermore, reliability is low because it is difficult to prevent data tampering and ensure consistency. Furthermore, there are limited ways to regularly check device status and data analysis results.

[1954] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1955] In this invention, the server includes: means for updating IoT device settings using generative artificial intelligence; means for profiling IoT device data using blockchain technology; means for automatically collecting IoT device data and generating data reports using generative artificial intelligence; means for a user to input device information, which the server receives and registers in the blockchain; means for the server to analyze data acquired from the device and generate optimal setting values; and means for the terminal to provide data analysis results using generative artificial intelligence. This automates IoT device setting updates, data profiling, and data analysis, improves collaboration between devices, prevents data tampering, and ensures consistency. Furthermore, it becomes possible to periodically grasp the device status and data analysis results.

[1956] "Generative AI" is an AI technology that automatically generates new configuration information and data analysis results based on input information provided by users and existing device data.

[1957] An "IoT device" is a physical device or sensor that is connected to the Internet and capable of sending and receiving data.

[1958] The "means for updating settings" is a method that uses generative artificial intelligence to analyze the setting information of an IoT device, generate optimal setting values ​​as needed, and automatically change the device settings.

[1959] "Blockchain technology" is a technology for recording data transaction history securely and in a decentralized manner, and has the advantage of preventing data tampering and unauthorized access.

[1960] "Means of data profiling" refers to a method of registering initial information about IoT devices using blockchain technology to identify and monitor the devices.

[1961] "Means for automatically collecting data" refers to a method for acquiring data from IoT devices periodically or in real time and sending it to a server.

[1962] The "means for generating data reports" refers to a method of analyzing collected data using generative artificial intelligence and automatically creating reports in a format that is easy for users to understand.

[1963] A "user" is a person or company that operates the system, inputs information, or receives reports.

[1964] The "server" is a central control unit that receives data from IoT devices, analyzes it, and registers it on the blockchain.

[1965] A "terminal" is a device operated by a user that uses generative artificial intelligence to analyze data and provide results.

[1966] "Profiling" is a procedure for compiling and managing unique information about IoT devices.

[1967] "Transparency" means that data is not tampered with and correct information can always be referenced.

[1968] "Means for managing data change history" refers to a method that uses blockchain technology to record the history of data changes and make them available for later review.

[1969] The "means of ensuring consistency" refers to a method of consistently managing the device's configuration status by registering configuration information generated using generative artificial intelligence on the blockchain.

[1970] "Data analysis results" are information and reports generated after analyzing acquired device data using generative artificial intelligence.

[1971] This invention is a system that combines generative artificial intelligence and blockchain technology to automate configuration updates, data profiling, and data analysis of IoT devices, improving connectivity between devices.

[1972] System configuration

[1973] 1. Generative Artificial Intelligence

[1974] Generative AI automatically generates new configuration information and data analysis results based on user-provided input information and existing device data. Specifically, the AI ​​engine periodically analyzes device data and proposes and implements configuration updates as needed. This AI engine applies natural language processing and machine learning technologies (e.g., TensorFlow and PyTorch).

[1975] 2. Blockchain Technology

[1976] Blockchain technology registers IoT device data in a distributed database, ensuring transparency and consistency. This technology allows device operation history and configuration change history to be stored without tampering, making troubleshooting and device management easier. Blockchain technology uses existing distributed ledger technologies (e.g., Hyperledger Fabric and Ethereum).

[1977] Example of operation

[1978] Device Profiling

[1979] 1. The user enters the information required to register a new IoT device into the system. For example, if a temperature sensor is to be installed in the "living room," the user enters the device name, location, and type.

[1980] 2. The server receives this information and uses blockchain technology to profile the device, specifically registering the device ID, registration timestamp, and other initial data on the blockchain.

[1981] Update device settings

[1982] 1. A user enters a new device setting, for example, setting a temperature sensor threshold to 25 degrees.

[1983] 2. The device uses generative artificial intelligence to analyze the input setting information, evaluate whether the settings are appropriate, and generate optimal setting values.

[1984] 3. The server registers the generated configuration information on the blockchain and updates the device configuration.

[1985] Automatic generation of data reports

[1986] 1. The server retrieves the latest temperature sensor data from the blockchain. This process is performed periodically.

[1987] 2. The device uses generative artificial intelligence to analyze this data and automatically generate a statistical report of temperature fluctuations.

[1988] 3. The generated report is provided to the user or used for further analysis within the system.

[1989] Specific prompt examples

[1990] Prompt: "I want to update the temperature sensor settings on my IoT device. Set the new threshold to 25 degrees."

[1991] This system makes it easier for users to manage IoT devices and analyze data, improving the connectivity between devices. In addition, the use of blockchain technology ensures data transparency and consistency, enabling reliable data management.

[1992] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1993] Device profiling process steps

[1994] Step 1

[1995] The user enters registration information for a new IoT device (e.g., device name, location, device type).

