system

Abstract

We provide a system that supports effective decarbonization actions at the individual level and contributes to the realization of a sustainable society. [Solution] A system including means for acquiring household electricity usage data, means for transmitting the acquired electricity usage data to a server, means for analyzing the acquired data on the server and identifying electricity consumption patterns, means for inputting and transmitting user environmental behavior data to the server, means for analyzing the user's environmental behavior data and awarding points, means for generating suggestions using AI based on electricity usage data and environmental behavior data, means for transmitting suggestions from the server to the user's terminal, means for the user to perform the suggested actions and input feedback, and means for sharing data and listing environmentally friendly products and displaying them to the user.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] As the problem of climate change becomes more serious, decarbonization actions are required in each household. However, in many households, there is a lack of information about their own power consumption situation and specific improvement measures, so it is difficult to effectively practice decarbonization actions. In addition, since there is no system that proposes specific environmental actions suitable for individual households or users, the realization of a sustainable society is getting further away. The purpose of this invention is to utilize household power usage data and environmental behavior data, propose optimal decarbonization actions for each household, support effective decarbonization actions at the individual level, and contribute to the realization of a sustainable society.

Means for Solving the Problems

[0005] To solve the above problems, we provide a system that includes the following means: means for acquiring household electricity usage data; means for transmitting the acquired electricity usage data to a server; means for analyzing the acquired data on the server and identifying electricity consumption patterns; means for inputting and transmitting user environmental behavior data to the server; means for analyzing the user's environmental behavior data and awarding points; means for generating suggestions using AI based on electricity usage data and environmental behavior data; means for transmitting suggestions from the server to the user's terminal; means for the user to perform the suggested actions and input feedback; and means for sharing data and listing environmentally friendly products and displaying them to the user.

[0006] "Household electricity usage data" refers to numerical data that shows the state of electricity consumption within a household, and is information obtained through smart meters and electricity measuring devices.

[0007] A "server" is a computer system that stores, processes, and analyzes data on a network, and is the central computer for receiving data transmitted from user terminals.

[0008] "Electricity consumption patterns" refer to data analysis results that show trends and characteristics of electricity usage within a household, including information such as times of day when consumption is high and peak usage times.

[0009] "Environmental behavior data" refers to data that records environmentally friendly actions taken by users (e.g., recycling, water conservation, efficient energy use, etc.), and is acquired through user input or sensor devices.

[0010] "Generative AI" refers to algorithms and systems that use artificial intelligence technology to analyze data and generate conclusions or suggestions.

[0011] A "suggestion" is a specific action for decarbonization or environmental protection that the generating AI recommends to the user based on the data it has analyzed.

[0012] "Feedback" is information that reports the results and status of an action after the user has performed the suggested action, and it is sent to the system's database.

[0013] "Points" are numerical values ​​assigned to evaluate the effectiveness and contribution of a user's environmental actions, and serve as a standard for quantitatively evaluating user behavior.

[0014] "Environmentally friendly products" are products that have a low environmental impact during manufacturing and use, and are also called eco-products or sustainable products.

[0015] The "Dekabo Score" is a score used as an evaluation criterion for environmentally friendly products, and is an index that quantifies the environmental contribution of each product. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9]Shows an emotion map to which a plurality of emotions are mapped. [Figure 10] 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

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of 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.

[0020] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention is a system that uses household electricity data and environmental behavior data to propose optimal decarbonization actions to users. The system consists of a terminal installed in the user's home, a server that analyzes the data and generates proposals, and an interface for notifying the user of the proposals. A specific embodiment of this system is described below.

[0038] System Configuration

[0039] 1. Terminal

[0040] It collects electricity usage data from smart meters and environmental sensors within the user's home.

[0041] It provides an interface for users to input environmental actions they have taken.

[0042] Receives suggestions from the server and notifies the user.

[0043] 2. Server

[0044] It receives and stores power usage data and environmental behavior data transmitted from the terminal.

[0045] The received data is analyzed to identify household electricity consumption patterns.

[0046] The system evaluates users' environmental behavior and assigns them points (green score).

[0047] Using AI generated from electricity data and environmental behavior data, we propose specific decarbonization actions to users.

[0048] We will save the feedback in the database and use it to improve future proposals.

[0049] Program processing (explained in natural language)

[0050] Collection and analysis of power data

[0051] 1. Terminal: Obtains daily electricity usage data from the home's smart meter.

[0052] 2. Terminal: Sends the acquired data to the server.

[0053] 3. Server: Stores the transmitted power usage data in the database.

[0054] 4. Server: Analyzes stored data to identify household power consumption patterns. For example, it extracts peak hours and periods of high consumption.

[0055] Input and evaluation of environmental behavioral data

[0056] 1. User: Inputs their daily environmental actions (e.g., recycled, water-saving, etc.) into the terminal.

[0057] 2. Terminal: Sends the entered data to the server.

[0058] 3. Server: Stores the transmitted environmental behavior data in a database and assigns points (green score) based on the behavior.

[0059] Proposal generation and notification

[0060] 1. Server: Uses AI generated based on power consumption patterns and environmental behavior data to suggest effective decarbonization actions to users.

[0061] 2. Server: For example, it generates specific suggestions such as, "Raise the air conditioner's temperature setting by 1 degree to reduce power consumption at night."

[0062] 3. Server: Sends the generated suggestions to the user's terminal.

[0063] 4. Terminal: Receives proposals and notifies the user.

[0064] Gathering feedback and sharing data

[0065] 1. User: Perform the suggested action and input the result into the terminal.

[0066] 2. Terminal: Sends the input feedback to the server.

[0067] 3. Server: Saves feedback data and uses it for future proposals.

[0068] 4. Server: Shares collected data with companies and power companies to promote decarbonization measures.

[0069] Specific example

[0070] For example, if a household uses its air conditioner day and night, its power consumption data is sent to a server. The server identifies peak times and, using the green score as a reference, suggests raising the air conditioner's temperature setting by one degree at night. This suggestion is notified to the user's device, and feedback on whether the user has taken action is collected to improve the accuracy of future suggestions.

[0071] In this way, the system integrates and manages household electricity consumption and environmental behavior, providing support for users to effectively implement decarbonization actions.

[0072] The following describes the processing flow.

[0073] Step 1:

[0074] Terminal: Periodically acquires electricity usage data from smart meters installed in homes. For example, it measures and stores electricity usage every 10 minutes.

[0075] Step 2:

[0076] Terminal: Sends acquired power usage data to the server. A secure communication protocol (e.g., HTTPS) is used for transmission.

[0077] Step 3:

[0078] Server: Stores the transmitted power usage data in the database. A timestamp is added to the data during storage to maintain data integrity.

[0079] Step 4:

[0080] Server: Analyzes stored data to identify household power consumption patterns. Specifically, it extracts peak hours and high-consumption periods on specific days.

[0081] Step 5:

[0082] User: Inputs daily environmental actions (e.g., waste sorting, water conservation, etc.) into the terminal's interface. Input can be done via touchscreen or voice input.

[0083] Step 6:

[0084] Terminal: Sends the entered environmental behavior data to the server. Immediately after sending, a data confirmation screen is displayed to allow the user to reconfirm the entered information.

[0085] Step 7:

[0086] Server: Stores the submitted environmental action data in a database and assigns points (green score) to each action. For example, 50 points are awarded for recycling.

[0087] Step 8:

[0088] Server: Uses AI generated from power consumption patterns and environmental behavior data to suggest decarbonization actions suitable for the user. The suggestions are customized based on context, time of day, and consumption levels.

