system

Abstract

We provide the system. [Solution] A means of storing transaction information in an immutable and transparent format using distributed ledger technology, A computational means using an artificial intelligence model for forecasting demand and supply, A means for determining the optimal transaction price based on the aforementioned forecast and autonomously matching trading partners, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern centralized power supply systems, there is a problem that it is difficult to effectively utilize surplus power generated by renewable energy. In particular, since the supply amount of renewable energy depends on the weather and time zone, it is unstable, and a mechanism for efficiently distributing the surplus to other consumers is required. Also, in conventional trading systems, the transparency and security of transactions are insufficient, and in particular, a fair market for small businesses and individuals to freely trade electricity has not been established, which is an issue.

Means for Solving the Problems

[0005] This invention provides a mechanism to prevent fraud by storing transaction information in a highly transparent format using distributed ledger technology. Furthermore, it utilizes artificial intelligence models to predict supply and demand, thereby enabling real-time evaluation of future supply and demand balances and the presentation of optimal transaction conditions. In addition, by introducing means for determining appropriate transaction prices and autonomously matching trading partners using the predicted information, it promotes the efficient trading of surplus electricity and the effective use of renewable energy. This makes it possible to create an environment in which individuals and small businesses can trade electricity fairly and safely.

[0006] A "distributed ledger" is a database technology used to hold immutable transaction data that is shared among multiple network participants.

[0007] "Transaction information" refers to information that includes details such as the quantity, price, and trading partners related to the buying and selling of electricity.

[0008] "Transparency" refers to a state where transaction details and processes are publicly available and not hidden from anyone.

[0009] An "artificial intelligence model" refers to an algorithm or computational model that allows a computer to automatically learn from data and make predictions and decisions about the future.

[0010] "Supply-demand balance" refers to a state of equilibrium between the supply and demand of goods such as electricity, with the ideal being a state where supply and demand are equal.

[0011] "Terms of trade" refer to the conditions for executing a transaction, such as quantity, price, duration, and counterparty.

[0012] "Transaction price" refers to the price per unit quantity of electricity bought and sold in a specific transaction.

[0013] "Matching" refers to the process of appropriately selecting buyers and sellers to complete transactions. [Brief explanation of the drawing]

[0014] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

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

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

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

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

[0019] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

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

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention provides an embodiment of a decentralized energy trading system that can efficiently trade surplus renewable energy. This system is implemented in the following manner.

[0036] First, the terminal acquires power consumption and supply data from the user's location. The terminal sends this data to the server at regular intervals. Upon receiving this data, the server performs the necessary preprocessing and then stores it in the database. Preprocessing includes data cleansing and imputation of missing values.

[0037] Next, the server runs an artificial intelligence model using the collected data. The AI ​​agent utilizes the collected data and external data (e.g., weather information and usage patterns) to predict future electricity demand and supply. This makes it possible to anticipate situations of oversupply or overdemand.

[0038] Subsequently, the server executes a pricing algorithm based on the predicted supply and demand data. This algorithm considers the balance of supply and demand to calculate the optimal price. Furthermore, an AI agent autonomously matches trading partners based on the predicted information. The terminal notifies the user of the transaction price and information about the trading partner.

[0039] Once a transaction is completed, the server records the transaction details on the blockchain, ensuring a secure and transparent transaction environment. The recorded information is immutable and can be used for future audits and verifications.

[0040] Once a transaction is completed, the user is notified via their terminal. Users can verify that their electricity was traded appropriately and contribute to further system improvements by providing feedback.

[0041] For example, if user A has a surplus of 10kWh of electricity during the day, the AI ​​agent will identify user B's shortage and match them so that A can sell 5kWh to B at a fair price. Once a transaction is completed between A and B, the information is recorded on the blockchain, and notifications are sent to both A and B's devices. In this way, individual users can efficiently trade their surplus electricity, maximizing the use of renewable energy.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The terminal collects real-time data on electricity consumption and generation from the user's home or business. This is done using smart meters and home energy management systems. The collected data is then transmitted to a server at regular intervals.

[0045] Step 2:

[0046] Before saving the received data to the database, the server performs preprocessing such as noise reduction and missing value imputation. This ensures data accuracy and makes it suitable as foundational data for analysis and prediction.

[0047] Step 3:

[0048] The server inputs pre-processed data into an artificial intelligence model to generate forecasts of each user's future electricity demand and supply. The AI ​​agent makes more accurate forecasts by referencing weather data and historical consumption patterns.

[0049] Step 4:

[0050] The server executes a pricing algorithm based on supply and demand data predicted by the AI ​​agent. This algorithm calculates the optimal transaction price considering current market conditions. At this stage, it also performs transaction matching to select appropriate buyers and sellers.

[0051] Step 5:

[0052] Once the transaction terms are determined, the server records the transaction details on the blockchain. This ensures the transparency and security of the transaction and helps prevent fraud. This information is used for subsequent audits and verification by the transaction parties.

[0053] Step 6:

[0054] The terminal notifies the user that a transaction has been completed. The user can then view the transaction details (trading partner, price, quantity, etc.). The user can also provide feedback, which the server collects to improve the system.

[0055] (Example 1)

[0056] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0057] Conventional renewable energy trading systems have problems in that they cannot adequately respond to fluctuations in electricity supply and demand. Furthermore, the transparency and security of trading information are insufficient, making reliable energy management difficult. In addition, the lack of sufficient demand forecasting and price optimization using artificial intelligence hinders efficient trading.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] In this invention, the server includes means for using information equipment to receive transaction requests and collect data on power usage and supply; storage means for preprocessing the collected data and storing it in a consistent state; and computation means for predicting future power demand and supply while referring to external environmental data using an artificial intelligence model. This makes it possible to respond flexibly to fluctuations in power supply and demand and to efficiently conduct transparent and reliable energy trading.

[0060] A "transaction request" is a request made to the system to initiate a transaction in response to the supply or demand situation of electricity.

[0061] "Information equipment" refers to measuring devices and terminals installed at user sites to collect data related to power usage and supply.

[0062] "Preprocessing" refers to the process of adding missing values ​​and correcting outliers to collected data to make it consistent.

[0063] A "memory device" is a database or similar data storage system that stores pre-processed data and uses it for subsequent analysis or model training.

[0064] An "artificial intelligence model" is a mathematical model that uses collected data and external environmental data to predict future electricity demand and supply.

[0065] "Distributed ledger technology" is blockchain technology used to record transaction details in an immutable format, thereby increasing transparency and security.

[0066] The "computational means" refers to the process of using an artificial intelligence model to calculate future electricity demand and supply, and to generate data for efficient trading.

[0067] The "transaction price" is the fair price in the trading of electricity, calculated based on the balance of supply and demand.

[0068] "User" refers to an individual or organization participating in this system as a power supplier or consumer that trades surplus electricity.

[0069] This invention is a decentralized trading system for efficiently trading surplus renewable energy. The method for implementing this system is described below.

[0070] First, the terminal is installed at the user's location and collects power consumption and supply data through a power meter. The collected data is sent to the server at regular intervals. The server then performs data preprocessing. Preprocessing includes imputing missing values ​​and correcting outliers, and the Python Pandas library is used for this purpose.

[0071] Preprocessed data is stored in a database management system (DBMS) on the server. MySQL® is used as the DBMS for data storage and management. The data is accumulated in an organized format in preparation for subsequent analysis.

[0072] Next, the server runs an artificial intelligence (AI) model. This AI model is built using TENSORFLOW® and combines data with external environmental information (e.g., weather data) to predict future electricity demand and supply. This prediction makes it possible to understand in advance situations of electricity oversupply or overdemand. Based on the predicted data, a pricing algorithm calculates the optimal transaction price.

[0073] Furthermore, an AI agent autonomously matches trading partners. Once a transaction is completed, the server records the transaction details on a distributed ledger (blockchain), ensuring secure and transparent transactions. This record is immutable and can be used for subsequent audits and transaction history verification.

[0074] After the transaction is completed, the terminal notifies the user of the transaction details and feedback. The user can review the electricity transaction details and provide feedback to the system, which helps to further improve the system.

[0075] As a concrete example, if user A has 10kWh of surplus electricity during the day, the AI ​​agent will identify user B's shortage and match them so that A can sell 5kWh to B at a fair price. This transaction will be notified to and recorded on the terminals of both users A and B.

[0076] Example of a prompt:

[0077] "Explain how User A can efficiently trade the 10kWh of surplus electricity they generated during the day. Also, show how the AI ​​agent will handle matching and pricing."

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The terminal acquires real-time data on power consumption and supply via a power meter installed at the user's site. The input is data from the power meter, and the output is raw power usage data. Each data point is recorded with a time stamp and stored for later processing. Specifically, the terminal collects data every 10 minutes and temporarily stores it in its internal memory.

[0081] Step 2:

[0082] The terminal periodically sends the collected data to the server. The input is the accumulated power data, and the output is the data packets sent to the server. Data transmission takes place over the internet, and security is ensured using protocols such as SSL. Specifically, the terminal sends the data in batches every hour.

[0083] Step 3:

[0084] The server preprocesses the received power data. The input is data sent from the terminal, and the output is a cleansed, consistent dataset. Data cleansing includes imputing missing values ​​and detecting outliers. Specifically, the server cleans the data using the Pandas library and corrects outliers using statistical methods.

[0085] Step 4:

[0086] The server stores pre-processed data in a database. The input is a consistent dataset, and the output is the state in which it is stored in the database. MySQL is used for database management, and query optimization is performed on a time basis. Specifically, the server periodically inserts data using SQL statements and sets up indexes that enable efficient searching.

[0087] Step 5:

[0088] The server uses database data and external environmental data to predict electricity demand and supply using an artificial intelligence model. The input is electricity consumption data and weather data stored in the database, and the output is time-series data of predicted electricity demand and supply. A neural network model using TensorFlow operates to estimate future values. Specifically, the server retrains the model daily to generate new predictions.

[0089] Step 6:

[0090] The server executes a pricing algorithm based on predicted supply and demand data. The input is the predicted data, and the output is the optimal transaction price. The pricing algorithm dynamically adjusts the price, taking into account the balance between supply and demand. Specifically, the server measures price elasticity and calculates the price based on a demand response model.

[0091] Step 7:

[0092] The server uses an AI agent to match trading partners. The input is predicted supply and demand, and the optimal price; the output is a list of matched trading pairs. The AI ​​agent utilizes machine learning algorithms to select the best partner. Specifically, the server analyzes predicted data and historical trading history to perform partner matching.

[0093] Step 8:

[0094] The terminal notifies the user of the details of a transaction once it is completed. The input is transaction information from the server, and the output is a notification message to the user. The terminal informs the user of the transaction details, counterparty, and price in real time. Specifically, the terminal communicates with the user using push notifications or email.

[0095] Step 9:

[0096] The server records transaction details using distributed ledger technology. The input is transaction detail data, and the output is recorded blockchain data. The use of blockchain technology guarantees the transparency and security of transactions. Specifically, the server sends the transaction to the blockchain network, where it is recorded on all nodes.

[0097] (Application Example 1)

[0098] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0099] To ensure the efficient trading and maximum utilization of renewable energy, it is necessary to accurately predict fluctuations in electricity demand and to have means to resolve surpluses or shortages. Furthermore, a system is needed that manages information transparently and securely, allowing users to easily understand their own energy usage. However, current systems struggle to fully meet these requirements.

[0100] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0101] In this invention, the server includes means for storing transaction information in an immutable and transparent format using distributed database technology; computation means for forecasting supply and demand using machine learning models; means for determining the optimal transaction price based on the forecast and autonomously matching trading partners; means for visualizing power supply status and transaction proposals via a user interface; and means for providing transaction history and energy usage trends on mobile devices. This enables efficient trading of renewable energy and promotes user understanding through visualization.

[0102] "Distributed database technology" is a technology that stores data in a distributed manner across multiple locations, ensuring data transparency and immutability without requiring a central administrator.

[0103] A "machine learning model" is a system of algorithms that learns patterns and rules from past data and has the ability to predict future demand and supply.

[0104] "Visualization of power supply status" is a technology that displays the energy supply status in a way that is easy for users to understand, using methods such as graphs and charts.

[0105] A "mobile device" is a portable electronic device capable of acquiring, processing, and transmitting information, and in this context, it specifically refers to smartphones and tablets.

[0106] A "user interface" is a component of software that provides the operating screen and display method for exchanging information between the user and the system.

[0107] This invention is a system for enabling the efficient trading of renewable energy. Specific embodiments are described below.

[0108] The server first uses distributed database technology to store transaction information reported by each user in an immutable and transparent format. This ensures the security and integrity of transactions.

[0109] Next, the server uses machine learning models to analyze historical data and energy data collected in real time. This modeling prepares the server to effectively forecast supply and demand and provide optimal trading conditions. External weather data and market trend data are used in this process.

[0110] On user terminals, mobile devices such as smartphones visualize and present power supply status and trading proposals to users in real time. The user interface is designed to be intuitive and easy to understand, making it easy for users to trade energy. For example, if there is a surplus of electricity, an option to easily sell it will be displayed within the application.

[0111] As a concrete example of implementation, the server inputs the following prompt to the AI ​​model: "We have 5kWh of surplus electricity. Please suggest the best trading partner and price. Factors to consider are demand, weather information, and existing electricity prices." This allows the AI ​​to provide the optimal solution, enabling the user to make a quick decision.