[1996] Input: Device information (Device name: Temperature sensor, Location: Living room, Type: Temperature sensor)

[1997] What happens: A user enters information through a web interface or a mobile app.

[1998] Step 2

[1999] The server receives the entered information and generates a device ID.

[2000] Input: Device information entered by the user

[2001] Data processing: The server analyzes the device information and generates a unique device ID.

[2002] Output: Generated device ID

[2003] Step 3

[2004] The server registers the device ID, registration timestamp, and initial data on the blockchain.

[2005] Input: Device ID, registration timestamp, initial data (e.g., initial settings for a temperature sensor)

[2006] Data calculation: Calling blockchain API and recording device information on the distributed ledger

[2007] Output: Device profile registered on the blockchain

[2008] Steps for updating device settings

[2009] Step 1

[2010] The user enters a new device setting (e.g., a temperature sensor threshold).

[2011] Input: New device setting (e.g. threshold 25 degrees)

[2012] Specific operation: The user inputs a new threshold value on the setting input screen and presses the send button.

[2013] Step 2

[2014] The terminal receives the entered setting information and analyzes it.

[2015] Input: New device configuration information

[2016] Data processing: The device receives the setting information and converts it into the appropriate data format.

[2017] Output: Parsable configuration data

[2018] Step 3

[2019] The terminal uses generative artificial intelligence to generate optimal setting values ​​based on the input setting information.

[2020] Input: Parsable configuration data

[2021] Data calculation: Generative AI analyzes past data and current conditions to calculate optimal settings

[2022] Output: Recommended optimal settings

[2023] Step 4

[2024] The server registers the generated configuration information on the blockchain and updates the device configuration.

[2025] Input: Recommended optimal settings

[2026] Data calculation: Register new configuration information through the blockchain API

[2027] Output: The latest device configuration recorded on the blockchain

[2028] Processing steps for automatic generation of data reports

[2029] Step 1

[2030] The server retrieves the latest device data from the blockchain.

[2031] Input: Device ID

[2032] Data computation: Send a data retrieval request to the blockchain network

[2033] Output: Latest device data

[2034] Step 2

[2035] The device uses generative artificial intelligence to analyze the acquired data and automatically generate a data report.

[2036] Input: Latest device data

[2037] Data Calculation: Generative AI analyzes data and formats statistics and trends into reports

[2038] Output: Auto-generated data report

[2039] Step 3

[2040] The server provides the generated report to the user.

[2041] Input: Auto-generated data report

[2042] What happens: The server displays the report on a dashboard or emails it to the user.

[2043] Output: User-accessible data reports

[2044] (Application example 1)

[2045] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2046] Traditionally, configuring and optimizing factory robots required a lot of manual work and time, resulting in reduced productivity. In addition, it was difficult to ensure transparency and consistency in IoT device data management, leading to issues with data reliability. Furthermore, automating device configuration change history and behavior analysis required complex processes and human intervention, making troubleshooting and efficient operation difficult.

[2047] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[2048] In this invention, the server includes means for updating IoT device settings using generative artificial intelligence, means for profiling IoT device data using blockchain technology, means for automatically collecting IoT device data and generating data reports using generative artificial intelligence, and means for configuring and optimizing factory robot operations. This enables automation of factory robot configuration and optimization, ensures transparency and consistency in IoT device data management, and enables efficient and reliable device operation.

[2049] "Generative AI" refers to AI that automatically generates new configuration information and data analysis results based on input information provided by the user and existing device data.

[2050] "IoT devices" are various devices that are interconnected via the Internet and are capable of collecting and communicating data.

[2051] "Means for updating settings" refers to means for optimizing the settings of IoT devices using generative artificial intelligence and automatically applying the latest settings.

[2052] "Blockchain technology" is a technology that builds a distributed database of data and ensures its transparency and consistency.

[2053] "Means for profiling data" refers to a means for using blockchain technology to perform detailed analysis of IoT device data and evaluate the device's condition and performance.

[2054] "Means for generating data reports" refers to means for analyzing data collected from IoT devices using generative artificial intelligence and outputting the results in the form of reports on a regular basis.

[2055] A "factory robot" is a robot designed to automatically perform various tasks in a factory.

[2056] "Means for setting and optimizing operation" refers to means for optimizing the operation settings of factory robots and adjusting them so that they can perform work efficiently and effectively.

[2057] This invention is a system that configures and optimizes the operation of factory robots and ensures transparency and consistency in data management of IoT devices. This system is composed of a combination of generative artificial intelligence and blockchain technology, and uses the following hardware and software:

[2058] Hardware and software used

[2059] Hardware: Various IoT sensors installed in the factory (temperature sensors, pressure sensors, speed sensors, etc.), factory robots that change settings

[2060] software:

[2061] Generative AI: Python AI libraries (e.g., TensorFlow)

[2062] Blockchain: A custom blockchain implemented in Python

[2063] Data analysis: A data processing system (e.g., Pandas, Numpy) for analyzing data streams from virtual sensors in real time.