[0089] Step 9:

[0090] Server: Sends the generated suggestion (e.g., "Raise the air conditioner's temperature by 1 degree to reduce nighttime power consumption") to the user's terminal.

[0091] Step 10:

[0092] Terminal: Receives proposals and notifies the user. Notifications are delivered via push notifications or alerts.

[0093] Step 11:

[0094] User: Perform the suggested action and input the result into the terminal. For example, input "Raised the air conditioner temperature by 1 degree."

[0095] Step 12:

[0096] Terminal: Sends the entered feedback to the server. The feedback includes the date and time the action was performed and its specific details.

[0097] Step 13:

[0098] Server: Saves feedback data and uses it to improve future proposals. For example, it evaluates implementation rates and effectiveness to optimize the next proposal.

[0099] Step 14:

[0100] Server: The collected data is shared with partner companies and power companies. The data is processed into statistical data and shared while ensuring security.

[0101] Step 15:

[0102] User's device: Displays a list of environmentally friendly products based on their DecaVo score, allowing the user to select from them. Each product includes a detailed description and rating.

[0103] Step 16:

[0104] User: Select an environmentally friendly product from the list and proceed with the purchase. The purchase process is conducted via the device, and the purchase data is sent to the server after confirmation.

[0105] Step 17:

[0106] Server: Saves purchase data to the database and updates the Decabo score. Contribution from product purchases is reflected in the Green score.

[0107] Through the steps outlined above, this system supports users in effectively implementing decarbonization actions.

[0108] (Example 1)

[0109] 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."

[0110] There is a need for a means to integrate and manage household electricity usage data and environmental behavior data to propose effective decarbonization actions to users. Conventional systems often only collect and analyze electricity usage data, lacking sufficient integration with environmental behavior data, resulting in limited concrete suggestions to users. Furthermore, it has been difficult to utilize the results of these suggestions as feedback, making it challenging to improve the accuracy of the suggestions.

[0111] 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.

[0112] In this invention, the server includes means for acquiring household electricity usage data, means for transmitting the acquired electricity usage data to the server, means for analyzing the data acquired by the server and identifying electricity consumption patterns, means for inputting and transmitting user environmental behavior data to the server, means for analyzing the user's environmental behavior data and assigning evaluation points, means for generating suggestions using an artificial intelligence model generated based on electricity usage data and environmental behavior data, means for transmitting suggestions from the server to the user's terminal, means for the user to perform the suggested actions and input feedback, means for collecting and accumulating data and reflecting the analysis results as feedback in the next suggestion, and means for sharing the collected data and promoting decarbonization measures. This makes it possible to integrate and manage electricity usage data and environmental behavior data and propose individually optimized decarbonization actions to users. Furthermore, by utilizing the results of the suggestions as feedback, it is possible to improve the accuracy of the suggestions.

[0113] "Household electricity usage data" refers to information that shows the amount of electricity consumed within a household and its usage patterns by time of day.

[0114] "Means of acquisition" refers to devices or methods for automatically collecting data using sensors, smart meters, etc.

[0115] A "server" refers to a computer system used to receive, analyze, and store power data and environmental behavior data.

[0116] "Means of analysis" refer to algorithms and software that process collected data to identify power consumption patterns.

[0117] "Environmental behavior data" refers to information about environmental protection actions taken by users (e.g., recycling, water conservation).

[0118] "Evaluation points" are values ​​used to quantitatively evaluate a user's environmental behavior and are indicators of the user's contribution.

[0119] A "generating artificial intelligence model" is a machine learning model or algorithm used to generate suggestions for users based on collected data.

[0120] A "suggestion" is information that indicates specific actions the user should take (e.g., adjusting the air conditioner's temperature settings).

[0121] "Means of input" refers to the interface (e.g., a smartphone application) that users use to input data or feedback.

[0122] "Feedback" is information used to report the results of a user taking a suggested action.

[0123] "Integrated management" is a method of centrally managing electricity usage data and environmental behavior data, and analyzing them in relation to each other.

[0124] "Means of promotion" refers to the methods and mechanisms for executing a plan and achieving its goals.

[0125] This invention is a system that collects and analyzes household electricity usage data and environmental behavior data, and proposes optimal decarbonization actions to the user. Specifically, it includes a terminal installed in the home, a server that analyzes the data and generates proposals, and an interface for notifying the user of the proposals. A specific embodiment of this system is described below.

[0126] System Configuration

[0127] 1. Terminal

[0128] The system collects electricity usage data from smart meters and environmental sensors installed in the user's home.

[0129] It provides an interface for users to input their environmental actions. Specifically, it uses a smartphone application or a web interface.

[0130] Receives suggestions from the server and notifies the user.

[0131] 2. Server

[0132] It is equipped with a database for receiving and storing power usage data and environmental behavior data transmitted from terminals.

[0133] The received data is analyzed to identify the electricity consumption patterns of each household. For example, it is possible to analyze increases and decreases in consumption during specific time periods.

[0134] It has an evaluation algorithm that assesses users' environmental behavior and assigns points (green score).

[0135] Using an AI model generated based on electricity data and environmental behavior data, we propose specific decarbonization actions to users.

[0136] The proposal and feedback will be saved in the database and used for future proposals.

[0137] 3. Interface

[0138] An interface for users to review and take suggested actions. Specifically, this can be achieved through application push notifications, email, or a web-based dashboard.

[0139] It also provides an interface for users to perform suggested actions and input the results as feedback.

[0140] Specific example

[0141] For example, if a household uses its air conditioner day and night, its power usage data is sent to a server. The server analyzes the received data and discovers that power consumption is particularly high at night. At the same time, it also considers the user's green score for environmental actions taken. Based on this, a generative AI model generates a specific suggestion, such as "raise the air conditioner's temperature setting by 1 degree at night." This suggestion is notified to the user's device, and feedback on whether the user has taken action is also collected to improve the accuracy of future suggestions.

[0142] Examples of prompts for generative AI models

[0143] "Based on household electricity usage data and user environmental behavior data, please propose effective decarbonization actions to users. Specifically, please provide detailed suggestions regarding adjustments to air conditioner settings and usage times."

[0144] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0145] Step 1:

[0146] Terminal: Acquires daily electricity usage data from home smart meters and environmental sensors.

[0147] Input: Power usage data from smart meters and environmental sensors.

[0148] Output: Acquired power usage data

[0149] Specific operation: Collects power consumption information transmitted from smart meters at regular time intervals (e.g., every 30 minutes). Similarly, collects data such as temperature and humidity from environmental sensors.

[0150] Step 2:

[0151] Terminal: Transmits acquired power usage data to the server at regular intervals.

[0152] Input: Acquired power usage data

[0153] Output: Power usage data sent to the server

[0154] Specific operation: Collected power usage data is sent to a server in batch processing or in real time. Data is typically transmitted via Wi-Fi or a wired network.

[0155] Step 3:

[0156] Server: Receives transmitted power usage data and stores it in the database.

[0157] Input: Power usage data transmitted from the terminal.

[0158] Output: Power usage data stored in the database

[0159] Specific operation: After initial processing of received data (e.g., data cleansing, normalization), it is saved to the database as time-series data.

[0160] Step 4:

[0161] Server: Analyzes stored power usage data to identify power consumption patterns for each household.