[0112] In this way, the present invention constructs a system that efficiently trades renewable energy and aims to maximize the utilization of energy resources.

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The server periodically retrieves power consumption and supply data from each user's terminal. This data is received as input, and before recording it in a distributed database, it undergoes data cleansing, correcting inconsistent data and imputing missing values. The clean data is then stored as output for further processing.

[0116] Step 2:

[0117] The server runs a machine learning model based on stored consumption and supply data. External data, such as weather information, is also input to predict future electricity demand and supply. Through data processing, the model analyzes past and present information to learn demand and supply patterns. The output is forecast data for demand and supply.

[0118] Step 3:

[0119] The server receives predicted supply and demand data as input and executes a pricing algorithm. This algorithm considers the balance of supply and demand and calculates the optimal transaction price. A specific transaction price is generated as output. Here, a generation AI model is utilized, and price adjustment prompts are used.

[0120] Step 4:

[0121] The server matches trading partners based on calculated transaction prices and forecast data. It generates a list of potential trading partners and autonomously selects the most suitable partner based on this list. The input is forecast data and price information, and the output is information on the matched trading partner.

[0122] Step 5:

[0123] When a transaction is completed, the server records the transaction details in a distributed database and sends a notification to the information terminal. To ensure transparency and verification of transactions, blockchain technology is used to maintain an immutable history. This allows users to verify the transaction results on their own devices.

[0124] Step 6:

[0125] Users provide feedback based on transaction information received through their devices. This feedback is sent to the server and used to improve the system. The input is user feedback information, and the output is data for system updates.

[0126] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0127] This invention provides an embodiment of a decentralized energy trading system that enables the efficient and emotionally relatable trading of surplus renewable energy in a user-friendly manner. This system is implemented in the following manner:

[0128] First, the terminal acquires data on power consumption and supply from the user's facility and transmits it to the server. The terminal is equipped with an emotion recognition sensor to analyze the user's facial expressions and tone of voice, and constantly captures and transmits the user's emotional data to the server. This makes it possible to analyze the user's emotions regarding their transaction experience in real time.

[0129] The server uses the received power data to run an artificial intelligence model. The AI ​​agent analyzes the data and predicts future power demand and supply. Furthermore, it uses an emotion engine to evaluate the user's emotional state and uses that data to determine how the user is psychologically influenced in transactions.

[0130] Next, the server combines predicted supply and demand data with sentiment information to adjust trading prices. For example, if a user is feeling stressed, it can set flexible prices to smooth the trading process. Furthermore, to increase user satisfaction during trading, the sentiment engine provides customized trading conditions tailored to each user.

[0131] Once a transaction is completed, the server records the transaction details on the blockchain in an immutable and transparent format and sends a transaction completion notification to the terminal. These notifications also include feedback generated by the sentiment engine, allowing users to register comments about their transaction experience. The server uses this feedback to refine the sentiment model, enabling more accurate customer satisfaction ratings.

[0132] For example, if user C wants to sell 10kWh of surplus electricity, and the system determines from the user's expression that they are feeling uneasy about the transaction, it will offer clear explanations and a slightly higher purchase price to complete the transaction quickly and efficiently. In this way, a transaction process that takes the user's emotional state into consideration can be realized, promoting the spread of renewable energy and improving user satisfaction.

[0133] The following describes the processing flow.

[0134] Step 1:

[0135] The terminals are installed in users' homes and businesses to collect data on power consumption and generation. In addition, the terminals are equipped with emotion recognition sensors that capture emotional data from the user's facial expressions and voice. This data is transmitted to a server in real time.

[0136] Step 2:

[0137] The server preprocesses the received power data, performing noise reduction and missing data imputation, before storing it in a database. Simultaneously, it analyzes emotional data and extracts the user's current emotional state. This process allows the server to maintain an emotional profile for each user.

[0138] Step 3:

[0139] The server inputs pre-processed power data into an artificial intelligence model to predict future power demand and supply. This prediction also utilizes external data such as the user's past consumption patterns and weather.

[0140] Step 4:

[0141] The server sets the optimal trading price for each individual user based on prediction results and sentiment data. Here, the sentiment engine adjusts the price and trading conditions, especially when the user is feeling anxious or seeking high satisfaction.

[0142] Step 5:

[0143] Once the transaction terms are determined, the server records that information on the blockchain. This ensures transaction transparency and reduces the risk of fraud. Subsequently, a notification of the successful transaction is sent to the user's device.

[0144] Step 6:

[0145] The terminal provides users with detailed transaction information, including trading partners, price, transaction volume, and sentiment-based feedback. Users can also provide feedback on transactions through the terminal. This feedback is collected on the server and used to improve the system.

[0146] (Example 2)

[0147] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0148] In renewable energy trading, conventional systems have failed to ensure transparency and reliability of transaction information, and have made it difficult to provide flexible trading conditions that take user sentiment into consideration. Therefore, there is a need for a system that improves the user experience and facilitates smoother transactions.

[0149] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0150] In this invention, the server includes means for storing transaction information in an immutable and transparent format using distributed ledger technology, means for predicting supply and demand and determining the optimal transaction price using an artificial intelligence model, and means for utilizing an emotion engine that analyzes the user's emotional state and reflects it in the transaction conditions. This makes it possible to provide flexible transaction conditions that take user emotions into consideration, while ensuring the reliability and transparency of transactions.

[0151] "Distributed ledger technology" is a database technology that is managed in a distributed manner by multiple computers, and is used to ensure the immutability and transparency of information.

[0152] "Transaction information" refers to data related to the buying and selling of electricity, including information such as transaction conditions, quantity, price, and participants.

[0153] An "artificial intelligence model" refers to an algorithm and its entire implementation used to predict future demand and supply using machine learning and data analysis techniques.

[0154] "Computational means" refers to a device or software that has the computing power to analyze data and perform certain processing.

[0155] An "emotion engine" is a system or software that processes user emotional data, analyzes their emotional state, and reflects that information in transaction conditions.

[0156] A "prompt message" is text data output by a generative AI model that presents specific conditions or instructions.

[0157] "Feedback" refers to data such as opinions and impressions provided by users based on their transaction experience, and is used to improve the system.

[0158] This invention is a system for efficiently trading surplus renewable energy in a way that respects user emotions. The system consists of multiple terminals and a central server. The terminals are installed in facilities owned by the users and are equipped with smart meters and emotion recognition sensors. The smart meters measure the amount of electricity consumed and supplied, and the emotion recognition sensors analyze the user's facial expressions and voice to generate emotion data.

[0159] The data collected by the device is transmitted to a server via the network. The server processes the received data and runs an artificial intelligence model to predict future electricity demand and supply. Machine learning algorithms are used in the AI ​​model's calculations, and historical data is also utilized. Furthermore, the server uses an emotion engine to analyze the user's emotional state and reflect this in the transaction conditions.

[0160] When a user requests to buy or sell electricity, the server sends a prompt to a generating AI model, which then generates optimal transaction conditions based on the user's emotions. For example, a prompt might say, "User C wants to sell 10kWh of surplus renewable energy. He is feeling anxious about the transaction. In this situation, please suggest how to adjust the transaction conditions to improve his satisfaction."

[0161] When a transaction is completed, the server records the transaction details on a distributed ledger technology, i.e., a blockchain, to ensure transparency and immutability. It also sends a transaction completion notification to the terminal, allowing the user to register feedback on their transaction experience. This feedback is aggregated on the server and used to improve the sentiment model.

[0162] As a concrete example, when user C sells 10kWh of surplus electricity, if the user's facial expression data detects anxiety about the transaction, the server uses prompt messages to formulate transaction conditions that provide reassurance. For example, it might expedite processing, provide clear explanations, and offer a higher-than-usual purchase price. This ensures a smooth transaction and improves user satisfaction.

[0163] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0164] Step 1:

[0165] The terminal collects data from smart meters and emotion recognition sensors installed at the user's facility. It takes in power consumption and supply measured by the smart meters, and emotion data based on facial expressions and voices detected by the emotion recognition sensors as input, and prepares to temporarily store this data.

[0166] Step 2:

[0167] The terminal sends the collected power and emotion data to the server. The output at this stage is a dataset showing power consumption, supply, and the user's emotional state. This dataset is then transferred to the server via the network to proceed to the next analysis process.

[0168] Step 3:

[0169] The server stores data received from terminals in a database and applies an artificial intelligence model to the power data. The input here is power consumption and supply data, and machine learning algorithms are used for analysis and prediction calculations to obtain predictions of future power demand and supply as output.

[0170] Step 4:

[0171] The server uses an emotion engine to analyze the received emotional data. The input for this process is data indicating the user's emotional state. The emotion engine evaluates the user's psychological state, analyzes how the user is being affected by the transaction, and outputs the results.

[0172] Step 5:

[0173] The server integrates predicted power supply and demand data with sentiment analysis results and inputs prompts into a generative AI model. The input includes the user's power surplus, sentiment state, and market forecast. The generative AI model analyzes the prompts and outputs flexible trading conditions and prices. These results are used to adjust trading prices and set conditions.

[0174] Step 6:

[0175] The server completes transactions based on optimized transaction conditions and records transaction information using distributed ledger technology in an immutable and transparent format. The recorded output is data detailing the contents of the transaction, and by storing this information on the blockchain, the immutability and transparency of the transaction are ensured.

[0176] Step 7:

[0177] After a transaction is completed, the server sends a notification of the transaction result to the terminal, and the user can provide feedback. The output received by the terminal is the transaction details and a request for feedback from the user. The feedback entered by the user is sent to the server and used to improve the sentiment model.

[0178] (Application Example 2)

[0179] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0180] Trading surplus renewable energy efficiently while considering the user's psychological state has been difficult with conventional systems. Furthermore, to improve the user trading experience, there is a need for a means to enable emotion-based interaction while ensuring trading flexibility and transparency.

[0181] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0182] In this invention, the server includes means for storing transaction information in an immutable and transparent format using distributed database technology, computation means using machine learning models for forecasting supply and demand, and means for recognizing the user's emotional state and presenting adjusted transaction conditions based on that information. This enables flexible energy trading that takes user emotions into consideration.

[0183] "Distributed database technology" is a technology that stores data across multiple nodes rather than on a central server, enabling highly transparent and fault-tolerant data management.

[0184] A "machine learning model" is a collection of mathematical algorithms that learn patterns from data and perform predictions and classifications, and is used to forecast energy demand and supply.

[0185] "Means of recognizing emotional states" refers to technologies that evaluate and analyze emotions by analyzing data such as a user's facial expressions and voice.

[0186] "Means of presenting trading conditions" refers to an interface that provides users with conditions such as price, quantity, and timing for a transaction, in order to optimize the transaction.

[0187] The system for implementing this invention mainly consists of a server, a user terminal, and related sensors. The server uses distributed database technology to record and manage transaction information while ensuring transparency and immutability. This allows users to ensure the reliability of their transactions.

[0188] Furthermore, the server runs a machine learning model that uses historical data to predict energy demand and supply. This model is implemented using a deep learning framework such as TensorFlow and has the ability to learn patterns from diverse datasets. This allows users to understand future energy supply conditions in advance and optimize their trading decisions.

[0189] Sensors and cameras and microphones built into smartphones are used to understand the user's psychological state from their facial expressions and voice. This allows for the analysis of facial expressions and voice tone using OpenCV and Google® Cloud Speech API, and the user's emotional state can be transmitted to the server in real time. Based on this emotional information, the server personalizes the user's trading experience and determines the optimal trading conditions.

[0190] For example, if the system determines that a user is experiencing stress, the conditions suggested for smoother transactions are adjusted, and a more user-friendly interface is provided. Furthermore, the sentiment model is refined based on feedback provided after transactions and reflected in future transactions.

[0191] An example of a prompt in a generative AI model is: "Suggest how to optimize surplus electricity trading for users with high stress levels, including pricing and user interface ideas." This allows for a more personalized customer experience and promotes the efficient use of renewable energy.

[0192] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0193] Step 1:

[0194] The user terminal uses its built-in camera and microphone to capture the user's facial expressions and voice. This allows for the acquisition of real-time emotional data of the user. The input is the user's facial expressions and voice, and the output is emotional data. This data is converted into emotional states through image analysis using OpenCV and voice analysis using the Google Cloud Speech API.

[0195] Step 2:

[0196] The user's terminal sends the acquired emotional data to the server. The server takes this emotional data as input to a machine learning model and begins the analysis. The input is the emotional data, and the output is the analysis result. This analysis clarifies the user's psychological state, such as stress and satisfaction levels.

[0197] Step 3:

[0198] The server uses a machine learning model to predict energy demand and supply, referencing accumulated transaction data and external information. The input is historical transaction data and external information, and the output is the prediction result. This prediction result serves as an important indicator for understanding the future balance of electricity supply and demand.

[0199] Step 4:

[0200] The server integrates the results of sentiment data analysis and energy supply and demand forecasts to determine the optimal trading conditions for each individual user. The inputs are sentiment analysis results and supply and demand forecasts, while the output is the optimized trading conditions. The AI ​​agent combines this data to derive prices and conditions that are appropriate to the user's psychological state.

[0201] Step 5:

[0202] The server notifies the user terminal of the determined trading conditions and prompts the user to participate in the trade. The input is the optimized trading conditions, and the output is a notification message to the user. This notification also includes feedback based on sentiment data, allowing the user to quickly check their trading status.