[2064] System processing overview

[2065] The server automatically collects data from IoT sensors in the factory and uses generative artificial intelligence to generate optimal operating settings, which are then stored transparently using blockchain technology.

[2066] The server first collects sensor data and analyzes it in real time. Based on this data, generative artificial intelligence automatically generates optimal robot operation settings, enabling automated factory robot configuration and optimization.

[2067] The generated configuration information and analysis results are stored using blockchain technology, ensuring data transparency and consistency of change history. This data management method enables efficient and reliable device operation.

[2068] Specific examples

[2069] A system that optimizes the motor speed and grip force of a robot in a factory continuously collects data from sensors and uses that data to calculate the optimal settings using generative artificial intelligence. Specifically, the AI ​​takes the current sensor values ​​and generates the appropriate settings. This new setting is stored on the blockchain, ensuring the device's authenticity and transparency.

[2070] Example prompts to input to the generative AI model

[2071] "Calculate the optimal motor speed and grip force for this system based on the latest sensor data."

[2072] Specific prompt:

[2073] "Calculate the optimal motor speed and grip force for the robot based on the following sensor data:

[2074] {

[2075] 'current_speed': 1.0,

[2076] 'current_grip': 1.0

[2077] }"

[2078] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2079] Step 1:

[2080] The server collects data in real time from various IoT sensors in the factory. The input from the sensors is various types of data such as temperature, pressure, speed, etc. The server acquires these inputs and stores them in a database.

[2081] Step 2:

[2082] The server inputs the collected sensor data into a generative AI model, which analyzes the data and generates optimal motion settings. This generative AI calculates optimal settings for motor speed and grip force, for example.

[2083] Step 3:

[2084] The terminal receives the optimal operational settings output by the generative AI and transmits the results to the factory robots, where the input is the setting value output by the AI ​​model and the output is the new operational setting value applied to the robot.

[2085] Step 4:

[2086] The server registers the generated operational configuration and related sensor data on the blockchain. The input is the operational configuration and sensor data, and the output is the data stored on the blockchain. This process ensures data transparency and a change history.

[2087] Step 5:

[2088] The server periodically retrieves data from the blockchain and generates a data report, where the input is the data retrieved from the blockchain and the output is the data report provided to the user.

[2089] Step 6:

[2090] Users can refer to the generated data report to check the operation status of the factory robot and the optimization status of the settings, and can make any further adjustments necessary based on this.

[2091] These processing steps enable automated configuration and optimization of factory robots, and improve transparency and reliability of data management.

[2092] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[2093] This invention relates to an IoT device management system that combines generative artificial intelligence, blockchain technology, and an emotion engine to automate the process of updating IoT device settings, data profiling, and automatic data report generation, and also enables device settings adjustment and report display based on the user's emotional state.

[2094] System configuration

[2095] Generative artificial intelligence (AI)

[2096] Generative AI automatically generates new configuration information and data analysis results based on user-provided input and existing device data. The AI ​​engine periodically analyzes device data and proposes and implements configuration updates as needed.

[2097] Blockchain technology

[2098] Blockchain technology allows IoT device data to be registered in a distributed database, ensuring transparency and consistency. This technology ensures that device operation history and configuration change history are tamper-proof, facilitating troubleshooting and device management.

[2099] Emotion Engine

[2100] The emotion engine recognizes the user's emotional state in real time and provides that information to the system, which can then dynamically adjust the settings of IoT devices and how data reports are displayed based on the user's emotional state.

[2101] Program processing overview

[2102] Device Profiling

[2103] 1. A user enters the necessary information to register a new IoT device into the system (e.g., device name, location, type, etc.).

[2104] 2. The server receives this information and generates a device ID.

[2105] 3. The server creates a payload containing the generated device ID and registration information and sends an HTTP POST request to the Blockchain API.

[2106] 4. The server receives the response from the blockchain API and verifies whether the device was successfully registered.

[2107] Get Device Data

[2108] 1. The server sends an HTTP GET request using a specific device ID to retrieve data about that device from the blockchain.

[2109] 2. The server receives the response from the blockchain API and parses the data in JSON format.

[2110] 3. The server provides the analyzed device data to applications and users.

[2111] Update device settings

[2112] 1. The user inputs a new setting value (e.g., threshold value).

[2113] 2. Based on the settings entered by the device, the device sends a request to the generative AI API to generate the optimal settings.

[2114] 3. The device receives the setting values ​​generated in response from the generative AI API.

[2115] 4. The server creates a payload containing the new configuration values ​​and sends an update device configuration request to the blockchain API.

[2116] 5. The server receives the response from the Blockchain API and verifies whether the configuration update was successful.

[2117] Automatic generation of data reports

[2118] 1. The server sends an HTTP GET request to retrieve the latest device data from the blockchain using the specific device ID.

[2119] 2. The server receives the response from the blockchain API and parses the data in JSON format.

[2120] 3. The device analyzes the device data and sends it to the generative AI API, which then sends a request to generate a data report.