[0162] Input: Power usage data stored in the database

[0163] Output: Analyzed power consumption patterns

[0164] Specific operation: Use data analysis algorithms to identify significant fluctuations in power consumption during specific time periods (e.g., daytime and nighttime). Extract peak consumption levels and distinct consumption patterns.

[0165] Step 5:

[0166] User: Inputs their daily environmental actions (e.g., recycled, water-saving) into the application on their device.

[0167] Input: User-generated environmental behavior data

[0168] Output: Environmental behavior data entered into the terminal

[0169] Specific operation: The user enters the environmental actions they have taken into a form using a smartphone application or web interface.

[0170] Step 6:

[0171] Terminal: Sends the entered environmental behavior data to the server.

[0172] Input: User-generated environmental behavior data

[0173] Output: Environmental behavior data sent to the server

[0174] Specific operation: Input data is sent to the server in real time. Data is usually transferred securely using an API.

[0175] Step 7:

[0176] Server: Stores the transmitted environmental behavior data in a database and assigns points (green score) based on the behavior.

[0177] Input: Environmental behavior data sent to the server

[0178] Output: Environmental behavior data saved in the database and assigned points.

[0179] Specific operation: Received environmental behavior data is stored in a database, and an algorithm is applied to calculate a green score based on the importance of each behavior.

[0180] Step 8:

[0181] Server: Uses an AI model generated based on power consumption patterns and environmental behavior data to suggest effective decarbonization actions to users.

[0182] Input: Power consumption patterns and environmental behavior data

[0183] Output: Generated proposals

[0184] Specific operation: Using a generative AI model, the system generates optimal action suggestions for the user from accumulated data (e.g., adjusting the air conditioner temperature setting, changing the usage time).

[0185] Step 9:

[0186] Server: Sends the generated suggestions to the user's terminal.

[0187] Input: Generated proposal

[0188] Output: Suggestions sent to the user's terminal

[0189] Specific actions: The generated suggestions are sent to the user's device via the API. They are displayed as push notifications or in-app messages.

[0190] Step 10:

[0191] User: Perform the suggested action and input the result as feedback into the device.

[0192] Input: Result of the proposed action

[0193] Output: Feedback data entered into the terminal

[0194] Specific action: The user performs the suggested action and inputs the result (e.g., increased the air conditioner temperature by 1 degree) as feedback to the application.

[0195] Step 11:

[0196] Terminal: Sends the input feedback to the server.

[0197] Input: Feedback data

[0198] Output: Feedback data sent to the server

[0199] Specific action: Send feedback data to the server in real time.

[0200] Step 12:

[0201] Server: Saves feedback data and uses it for future proposals.

[0202] Input: Feedback data sent to the server

[0203] Output: Next proposals utilizing feedback data

[0204] Specific operation: Feedback data is saved to a database and reflected in the next proposal generation algorithm to improve the accuracy of the proposals.

[0205] Step 13:

[0206] Server: Shares collected data with companies and power companies to promote decarbonization measures.

[0207] Input: Power usage data and environmental activity data stored on the server

[0208] Output: Shared data

[0209] Specific actions: Statistically process data and share it with businesses and power companies. This will provide foundational data to promote widespread decarbonization measures.

[0210] (Application Example 1)

[0211] 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."

[0212] Modern energy efficiency issues involve challenges in both optimizing electricity consumption in homes and stores and promoting environmentally conscious behavior. In particular, brick-and-mortar stores often have high electricity consumption, hindering cost reduction and environmental impact mitigation. Furthermore, there is a lack of concrete and effective methods to raise employee environmental awareness and translate it into concrete actions. Therefore, these challenges need to be addressed by integrating and managing store electricity usage data and environmental behavior data, and by providing specific suggestions to employees.

[0213] 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.

[0214] In this invention, the server includes means for acquiring store power usage data and proposing actions to reduce power consumption, means for evaluating employee environmental behavior data and awarding points, and means for analyzing the acquired data and identifying power consumption patterns. This makes it possible to optimize power consumption in stores and propose ways to raise employees' environmental awareness.

[0215] Definitions of important words

[0216] "Household electricity usage data"

[0217] This refers to numerical information that shows the usage status of all electrical appliances and equipment used in a household.

[0218] "Means of acquisition"

[0219] This refers to devices and technologies used to measure and collect data.

[0220] "server"

[0221] A computer system is a computer system that receives, stores, and analyzes data, and then transmits it to other devices.

[0222] "Power consumption patterns"

[0223] This refers to the periodic fluctuations or trends in electricity usage in a particular household or store.

[0224] "Environmental behavior data"

[0225] This refers to records and reported data of environmentally friendly actions taken by users and employees.

[0226] "Means of awarding points"

[0227] This refers to systems and technologies that evaluate the environmental behavior of users and employees and award points as incentives.

[0228] “Generation AI”

[0229] This refers to artificial intelligence that automatically generates appropriate suggestions for users and employees based on acquired data.

[0230] "Means for generating proposals"

[0231] This refers to a technology that analyzes acquired data and creates suggestions to encourage effective actions from users and employees.

[0232] "Method for sending proposals"

[0233] This refers to communication technologies and systems used to notify users' or employees' terminals of proposals from a server.

[0234] "Methods for providing feedback"

[0235] This refers to an interface or system for users or employees to report the results of performing suggested actions.

[0236] "Data sharing"

[0237] This refers to coordinating and communicating collected data with other relevant systems and organizations.

[0238] "Environmentally friendly products"

[0239] This refers to products that are energy-efficient and designed to reduce their environmental impact.

[0240] "Actions to reduce electricity consumption"

[0241] This refers to specific actions or operations taken to reduce electricity consumption.

[0242] "Store electricity usage data"

[0243] This refers to numerical information that shows the usage status of all electrical equipment and facilities in a store.

[0244] "Employee environmental behavior data"

[0245] This refers to records and reports of environmentally friendly actions taken by store employees.

[0246] Modes for carrying out the invention

[0247] This invention is a system that integrates and manages electricity usage data and environmental behavior data in homes and stores, and proposes effective environmental actions. The embodiments for carrying out this invention are described below.

[0248] System Configuration

[0249] 1. Terminal

[0250] Hardware: Smartphones, tablets

[0251] Software: Applications for data acquisition and input

[0252] Features: Acquisition of electricity usage data for homes and stores, input of user and employee environmental behavior data, receiving and displaying suggestions, input of feedback.

[0253] 2. Sensor

[0254] Smart meter: Real-time acquisition of electricity usage data

[0255] Environmental sensors: Acquisition of ambient environmental data (temperature, humidity, etc.)

[0256] 3. Server

[0257] Hardware: Cloud servers (e.g., AWS, Google Cloud)

[0258] Software: Data analysis platform, database (Firebase Realtime Database), generative AI (GPT-3, OpenAI)

[0259] Functions: Receiving, storing, analyzing, generating suggestions, and sending data.

[0260] Program processing

[0261] Data collection and transmission

[0262] Terminal: Acquires real-time electricity usage data from homes and stores using smart meters and environmental sensors, and transmits it to a cloud server.

[0263] Server: Receives transmitted power usage data and stores it in a database. Analyzes the retrieved data to identify consumption patterns.

[0264] Input and evaluation of environmental behavioral data

[0265] Users and employees: Input their daily environmental actions (e.g., recycling, energy saving) into a terminal application.

[0266] Terminal: Sends the entered environmental behavior data to the server.

[0267] Server: Stores the transmitted environmental behavior data in a database and assigns points (green score).