[0203] Step 6:

[0204] Once a transaction is completed, the server records the transaction details in a distributed database and collects feedback from the user. Inputs are the transaction details and user feedback, while outputs are the recorded transaction data and feedback information. This ensures transaction reliability and leads to further enhancement of the sentiment model.

[0205] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0206] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0207] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0208] [Second Embodiment]

[0209] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0210] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0211] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0212] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0213] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0214] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0215] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0216] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0217] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0218] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0219] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0220] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0221] This invention provides an embodiment of a decentralized energy trading system that can efficiently trade surplus renewable energy. This system is implemented in the following manner.

[0222] First, the terminal acquires power consumption and supply data from the user's location. The terminal sends this data to the server at regular intervals. Upon receiving this data, the server performs the necessary preprocessing and then stores it in the database. Preprocessing includes data cleansing and imputation of missing values.

[0223] Next, the server runs an artificial intelligence model using the collected data. The AI ​​agent utilizes the collected data and external data (e.g., weather information and usage patterns) to predict future electricity demand and supply. This makes it possible to anticipate situations of oversupply or overdemand.

[0224] Subsequently, the server executes a pricing algorithm based on the predicted supply and demand data. This algorithm considers the balance of supply and demand to calculate the optimal price. Furthermore, an AI agent autonomously matches trading partners based on the predicted information. The terminal notifies the user of the transaction price and information about the trading partner.

[0225] Once a transaction is completed, the server records the transaction details on the blockchain, ensuring a secure and transparent transaction environment. The recorded information is immutable and can be used for future audits and verifications.

[0226] Once a transaction is completed, the user is notified via their terminal. Users can verify that their electricity was traded appropriately and contribute to further system improvements by providing feedback.

[0227] For example, if user A has a surplus of 10kWh of electricity during the day, the AI ​​agent will identify user B's shortage and match them so that A can sell 5kWh to B at a fair price. Once a transaction is completed between A and B, the information is recorded on the blockchain, and notifications are sent to both A and B's devices. In this way, individual users can efficiently trade their surplus electricity, maximizing the use of renewable energy.

[0228] The following describes the processing flow.

[0229] Step 1:

[0230] The terminal collects real-time data on electricity consumption and generation from the user's home or business. This is done using smart meters and home energy management systems. The collected data is then transmitted to a server at regular intervals.

[0231] Step 2:

[0232] Before saving the received data to the database, the server performs preprocessing such as noise reduction and missing value imputation. This ensures data accuracy and makes it suitable as foundational data for analysis and prediction.

[0233] Step 3:

[0234] The server inputs pre-processed data into an artificial intelligence model to generate forecasts of each user's future electricity demand and supply. The AI ​​agent makes more accurate forecasts by referencing weather data and historical consumption patterns.

[0235] Step 4:

[0236] The server executes a pricing algorithm based on supply and demand data predicted by the AI ​​agent. This algorithm calculates the optimal transaction price considering current market conditions. At this stage, it also performs transaction matching to select appropriate buyers and sellers.

[0237] Step 5:

[0238] Once the transaction terms are determined, the server records the transaction details on the blockchain. This ensures the transparency and security of the transaction and helps prevent fraud. This information is used for subsequent audits and verification by the transaction parties.

[0239] Step 6:

[0240] The terminal notifies the user that a transaction has been completed. The user can then view the transaction details (trading partner, price, quantity, etc.). The user can also provide feedback, which the server collects to improve the system.

[0241] (Example 1)

[0242] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0243] Conventional renewable energy trading systems have problems in that they cannot adequately respond to fluctuations in electricity supply and demand. Furthermore, the transparency and security of trading information are insufficient, making reliable energy management difficult. In addition, the lack of sufficient demand forecasting and price optimization using artificial intelligence hinders efficient trading.

[0244] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0245] In this invention, the server includes means for using information equipment to receive transaction requests and collect data on power usage and supply; storage means for preprocessing the collected data and storing it in a consistent state; and computation means for predicting future power demand and supply while referring to external environmental data using an artificial intelligence model. This makes it possible to respond flexibly to fluctuations in power supply and demand and to efficiently conduct transparent and reliable energy trading.

[0246] A "transaction request" is a request made to the system to initiate a transaction in response to the supply or demand situation of electricity.

[0247] "Information equipment" refers to measuring devices and terminals installed at user sites to collect data related to power usage and supply.

[0248] "Preprocessing" refers to the process of adding missing values ​​and correcting outliers to collected data to make it consistent.

[0249] A "memory device" is a database or similar data storage system that stores pre-processed data and uses it for subsequent analysis or model training.

[0250] An "artificial intelligence model" is a mathematical model that uses collected data and external environmental data to predict future electricity demand and supply.

[0251] "Distributed ledger technology" is blockchain technology used to record transaction details in an immutable format, thereby increasing transparency and security.

[0252] The "computational means" refers to the process of using an artificial intelligence model to calculate future electricity demand and supply, and to generate data for efficient trading.

[0253] The "transaction price" is the fair price in the trading of electricity, calculated based on the balance of supply and demand.

[0254] "User" refers to an individual or organization participating in this system as a power supplier or consumer that trades surplus electricity.

[0255] This invention is a decentralized trading system for efficiently trading surplus renewable energy. The method for implementing this system is described below.

[0256] First, the terminal is installed at the user's location and collects power consumption and supply data through a power meter. The collected data is sent to the server at regular intervals. The server then performs data preprocessing. Preprocessing includes imputing missing values ​​and correcting outliers, and the Python Pandas library is used for this purpose.

[0257] Preprocessed data is stored in a database management system (DBMS) on the server. MySQL is used for data storage and management. The data is accumulated in an organized format in preparation for subsequent analysis.

[0258] Next, the server runs an artificial intelligence (AI) model. This AI model, built using TensorFlow, combines data with external environmental information (e.g., weather data) to predict future electricity demand and supply. This prediction makes it possible to anticipate situations of electricity oversupply or overdemand. Based on the predicted data, a pricing algorithm calculates the optimal transaction price.

[0259] Furthermore, an AI agent autonomously matches trading partners. Once a transaction is completed, the server records the transaction details on a distributed ledger (blockchain), ensuring secure and transparent transactions. This record is immutable and can be used for subsequent audits and transaction history verification.

[0260] After the transaction is completed, the terminal notifies the user of the transaction details and feedback. The user can review the electricity transaction details and provide feedback to the system, which helps to further improve the system.

[0261] As a concrete example, if user A has 10kWh of surplus electricity during the day, the AI ​​agent will identify user B's shortage and match them so that A can sell 5kWh to B at a fair price. This transaction will be notified to and recorded on the terminals of both users A and B.

[0262] Example of a prompt:

[0263] "Explain how User A can efficiently trade the 10kWh of surplus electricity they generated during the day. Also, show how the AI ​​agent will handle matching and pricing."

[0264] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0265] Step 1:

[0266] The terminal acquires real-time data on power consumption and supply via a power meter installed at the user's site. The input is data from the power meter, and the output is raw power usage data. Each data point is recorded with a time stamp and stored for later processing. Specifically, the terminal collects data every 10 minutes and temporarily stores it in its internal memory.

[0267] Step 2:

[0268] The terminal periodically sends the collected data to the server. The input is the accumulated power data, and the output is the data packets sent to the server. Data transmission takes place over the internet, and security is ensured using protocols such as SSL. Specifically, the terminal sends the data in batches every hour.

[0269] Step 3:

[0270] The server preprocesses the received power data. The input is data sent from the terminal, and the output is a cleansed, consistent dataset. Data cleansing includes imputing missing values ​​and detecting outliers. Specifically, the server cleans the data using the Pandas library and corrects outliers using statistical methods.

[0271] Step 4:

[0272] The server stores pre-processed data in a database. The input is a consistent dataset, and the output is the state in which it is stored in the database. MySQL is used for database management, and query optimization is performed on a time basis. Specifically, the server periodically inserts data using SQL statements and sets up indexes that enable efficient searching.

[0273] Step 5:

[0274] The server uses database data and external environmental data to predict electricity demand and supply using an artificial intelligence model. The input is electricity consumption data and weather data stored in the database, and the output is time-series data of predicted electricity demand and supply. A neural network model using TensorFlow operates to estimate future values. Specifically, the server retrains the model daily to generate new predictions.

[0275] Step 6:

[0276] The server executes a pricing algorithm based on predicted supply and demand data. The input is the predicted data, and the output is the optimal transaction price. The pricing algorithm dynamically adjusts the price, taking into account the balance between supply and demand. Specifically, the server measures price elasticity and calculates the price based on a demand response model.

[0277] Step 7:

[0278] The server uses an AI agent to match trading partners. The input is predicted supply and demand, and the optimal price; the output is a list of matched trading pairs. The AI ​​agent utilizes machine learning algorithms to select the best partner. Specifically, the server analyzes predicted data and historical trading history to perform partner matching.

[0279] Step 8:

[0280] The terminal notifies the user of the details of a transaction once it is completed. The input is transaction information from the server, and the output is a notification message to the user. The terminal informs the user of the transaction details, counterparty, and price in real time. Specifically, the terminal communicates with the user using push notifications or email.

[0281] Step 9:

[0282] The server records the transaction details using distributed ledger technology. The input is the transaction detail data, and the output is the recorded blockchain data. By using blockchain technology, the transparency and security of transactions are guaranteed. As a specific operation, the server sends the transaction to the blockchain network and it is recorded by all nodes.

[0283] (Application Example 1)

[0284] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0285] In order to achieve efficient transactions and maximum utilization of renewable energy, it is necessary to accurately predict fluctuations in power demand and eliminate excess or shortage situations. Also, a mechanism is required to manage information transparently and securely so that users can easily grasp their own energy usage status. However, it is difficult for the current system to fully meet these requirements.

[0286] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0287] In this invention, the server includes means for storing transaction information in an immutable and highly transparent format using distributed database technology, calculation means for predicting demand and supply using a machine learning model, means for determining an optimal transaction price based on the prediction and autonomously matching trading partners, means for visualizing the power supply situation and transaction proposals via a user interface, and means for providing transaction history and energy usage trends on a mobile device. Thereby, efficient transactions of renewable energy and promotion of understanding by visualization to users become possible.

[0288] "Distributed database technology" is a technology that stores data in a distributed manner across multiple locations, ensuring data transparency and immutability without requiring a central administrator.

[0289] A "machine learning model" is a system of algorithms that learns patterns and rules from past data and has the ability to predict future demand and supply.

[0290] "Visualization of power supply status" is a technology that displays the energy supply status in a way that is easy for users to understand, using methods such as graphs and charts.

[0291] A "mobile device" is a portable electronic device capable of acquiring, processing, and transmitting information, and in this context, it specifically refers to smartphones and tablets.

[0292] A "user interface" is a component of software that provides the operating screen and display method for exchanging information between the user and the system.

[0293] This invention is a system for enabling the efficient trading of renewable energy. Specific embodiments are described below.

[0294] The server first uses distributed database technology to store transaction information reported by each user in an immutable and transparent format. This ensures the security and integrity of transactions.

[0295] Next, the server uses machine learning models to analyze historical data and energy data collected in real time. This modeling prepares the server to effectively forecast supply and demand and provide optimal trading conditions. External weather data and market trend data are used in this process.

[0296] On user terminals, mobile devices such as smartphones visualize and present power supply status and trading proposals to users in real time. The user interface is designed to be intuitive and easy to understand, making it easy for users to trade energy. For example, if there is a surplus of electricity, an option to easily sell it will be displayed within the application.

[0297] As a concrete example of implementation, the server inputs the following prompt to the AI ​​model: "We have 5kWh of surplus electricity. Please suggest the best trading partner and price. Factors to consider are demand, weather information, and existing electricity prices." This allows the AI ​​to provide the optimal solution, enabling the user to make a quick decision.

[0298] In this way, the present invention constructs a system that efficiently trades renewable energy and aims to maximize the utilization of energy resources.

[0299] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0300] Step 1:

[0301] The server periodically retrieves power consumption and supply data from each user's terminal. This data is received as input, and before recording it in a distributed database, it undergoes data cleansing, correcting inconsistent data and imputing missing values. The clean data is then stored as output for further processing.

[0302] Step 2:

[0303] The server runs a machine learning model based on stored consumption and supply data. External data, such as weather information, is also input to predict future electricity demand and supply. Through data processing, the model analyzes past and present information to learn demand and supply patterns. The output is forecast data for demand and supply.

[0304] Step 3:

[0305] The server receives the predicted demand and supply data as input and executes a price-setting algorithm. This algorithm takes into account the balance between demand and supply and calculates the optimal trading price. As output, a specific trading price is generated. Here, the generated AI model is utilized and a price adjustment prompt is used.

[0306] Step 4:

[0307] Based on the calculated trading price and prediction data, the server performs matching of trading partners. A candidate list of trading partners is generated, and the most appropriate partner is autonomously selected based on it. The input is prediction data and price information, and the output is information on the matched trading partner.

[0308] Step 5:

[0309] If a transaction is concluded, the server records the details of the transaction in a distributed database and sends a notification to the information terminal. To ensure the transparency and confirmation of the transaction, blockchain technology is used to keep the history immutable. As a result, users can check the transaction results on their own terminals.

[0310] Step 6:

[0311] The user provides feedback based on the transaction information received through the terminal. This feedback is sent to the server and utilized for system improvement. The input is the feedback information from the user, and data for system update is obtained as output.