[2121] 4. The device receives the generated data report in response from the generative AI API.

[2122] 5. The server provides the generated data report to the user or uses it for further data analysis within the system.

[2123] Use of emotion engine

[2124] 1. The emotion engine recognizes the user's emotional state in real time.

[2125] 2. The emotion engine sends the recognized emotion data to the system, which dynamically adjusts the device settings and how data reports are displayed.

[2126] 3. The server registers the emotion data on the blockchain to ensure transparency and consistency.

[2127] Specific examples

[2128] Device Profiling Example

[2129] 1. The user enters the temperature sensor registration information (e.g., name, location, type).

[2130] 2. The server receives the entered information and generates a device ID.

[2131] 3. The server creates a payload containing the device ID and registration information and sends an HTTP POST request to the Blockchain API.

[2132] 4. The server receives the response from the blockchain API and verifies whether the device was successfully registered.

[2133] Example of updating device settings

[2134] 1. The user enters a new threshold of 25 degrees for the temperature sensor.

[2135] 2. The device sends a request to the AI ​​API for the input thresholds to generate the optimal settings.

[2136] 3. The device receives the setting values ​​generated in response from the generative AI API.

[2137] 4. The server creates a payload containing the new configuration values ​​and sends an update device configuration request to the blockchain API.

[2138] 5. The server receives the response from the Blockchain API and verifies whether the configuration update was successful.

[2139] Example of automatic data report generation

[2140] 1. The server sends an HTTP GET request using the temperature sensor ID to retrieve the latest device data from the blockchain.

[2141] 2. The server receives the response from the blockchain API and parses the data in JSON format.

[2142] 3. The device analyzes the device data and sends it to the generative AI API, which then sends a request to generate a data report.

[2143] 4. The device receives the generated data report in response from the generative AI API.

[2144] 5. The server provides the generated data report to the user or uses it for further data analysis within the system.

[2145] Example of using the emotion engine

[2146] 1. The emotion engine recognizes the user's emotions in real time and sends the emotion data to the system.

[2147] 2. Your device will adjust device settings and how data reports are displayed based on emotion data.

[2148] 3. The server registers the emotion data on the blockchain to ensure transparency and consistency.

[2149] This system improves the connectivity of IoT devices, making it easier for users to manage devices and analyze data. Furthermore, the use of an emotion engine enables optimal operation based on the user's emotional state, improving the user experience. Furthermore, the use of blockchain technology makes the device's operational history transparent, enabling reliable data management.

[2150] The processing flow will be explained below.

[2151] Device Profiling

[2152] Step 1:

[2153] The user enters the registration information for a new IoT device (e.g., device name, location, type, etc.) into the system's input form.

[2154] Step 2:

[2155] The server takes the information provided by the user and generates a new device ID.

[2156] Step 3:

[2157] The server creates a payload containing the device ID and the registration information provided by the user and sends an HTTP POST request to the Blockchain API.

[2158] Step 4:

[2159] The server receives the response sent from the Blockchain API and verifies whether the device was successfully registered.

[2160] Get Device Data

[2161] Step 1:

[2162] The server sends an HTTP GET request using a specific device ID to retrieve data about that device from the blockchain.

[2163] Step 2:

[2164] The server receives the response sent from the blockchain API and parses the data in JSON format.

[2165] Step 3:

[2166] The server converts the parsed device data into a format for presentation to the application user.

[2167] Step 4:

[2168] Access, view, and manipulate device data provided by the user.

[2169] Update device settings

[2170] Step 1:

[2171] The user enters new device setting values ​​(e.g., thresholds) into the system's input form.

[2172] Step 2:

[2173] Based on the settings entered by the user, the device sends a request to a generative artificial intelligence API to generate the optimal settings.

[2174] Step 3:

[2175] The device receives the setting values ​​generated in the response sent from the generative artificial intelligence API.

[2176] Step 4:

[2177] The server creates a payload containing the new configuration values ​​and sends an update device configuration request to the blockchain API.

[2178] Step 5:

[2179] The server receives the response sent from the Blockchain API and checks whether the configuration update was completed successfully.

[2180] Automatic generation of data reports

[2181] Step 1:

[2182] The server sends an HTTP GET request using the specific device ID to retrieve the latest device data from the blockchain.

[2183] Step 2:

[2184] The server receives the response sent from the blockchain API and parses the data in JSON format.

[2185] Step 3:

[2186] The device data analyzed by the terminal is sent to a generative artificial intelligence API, which then sends a request to generate a data report.

[2187] Step 4:

[2188] The device receives the data report generated in response from the generative artificial intelligence API.

[2189] Step 5:

[2190] The server converts the generated data report into a format for presentation to the user.

[2191] Use of emotion engine

[2192] Step 1:

[2193] The emotion engine analyzes the user's facial expressions, voice, etc. to recognize the user's emotional state in real time.

[2194] Step 2:

[2195] The emotion engine transmits the recognized emotion data to the server.