[0268] Proposal generation and notification

[0269] Server: Based on power consumption patterns and environmental behavior data, it uses generated AI to propose specific decarbonization actions.

[0270] Server: Generates suggestions and sends them to user or employee terminals. For example, it generates specific suggestions such as "raise the air conditioner temperature by 1 degree."

[0271] Terminal: Receives generated proposals and notifies users and employees.

[0272] Gathering feedback and sharing data

[0273] Users and employees: Perform the suggested action and enter the result into the terminal.

[0274] Terminal: Transmits the input feedback to the server.

[0275] Server: Saves the feedback data and utilizes it for subsequent proposals. Also, shares the collected data with relevant enterprises and service providers to promote decarbonization measures.

[0276] Specific example

[0277] For example, the power usage data of a certain store is transmitted to the server, and it is analyzed that the consumption peaks in a specific time period. The server uses generative AI to generate a proposal such as "Dim the daytime lighting by 50%" for the employees and notifies the terminal. When the employees execute the proposal and report the results on the terminal, the server receives the feedback and reflects it in the next proposal. This can optimize the power consumption of the store and reduce the environmental load.

[0278] Example of input prompt for the generative AI model

[0279] "Based on the power usage data and environmental behavior data of the store, please propose a method to optimize store operations. Specifically, generate proposals for actions to reduce power consumption (e.g., adjusting the set temperature of the air conditioner, dimming the lighting, etc.) and notify the employees."

[0280] In this way, the present invention integrally manages and proposes power usage and environmental behavior in homes and stores, and provides support for users and employees to effectively implement decarbonization actions.

[0281] The flow of specific processing in Application Example 1 will be described using FIG. 12.

[0282] Processing steps of the program

[0283] Step 1:

[0284] Collection of power data

[0285] The terminal obtains the power usage data of homes and stores in real time from smart meters and environmental sensors.

[0286] Input: Measurement values of smart meters and environmental sensors.

[0287] Output: The acquired power data.

[0288] Specific operation: The smart meter measures the power usage every minute and sends the data to the terminal. The terminal sends this data to the cloud server in an appropriate format.

[0289] Step 2:

[0290] Analysis and storage of power data

[0291] The server receives the power data sent from the terminal and stores it in the database. Then, it analyzes the stored data to identify the power consumption pattern.

[0292] Input: The acquired power data.

[0293] Output: The analyzed power consumption pattern.

[0294] Specific operation: The server stores the power usage data in the database and executes an analysis algorithm to identify the daily consumption pattern. For example, it calculates the average consumption for each time period to extract the peak time periods.

[0295] Step 3:

[0296] Input of environmental behavior data

[0297] Users and employees input their daily environmental behaviors into the terminal application.

[0298] Input: Environmental behavior data (e.g., recycling, energy-saving behaviors). <Output: The input environmental behavior data.

[0300] Specific operation: Users and employees use smartphone or tablet apps to select and input specific environmental behaviors. This data is sent to the cloud server in real time.

[0301] Step 4:

[0302] Evaluation and Point Assignment of Environmental Behavior Data

[0303] The server receives the sent environmental behavior data, saves it in the database, and then assigns points (green scores) based on the content of the behavior.

[0304] Input: The input environmental behavior data.

[0305] Output: Points (green scores).

[0306] Specific operation: The server scores the environmental behavior data based on an evaluation algorithm and saves the result in the database. For example, points are added according to the frequency of recycling.

[0307] Step 5:

[0308] Proposal Generation

[0309] The server uses generative AI to propose specific decarbonization actions based on power consumption patterns and environmental behavior data.

[0310] Input: Power consumption patterns, environmental behavior data.

[0311] Output: The generated proposals.

[0312] Specific operation: The server inputs the analyzed data into the generating AI and generates optimal suggestions based on the prompt. For example, the prompt might be, "Please suggest ways to optimize store operations based on store power usage data and environmental behavior data."

[0313] Step 6:

[0314] Notification of proposal

[0315] The server sends the generated suggestions to the user's or employee's device and provides notifications.

[0316] Input: Generated suggestions.

[0317] Output: Notification to the terminal.

[0318] Specific operation: The server sends the suggestion as a push notification to the device. The device receives the notification and displays it in the app.

[0319] Step 7:

[0320] Gathering feedback

[0321] Users and employees perform the suggested actions and input the results into the terminal.

[0322] Input: Feedback data (results of action execution).

[0323] Output: Input feedback data.

[0324] Specific operation: After performing the suggested actions, users and employees input the results and effects into a terminal application. This data is sent to a cloud server in real time.

[0325] Step 8:

[0326] Feedback analysis and data sharing

[0327] The server analyzes the feedback data and uses it to improve the accuracy of future suggestions. It also shares the collected data with relevant companies and service providers.

[0328] Input: The input feedback data.

[0329] Output: Analysis results and shared data.

[0330] Specific operation: The server analyzes the feedback data and runs an algorithm to evaluate the effectiveness of the suggestions. The analysis results are stored in a database, and data for sharing is generated.

[0331] 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.

[0332] This invention is a system that combines a generative AI and an emotion engine using household electricity data and environmental behavior data to propose optimal decarbonization actions to the user. The system includes the following main components:

[0333] System Configuration

[0334] 1. Terminal

[0335] Electricity usage data is collected from smart meters and environmental sensors installed in homes.

[0336] It provides an interface for users to input environmental actions they have taken.

[0337] It provides an emotion input interface for recognizing the user's emotions.

[0338] Receives suggestions from the server and notifies the user.

[0339] 2. Server

[0340] It receives and stores power usage data, environmental behavior data, and emotional data transmitted from the device.

[0341] The received data is analyzed to identify household electricity consumption patterns.

[0342] The system evaluates users' environmental behavior and assigns them points (green score).

[0343] Using AI generated from electricity usage data and environmental behavior data, we propose specific decarbonization actions to users.

[0344] The emotion engine analyzes user emotions and optimizes suggestions accordingly.

[0345] We will save the feedback in the database and use it to improve future proposals.

[0346] Program processing (explained in natural language)

[0347] Collection and analysis of power data

[0348] 1. Terminal: Periodically acquires daily electricity usage data from the home's smart meter. For example, it measures and stores electricity usage every 10 minutes.

[0349] 2. Terminal: Sends acquired power usage data to the server. A secure communication protocol (e.g., HTTPS) is used for transmission.

[0350] 3. Server: Stores the transmitted power usage data in a database. A timestamp is added to the data during storage to maintain data integrity.

[0351] 4. Server: Analyzes stored data to identify household power consumption patterns. Specifically, it extracts peak hours and high-consumption periods on specific days.

[0352] Input and evaluation of environmental behavioral data

[0353] 1. User: Inputs daily environmental actions (e.g., waste sorting, water conservation, etc.) into the terminal's interface. Touchscreen or voice input can be used for input.

[0354] 2. Terminal: Sends the entered environmental behavior data to the server. Immediately after sending, a data confirmation screen is displayed to allow the user to reconfirm the entered information.

[0355] 3. Server: Stores the submitted environmental behavior data in a database and assigns points (green score) based on the behavior. For example, 50 points are awarded for recycling.

[0356] Proposal generation and notification

[0357] 1. Server: Uses AI generated based on power consumption patterns and environmental behavior data to suggest effective decarbonization actions to users. Suggestions are customized based on context, time of day, and consumption levels.

[0358] 2. Server: For example, it generates specific suggestions such as, "Raise the air conditioner's temperature setting by 1 degree to reduce power consumption at night."