[0312] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0313] This invention provides an embodiment of a decentralized energy trading system that enables the efficient and emotionally relatable trading of surplus renewable energy in a user-friendly manner. This system is implemented in the following manner:

[0314] First, the terminal acquires data on power consumption and supply from the user's facility and transmits it to the server. The terminal is equipped with an emotion recognition sensor to analyze the user's facial expressions and tone of voice, and constantly captures and transmits the user's emotional data to the server. This makes it possible to analyze the user's emotions regarding their transaction experience in real time.

[0315] The server uses the received power data to run an artificial intelligence model. The AI ​​agent analyzes the data and predicts future power demand and supply. Furthermore, it uses an emotion engine to evaluate the user's emotional state and uses that data to determine how the user is psychologically influenced in transactions.

[0316] Next, the server combines predicted supply and demand data with sentiment information to adjust trading prices. For example, if a user is feeling stressed, it can set flexible prices to smooth the trading process. Furthermore, to increase user satisfaction during trading, the sentiment engine provides customized trading conditions tailored to each user.

[0317] Once a transaction is completed, the server records the transaction details on the blockchain in an immutable and transparent format and sends a transaction completion notification to the terminal. These notifications also include feedback generated by the sentiment engine, allowing users to register comments about their transaction experience. The server uses this feedback to refine the sentiment model, enabling more accurate customer satisfaction ratings.

[0318] For example, if user C wants to sell 10kWh of surplus electricity, and the system determines from the user's expression that they are feeling uneasy about the transaction, it will offer clear explanations and a slightly higher purchase price to complete the transaction quickly and efficiently. In this way, a transaction process that takes the user's emotional state into consideration can be realized, promoting the spread of renewable energy and improving user satisfaction.

[0319] The following describes the processing flow.

[0320] Step 1:

[0321] The terminals are installed in users' homes and businesses to collect data on power consumption and generation. In addition, the terminals are equipped with emotion recognition sensors that capture emotional data from the user's facial expressions and voice. This data is transmitted to a server in real time.

[0322] Step 2:

[0323] The server preprocesses the received power data, performing noise reduction and missing data imputation, before storing it in a database. Simultaneously, it analyzes emotional data and extracts the user's current emotional state. This process allows the server to maintain an emotional profile for each user.

[0324] Step 3:

[0325] The server inputs pre-processed power data into an artificial intelligence model to predict future power demand and supply. This prediction also utilizes external data such as the user's past consumption patterns and weather.

[0326] Step 4:

[0327] The server sets the optimal trading price for each individual user based on prediction results and sentiment data. Here, the sentiment engine adjusts the price and trading conditions, especially when the user is feeling anxious or seeking high satisfaction.

[0328] Step 5:

[0329] Once the transaction terms are determined, the server records that information on the blockchain. This ensures transaction transparency and reduces the risk of fraud. Subsequently, a notification of the successful transaction is sent to the user's device.

[0330] Step 6:

[0331] The terminal provides users with detailed transaction information, including trading partners, price, transaction volume, and sentiment-based feedback. Users can also provide feedback on transactions through the terminal. This feedback is collected on the server and used to improve the system.

[0332] (Example 2)

[0333] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0334] In renewable energy trading, conventional systems have failed to ensure transparency and reliability of transaction information, and have made it difficult to provide flexible trading conditions that take user sentiment into consideration. Therefore, there is a need for a system that improves the user experience and facilitates smoother transactions.

[0335] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0336] In this invention, the server includes means for storing transaction information in an immutable and transparent format using distributed ledger technology, means for predicting supply and demand and determining the optimal transaction price using an artificial intelligence model, and means for utilizing an emotion engine that analyzes the user's emotional state and reflects it in the transaction conditions. This makes it possible to provide flexible transaction conditions that take user emotions into consideration, while ensuring the reliability and transparency of transactions.

[0337] "Distributed ledger technology" is a database technology that is managed in a distributed manner by multiple computers, and is used to ensure the immutability and transparency of information.

[0338] "Transaction information" refers to data related to the buying and selling of electricity, including information such as transaction conditions, quantity, price, and participants.

[0339] An "artificial intelligence model" refers to an algorithm and its entire implementation used to predict future demand and supply using machine learning and data analysis techniques.

[0340] "Computational means" refers to a device or software that has the computing power to analyze data and perform certain processing.

[0341] An "emotion engine" is a system or software that processes user emotional data, analyzes their emotional state, and reflects that information in transaction conditions.

[0342] A "prompt message" is text data output by a generative AI model that presents specific conditions or instructions.

[0343] "Feedback" refers to data such as opinions and impressions provided by users based on their transaction experience, and is used to improve the system.

[0344] This invention is a system for efficiently trading surplus renewable energy in a way that respects user emotions. The system consists of multiple terminals and a central server. The terminals are installed in facilities owned by the users and are equipped with smart meters and emotion recognition sensors. The smart meters measure the amount of electricity consumed and supplied, and the emotion recognition sensors analyze the user's facial expressions and voice to generate emotion data.

[0345] The data collected by the device is transmitted to a server via the network. The server processes the received data and runs an artificial intelligence model to predict future electricity demand and supply. Machine learning algorithms are used in the AI ​​model's calculations, and historical data is also utilized. Furthermore, the server uses an emotion engine to analyze the user's emotional state and reflect this in the transaction conditions.

[0346] When a user requests to buy or sell electricity, the server sends a prompt to a generating AI model, which then generates optimal transaction conditions based on the user's emotions. For example, a prompt might say, "User C wants to sell 10kWh of surplus renewable energy. He is feeling anxious about the transaction. In this situation, please suggest how to adjust the transaction conditions to improve his satisfaction."

[0347] When a transaction is completed, the server records the transaction details on a distributed ledger technology, i.e., a blockchain, to ensure transparency and immutability. It also sends a transaction completion notification to the terminal, allowing the user to register feedback on their transaction experience. This feedback is aggregated on the server and used to improve the sentiment model.

[0348] As a concrete example, when user C sells 10kWh of surplus electricity, if the user's facial expression data detects anxiety about the transaction, the server uses prompt messages to formulate transaction conditions that provide reassurance. For example, it might expedite processing, provide clear explanations, and offer a higher-than-usual purchase price. This ensures a smooth transaction and improves user satisfaction.

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

[0350] Step 1:

[0351] The terminal collects data from smart meters and emotion recognition sensors installed at the user's facility. It takes in power consumption and supply measured by the smart meters, and emotion data based on facial expressions and voices detected by the emotion recognition sensors as input, and prepares to temporarily store this data.

[0352] Step 2:

[0353] The terminal sends the collected power and emotion data to the server. The output at this stage is a dataset showing power consumption, supply, and the user's emotional state. This dataset is then transferred to the server via the network to proceed to the next analysis process.

[0354] Step 3:

[0355] The server stores data received from terminals in a database and applies an artificial intelligence model to the power data. The input here is power consumption and supply data, and machine learning algorithms are used for analysis and prediction calculations to obtain predictions of future power demand and supply as output.

[0356] Step 4:

[0357] The server uses an emotion engine to analyze the received emotional data. The input for this process is data indicating the user's emotional state. The emotion engine evaluates the user's psychological state, analyzes how the user is being affected by the transaction, and outputs the results.

[0358] Step 5:

[0359] The server integrates predicted power supply and demand data with sentiment analysis results and inputs prompts into a generative AI model. The input includes the user's power surplus, sentiment state, and market forecast. The generative AI model analyzes the prompts and outputs flexible trading conditions and prices. These results are used to adjust trading prices and set conditions.

[0360] Step 6:

[0361] The server completes transactions based on optimized transaction conditions and records transaction information using distributed ledger technology in an immutable and transparent format. The recorded output is data detailing the contents of the transaction, and by storing this information on the blockchain, the immutability and transparency of the transaction are ensured.

[0362] Step 7:

[0363] After a transaction is completed, the server sends a notification of the transaction result to the terminal, and the user can provide feedback. The output received by the terminal is the transaction details and a request for feedback from the user. The feedback entered by the user is sent to the server and used to improve the sentiment model.

[0364] (Application Example 2)

[0365] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0366] Trading surplus renewable energy efficiently while considering the user's psychological state has been difficult with conventional systems. Furthermore, to improve the user trading experience, there is a need for a means to enable emotion-based interaction while ensuring trading flexibility and transparency.

[0367] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0368] In this invention, the server includes means for storing transaction information in an immutable and transparent format using distributed database technology, computation means using machine learning models for forecasting supply and demand, and means for recognizing the user's emotional state and presenting adjusted transaction conditions based on that information. This enables flexible energy trading that takes user emotions into consideration.

[0369] "Distributed database technology" is a technology that stores data across multiple nodes rather than on a central server, enabling highly transparent and fault-tolerant data management.

[0370] A "machine learning model" is a collection of mathematical algorithms that learn patterns from data and perform predictions and classifications, and is used to forecast energy demand and supply.

[0371] "Means of recognizing emotional states" refers to technologies that evaluate and analyze emotions by analyzing data such as a user's facial expressions and voice.

[0372] "Means of presenting trading conditions" refers to an interface that provides users with conditions such as price, quantity, and timing for a transaction, in order to optimize the transaction.

[0373] The system for implementing this invention mainly consists of a server, a user terminal, and related sensors. The server uses distributed database technology to record and manage transaction information while ensuring transparency and immutability. This allows users to ensure the reliability of their transactions.

[0374] Furthermore, the server runs a machine learning model that uses historical data to predict energy demand and supply. This model is implemented using a deep learning framework such as TensorFlow and has the ability to learn patterns from diverse datasets. This allows users to understand future energy supply conditions in advance and optimize their trading decisions.

[0375] Sensors and cameras and microphones built into smartphones are used to understand the user's psychological state from their facial expressions and voice. This allows OpenCV and the Google Cloud Speech API to analyze facial expressions and voice tone, and send the user's emotional state to the server in real time. Based on this emotional information, the server personalizes the user's trading experience and determines the optimal trading conditions.

[0376] For example, if the system determines that a user is experiencing stress, the conditions suggested for smoother transactions are adjusted, and a more user-friendly interface is provided. Furthermore, the sentiment model is refined based on feedback provided after transactions and reflected in future transactions.

[0377] An example of a prompt in a generative AI model is: "Suggest how to optimize surplus electricity trading for users with high stress levels, including pricing and user interface ideas." This allows for a more personalized customer experience and promotes the efficient use of renewable energy.

[0378] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0379] Step 1:

[0380] The user terminal uses its built-in camera and microphone to capture the user's facial expressions and voice. This allows for the acquisition of real-time emotional data of the user. The input is the user's facial expressions and voice, and the output is emotional data. This data is converted into emotional states through image analysis using OpenCV and voice analysis using the Google Cloud Speech API.

[0381] Step 2:

[0382] The user's terminal sends the acquired emotional data to the server. The server takes this emotional data as input to a machine learning model and begins the analysis. The input is the emotional data, and the output is the analysis result. This analysis clarifies the user's psychological state, such as stress and satisfaction levels.

[0383] Step 3:

[0384] The server uses a machine learning model to predict energy demand and supply, referencing accumulated transaction data and external information. The input is historical transaction data and external information, and the output is the prediction result. This prediction result serves as an important indicator for understanding the future balance of electricity supply and demand.

[0385] Step 4:

[0386] The server integrates the results of sentiment data analysis and energy supply and demand forecasts to determine the optimal trading conditions for each individual user. The inputs are sentiment analysis results and supply and demand forecasts, while the output is the optimized trading conditions. The AI ​​agent combines this data to derive prices and conditions that are appropriate to the user's psychological state.

[0387] Step 5:

[0388] The server notifies the user terminal of the determined trading conditions and prompts the user to participate in the trade. The input is the optimized trading conditions, and the output is a notification message to the user. This notification also includes feedback based on sentiment data, allowing the user to quickly check their trading status.

[0389] Step 6:

[0390] Once a transaction is completed, the server records the transaction details in a distributed database and collects feedback from the user. Inputs are the transaction details and user feedback, while outputs are the recorded transaction data and feedback information. This ensures transaction reliability and leads to further enhancement of the sentiment model.

[0391] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0392] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0393] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0394] [Third Embodiment]

[0395] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0396] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0397] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0398] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0399] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0400] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0401] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0402] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0403] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0404] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0405] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0406] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0407] This invention provides an embodiment of a decentralized energy trading system that can efficiently trade surplus renewable energy. This system is implemented in the following manner.

[0408] First, the terminal acquires power consumption and supply data from the user's location. The terminal sends this data to the server at regular intervals. Upon receiving this data, the server performs the necessary preprocessing and then stores it in the database. Preprocessing includes data cleansing and imputation of missing values.

[0409] Next, the server runs an artificial intelligence model using the collected data. The AI ​​agent utilizes the collected data and external data (e.g., weather information and usage patterns) to predict future electricity demand and supply. This makes it possible to anticipate situations of oversupply or overdemand.

[0410] Subsequently, the server executes a pricing algorithm based on the predicted supply and demand data. This algorithm considers the balance of supply and demand to calculate the optimal price. Furthermore, an AI agent autonomously matches trading partners based on the predicted information. The terminal notifies the user of the transaction price and information about the trading partner.

[0411] Once a transaction is completed, the server records the transaction details on the blockchain, ensuring a secure and transparent transaction environment. The recorded information is immutable and can be used for future audits and verifications.

[0412] Once a transaction is completed, the user is notified via their terminal. Users can verify that their electricity was traded appropriately and contribute to further system improvements by providing feedback.