[2196] Step 3:

[2197] The server uses the received emotion data to dynamically adjust device settings and how data reports are displayed accordingly.

[2198] Step 4:

[2199] The server registers the emotion data on the blockchain, ensuring transparency and consistency.

[2200] Specific examples

[2201] Device Profiling Example

[2202] Step 1:

[2203] The user enters the temperature sensor registration information (e.g., name, location, and type) into the system's input form.

[2204] Step 2:

[2205] The server takes the information provided by the user and generates a new device ID.

[2206] Step 3:

[2207] The server creates a payload containing the device ID and registration information and sends an HTTP POST request to the Blockchain API.

[2208] Step 4:

[2209] The server receives the response sent from the Blockchain API and verifies whether the device was successfully registered.

[2210] Example of updating device settings

[2211] Step 1:

[2212] The user enters the new temperature sensor threshold of 25 degrees into the system's input form.

[2213] Step 2:

[2214] The device sends a request to the artificial intelligence API for generating the input threshold value, and generates the optimal settings.

[2215] Step 3:

[2216] The device receives the setting values ​​generated in the response sent from the generative artificial intelligence API.

[2217] Step 4:

[2218] The server creates a payload containing the new configuration values ​​and sends an update device configuration request to the blockchain API.

[2219] Step 5:

[2220] The server receives the response sent from the Blockchain API and checks whether the configuration update was completed successfully.

[2221] Example of automatic data report generation

[2222] Step 1:

[2223] The server sends an HTTP GET request using the temperature sensor ID to retrieve the latest device data from the blockchain.

[2224] Step 2:

[2225] The server receives the response sent from the blockchain API and parses the data in JSON format.

[2226] Step 3:

[2227] The device data analyzed by the terminal is sent to a generative artificial intelligence API, which then sends a request to generate a data report.

[2228] Step 4:

[2229] The device receives the data report generated in response from the generative artificial intelligence API.

[2230] Step 5:

[2231] The server converts the generated data report into a format for presentation to the user.

[2232] Example of using the emotion engine

[2233] Step 1:

[2234] The emotion engine recognizes the user's emotions in real time and transmits the emotion data to the server.

[2235] Step 2:

[2236] Your device will adjust device settings and how data reports are displayed based on your emotional data.

[2237] Step 3:

[2238] The server registers the emotion data on the blockchain, ensuring transparency and consistency.

[2239] Example 2

[2240] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2241] As the number of IoT devices increases, managing them and updating their settings becomes more complex. Furthermore, there is an increasing demand for device behavior and display methods that take into account the user's emotional state. Furthermore, while it is important to ensure the transparency and integrity of device data, no effective system exists to address these challenges. To solve these problems, an innovative system combining generative artificial intelligence, blockchain technology, and an emotion engine is needed.

[2242] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2243] In this invention, the server includes means for updating IoT device settings using generative artificial intelligence, means for profiling IoT device data using blockchain technology, means for automatically collecting IoT device data and generating data reports using generative artificial intelligence, means for recognizing a user's emotional state in real time using an emotion engine and dynamically adjusting the IoT device settings and display of the data report, and means for dynamically adjusting the display method of the IoT device settings and data report based on the user's emotional state. This enables optimal device settings and data report display according to the user's emotional state, while ensuring the transparency and consistency of device data.

[2244] "Generative AI" is an AI technology that automatically generates new configuration information and data analysis results based on input information provided by users and existing device data.

[2245] "Blockchain technology" is a technology that registers data in a distributed database and keeps operation history and setting change history untampered in order to ensure data transparency and consistency.

[2246] The "emotion engine" is a technology that recognizes a user's emotional state in real time and provides that information to the system, dynamically adjusting device settings and the way data reports are displayed based on the user's emotional state.

[2247] An "IoT device" is a physical device that can communicate with other devices and systems over the Internet.

[2248] "Profiling" is the process of collecting data about a specific target (in this case, an IoT device) and analyzing its characteristics and behavioral patterns.

[2249] A "data report" is a report generated based on collected and analyzed data, often including visual graphs and statistics.

[2250] "Payload" refers to the actual portion of data exchanged between systems, that is, the information actually sent from the sender to the receiver.

[2251] "Unique Device ID" means a unique identifier used to distinguish a particular device from all other devices.

[2252] An "HTTP request" is a request sent from a client to a server when accessing a resource on the web, and includes methods such as GET and POST.

[2253] MODE FOR CARRYING OUT THE INVENTION

[2254] This invention relates to an IoT device management system that combines generative artificial intelligence, blockchain technology, and an emotion engine to automate the process of updating IoT device settings, data profiling, and automatic data report generation, and also enables device settings adjustment and report display based on the user's emotional state.

[2255] Hardware and software used

[2256] The server is the hardware that handles the main processing of the system. It runs multiple services, profiling device data, updating settings, and generating data reports.

[2257] A terminal is a device (e.g., PC, smartphone, tablet) through which a user interacts with the system. The terminal receives input from the user and sends requests to the server or the generative AI API based on that input.