[0359] 3. Server: Sends the generated suggestions to the user's terminal.

[0360] 4. Device: Receives proposals and notifies the user. Notifications are sent via push notifications or alerts.

[0361] Collection and analysis of emotional data

[0362] 1. User: Enter your daily emotional state (e.g., happy, sad, stressed, etc.) into the device's emotion input interface.

[0363] 2. Terminal: Sends emotional data to the server. Encrypted communication is used for transmission to protect user privacy.

[0364] 3. Server: Stores emotion data in a database and analyzes it using an emotion engine. Based on the analysis results, it generates suggestions tailored to the user's state.

[0365] Gathering feedback and sharing data

[0366] 1. User: Perform the suggested action and input the result into the terminal. For example, input "Raised the air conditioner temperature by 1 degree."

[0367] 2. Terminal: Sends the entered feedback to the server. The feedback includes the date and time the action was performed and its specific details.

[0368] 3. Server: Saves feedback data and uses it to improve future proposals. For example, it evaluates implementation rates and effectiveness to optimize the next proposal.

[0369] 4. Server: The collected data is shared with partner companies and power companies. The data is processed into statistical data and shared while ensuring security.

[0370] Specific example

[0371] For example, if a household uses its air conditioner day and night, its power consumption data is sent to a server. The server identifies peak times and, taking sentiment data into consideration, suggests raising the air conditioner's temperature setting by one degree at night. This suggestion is notified to the user's device, and feedback on whether the user acted on it is also collected to improve the accuracy of future suggestions.

[0372] If emotional data is analyzed as indicating "fatigue" or "stress," the suggestion will be customized to best suit the user's condition, such as "set the room temperature to a comfortable level so you can go to bed earlier," rather than simply "raise the air conditioner temperature."

[0373] In this way, the system integrates and manages household electricity consumption and environmental behavior, utilizes emotional data to provide users with optimized suggestions, and supports effective decarbonization actions.

[0374] The following describes the processing flow.

[0375] Step 1:

[0376] Terminal: Acquires electricity usage data from the home's smart meter every 10 minutes. For example, it records the amount of electricity consumed during each time period.

[0377] Step 2:

[0378] Terminal: Sends acquired power usage data to the server at regular intervals. A secure communication protocol (HTTPS) is used for transmission.

[0379] Step 3:

[0380] Server: Stores transmitted power usage data in a database. The data is stored with a timestamp to maintain consistency.

[0381] Step 4:

[0382] Server: Analyzes stored data to identify household power consumption patterns. Specifically, it extracts peak hours and high-consumption periods on specific days.

[0383] Step 5:

[0384] User: Inputs daily environmental actions (e.g., waste sorting, water conservation, energy saving) into the terminal's interface. Touchscreen or voice recognition can be used for input.

[0385] Step 6:

[0386] Terminal: Transmits entered environmental behavior data to the server in real time. A confirmation screen is displayed immediately after user input, allowing for reconfirmation.

[0387] Step 7:

[0388] Server: Stores the submitted environmental action data in a database and assigns points (green score) to each action. For example, 50 points are awarded for recycling.

[0389] Step 8:

[0390] User: Enter your current emotional state (e.g., happy, sad, stressed) into the device's emotion input interface. This can also utilize facial recognition or voice input.

[0391] Step 9:

[0392] Device: Sends emotional data to the server. Encrypted communication is used to send emotional data and protect user privacy.

[0393] Step 10:

[0394] Server: Stores emotional data in a database and analyzes it using an emotion engine. Based on the analysis results, it identifies the user's current emotional state.

[0395] Step 11:

[0396] Server: Uses AI generated based on power consumption patterns, environmental behavior data, and emotional data to suggest effective decarbonization actions to users. The suggestions are customized according to the user's situation and emotions.

[0397] Step 12:

[0398] Server: For example, it generates specific suggestions such as "raise the air conditioner's temperature by one degree to reduce nighttime power consumption" or "take a walk in a green space to reduce stress."

[0399] Step 13:

[0400] Server: Sends the generated suggestions to the user's terminal.

[0401] Step 14:

[0402] Terminal: Receives proposals and notifies the user. Notifications are displayed as push notifications or alerts.

[0403] Step 15:

[0404] User: Perform the suggested action and input the result into the terminal. For example, report, "Raised the air conditioner temperature by 1 degree."

[0405] Step 16:

[0406] Terminal: Sends the entered feedback to the server. The feedback includes the date and time the action was performed and its specific details.

[0407] Step 17:

[0408] Server: Saves and analyzes feedback data to improve future suggestions. For example, it evaluates the implementation rate and effectiveness of actions to optimize the next suggestion.

[0409] Step 18:

[0410] Server: Shares collected data with companies and power companies. The data is processed into statistical information and provided while ensuring security.

[0411] Step 19:

[0412] Terminal: Lists and displays environmentally friendly products based on their DecaVo score to the user. Products include detailed descriptions and user ratings.

[0413] Step 20:

[0414] User: Select an environmentally friendly product from the list and proceed with the purchase. The purchase process is conducted through secure online payment.

[0415] Step 21:

[0416] Server: Saves purchase data to the database and updates the Decabo score. Contribution from product purchases is reflected in the Green score.

[0417] Through the steps described above, this system considers the user's power consumption patterns and emotional state, proposes optimal and effective decarbonization actions, and supports the realization of a sustainable society.

[0418] (Example 2)

[0419] 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".

[0420] While conventional home energy management systems collected and analyzed electricity usage data, they were unable to integrate environmental behavior and emotional data to provide optimal recommendations. Furthermore, providing personalized feedback and suggestions to users in real time was difficult, resulting in insufficient efforts to improve user motivation and encourage continuous environmental improvement behaviors.

[0421] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for storing household power usage data with a timestamp and identifying household power consumption patterns, means for analyzing the user's environmental behavior data and assigning points, and means for proposing optimal decarbonization actions using an AI model generated based on power usage data and environmental behavior data. This enables the integrated analysis and evaluation of the user's power consumption patterns, environmental behavior, and emotional data, and makes it possible to propose specific decarbonization actions optimized for the user.

[0422] "Electricity usage data" refers to data that records household electricity consumption on an hourly basis.

[0423] A "secure communication protocol" is a means of communication used to ensure the security of data transmission. Specifically, HTTPS is an example of such a protocol.

[0424] A "timestamp" is information that records the date and time when data was acquired.

[0425] "Power consumption patterns" refer to data that shows trends and characteristics of power usage during specific time periods or dates.

[0426] "Environmental behavior data" refers to data that records users' daily actions taken to protect the environment. Specifically, this includes information such as waste sorting and water conservation practices.

[0427] A "generative AI model" is a system that generates optimal suggestions or predictions from specific input data based on artificial intelligence algorithms.

[0428] "Feedback" refers to information about the results and circumstances of a user's actions after performing a suggested action.

[0429] "Emotional data" refers to data that records a user's emotional state (e.g., happy, sad, stressed, etc.).

[0430] "Points" are rewards or scores awarded to users for their environmental actions.

[0431] A "notification" is a means of communicating information from a server to a user, and is usually done in the form of push notifications or alerts on the device.

[0432] "Data sharing" refers to the act of securely sharing collected data as statistical information with partner companies, power companies, and other relevant parties.

[0433] "Listing" refers to displaying products or information in a list format based on specific criteria.