[0413] For example, if user A has a surplus of 10kWh of electricity during the day, the AI ​​agent will identify user B's shortage and match them so that A can sell 5kWh to B at a fair price. Once a transaction is completed between A and B, the information is recorded on the blockchain, and notifications are sent to both A and B's devices. In this way, individual users can efficiently trade their surplus electricity, maximizing the use of renewable energy.

[0414] The following describes the processing flow.

[0415] Step 1:

[0416] The terminal collects real-time data on electricity consumption and generation from the user's home or business. This is done using smart meters and home energy management systems. The collected data is then transmitted to a server at regular intervals.

[0417] Step 2:

[0418] Before saving the received data to the database, the server performs preprocessing such as noise reduction and missing value imputation. This ensures data accuracy and makes it suitable as foundational data for analysis and prediction.

[0419] Step 3:

[0420] The server inputs pre-processed data into an artificial intelligence model to generate forecasts of each user's future electricity demand and supply. The AI ​​agent makes more accurate forecasts by referencing weather data and historical consumption patterns.

[0421] Step 4:

[0422] The server executes a pricing algorithm based on supply and demand data predicted by the AI ​​agent. This algorithm calculates the optimal transaction price considering current market conditions. At this stage, it also performs transaction matching to select appropriate buyers and sellers.

[0423] Step 5:

[0424] Once the transaction terms are determined, the server records the transaction details on the blockchain. This ensures the transparency and security of the transaction and helps prevent fraud. This information is used for subsequent audits and verification by the transaction parties.

[0425] Step 6:

[0426] The terminal notifies the user that a transaction has been completed. The user can then view the transaction details (trading partner, price, quantity, etc.). The user can also provide feedback, which the server collects to improve the system.

[0427] (Example 1)

[0428] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0429] Conventional renewable energy trading systems have problems in that they cannot adequately respond to fluctuations in electricity supply and demand. Furthermore, the transparency and security of trading information are insufficient, making reliable energy management difficult. In addition, the lack of sufficient demand forecasting and price optimization using artificial intelligence hinders efficient trading.

[0430] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0431] In this invention, the server includes means for using information equipment to receive transaction requests and collect data on power usage and supply; storage means for preprocessing the collected data and storing it in a consistent state; and computation means for predicting future power demand and supply while referring to external environmental data using an artificial intelligence model. This makes it possible to respond flexibly to fluctuations in power supply and demand and to efficiently conduct transparent and reliable energy trading.

[0432] A "transaction request" is a request made to the system to initiate a transaction in response to the supply or demand situation of electricity.

[0433] "Information equipment" refers to measuring devices and terminals installed at user sites to collect data related to power usage and supply.

[0434] "Preprocessing" refers to the process of adding missing values ​​and correcting outliers to collected data to make it consistent.

[0435] A "memory device" is a database or similar data storage system that stores pre-processed data and uses it for subsequent analysis or model training.

[0436] An "artificial intelligence model" is a mathematical model that uses collected data and external environmental data to predict future electricity demand and supply.

[0437] "Distributed ledger technology" is blockchain technology used to record transaction details in an immutable format, thereby increasing transparency and security.

[0438] The "computational means" refers to the process of using an artificial intelligence model to calculate future electricity demand and supply, and to generate data for efficient trading.

[0439] The "transaction price" is the fair price in the trading of electricity, calculated based on the balance of supply and demand.

[0440] "User" refers to an individual or organization participating in this system as a power supplier or consumer that trades surplus electricity.

[0441] This invention is a decentralized trading system for efficiently trading surplus renewable energy. The method for implementing this system is described below.

[0442] First, the terminal is installed at the user's location and collects power consumption and supply data through a power meter. The collected data is sent to the server at regular intervals. The server then performs data preprocessing. Preprocessing includes imputing missing values ​​and correcting outliers, and the Python Pandas library is used for this purpose.

[0443] Preprocessed data is stored in a database management system (DBMS) on the server. MySQL is used for data storage and management. The data is accumulated in an organized format in preparation for subsequent analysis.

[0444] Next, the server runs an artificial intelligence (AI) model. This AI model, built using TensorFlow, combines data with external environmental information (e.g., weather data) to predict future electricity demand and supply. This prediction makes it possible to anticipate situations of electricity oversupply or overdemand. Based on the predicted data, a pricing algorithm calculates the optimal transaction price.

[0445] Furthermore, an AI agent autonomously matches trading partners. Once a transaction is completed, the server records the transaction details on a distributed ledger (blockchain), ensuring secure and transparent transactions. This record is immutable and can be used for subsequent audits and transaction history verification.

[0446] After the transaction is completed, the terminal notifies the user of the transaction details and feedback. The user can review the electricity transaction details and provide feedback to the system, which helps to further improve the system.

[0447] As a concrete example, if user A has 10kWh of surplus electricity during the day, the AI ​​agent will identify user B's shortage and match them so that A can sell 5kWh to B at a fair price. This transaction will be notified to and recorded on the terminals of both users A and B.

[0448] Example of a prompt:

[0449] "Explain how User A can efficiently trade the 10kWh of surplus electricity they generated during the day. Also, show how the AI ​​agent will handle matching and pricing."

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

[0451] Step 1:

[0452] The terminal acquires real-time data on power consumption and supply via a power meter installed at the user's site. The input is data from the power meter, and the output is raw power usage data. Each data point is recorded with a time stamp and stored for later processing. Specifically, the terminal collects data every 10 minutes and temporarily stores it in its internal memory.

[0453] Step 2:

[0454] The terminal periodically sends the collected data to the server. The input is the accumulated power data, and the output is the data packets sent to the server. Data transmission takes place over the internet, and security is ensured using protocols such as SSL. Specifically, the terminal sends the data in batches every hour.

[0455] Step 3:

[0456] The server preprocesses the received power data. The input is data sent from the terminal, and the output is a cleansed, consistent dataset. Data cleansing includes imputing missing values ​​and detecting outliers. Specifically, the server cleans the data using the Pandas library and corrects outliers using statistical methods.

[0457] Step 4:

[0458] The server stores pre-processed data in a database. The input is a consistent dataset, and the output is the state in which it is stored in the database. MySQL is used for database management, and query optimization is performed on a time basis. Specifically, the server periodically inserts data using SQL statements and sets up indexes that enable efficient searching.

[0459] Step 5:

[0460] The server uses database data and external environmental data to predict electricity demand and supply using an artificial intelligence model. The input is electricity consumption data and weather data stored in the database, and the output is time-series data of predicted electricity demand and supply. A neural network model using TensorFlow operates to estimate future values. Specifically, the server retrains the model daily to generate new predictions.

[0461] Step 6:

[0462] The server executes a pricing algorithm based on predicted supply and demand data. The input is the predicted data, and the output is the optimal transaction price. The pricing algorithm dynamically adjusts the price, taking into account the balance between supply and demand. Specifically, the server measures price elasticity and calculates the price based on a demand response model.

[0463] Step 7:

[0464] The server uses an AI agent to match trading partners. The input is predicted supply and demand, and the optimal price; the output is a list of matched trading pairs. The AI ​​agent utilizes machine learning algorithms to select the best partner. Specifically, the server analyzes predicted data and historical trading history to perform partner matching.

[0465] Step 8:

[0466] The terminal notifies the user of the details of a transaction once it is completed. The input is transaction information from the server, and the output is a notification message to the user. The terminal informs the user of the transaction details, counterparty, and price in real time. Specifically, the terminal communicates with the user using push notifications or email.

[0467] Step 9:

[0468] The server records transaction details using distributed ledger technology. The input is transaction detail data, and the output is recorded blockchain data. The use of blockchain technology guarantees the transparency and security of transactions. Specifically, the server sends the transaction to the blockchain network, where it is recorded on all nodes.

[0469] (Application Example 1)

[0470] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0471] To ensure the efficient trading and maximum utilization of renewable energy, it is necessary to accurately predict fluctuations in electricity demand and to have means to resolve surpluses or shortages. Furthermore, a system is needed that manages information transparently and securely, allowing users to easily understand their own energy usage. However, current systems struggle to fully meet these requirements.

[0472] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0473] In this invention, the server includes means for storing transaction information in an immutable and transparent format using distributed database technology; computation means for forecasting supply and demand using machine learning models; means for determining the optimal transaction price based on the forecast and autonomously matching trading partners; means for visualizing power supply status and transaction proposals via a user interface; and means for providing transaction history and energy usage trends on mobile devices. This enables efficient trading of renewable energy and promotes user understanding through visualization.

[0474] "Distributed database technology" is a technology that stores data in a distributed manner across multiple locations, ensuring data transparency and immutability without requiring a central administrator.

[0475] A "machine learning model" is a system of algorithms that learns patterns and rules from past data and has the ability to predict future demand and supply.

[0476] "Visualization of power supply status" is a technology that displays the energy supply status in a way that is easy for users to understand, using methods such as graphs and charts.

[0477] A "mobile device" is a portable electronic device capable of acquiring, processing, and transmitting information, and in this context, it specifically refers to smartphones and tablets.

[0478] A "user interface" is a component of software that provides the operating screen and display method for exchanging information between the user and the system.

[0479] This invention is a system for enabling the efficient trading of renewable energy. Specific embodiments are described below.

[0480] The server first uses distributed database technology to store transaction information reported by each user in an immutable and transparent format. This ensures the security and integrity of transactions.

[0481] Next, the server uses machine learning models to analyze historical data and energy data collected in real time. This modeling prepares the server to effectively forecast supply and demand and provide optimal trading conditions. External weather data and market trend data are used in this process.

[0482] On user terminals, mobile devices such as smartphones visualize and present power supply status and trading proposals to users in real time. The user interface is designed to be intuitive and easy to understand, making it easy for users to trade energy. For example, if there is a surplus of electricity, an option to easily sell it will be displayed within the application.

[0483] As a concrete example of implementation, the server inputs the following prompt to the AI ​​model: "We have 5kWh of surplus electricity. Please suggest the best trading partner and price. Factors to consider are demand, weather information, and existing electricity prices." This allows the AI ​​to provide the optimal solution, enabling the user to make a quick decision.

[0484] In this way, the present invention constructs a system that efficiently trades renewable energy and aims to maximize the utilization of energy resources.

[0485] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0486] Step 1:

[0487] The server periodically retrieves power consumption and supply data from each user's terminal. This data is received as input, and before recording it in a distributed database, it undergoes data cleansing, correcting inconsistent data and imputing missing values. The clean data is then stored as output for further processing.

[0488] Step 2:

[0489] The server runs a machine learning model based on stored consumption and supply data. External data, such as weather information, is also input to predict future electricity demand and supply. Through data processing, the model analyzes past and present information to learn demand and supply patterns. The output is forecast data for demand and supply.

[0490] Step 3:

[0491] The server receives predicted supply and demand data as input and executes a pricing algorithm. This algorithm considers the balance of supply and demand and calculates the optimal transaction price. A specific transaction price is generated as output. Here, a generation AI model is utilized, and price adjustment prompts are used.

[0492] Step 4:

[0493] The server matches trading partners based on calculated transaction prices and forecast data. It generates a list of potential trading partners and autonomously selects the most suitable partner based on this list. The input is forecast data and price information, and the output is information on the matched trading partner.

[0494] Step 5:

[0495] When a transaction is completed, the server records the transaction details in a distributed database and sends a notification to the information terminal. To ensure transparency and verification of transactions, blockchain technology is used to maintain an immutable history. This allows users to verify the transaction results on their own devices.

[0496] Step 6:

[0497] Users provide feedback based on transaction information received through their devices. This feedback is sent to the server and used to improve the system. The input is user feedback information, and the output is data for system updates.

[0498] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0499] This invention provides an embodiment of a decentralized energy trading system that enables the efficient and emotionally relatable trading of surplus renewable energy in a user-friendly manner. This system is implemented in the following manner:

[0500] First, the terminal acquires data on power consumption and supply from the user's facility and transmits it to the server. The terminal is equipped with an emotion recognition sensor to analyze the user's facial expressions and tone of voice, and constantly captures and transmits the user's emotional data to the server. This makes it possible to analyze the user's emotions regarding their transaction experience in real time.

[0501] The server uses the received power data to run an artificial intelligence model. The AI ​​agent analyzes the data and predicts future power demand and supply. Furthermore, it uses an emotion engine to evaluate the user's emotional state and uses that data to determine how the user is psychologically influenced in transactions.

[0502] Next, the server combines predicted supply and demand data with sentiment information to adjust trading prices. For example, if a user is feeling stressed, it can set flexible prices to smooth the trading process. Furthermore, to increase user satisfaction during trading, the sentiment engine provides customized trading conditions tailored to each user.

[0503] Once a transaction is completed, the server records the transaction details on the blockchain in an immutable and transparent format and sends a transaction completion notification to the terminal. These notifications also include feedback generated by the sentiment engine, allowing users to register comments about their transaction experience. The server uses this feedback to refine the sentiment model, enabling more accurate customer satisfaction ratings.

[0504] For example, if user C wants to sell 10kWh of surplus electricity, and the system determines from the user's expression that they are feeling uneasy about the transaction, it will offer clear explanations and a slightly higher purchase price to complete the transaction quickly and efficiently. In this way, a transaction process that takes the user's emotional state into consideration can be realized, promoting the spread of renewable energy and improving user satisfaction.

[0505] The following describes the processing flow.