[2258] Generative AI is software that automatically generates new configuration information and data analysis results based on input information provided by the user and existing device data.

[2259] Blockchain technology is used to ensure transparency and consistency of devices by registering device data and operation history in a distributed database.

[2260] An emotion engine is software that recognizes a user's emotional state in real time and provides that information to a system that can dynamically adjust the settings of IoT devices and the way data reports are displayed based on the user's emotional state.

[2261] System processing overview and specific examples

[2262] Device profiling examples

[2263] The user enters the temperature sensor's registration information (e.g., name, location, and type). The server receives the entered information and generates a unique device ID. The server then creates a payload containing the generated device ID and registration information and sends an HTTP POST request to the blockchain API. The server then receives a response from the blockchain API and confirms whether the device has been successfully registered.

[2264] Example of updating device settings

[2265] The user inputs a new threshold value for the temperature sensor (e.g., 25 degrees). The device sends a request to the generative AI API based on the input threshold value to generate optimal settings. The device receives the generated setting value in the response from the generative AI API and sends it to the server. The server creates a payload containing the new setting value and sends a device setting update request to the blockchain API. Finally, the server receives the response from the blockchain API and confirms that the setting update was completed successfully.

[2266] Example of automatic generation of data reports

[2267] The server uses the temperature sensor ID to send an HTTP GET request to retrieve the latest device data from the blockchain. The server receives the response from the blockchain API and parses the data in JSON format. The device then sends the parsed device data to the generative AI API and sends a request to generate a data report. The device then receives the generated data report in the response from the generative AI API and provides it to the server. The server then provides the generated data report to the user or uses it for further data analysis within the system.

[2268] Examples of using the emotion engine

[2269] The emotion engine recognizes users' emotions in real time and transmits the emotion data to the system. The device adjusts device settings and data report display methods based on the emotion data. The server registers the emotion data on the blockchain to ensure transparency and consistency.

[2270] Prompt Sentence Examples

[2271] "Analyze the following device data and generate a data report: Temperature sensor ID: 123, Temperature: 25°C, Humidity: 60%"

[2272] This invention allows users to easily manage IoT devices and analyze data. Furthermore, the use of an emotion engine enables optimal operation according to the user's emotional state, improving the user experience. Furthermore, the use of blockchain technology makes the device's operational history transparent, enabling highly reliable data management.

[2273] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2274] Device Profiling

[2275] Step 1:

[2276] The user accesses the system's UI and enters information about the new IoT device (e.g., device name, location, and type).

[2277] Input: Device information such as device name, location, and type

[2278] Output: User input information

[2279] What happens: A user navigates through a form, filling in the fields and finally clicking the submit button.

[2280] Step 2:

[2281] The server receives input information from the user.

[2282] Input: User input information

[2283] Output: A set of information to generate a device ID

[2284] What it does: The server stores the received information in a temporary database, then generates a unique device ID based on the entered data.

[2285] Step 3:

[2286] The server generates a unique device ID.

[2287] Input: User input information

[2288] Output: Unique device ID

[2289] What happens: The server uses a UUID (Universal Unique Identifier) ​​generator to generate a unique ID.

[2290] Step 4:

[2291] The server combines the generated device ID and the user's input information into a payload and sends an HTTP POST request to the blockchain API.

[2292] Input: Device ID, user input information

[2293] Output: A request to the Blockchain API

[2294] What happens: The server creates a new payload and sends an HTTP POST request to the blockchain API.

[2295] Step 5:

[2296] The server receives the response from the Blockchain API and confirms that the device was successfully registered.

[2297] Input: Response from the Blockchain API

[2298] Output: Confirmation of registration result

[2299] Specific operation: The server checks the response status code to see if the registration was successful. If successful, it saves the device information in the database.

[2300] Update device settings

[2301] Step 1:

[2302] The user inputs new device setting values ​​(e.g., thresholds) on the system setting screen.

[2303] Input: New setting value (e.g. threshold)

[2304] Output: User configuration information

[2305] Specific behavior: The user enters a new threshold in the settings input form and clicks the save button.

[2306] Step 2:

[2307] The device sends a request to the generative artificial intelligence API based on the input settings.

[2308] Input: New setting value

[2309] Output: A request to the generative AI API

[2310] Specific operation: The device creates a request payload containing the new setting values ​​and sends an HTTP POST request to the generative AI API.

[2311] Step 3:

[2312] The device receives the setting values ​​generated in response from the generative artificial intelligence API.

[2313] Input: Response from the generative AI API

[2314] Output: Optimal setting

[2315] Specific operation: Analyze the response data received by the device and extract the optimal setting values.

[2316] Step 4:

[2317] The server creates a payload containing the new configuration values ​​and sends an update device configuration request to the blockchain API.

[2318] Input: Optimal setting

[2319] Output: An update request to the blockchain API

[2320] What happens: The server creates the update payload and sends an HTTP POST request to the blockchain API.