[0434] This invention is a system that utilizes household electricity usage data and environmental behavior data, and combines a generative AI model and an emotion engine to propose optimal decarbonization actions to users. This system includes a smart meter for acquiring household electricity usage data, a terminal for inputting the user's environmental behavior, and a server for analyzing and storing the data. The following describes each component of this invention and its operation.

[0435] Components

[0436] 1. Smart meters and sensors: These are installed in homes and periodically collect electricity usage data every 10 minutes. For example, they collect data such as "10:00 AM - 0.45kWh" and "10:10 AM - 0.50kWh".

[0437] 2. Terminal: An interface for users to input environmental actions within the home (such as waste sorting and water conservation). It can use a touchscreen or voice input. For example, one might input, "Today I sorted my trash."

[0438] 3. Server: Stores and analyzes collected data and generates suggestions. The server stores the data in a database with timestamps and identifies power consumption patterns. For example, it stores the data with a timestamp such as "2023-10-05 10:10:00 - 0.50kWh". Furthermore, it uses a secure communication protocol (HTTPS) to exchange data.

[0439] 4. Generative AI Model: The generative AI model on the server proposes optimal decarbonization actions based on power consumption data and environmental behavior data. For example, it generates suggestions such as "Raise the air conditioner's temperature setting by 1 degree to reduce power consumption at night."

[0440] 5. Emotion Engine: Analyzes user emotion data and optimizes suggestions. Emotion data is transmitted to the server via encrypted communication and analyzed. For example, if a user inputs "I'm stressed," the system will make suggestions such as "Adjust the temperature setting for relaxation."

[0441] Specific example

[0442] For example, if a household uses its air conditioner day and night, its power consumption data is sent from the smart meter to a server. The server identifies peak times and, taking emotional data into consideration, suggests "raising the air conditioner's temperature setting by one degree at night." This suggestion is notified to the user's device, and feedback is collected on whether the user has taken action. Furthermore, if the user's emotional data is analyzed as "fatigue" or "stress," the suggestion is customized from "raising the air conditioner's temperature setting" to "setting the room temperature to a comfortable level so you can go to bed earlier."

[0443] Example of a prompt

[0444] Examples of prompt statements to input into a generative AI model include the following:

[0445] "Based on household electricity consumption data and environmental behavior data, propose the optimal decarbonization actions. If the user's emotional state is 'fatigue,' the proposal should be especially considerate of the user's physical condition."

[0446] With the above configuration and operation, the present invention is a system that comprehensively manages the user's power consumption patterns and environmental behavior, and utilizes emotional data to support effective and personalized decarbonization actions.

[0447] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0448] Step 1:

[0449] Terminal: Acquires electricity usage data from smart meters. For example, it acquires data such as "10:00 AM - 0.45kWh" and "10:10 AM - 0.50kWh" as electricity consumption every 10 minutes. The input is real-time data from the smart meter, and the output is electricity usage data temporarily stored in the terminal.

[0450] Step 2:

[0451] Terminal: Sends acquired power usage data to the server. A secure communication protocol (e.g., HTTPS) is used for this transmission. The input is temporarily stored power usage data, and the output is the data sent to the server. Specifically, the terminal encodes the data and sends it to the server using the HTTPS protocol.

[0452] Step 3:

[0453] Server: Stores transmitted power usage data in a database. During the storage process, a timestamp is added to the data. The input is the raw power usage data sent to the server, and the output is the timestamped data stored in the database. Specifically, the server uses a database management system to organize and store the data.

[0454] Step 4:

[0455] Server: Analyzes data stored in the database to identify power consumption patterns. Input is time-stamped power usage data, and output is power consumption patterns identified based on the analysis (e.g., peak hours and high-consumption periods). Specifically, the server uses statistical analysis software to extract consumption patterns.

[0456] Step 5:

[0457] User: Inputs daily environmental actions (e.g., waste sorting, water conservation) into the terminal's interface. Input can be done using a touchscreen or voice input. The input is raw environmental action data from the user, and the output is environmental action data temporarily stored as input data within the terminal.

[0458] Step 6:

[0459] Terminal: Sends the entered environmental behavior data to the server. A data confirmation screen is displayed to allow the user to reconfirm the entered information. The input is temporarily stored environmental behavior data, and the output is the data sent to the server. Specifically, the terminal encodes the data and sends it to the server using a secure communication protocol.

[0460] Step 7:

[0461] Server: Stores the submitted environmental behavior data in a database and assigns points (green scores). The input is the raw environmental behavior data sent to the server, and the output is the data with points attached, stored in the database. Specifically, the server calculates the score using a point-assigning algorithm and adds it to the database.

[0462] Step 8:

[0463] Server: Uses a generative AI model based on power consumption patterns and environmental behavior data to suggest effective decarbonization actions to the user. The input is power consumption pattern data and environmental behavior data stored on the server, and the output is specific suggestions generated by the generative AI model. Specifically, the server sends the prompt example "Please tell me effective actions to reduce tonight's peak power consumption" to the generative AI model and receives suggestions.

[0464] Step 9:

[0465] Server: Sends the generated proposals to the user's terminal. The input is the generated decarbonization action proposals, and the output is the content of the proposals sent to the user's terminal. Specifically, the server encodes the proposals and sends them to the terminal using a secure communication protocol.

[0466] Step 10:

[0467] Terminal: Receives suggestions and notifies the user. Notifications are made via push notifications or alerts. Input is suggestion data received from the server, and output is a notification message displayed to the user. Specifically, the terminal uses a notification API to display suggestions to the user.

[0468] Step 11:

[0469] User: Inputs their daily emotional state (e.g., happy, sad, stressed) into the device's emotion input interface. The input is the user's raw emotional data, and the output is the emotional data temporarily stored as input data within the device.

[0470] Step 12:

[0471] The terminal sends emotion data to the server. Encrypted communication is used for transmission to protect user privacy. The input is temporarily stored emotion data, and the output is the data sent to the server. Specifically, the terminal encodes the data and sends it to the server using an encryption protocol.

[0472] Step 13:

[0473] Server: Stores emotional data in a database and analyzes it using an emotion engine. The input is raw emotional data sent to the server, and the output is the analysis result. Specifically, the server uses an emotion analysis algorithm to obtain data corresponding to the user's state.

[0474] Step 14:

[0475] Server: Executes the proposed action and inputs the result into the terminal. The input is user feedback data, and the output is the feedback data temporarily stored as input data within the terminal.

[0476] Step 15:

[0477] Terminal: Sends the input feedback to the server. The input is temporarily stored feedback data, and the output is the data sent to the server. Specifically, the terminal encodes the data and sends it to the server using a secure communication protocol.

[0478] Step 16:

[0479] Server: Stores feedback data and uses it for future proposals. Input is raw feedback data sent to the server, and output is evaluation data stored in the database. Specifically, the server uses an evaluation algorithm to analyze the data in order to optimize the next proposal.

[0480] (Application Example 2)

[0481] 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".

[0482] In recent years, systems that optimize household electricity consumption and promote environmentally friendly behavior have become indispensable. However, conventional systems have been limited to collecting and analyzing electricity consumption data and environmental behavior data, and have suffered from low accuracy in recommendations because they do not adequately consider the user's emotional state or real-time feedback. Furthermore, if the suggested products or services do not match the user's emotional state, they are not well accepted, and this does not lead to effective decarbonization behavior.