[0506] Step 1:

[0507] The terminals are installed in users' homes and businesses to collect data on power consumption and generation. In addition, the terminals are equipped with emotion recognition sensors that capture emotional data from the user's facial expressions and voice. This data is transmitted to a server in real time.

[0508] Step 2:

[0509] The server preprocesses the received power data, performing noise reduction and missing data imputation, before storing it in a database. Simultaneously, it analyzes emotional data and extracts the user's current emotional state. This process allows the server to maintain an emotional profile for each user.

[0510] Step 3:

[0511] The server inputs pre-processed power data into an artificial intelligence model to predict future power demand and supply. This prediction also utilizes external data such as the user's past consumption patterns and weather.

[0512] Step 4:

[0513] The server sets the optimal trading price for each individual user based on prediction results and sentiment data. Here, the sentiment engine adjusts the price and trading conditions, especially when the user is feeling anxious or seeking high satisfaction.

[0514] Step 5:

[0515] Once the transaction terms are determined, the server records that information on the blockchain. This ensures transaction transparency and reduces the risk of fraud. Subsequently, a notification of the successful transaction is sent to the user's device.

[0516] Step 6:

[0517] The terminal provides users with detailed transaction information, including trading partners, price, transaction volume, and sentiment-based feedback. Users can also provide feedback on transactions through the terminal. This feedback is collected on the server and used to improve the system.

[0518] (Example 2)

[0519] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0520] In renewable energy trading, conventional systems have failed to ensure transparency and reliability of transaction information, and have made it difficult to provide flexible trading conditions that take user sentiment into consideration. Therefore, there is a need for a system that improves the user experience and facilitates smoother transactions.

[0521] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0522] In this invention, the server includes means for storing transaction information in an immutable and transparent format using distributed ledger technology, means for predicting supply and demand and determining the optimal transaction price using an artificial intelligence model, and means for utilizing an emotion engine that analyzes the user's emotional state and reflects it in the transaction conditions. This makes it possible to provide flexible transaction conditions that take user emotions into consideration, while ensuring the reliability and transparency of transactions.

[0523] "Distributed ledger technology" is a database technology that is managed in a distributed manner by multiple computers, and is used to ensure the immutability and transparency of information.

[0524] "Transaction information" refers to data related to the buying and selling of electricity, including information such as transaction conditions, quantity, price, and participants.

[0525] An "artificial intelligence model" refers to an algorithm and its entire implementation used to predict future demand and supply using machine learning and data analysis techniques.

[0526] "Computational means" refers to a device or software that has the computing power to analyze data and perform certain processing.

[0527] An "emotion engine" is a system or software that processes user emotional data, analyzes their emotional state, and reflects that information in transaction conditions.

[0528] A "prompt message" is text data output by a generative AI model that presents specific conditions or instructions.

[0529] "Feedback" refers to data such as opinions and impressions provided by users based on their transaction experience, and is used to improve the system.

[0530] This invention is a system for efficiently trading surplus renewable energy in a way that respects user emotions. The system consists of multiple terminals and a central server. The terminals are installed in facilities owned by the users and are equipped with smart meters and emotion recognition sensors. The smart meters measure the amount of electricity consumed and supplied, and the emotion recognition sensors analyze the user's facial expressions and voice to generate emotion data.

[0531] The data collected by the device is transmitted to a server via the network. The server processes the received data and runs an artificial intelligence model to predict future electricity demand and supply. Machine learning algorithms are used in the AI ​​model's calculations, and historical data is also utilized. Furthermore, the server uses an emotion engine to analyze the user's emotional state and reflect this in the transaction conditions.

[0532] When a user requests to buy or sell electricity, the server sends a prompt to a generating AI model, which then generates optimal transaction conditions based on the user's emotions. For example, a prompt might say, "User C wants to sell 10kWh of surplus renewable energy. He is feeling anxious about the transaction. In this situation, please suggest how to adjust the transaction conditions to improve his satisfaction."

[0533] When a transaction is completed, the server records the transaction details on a distributed ledger technology, i.e., a blockchain, to ensure transparency and immutability. It also sends a transaction completion notification to the terminal, allowing the user to register feedback on their transaction experience. This feedback is aggregated on the server and used to improve the sentiment model.

[0534] As a concrete example, when user C sells 10kWh of surplus electricity, if the user's facial expression data detects anxiety about the transaction, the server uses prompt messages to formulate transaction conditions that provide reassurance. For example, it might expedite processing, provide clear explanations, and offer a higher-than-usual purchase price. This ensures a smooth transaction and improves user satisfaction.

[0535] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0536] Step 1:

[0537] The terminal collects data from smart meters and emotion recognition sensors installed at the user's facility. It takes in power consumption and supply measured by the smart meters, and emotion data based on facial expressions and voices detected by the emotion recognition sensors as input, and prepares to temporarily store this data.

[0538] Step 2:

[0539] The terminal sends the collected power and emotion data to the server. The output at this stage is a dataset showing power consumption, supply, and the user's emotional state. This dataset is then transferred to the server via the network to proceed to the next analysis process.

[0540] Step 3:

[0541] The server stores data received from terminals in a database and applies an artificial intelligence model to the power data. The input here is power consumption and supply data, and machine learning algorithms are used for analysis and prediction calculations to obtain predictions of future power demand and supply as output.

[0542] Step 4:

[0543] The server uses an emotion engine to analyze the received emotional data. The input for this process is data indicating the user's emotional state. The emotion engine evaluates the user's psychological state, analyzes how the user is being affected by the transaction, and outputs the results.

[0544] Step 5:

[0545] The server integrates predicted power supply and demand data with sentiment analysis results and inputs prompts into a generative AI model. The input includes the user's power surplus, sentiment state, and market forecast. The generative AI model analyzes the prompts and outputs flexible trading conditions and prices. These results are used to adjust trading prices and set conditions.

[0546] Step 6:

[0547] The server completes transactions based on optimized transaction conditions and records transaction information using distributed ledger technology in an immutable and transparent format. The recorded output is data detailing the contents of the transaction, and by storing this information on the blockchain, the immutability and transparency of the transaction are ensured.

[0548] Step 7:

[0549] After a transaction is completed, the server sends a notification of the transaction result to the terminal, and the user can provide feedback. The output received by the terminal is the transaction details and a request for feedback from the user. The feedback entered by the user is sent to the server and used to improve the sentiment model.

[0550] (Application Example 2)

[0551] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0552] Trading surplus renewable energy efficiently while considering the user's psychological state has been difficult with conventional systems. Furthermore, to improve the user trading experience, there is a need for a means to enable emotion-based interaction while ensuring trading flexibility and transparency.

[0553] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0554] In this invention, the server includes means for storing transaction information in an immutable and transparent format using distributed database technology, computation means using machine learning models for forecasting supply and demand, and means for recognizing the user's emotional state and presenting adjusted transaction conditions based on that information. This enables flexible energy trading that takes user emotions into consideration.

[0555] "Distributed database technology" is a technology that stores data across multiple nodes rather than on a central server, enabling highly transparent and fault-tolerant data management.

[0556] A "machine learning model" is a collection of mathematical algorithms that learn patterns from data and perform predictions and classifications, and is used to forecast energy demand and supply.

[0557] "Means of recognizing emotional states" refers to technologies that evaluate and analyze emotions by analyzing data such as a user's facial expressions and voice.

[0558] "Means of presenting trading conditions" refers to an interface that provides users with conditions such as price, quantity, and timing for a transaction, in order to optimize the transaction.

[0559] The system for implementing this invention mainly consists of a server, a user terminal, and related sensors. The server uses distributed database technology to record and manage transaction information while ensuring transparency and immutability. This allows users to ensure the reliability of their transactions.

[0560] Furthermore, the server runs a machine learning model that uses historical data to predict energy demand and supply. This model is implemented using a deep learning framework such as TensorFlow and has the ability to learn patterns from diverse datasets. This allows users to understand future energy supply conditions in advance and optimize their trading decisions.

[0561] Sensors and cameras and microphones built into smartphones are used to understand the user's psychological state from their facial expressions and voice. This allows OpenCV and the Google Cloud Speech API to analyze facial expressions and voice tone, and send the user's emotional state to the server in real time. Based on this emotional information, the server personalizes the user's trading experience and determines the optimal trading conditions.

[0562] For example, if the system determines that a user is experiencing stress, the conditions suggested for smoother transactions are adjusted, and a more user-friendly interface is provided. Furthermore, the sentiment model is refined based on feedback provided after transactions and reflected in future transactions.

[0563] An example of a prompt in a generative AI model is: "Suggest how to optimize surplus electricity trading for users with high stress levels, including pricing and user interface ideas." This allows for a more personalized customer experience and promotes the efficient use of renewable energy.

[0564] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0565] Step 1:

[0566] The user terminal uses its built-in camera and microphone to capture the user's facial expressions and voice. This allows for the acquisition of real-time emotional data of the user. The input is the user's facial expressions and voice, and the output is emotional data. This data is converted into emotional states through image analysis using OpenCV and voice analysis using the Google Cloud Speech API.

[0567] Step 2:

[0568] The user's terminal sends the acquired emotional data to the server. The server takes this emotional data as input to a machine learning model and begins the analysis. The input is the emotional data, and the output is the analysis result. This analysis clarifies the user's psychological state, such as stress and satisfaction levels.

[0569] Step 3:

[0570] The server uses a machine learning model to predict energy demand and supply, referencing accumulated transaction data and external information. The input is historical transaction data and external information, and the output is the prediction result. This prediction result serves as an important indicator for understanding the future balance of electricity supply and demand.

[0571] Step 4:

[0572] The server integrates the results of sentiment data analysis and energy supply and demand forecasts to determine the optimal trading conditions for each individual user. The inputs are sentiment analysis results and supply and demand forecasts, while the output is the optimized trading conditions. The AI ​​agent combines this data to derive prices and conditions that are appropriate to the user's psychological state.

[0573] Step 5:

[0574] The server notifies the user terminal of the determined trading conditions and prompts the user to participate in the trade. The input is the optimized trading conditions, and the output is a notification message to the user. This notification also includes feedback based on sentiment data, allowing the user to quickly check their trading status.

[0575] Step 6:

[0576] Once a transaction is completed, the server records the transaction details in a distributed database and collects feedback from the user. Inputs are the transaction details and user feedback, while outputs are the recorded transaction data and feedback information. This ensures transaction reliability and leads to further enhancement of the sentiment model.

[0577] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0578] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0579] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0580] [Fourth Embodiment]

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

[0582] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0583] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0584] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0585] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0586] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0587] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0588] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0589] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0590] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0591] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0592] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0593] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0594] This invention provides an embodiment of a decentralized energy trading system that can efficiently trade surplus renewable energy. This system is implemented in the following manner.

[0595] First, the terminal acquires power consumption and supply data from the user's location. The terminal sends this data to the server at regular intervals. Upon receiving this data, the server performs the necessary preprocessing and then stores it in the database. Preprocessing includes data cleansing and imputation of missing values.

[0596] Next, the server runs an artificial intelligence model using the collected data. The AI ​​agent utilizes the collected data and external data (e.g., weather information and usage patterns) to predict future electricity demand and supply. This makes it possible to anticipate situations of oversupply or overdemand.

[0597] Subsequently, the server executes a pricing algorithm based on the predicted supply and demand data. This algorithm considers the balance of supply and demand to calculate the optimal price. Furthermore, an AI agent autonomously matches trading partners based on the predicted information. The terminal notifies the user of the transaction price and information about the trading partner.

[0598] Once a transaction is completed, the server records the transaction details on the blockchain, ensuring a secure and transparent transaction environment. The recorded information is immutable and can be used for future audits and verifications.

[0599] Once a transaction is completed, the user is notified via their terminal. Users can verify that their electricity was traded appropriately and contribute to further system improvements by providing feedback.

[0600] For example, if user A has a surplus of 10kWh of electricity during the day, the AI ​​agent will identify user B's shortage and match them so that A can sell 5kWh to B at a fair price. Once a transaction is completed between A and B, the information is recorded on the blockchain, and notifications are sent to both A and B's devices. In this way, individual users can efficiently trade their surplus electricity, maximizing the use of renewable energy.

[0601] The following describes the processing flow.

[0602] Step 1:

[0603] The terminal collects real-time data on electricity consumption and generation from the user's home or business. This is done using smart meters and home energy management systems. The collected data is then transmitted to a server at regular intervals.

[0604] Step 2:

[0605] Before saving the received data to the database, the server performs preprocessing such as noise reduction and missing value imputation. This ensures data accuracy and makes it suitable as foundational data for analysis and prediction.

[0606] Step 3:

[0607] The server inputs pre-processed data into an artificial intelligence model to generate forecasts of each user's future electricity demand and supply. The AI ​​agent makes more accurate forecasts by referencing weather data and historical consumption patterns.

[0608] Step 4:

[0609] The server executes a pricing algorithm based on supply and demand data predicted by the AI ​​agent. This algorithm calculates the optimal transaction price considering current market conditions. At this stage, it also performs transaction matching to select appropriate buyers and sellers.

[0610] Step 5:

[0611] Once the transaction terms are determined, the server records the transaction details on the blockchain. This ensures the transparency and security of the transaction and helps prevent fraud. This information is used for subsequent audits and verification by the transaction parties.

[0612] Step 6:

[0613] The terminal notifies the user that a transaction has been completed. The user can then view the transaction details (trading partner, price, quantity, etc.). The user can also provide feedback, which the server collects to improve the system.