[2321] Step 5:

[2322] The server receives the response from the Blockchain API and confirms that the configuration update was successful.

[2323] Input: Response from the Blockchain API

[2324] Output: Confirmation of update results

[2325] Specific behavior: The server checks the response status code to confirm that the settings were updated successfully. If successful, it saves the setting change history to the database.

[2326] Automatic generation of data reports

[2327] Step 1:

[2328] The server sends an HTTP GET request using the specific device ID to retrieve the latest device data from the blockchain.

[2329] Input: Device ID

[2330] Output: A get request to the Blockchain API

[2331] Specific operation: The server creates an HTTP GET request based on the device ID and sends it to the blockchain API.

[2332] Step 2:

[2333] The server receives the response from the blockchain API and parses the data in JSON format.

[2334] Input: Response from the Blockchain API

[2335] Output: Parsed device data

[2336] Specific operation: The response data received by the server is analyzed using a JSON parser.

[2337] Step 3:

[2338] The device data analyzed by the terminal is sent to a generative artificial intelligence API, which then sends a request to generate a data report.

[2339] Input: Parsed device data

[2340] Output: A generated request to the generative AI API

[2341] Specific operation: The device creates a request payload for generating a data report and sends an HTTP POST request to the generative artificial intelligence API.

[2342] Step 4:

[2343] The device receives the data report generated in response from the generative artificial intelligence API.

[2344] Input: Response from the generative AI API

[2345] Output: Data report

[2346] Specific operations: Analyze the response data received by the terminal and extract the generated data report.

[2347] Step 5:

[2348] The server provides the generated data report to the user or uses it for further data analysis within the system.

[2349] Input: Data Report

[2350] Output: Report display to user or internal data analysis

[2351] Specific Actions: The server displays the data report through the user interface and notifies the user, or stores it in a database for further analysis within the system.

[2352] Use of emotion engine

[2353] Step 1:

[2354] The emotion engine recognizes the user's emotions in real time and transmits the emotion data to the system.

[2355] Input: User's emotional state

[2356] Output: Emotion data

[2357] Specific operation: The emotion engine analyzes camera and sensor data to recognize the user's emotional state, and sends the recognized emotional data to the system.

[2358] Step 2:

[2359] Your device will adjust device settings and how data reports are displayed based on your emotion data.

[2360] Input: Emotion data

[2361] Output: How to view adjusted device settings and data reports

[2362] Specific behavior: Based on the emotion data received by the device, logic is applied to optimize settings and display methods, and actual device settings and display methods are changed.

[2363] Step 3:

[2364] The server registers the emotion data on the blockchain, ensuring transparency and consistency.

[2365] Input: Emotion data

[2366] Output: Record on the blockchain

[2367] Specific operation: The server creates a payload containing emotion data and sends an HTTP POST request to the blockchain API. After sending, it receives a response and confirms that the emotion data was successfully recorded.

[2368] (Application example 2)

[2369] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2370] In modern virtual stores, it is important to dynamically manage and adjust the environment using IoT devices to maximize the user experience. However, conventional systems do not adjust settings in real time based on the user's emotional state, making it difficult to optimize operations based on the user's emotions and behavior. Furthermore, there is a lack of means to ensure data transparency and consistency. Therefore, there is a need for a system that can appropriately adjust devices according to the user's emotional state and manage data in a secure and transparent manner.

[2371] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2372] In this invention, the server includes: means for updating IoT device settings using generative artificial intelligence; means for profiling IoT device data using blockchain technology; means for automatically collecting IoT device data and generating data reports using generative artificial intelligence; means for recognizing a user's emotional state in real time using an emotion engine and dynamically adjusting IoT device settings based on the state; and means for registering the emotion data obtained by the emotion engine in the blockchain to ensure transparency and consistency. This enables optimal adjustment of IoT device settings based on the user's emotional state, thereby realizing safe and transparent data management.

[2373] "Generative AI" is an AI technology that automatically generates new settings and analysis results based on input information provided by the user and existing data.

[2374] An "IoT device" is a physical device that communicates with each other over the Internet and collects and controls data.

[2375] "Blockchain technology" is a technology that uses a distributed database to ensure data transparency and consistency. This technology prevents data tampering and enables highly reliable data management.

[2376] "Data profiling" is the process of collecting data related to a particular device and analyzing its characteristics and patterns.

[2377] A "data report" is a report that analyzes collected data and summarizes it visually or in writing, making it easy to understand trends and problems in the data.

[2378] An "emotion engine" is a technology that recognizes a user's emotional state in real time and integrates that information with other systems.

[2379] "Transparency" refers to a state in which data and processes can be easily verified from the outside and are highly reliable.

[2380] "Consistency" refers to a state in which data is managed according to certain rules and standards, and its quality is always maintained.

[2381] "Real-time" refers to responding immediately to an event or process as it occurs.