[0483] 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. In this invention, the server includes means for acquiring household electricity usage data, means for analyzing environmental behavior data and assigning points, means for acquiring and analyzing user emotion data, means for generating suggestions according to the emotional state using a generating AI, means for transmitting the suggestions to the user's terminal, means for the user to perform the suggested actions and input feedback, and means for sharing data and listing environmentally friendly products and displaying them to the user. This makes it possible to propose optimal decarbonization actions that take into account the user's electricity consumption patterns and emotional state.

[0484] "Household electricity usage data" refers to data that shows the amount and patterns of electricity consumption used within a household.

[0485] A "server" is a computer system that collects, analyzes, stores, and notifies users of data.

[0486] "Generative AI" is an artificial intelligence model that generates optimal suggestions for users based on electricity usage data and environmental behavior data.

[0487] "Emotional data" refers to data that indicates a user's emotional state (for example, happy, sad, stressed, etc.).

[0488] An "emotion engine" is an algorithm that analyzes emotional data and evaluates the user's emotional state.

[0489] "Environmental behavior data" refers to data that shows environmentally friendly actions taken by users (e.g., recycling, waste sorting, energy saving, etc.).

[0490] "Means of awarding points" refers to a function that evaluates a user's environmental behavior and awards the user points (green score) commensurate with that behavior.

[0491] A "terminal" is a device used by a user (e.g., a smartphone, tablet, smart glasses, etc.) that provides an interface with the system.

[0492] "Feedback" refers to the process of a user performing an action suggested by the system and providing feedback on the results.

[0493] Modes for carrying out the invention

[0494] This invention is a system that analyzes household electricity data, environmental behavior data, and emotional data, and combines a generative AI and an emotion engine to propose optimal decarbonization actions to the user. This system will be implemented as a virtual store application called "Green Shopping Assistant" using smart glasses or a head-mounted display.

[0495] Key components of the system

[0496] 1. Terminal:

[0497] Get:

[0498] Daily electricity usage data is regularly acquired from home smart meters and environmental sensors. For example, electricity usage is measured and stored every 10 minutes.

[0499] Data entry:

[0500] It provides an interface for users to input environmental actions they have taken (e.g., waste sorting, water conservation). Input can be done via a touchscreen or voice input.

[0501] Acquiring emotion data:

[0502] Real-time acquisition of emotional data using devices equipped with an emotion engine (e.g., cameras with facial recognition software).

[0503] Proposal notification:

[0504] The server notifies the user of suggestions sent from the server. The notifications are made in real time and displayed on the user's smart glasses or head-mounted display.

[0505] 2. Server:

[0506] Data management:

[0507] Household electricity usage data, environmental behavior data, and emotional data are received using the HTTPS communication protocol and stored in a database.

[0508] Data analysis:

[0509] The system analyzes the received data to identify household electricity consumption patterns. Points (green score) are assigned based on environmental behavior data.

[0510] Suggestion generation:

[0511] Using a generative AI model (e.g., GPT-4), the system proposes optimal decarbonization actions based on the user's power consumption patterns and emotional data. For example: "Raise the air conditioner's temperature setting by 1 degree to reduce nighttime power consumption."

[0512] Emotion analysis:

[0513] An emotion engine is used to analyze emotional data and optimize suggestions based on the user's emotions. For example, if the user is tired, the suggestion might be "create a relaxing environment" rather than "raise the air conditioner temperature."

[0514] 3. Feedback function:

[0515] input:

[0516] When a user performs a suggested action, feedback is entered into the device. For example, "I raised the air conditioner temperature by 1 degree."

[0517] evaluation:

[0518] The server optimizes its next proposal based on the feedback data. The collected data is processed into statistical data and securely shared with partner companies and power companies.

[0519] Specific example

[0520] For example, if a household uses its air conditioner day and night, its power consumption data is sent to a server. The server identifies peak times and, taking sentiment data into consideration, generates specific suggestions such as "raise the air conditioner's temperature by one degree at night." This suggestion is notified to the user's smart glasses or head-mounted display in real time. If the user accepts the suggestion, their feedback is collected to improve the accuracy of future suggestions.

[0521] Example of a prompt

[0522] "Consider the user's household electricity data and environmental behavior data, and generate a proposal for the most optimal decarbonization actions under the following conditions."

[0523] Power Consumption Patterns: Based on current data, make suggestions regarding energy-saving home appliances.

[0524] Emotional state: If the user is tired, suggest products with relaxation effects.

[0525] Proposal details: Please specify the product you are proposing and the reasons for your proposal.

[0526] In this way, the system integrates and manages household electricity consumption and environmental behavior, utilizes emotional data to provide users with optimized suggestions, and supports effective decarbonization actions.

[0527] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0528] Step 1:

[0529] The device acquires electricity usage data from the home's smart meter and environmental sensors. This data includes the household's electricity consumption and is recorded every 10 minutes.

[0530] Step 2:

[0531] The terminal sends power usage data it acquires to the server using the HTTPS communication protocol. The transmitted data includes a timestamp and is encrypted to ensure security.

[0532] Step 3:

[0533] The server saves the received power usage data to a database. A timestamp is added to maintain data integrity during saving, and data backups are also performed.

[0534] Step 4:

[0535] The terminal provides an interface for users to input environmental behavior data. Users input behavior data (e.g., recycling, water conservation) using a touchscreen or voice input.

[0536] Step 5:

[0537] The device sends the entered environmental behavior data to the server. The user can reconfirm the sent data on a confirmation screen.

[0538] Step 6:

[0539] The server stores the received environmental behavior data in a database and performs analysis. The analysis includes a process of assigning points (green scores) based on the behavior. For example, 50 points might be awarded for recycling.

[0540] Step 7:

[0541] The device acquires user emotion data using a device equipped with an emotion engine. This emotion data includes the user's emotional state (e.g., happy, sad, stressed, etc.).

[0542] Step 8:

[0543] The device sends the acquired emotion data to the server. Encrypted communication is used for transmission to protect user privacy.

[0544] Step 9:

[0545] The server stores the received emotional data in a database and analyzes it using an emotion engine. Based on the analysis results, the user's emotional state is identified.

[0546] Step 10:

[0547] The server uses an AI model generated based on power consumption patterns, environmental behavior data, and emotional data to suggest optimal decarbonization actions to the user. For example, it might generate a specific suggestion such as "raise the air conditioner temperature by 1 degree to reduce power consumption at night."

[0548] Step 11:

[0549] The server generates suggestions and sends them to the user's device. The user is then notified in real time via their smart glasses or head-mounted display.

[0550] Step 12:

[0551] The user performs the suggested action and inputs feedback into the device. For example, they might input, "I raised the air conditioner temperature by 1 degree."

[0552] Step 13:

[0553] The device sends the input feedback to the server. The feedback includes the date and time the action was performed and its result.

[0554] Step 14:

[0555] The server stores the feedback data in a database and uses it to optimize future proposals. The collected feedback data is shared with partner companies and power companies as statistical data.

[0556] At each step, it is clearly explained what data the server, terminal, and user input, what data processing is performed, and what output is obtained. This processing flow enables a system that comprehensively manages the user's power consumption, environmental behavior, and emotional state, supporting optimal decarbonization actions.

[0557] 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.

[0558] 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.

[0559] 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.

[0560] [Second Embodiment]

[0561] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0562] 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.

[0563] 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).

[0564] 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.

[0565] 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.

[0566] 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).

[0567] 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.

[0568] 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.

[0569] 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.

[0570] 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.

[0571] 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.

[0572] 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".