[0614] (Example 1)

[0615] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0616] Conventional renewable energy trading systems have problems in that they cannot adequately respond to fluctuations in electricity supply and demand. Furthermore, the transparency and security of trading information are insufficient, making reliable energy management difficult. In addition, the lack of sufficient demand forecasting and price optimization using artificial intelligence hinders efficient trading.

[0617] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0618] In this invention, the server includes means for using information equipment to receive transaction requests and collect data on power usage and supply; storage means for preprocessing the collected data and storing it in a consistent state; and computation means for predicting future power demand and supply while referring to external environmental data using an artificial intelligence model. This makes it possible to respond flexibly to fluctuations in power supply and demand and to efficiently conduct transparent and reliable energy trading.

[0619] A "transaction request" is a request made to the system to initiate a transaction in response to the supply or demand situation of electricity.

[0620] "Information equipment" refers to measuring devices and terminals installed at user sites to collect data related to power usage and supply.

[0621] "Preprocessing" refers to the process of adding missing values ​​and correcting outliers to collected data to make it consistent.

[0622] A "memory device" is a database or similar data storage system that stores pre-processed data and uses it for subsequent analysis or model training.

[0623] An "artificial intelligence model" is a mathematical model that uses collected data and external environmental data to predict future electricity demand and supply.

[0624] "Distributed ledger technology" is blockchain technology used to record transaction details in an immutable format, thereby increasing transparency and security.

[0625] The "computational means" refers to the process of using an artificial intelligence model to calculate future electricity demand and supply, and to generate data for efficient trading.

[0626] The "transaction price" is the fair price in the trading of electricity, calculated based on the balance of supply and demand.

[0627] "User" refers to an individual or organization participating in this system as a power supplier or consumer that trades surplus electricity.

[0628] This invention is a decentralized trading system for efficiently trading surplus renewable energy. The method for implementing this system is described below.

[0629] First, the terminal is installed at the user's location and collects power consumption and supply data through a power meter. The collected data is sent to the server at regular intervals. The server then performs data preprocessing. Preprocessing includes imputing missing values ​​and correcting outliers, and the Python Pandas library is used for this purpose.

[0630] Preprocessed data is stored in a database management system (DBMS) on the server. MySQL is used for data storage and management. The data is accumulated in an organized format in preparation for subsequent analysis.

[0631] Next, the server runs an artificial intelligence (AI) model. This AI model, built using TensorFlow, combines data with external environmental information (e.g., weather data) to predict future electricity demand and supply. This prediction makes it possible to anticipate situations of electricity oversupply or overdemand. Based on the predicted data, a pricing algorithm calculates the optimal transaction price.

[0632] Furthermore, an AI agent autonomously matches trading partners. Once a transaction is completed, the server records the transaction details on a distributed ledger (blockchain), ensuring secure and transparent transactions. This record is immutable and can be used for subsequent audits and transaction history verification.

[0633] After the transaction is completed, the terminal notifies the user of the transaction details and feedback. The user can review the electricity transaction details and provide feedback to the system, which helps to further improve the system.

[0634] As a concrete example, if user A has 10kWh of surplus electricity during the day, the AI ​​agent will identify user B's shortage and match them so that A can sell 5kWh to B at a fair price. This transaction will be notified to and recorded on the terminals of both users A and B.

[0635] Example of a prompt:

[0636] "Explain how User A can efficiently trade the 10kWh of surplus electricity they generated during the day. Also, show how the AI ​​agent will handle matching and pricing."

[0637] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0638] Step 1:

[0639] The terminal acquires real-time data on power consumption and supply via a power meter installed at the user's site. The input is data from the power meter, and the output is raw power usage data. Each data point is recorded with a time stamp and stored for later processing. Specifically, the terminal collects data every 10 minutes and temporarily stores it in its internal memory.

[0640] Step 2:

[0641] The terminal periodically sends the collected data to the server. The input is the accumulated power data, and the output is the data packets sent to the server. Data transmission takes place over the internet, and security is ensured using protocols such as SSL. Specifically, the terminal sends the data in batches every hour.

[0642] Step 3:

[0643] The server preprocesses the received power data. The input is data sent from the terminal, and the output is a cleansed, consistent dataset. Data cleansing includes imputing missing values ​​and detecting outliers. Specifically, the server cleans the data using the Pandas library and corrects outliers using statistical methods.

[0644] Step 4:

[0645] The server stores pre-processed data in a database. The input is a consistent dataset, and the output is the state in which it is stored in the database. MySQL is used for database management, and query optimization is performed on a time basis. Specifically, the server periodically inserts data using SQL statements and sets up indexes that enable efficient searching.

[0646] Step 5:

[0647] The server uses database data and external environmental data to predict electricity demand and supply using an artificial intelligence model. The input is electricity consumption data and weather data stored in the database, and the output is time-series data of predicted electricity demand and supply. A neural network model using TensorFlow operates to estimate future values. Specifically, the server retrains the model daily to generate new predictions.

[0648] Step 6:

[0649] The server executes a pricing algorithm based on predicted supply and demand data. The input is the predicted data, and the output is the optimal transaction price. The pricing algorithm dynamically adjusts the price, taking into account the balance between supply and demand. Specifically, the server measures price elasticity and calculates the price based on a demand response model.

[0650] Step 7:

[0651] The server uses an AI agent to match trading partners. The input is predicted supply and demand, and the optimal price; the output is a list of matched trading pairs. The AI ​​agent utilizes machine learning algorithms to select the best partner. Specifically, the server analyzes predicted data and historical trading history to perform partner matching.

[0652] Step 8:

[0653] The terminal notifies the user of the details of a transaction once it is completed. The input is transaction information from the server, and the output is a notification message to the user. The terminal informs the user of the transaction details, counterparty, and price in real time. Specifically, the terminal communicates with the user using push notifications or email.

[0654] Step 9:

[0655] The server records transaction details using distributed ledger technology. The input is transaction detail data, and the output is recorded blockchain data. The use of blockchain technology guarantees the transparency and security of transactions. Specifically, the server sends the transaction to the blockchain network, where it is recorded on all nodes.

[0656] (Application Example 1)

[0657] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0658] To ensure the efficient trading and maximum utilization of renewable energy, it is necessary to accurately predict fluctuations in electricity demand and to have means to resolve surpluses or shortages. Furthermore, a system is needed that manages information transparently and securely, allowing users to easily understand their own energy usage. However, current systems struggle to fully meet these requirements.

[0659] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0660] In this invention, the server includes means for storing transaction information in an immutable and transparent format using distributed database technology; computation means for forecasting supply and demand using machine learning models; means for determining the optimal transaction price based on the forecast and autonomously matching trading partners; means for visualizing power supply status and transaction proposals via a user interface; and means for providing transaction history and energy usage trends on mobile devices. This enables efficient trading of renewable energy and promotes user understanding through visualization.

[0661] "Distributed database technology" is a technology that stores data in a distributed manner across multiple locations, ensuring data transparency and immutability without requiring a central administrator.

[0662] A "machine learning model" is a system of algorithms that learns patterns and rules from past data and has the ability to predict future demand and supply.

[0663] "Visualization of power supply status" is a technology that displays the energy supply status in a way that is easy for users to understand, using methods such as graphs and charts.

[0664] A "mobile device" is a portable electronic device capable of acquiring, processing, and transmitting information, and in this context, it specifically refers to smartphones and tablets.

[0665] A "user interface" is a component of software that provides the operating screen and display method for exchanging information between the user and the system.

[0666] This invention is a system for enabling the efficient trading of renewable energy. Specific embodiments are described below.

[0667] The server first uses distributed database technology to store transaction information reported by each user in an immutable and transparent format. This ensures the security and integrity of transactions.

[0668] Next, the server uses machine learning models to analyze historical data and energy data collected in real time. This modeling prepares the server to effectively forecast supply and demand and provide optimal trading conditions. External weather data and market trend data are used in this process.

[0669] On user terminals, mobile devices such as smartphones visualize and present power supply status and trading proposals to users in real time. The user interface is designed to be intuitive and easy to understand, making it easy for users to trade energy. For example, if there is a surplus of electricity, an option to easily sell it will be displayed within the application.

[0670] As a concrete example of implementation, the server inputs the following prompt to the AI ​​model: "We have 5kWh of surplus electricity. Please suggest the best trading partner and price. Factors to consider are demand, weather information, and existing electricity prices." This allows the AI ​​to provide the optimal solution, enabling the user to make a quick decision.

[0671] In this way, the present invention constructs a system that efficiently trades renewable energy and aims to maximize the utilization of energy resources.

[0672] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0673] Step 1:

[0674] The server periodically retrieves power consumption and supply data from each user's terminal. This data is received as input, and before recording it in a distributed database, it undergoes data cleansing, correcting inconsistent data and imputing missing values. The clean data is then stored as output for further processing.

[0675] Step 2:

[0676] The server runs a machine learning model based on stored consumption and supply data. External data, such as weather information, is also input to predict future electricity demand and supply. Through data processing, the model analyzes past and present information to learn demand and supply patterns. The output is forecast data for demand and supply.

[0677] Step 3:

[0678] The server receives predicted supply and demand data as input and executes a pricing algorithm. This algorithm considers the balance of supply and demand and calculates the optimal transaction price. A specific transaction price is generated as output. Here, a generation AI model is utilized, and price adjustment prompts are used.

[0679] Step 4:

[0680] The server matches trading partners based on calculated transaction prices and forecast data. It generates a list of potential trading partners and autonomously selects the most suitable partner based on this list. The input is forecast data and price information, and the output is information on the matched trading partner.

[0681] Step 5:

[0682] When a transaction is completed, the server records the transaction details in a distributed database and sends a notification to the information terminal. To ensure transparency and verification of transactions, blockchain technology is used to maintain an immutable history. This allows users to verify the transaction results on their own devices.

[0683] Step 6:

[0684] Users provide feedback based on transaction information received through their devices. This feedback is sent to the server and used to improve the system. The input is user feedback information, and the output is data for system updates.

[0685] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0686] This invention provides an embodiment of a decentralized energy trading system that enables the efficient and emotionally relatable trading of surplus renewable energy in a user-friendly manner. This system is implemented in the following manner:

[0687] First, the terminal acquires data on power consumption and supply from the user's facility and transmits it to the server. The terminal is equipped with an emotion recognition sensor to analyze the user's facial expressions and tone of voice, and constantly captures and transmits the user's emotional data to the server. This makes it possible to analyze the user's emotions regarding their transaction experience in real time.

[0688] The server uses the received power data to run an artificial intelligence model. The AI ​​agent analyzes the data and predicts future power demand and supply. Furthermore, it uses an emotion engine to evaluate the user's emotional state and uses that data to determine how the user is psychologically influenced in transactions.

[0689] Next, the server combines predicted supply and demand data with sentiment information to adjust trading prices. For example, if a user is feeling stressed, it can set flexible prices to smooth the trading process. Furthermore, to increase user satisfaction during trading, the sentiment engine provides customized trading conditions tailored to each user.

[0690] Once a transaction is completed, the server records the transaction details on the blockchain in an immutable and transparent format and sends a transaction completion notification to the terminal. These notifications also include feedback generated by the sentiment engine, allowing users to register comments about their transaction experience. The server uses this feedback to refine the sentiment model, enabling more accurate customer satisfaction ratings.

[0691] For example, if user C wants to sell 10kWh of surplus electricity, and the system determines from the user's expression that they are feeling uneasy about the transaction, it will offer clear explanations and a slightly higher purchase price to complete the transaction quickly and efficiently. In this way, a transaction process that takes the user's emotional state into consideration can be realized, promoting the spread of renewable energy and improving user satisfaction.

[0692] The following describes the processing flow.

[0693] Step 1:

[0694] The terminals are installed in users' homes and businesses to collect data on power consumption and generation. In addition, the terminals are equipped with emotion recognition sensors that capture emotional data from the user's facial expressions and voice. This data is transmitted to a server in real time.

[0695] Step 2:

[0696] The server preprocesses the received power data, performing noise reduction and missing data imputation, before storing it in a database. Simultaneously, it analyzes emotional data and extracts the user's current emotional state. This process allows the server to maintain an emotional profile for each user.

[0697] Step 3:

[0698] The server inputs pre-processed power data into an artificial intelligence model to predict future power demand and supply. This prediction also utilizes external data such as the user's past consumption patterns and weather.

[0699] Step 4:

[0700] The server sets the optimal trading price for each individual user based on prediction results and sentiment data. Here, the sentiment engine adjusts the price and trading conditions, especially when the user is feeling anxious or seeking high satisfaction.

[0701] Step 5:

[0702] Once the transaction terms are determined, the server records that information on the blockchain. This ensures transaction transparency and reduces the risk of fraud. Subsequently, a notification of the successful transaction is sent to the user's device.

[0703] Step 6:

[0704] The terminal provides users with detailed transaction information, including trading partners, price, transaction volume, and sentiment-based feedback. Users can also provide feedback on transactions through the terminal. This feedback is collected on the server and used to improve the system.

[0705] (Example 2)

[0706] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0707] In renewable energy trading, conventional systems have failed to ensure transparency and reliability of transaction information, and have made it difficult to provide flexible trading conditions that take user sentiment into consideration. Therefore, there is a need for a system that improves the user experience and facilitates smoother transactions.