[2382] This invention relates to an IoT device management system that combines generative artificial intelligence, blockchain technology, and an emotion engine. The system aims to improve the user experience in virtual stores using devices such as smart glasses.

[2383] The system configuration is as follows:

[2384] 1. Generative Artificial Intelligence

[2385] The server uses generative artificial intelligence (AI) to automatically generate new configuration information and data analysis results based on user-provided input information and existing device data. The AI ​​periodically analyzes device data and generates appropriate configuration updates and data reports.

[2386] 2. Blockchain Technology

[2387] The server uses blockchain technology to register IoT device data and configuration change history in a distributed database, ensuring transparency and consistency and preventing tampering with device operation history and data.

[2388] 3. Emotion Engine

[2389] The emotion engine recognizes the user's emotional state in real time and provides that data to the server, which then dynamically adjusts the settings of the IoT device and how data reports are displayed. The emotion data is also registered on the blockchain to ensure transparency and consistency.

[2390] Example of a system

[2391] An example of a virtual store assistant might be implemented as follows:

[2392] Adjusting lighting based on user's emotional state

[2393] As a user wearing smart glasses walks around a virtual store, the emotion engine recognizes the user's emotional state in real time. For example, if the user is "having fun," the generative AI will brighten the smart lighting in the store and set the color temperature to a warmer setting.

[2394] Device registration and configuration updates

[2395] Device information is entered from the terminal through the smart glasses, and the server registers it on the blockchain. Device settings are updated by generating optimal settings based on user input using generative artificial intelligence, which then registers the settings on the blockchain again.

[2396] Hardware and software used

[2397] 1. Hardware

[2398] Smart glasses (AR / VR headsets)

[2399] Emotion Recognition Camera

[2400] 2. Software

[2401] Emotion Engine API

[2402] AI Generator API

[2403] Blockchain API

[2404] Examples of prompt statements

[2405] An example of a prompt to input to a generative AI model is as follows:

[2406] Use the latest customer sentiment data to suggest optimal settings for smart lighting in your virtual store: for example, if the customer is "having fun," set the brightness to 100% and the color temperature to cool white.

[2407] This invention enables IoT devices in virtual stores to work together and optimize operations based on the user's emotional state. Blockchain technology also makes the device's operational history transparent, enabling highly reliable data management.

[2408] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2409] Step 1:

[2410] Registering devices

[2411] The user puts on the smart glasses and inputs information to register a new IoT device (e.g., device name, installation location, type) into the terminal via the smart glasses. The terminal receives this information and sends it to the server. The server generates a device ID based on this information and sends an HTTP POST request to register the device information on the blockchain. The response confirms that the device has been successfully registered.

[2412] Input: Device information (device name, location, type)

[2413] Output: Device ID, result registered on the blockchain

[2414] How it works: The user enters device information through the smart glasses, the device sends the information to the server, and the server registers it on the blockchain.

[2415] Step 2:

[2416] Update device settings

[2417] The user inputs new settings (e.g., thresholds) through the smart glasses. The device requests the input settings from the generative AI API, which generates optimal settings. The server registers the generated settings in the blockchain and responds by confirming whether the update was successful.

[2418] Input: New setting value (e.g. threshold)

[2419] Output: Generated optimal settings, results registered on the blockchain

[2420] How it works: The user inputs new settings through the smart glasses, the device sends them to the generative AI API, and the server registers the generated settings on the blockchain.

[2421] Step 3:

[2422] Automatic generation of data reports

[2423] The server sends an HTTP GET request using a specific device ID to retrieve the latest device data from the blockchain. The server sends the data retrieved in response to the generative AI API and requests it to generate a data report. The generated data report is then received and provided to the user.

[2424] Input: Device ID, data from blockchain

[2425] Output: Generated data report

[2426] How it works: The server retrieves device data from the blockchain, sends it to a generative AI API, and provides the generated data report to the user.

[2427] Step 4:

[2428] Use of emotion engine

[2429] The emotion engine recognizes the user's emotional state in real time, sends the recognized emotional data to the server, which registers the data on the blockchain, and dynamically adjusts the device settings based on the emotional data.

[2430] Input: Emotion data

[2431] Output: Dynamically adjusted device settings, emotion data registered on the blockchain

[2432] How it works: The emotion engine recognizes the user's emotional state in real time, the server registers the emotional data on the blockchain, and the device adjusts the device settings.

[2433] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2434] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2435] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2436] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2437] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2438] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2439] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2440] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2441] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2442] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2443] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2444] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2445] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2446] 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.

[2447] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific p...

Claims

1. A means of updating the configuration of IoT devices using generative artificial intelligence; and A means of profiling data from IoT devices using blockchain technology; means for automatically collecting data from the IoT devices and generating a data report using generative artificial intelligence; A system including:

2. The system according to claim 1 , further comprising means for ensuring transparency of the IoT device using blockchain technology and managing a change history of the data.

3. The system of claim 1 , further comprising a means for registering updated IoT device settings using the generative artificial intelligence in a blockchain to ensure data consistency.

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