[0708] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0709] In this invention, the server includes means for storing transaction information in an immutable and transparent format using distributed ledger technology, means for predicting supply and demand and determining the optimal transaction price using an artificial intelligence model, and means for utilizing an emotion engine that analyzes the user's emotional state and reflects it in the transaction conditions. This makes it possible to provide flexible transaction conditions that take user emotions into consideration, while ensuring the reliability and transparency of transactions.

[0710] "Distributed ledger technology" is a database technology that is managed in a distributed manner by multiple computers, and is used to ensure the immutability and transparency of information.

[0711] "Transaction information" refers to data related to the buying and selling of electricity, including information such as transaction conditions, quantity, price, and participants.

[0712] An "artificial intelligence model" refers to an algorithm and its entire implementation used to predict future demand and supply using machine learning and data analysis techniques.

[0713] "Computational means" refers to a device or software that has the computing power to analyze data and perform certain processing.

[0714] An "emotion engine" is a system or software that processes user emotional data, analyzes their emotional state, and reflects that information in transaction conditions.

[0715] A "prompt message" is text data output by a generative AI model that presents specific conditions or instructions.

[0716] "Feedback" refers to data such as opinions and impressions provided by users based on their transaction experience, and is used to improve the system.

[0717] This invention is a system for efficiently trading surplus renewable energy in a way that respects user emotions. The system consists of multiple terminals and a central server. The terminals are installed in facilities owned by the users and are equipped with smart meters and emotion recognition sensors. The smart meters measure the amount of electricity consumed and supplied, and the emotion recognition sensors analyze the user's facial expressions and voice to generate emotion data.

[0718] The data collected by the device is transmitted to a server via the network. The server processes the received data and runs an artificial intelligence model to predict future electricity demand and supply. Machine learning algorithms are used in the AI ​​model's calculations, and historical data is also utilized. Furthermore, the server uses an emotion engine to analyze the user's emotional state and reflect this in the transaction conditions.

[0719] When a user requests to buy or sell electricity, the server sends a prompt to a generating AI model, which then generates optimal transaction conditions based on the user's emotions. For example, a prompt might say, "User C wants to sell 10kWh of surplus renewable energy. He is feeling anxious about the transaction. In this situation, please suggest how to adjust the transaction conditions to improve his satisfaction."

[0720] When a transaction is completed, the server records the transaction details on a distributed ledger technology, i.e., a blockchain, to ensure transparency and immutability. It also sends a transaction completion notification to the terminal, allowing the user to register feedback on their transaction experience. This feedback is aggregated on the server and used to improve the sentiment model.

[0721] As a concrete example, when user C sells 10kWh of surplus electricity, if the user's facial expression data detects anxiety about the transaction, the server uses prompt messages to formulate transaction conditions that provide reassurance. For example, it might expedite processing, provide clear explanations, and offer a higher-than-usual purchase price. This ensures a smooth transaction and improves user satisfaction.

[0722] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0723] Step 1:

[0724] The terminal collects data from smart meters and emotion recognition sensors installed at the user's facility. It takes in power consumption and supply measured by the smart meters, and emotion data based on facial expressions and voices detected by the emotion recognition sensors as input, and prepares to temporarily store this data.

[0725] Step 2:

[0726] The terminal sends the collected power and emotion data to the server. The output at this stage is a dataset showing power consumption, supply, and the user's emotional state. This dataset is then transferred to the server via the network to proceed to the next analysis process.

[0727] Step 3:

[0728] The server stores data received from terminals in a database and applies an artificial intelligence model to the power data. The input here is power consumption and supply data, and machine learning algorithms are used for analysis and prediction calculations to obtain predictions of future power demand and supply as output.

[0729] Step 4:

[0730] The server uses an emotion engine to analyze the received emotional data. The input for this process is data indicating the user's emotional state. The emotion engine evaluates the user's psychological state, analyzes how the user is being affected by the transaction, and outputs the results.

[0731] Step 5:

[0732] The server integrates predicted power supply and demand data with sentiment analysis results and inputs prompts into a generative AI model. The input includes the user's power surplus, sentiment state, and market forecast. The generative AI model analyzes the prompts and outputs flexible trading conditions and prices. These results are used to adjust trading prices and set conditions.

[0733] Step 6:

[0734] The server completes transactions based on optimized transaction conditions and records transaction information using distributed ledger technology in an immutable and transparent format. The recorded output is data detailing the contents of the transaction, and by storing this information on the blockchain, the immutability and transparency of the transaction are ensured.

[0735] Step 7:

[0736] After a transaction is completed, the server sends a notification of the transaction result to the terminal, and the user can provide feedback. The output received by the terminal is the transaction details and a request for feedback from the user. The feedback entered by the user is sent to the server and used to improve the sentiment model.

[0737] (Application Example 2)

[0738] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0739] Trading surplus renewable energy efficiently while considering the user's psychological state has been difficult with conventional systems. Furthermore, to improve the user trading experience, there is a need for a means to enable emotion-based interaction while ensuring trading flexibility and transparency.

[0740] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0741] In this invention, the server includes means for storing transaction information in an immutable and transparent format using distributed database technology, computation means using machine learning models for forecasting supply and demand, and means for recognizing the user's emotional state and presenting adjusted transaction conditions based on that information. This enables flexible energy trading that takes user emotions into consideration.

[0742] "Distributed database technology" is a technology that stores data across multiple nodes rather than on a central server, enabling highly transparent and fault-tolerant data management.

[0743] A "machine learning model" is a collection of mathematical algorithms that learn patterns from data and perform predictions and classifications, and is used to forecast energy demand and supply.

[0744] "Means of recognizing emotional states" refers to technologies that evaluate and analyze emotions by analyzing data such as a user's facial expressions and voice.

[0745] "Means of presenting trading conditions" refers to an interface that provides users with conditions such as price, quantity, and timing for a transaction, in order to optimize the transaction.

[0746] The system for implementing this invention mainly consists of a server, a user terminal, and related sensors. The server uses distributed database technology to record and manage transaction information while ensuring transparency and immutability. This allows users to ensure the reliability of their transactions.

[0747] Furthermore, the server runs a machine learning model that uses historical data to predict energy demand and supply. This model is implemented using a deep learning framework such as TensorFlow and has the ability to learn patterns from diverse datasets. This allows users to understand future energy supply conditions in advance and optimize their trading decisions.

[0748] Sensors and cameras and microphones built into smartphones are used to understand the user's psychological state from their facial expressions and voice. This allows OpenCV and the Google Cloud Speech API to analyze facial expressions and voice tone, and send the user's emotional state to the server in real time. Based on this emotional information, the server personalizes the user's trading experience and determines the optimal trading conditions.

[0749] For example, if the system determines that a user is experiencing stress, the conditions suggested for smoother transactions are adjusted, and a more user-friendly interface is provided. Furthermore, the sentiment model is refined based on feedback provided after transactions and reflected in future transactions.

[0750] An example of a prompt in a generative AI model is: "Suggest how to optimize surplus electricity trading for users with high stress levels, including pricing and user interface ideas." This allows for a more personalized customer experience and promotes the efficient use of renewable energy.

[0751] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0752] Step 1:

[0753] The user terminal uses its built-in camera and microphone to capture the user's facial expressions and voice. This allows for the acquisition of real-time emotional data of the user. The input is the user's facial expressions and voice, and the output is emotional data. This data is converted into emotional states through image analysis using OpenCV and voice analysis using the Google Cloud Speech API.

[0754] Step 2:

[0755] The user's terminal sends the acquired emotional data to the server. The server takes this emotional data as input to a machine learning model and begins the analysis. The input is the emotional data, and the output is the analysis result. This analysis clarifies the user's psychological state, such as stress and satisfaction levels.

[0756] Step 3:

[0757] The server uses a machine learning model to predict energy demand and supply, referencing accumulated transaction data and external information. The input is historical transaction data and external information, and the output is the prediction result. This prediction result serves as an important indicator for understanding the future balance of electricity supply and demand.

[0758] Step 4:

[0759] The server integrates the results of sentiment data analysis and energy supply and demand forecasts to determine the optimal trading conditions for each individual user. The inputs are sentiment analysis results and supply and demand forecasts, while the output is the optimized trading conditions. The AI ​​agent combines this data to derive prices and conditions that are appropriate to the user's psychological state.

[0760] Step 5:

[0761] The server notifies the user terminal of the determined trading conditions and prompts the user to participate in the trade. The input is the optimized trading conditions, and the output is a notification message to the user. This notification also includes feedback based on sentiment data, allowing the user to quickly check their trading status.

[0762] Step 6:

[0763] Once a transaction is completed, the server records the transaction details in a distributed database and collects feedback from the user. Inputs are the transaction details and user feedback, while outputs are the recorded transaction data and feedback information. This ensures transaction reliability and leads to further enhancement of the sentiment model.

[0764] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0765] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0766] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0767] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0768] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0769] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0770] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0771] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0772] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0773] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0774] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0775] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0776] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0777] 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.

[0778] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0779] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0780] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0781] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0782] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0783] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0784] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0785] The following is further disclosed regarding the embodiments described above.

[0786] (Claim 1)

[0787] A means of storing transaction information in an immutable and transparent format using distributed ledger technology,

[0788] A computational means using an artificial intelligence model for forecasting demand and supply,

[0789] A means for determining the optimal transaction price based on the aforementioned forecast and autonomously matching trading partners,

[0790] A system that includes this.

[0791] (Claim 2)

[0792] The system according to claim 1, further comprising means for notifying a terminal of relevant information after a transaction is completed and for aggregating user feedback.

[0793] (Claim 3)

[0794] The system according to claim 1, wherein the artificial intelligence model is configured to predict future power consumption and generation using external data.

[0795] "Example 1"

[0796] (Claim 1)

[0797] Means of using information equipment to receive transaction requests and collect data on power usage and supply,

[0798] A storage means for preprocessing collected data and storing it in a consistent state,

[0799] A computational means that uses an artificial intelligence model to predict future electricity demand and supply while referring to external environmental data,

[0800] A means for calculating the optimal transaction price based on a situation of oversupply or overdemand,

[0801] A means of automatically selecting the users to whom the transaction will be executed,

[0802] A means of recording transaction details in an immutable format using distributed ledger technology,

[0803] A system that includes this.

[0804] (Claim 2)

[0805] The system according to claim 1, comprising notifying an information terminal of relevant information when a transaction is completed, and collecting user evaluations and integrating and storing the data.

[0806] (Claim 3)

[0807] The system according to claim 1, wherein the artificial intelligence model is configured to use environmental data to predict future power consumption and generation.

[0808] "Application Example 1"

[0809] (Claim 1)

[0810] A means of storing transaction information in an immutable and transparent format using distributed database technology,

[0811] A computational means using a machine learning model for forecasting demand and supply,

[0812] A means for determining the optimal transaction price based on the aforementioned forecast and autonomously matching trading partners,

[0813] A means of visualizing power supply status and trading proposals via a user interface,

[0814] A means of providing transaction history and energy usage trends on a mobile device,

[0815] A system that includes this.

[0816] (Claim 2)

[0817] The system according to claim 1, further comprising means for notifying information related to a transaction after it has been completed, collecting feedback from users, and contributing to the improvement of the system.

[0818] (Claim 3)

[0819] The system according to claim 1, wherein the machine learning model is configured to utilize external information to predict future energy consumption and generation, thereby enabling smarter energy management for the entire city.

[0820] "Example 2 of combining an emotion engine"

[0821] (Claim 1)

[0822] A means of storing transaction information in an immutable and transparent format using distributed ledger technology,

[0823] A computational means using an artificial intelligence model for forecasting demand and supply,

[0824] A means for determining the optimal transaction price based on the aforementioned forecast and autonomously matching trading partners,

[0825] A means of utilizing an emotion engine to analyze the user's emotional state and reflect it in the transaction conditions,

[0826] A system that includes this.

[0827] (Claim 2)

[0828] The system according to claim 1, further comprising means for notifying the terminal of relevant information after a transaction is completed, for aggregating user feedback, and for refining the sentiment model.

[0829] (Claim 3)

[0830] The system according to claim 1, wherein the artificial intelligence model is configured to predict future power consumption and generation using external data and to output prompt sentences adjusted based on the user's emotional state.

[0831] "Application example 2 when combining with an emotional engine"

[0832] (Claim 1)

[0833] A means of storing transaction information in an immutable and transparent format using distributed database technology,

[0834] A computational means using a machine learning model for forecasting demand and supply,

[0835] A means of recognizing the user's emotional state and presenting trading conditions adjusted based on that information,

[0836] A means for determining the optimal transaction price based on the aforementioned predicted and recognized emotions and for autonomously matching trading partners,

[0837] A system that includes this.

[0838] (Claim 2)

[0839] The system according to claim 1, further comprising means for notifying the device of relevant information after a transaction is completed, for aggregating emotional feedback from users, and for improving the emotional model.

[0840] (Claim 3)

[0841] The system according to claim 1, wherein the machine learning model is configured to predict future energy consumption and power generation using external information and to combine this with the results of the user's emotion recognition. [Explanation of symbols]

[0842] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of storing transaction information in an immutable and transparent format using distributed ledger technology, A computational means using an artificial intelligence model for forecasting demand and supply, A means for determining the optimal transaction price based on the aforementioned forecast and autonomously matching trading partners, A system that includes this.

2. The system according to claim 1, further comprising means for notifying a terminal of relevant information after a transaction is completed and for aggregating user feedback.

3. The system according to claim 1, wherein the artificial intelligence model is configured to predict future power consumption and power generation using external data.

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