[0573] This invention is a system that uses household electricity data and environmental behavior data to propose optimal decarbonization actions to users. The system consists of a terminal installed in the user's home, a server that analyzes the data and generates proposals, and an interface for notifying the user of the proposals. A specific embodiment of this system is described below.

[0574] System Configuration

[0575] 1. Terminal

[0576] It collects electricity usage data from smart meters and environmental sensors within the user's home.

[0577] It provides an interface for users to input environmental actions they have taken.

[0578] Receives suggestions from the server and notifies the user.

[0579] 2. Server

[0580] It receives and stores power usage data and environmental behavior data transmitted from the terminal.

[0581] The received data is analyzed to identify household electricity consumption patterns.

[0582] The system evaluates users' environmental behavior and assigns them points (green score).

[0583] Using AI generated from electricity data and environmental behavior data, we propose specific decarbonization actions to users.

[0584] We will save the feedback in the database and use it to improve future proposals.

[0585] Program processing (explained in natural language)

[0586] Collection and analysis of power data

[0587] 1. Terminal: Obtains daily electricity usage data from the home's smart meter.

[0588] 2. Terminal: Sends the acquired data to the server.

[0589] 3. Server: Stores the transmitted power usage data in the database.

[0590] 4. Server: Analyzes stored data to identify household power consumption patterns. For example, it extracts peak hours and periods of high consumption.

[0591] Input and evaluation of environmental behavioral data

[0592] 1. User: Inputs their daily environmental actions (e.g., recycled, water-saving, etc.) into the terminal.

[0593] 2. Terminal: Sends the entered data to the server.

[0594] 3. Server: Stores the transmitted environmental behavior data in a database and assigns points (green score) based on the behavior.

[0595] Proposal generation and notification

[0596] 1. Server: Uses AI generated based on power consumption patterns and environmental behavior data to suggest effective decarbonization actions to users.

[0597] 2. Server: For example, it generates specific suggestions such as, "Raise the air conditioner's temperature setting by 1 degree to reduce power consumption at night."

[0598] 3. Server: Sends the generated suggestions to the user's terminal.

[0599] 4. Terminal: Receives proposals and notifies the user.

[0600] Gathering feedback and sharing data

[0601] 1. User: Perform the suggested action and input the result into the terminal.

[0602] 2. Terminal: Sends the input feedback to the server.

[0603] 3. Server: Saves feedback data and uses it for future proposals.

[0604] 4. Server: Shares collected data with companies and power companies to promote decarbonization measures.

[0605] Specific example

[0606] For example, if a household uses its air conditioner day and night, its power consumption data is sent to a server. The server identifies peak times and, using the green score as a reference, suggests raising the air conditioner's temperature setting by one degree at night. This suggestion is notified to the user's device, and feedback on whether the user has taken action is collected to improve the accuracy of future suggestions.

[0607] In this way, the system integrates and manages household electricity consumption and environmental behavior, providing support for users to effectively implement decarbonization actions.

[0608] The following describes the processing flow.

[0609] Step 1:

[0610] Terminal: Periodically acquires electricity usage data from smart meters installed in homes. For example, it measures and stores electricity usage every 10 minutes.

[0611] Step 2:

[0612] Terminal: Sends acquired power usage data to the server. A secure communication protocol (e.g., HTTPS) is used for transmission.

[0613] Step 3:

[0614] Server: Stores the transmitted power usage data in the database. A timestamp is added to the data during storage to maintain data integrity.

[0615] Step 4:

[0616] Server: Analyzes stored data to identify household power consumption patterns. Specifically, it extracts peak hours and high-consumption periods on specific days.

[0617] Step 5:

[0618] User: Inputs daily environmental actions (e.g., waste sorting, water conservation, etc.) into the terminal's interface. Input can be done via touchscreen or voice input.

[0619] Step 6:

[0620] Terminal: Sends the entered environmental behavior data to the server. Immediately after sending, a data confirmation screen is displayed to allow the user to reconfirm the entered information.

[0621] Step 7:

[0622] Server: Stores the submitted environmental action data in a database and assigns points (green score) to each action. For example, 50 points are awarded for recycling.

[0623] Step 8:

[0624] Server: Uses AI generated from power consumption patterns and environmental behavior data to suggest decarbonization actions suitable for the user. The suggestions are customized based on context, time of day, and consumption levels.

[0625] Step 9:

[0626] Server: Sends the generated suggestion (e.g., "Raise the air conditioner's temperature by 1 degree to reduce nighttime power consumption") to the user's terminal.

[0627] Step 10:

[0628] Terminal: Receives proposals and notifies the user. Notifications are delivered via push notifications or alerts.

[0629] Step 11:

[0630] User: Perform the suggested action and input the result into the terminal. For example, input "Raised the air conditioner temperature by 1 degree."

[0631] Step 12:

[0632] Terminal: Sends the entered feedback to the server. The feedback includes the date and time the action was performed and its specific details.

[0633] Step 13:

[0634] Server: Saves feedback data and uses it to improve future proposals. For example, it evaluates implementation rates and effectiveness to optimize the next proposal.

[0635] Step 14:

[0636] Server: The collected data is shared with partner companies and power companies. The data is processed into statistical data and shared while ensuring security.

[0637] Step 15:

[0638] User's device: Displays a list of environmentally friendly products based on their DecaVo score, allowing the user to select from them. Each product includes a detailed description and rating.

[0639] Step 16:

[0640] User: Select an environmentally friendly product from the list and proceed with the purchase. The purchase process is conducted via the device, and the purchase data is sent to the server after confirmation.

[0641] Step 17:

[0642] Server: Saves purchase data to the database and updates the Decabo score. Contribution from product purchases is reflected in the Green score.

[0643] Through the steps outlined above, this system supports users in effectively implementing decarbonization actions.

[0644] (Example 1)

[0645] 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".

[0646] There is a need for a means to integrate and manage household electricity usage data and environmental behavior data to propose effective decarbonization actions to users. Conventional systems often only collect and analyze electricity usage data, lacking sufficient integration with environmental behavior data, resulting in limited concrete suggestions to users. Furthermore, it has been difficult to utilize the results of these suggestions as feedback, making it challenging to improve the accuracy of the suggestions.

[0647] 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.

[0648] In this invention, the server includes means for acquiring household electricity usage data, means for transmitting the acquired electricity usage data to the server, means for analyzing the data acquired by the server and identifying electricity consumption patterns, means for inputting and transmitting user environmental behavior data to the server, means for analyzing the user's environmental behavior data and assigning evaluation points, means for generating suggestions using an artificial intelligence model generated based on electricity usage data and environmental behavior data, means for transmitting suggestions from the server to the user's terminal, means for the user to perform the suggested actions and input feedback, means for collecting and accumulating data and reflecting the analysis results as feedback in the next suggestion, and means for sharing the collected data and promoting decarbonization measures. This makes it possible to integrate and manage electricity usage data and environmental behavior data and propose individually optimized decarbonization actions to users. Furthermore, by utilizing the results of the suggestions as feedback, it is possible to improve the accuracy of the suggestions.

[0649] "Household electricity usage data" refers to information that shows the amount of electricity consumed within a household and its usage patterns by time of day.

[0650] "Means of acquisition" refers to devices or methods for automatically collecting data using sensors, smart meters, etc.

[0651] A "server" refers to a computer system used to receive, analyze, and store power data and environmental behavior data.

[0652] "Means of analysis" refer to algorithms and software that process collected data to identify power consumption patterns.

[0653] "Environmental behavior data" refers to information about environmental protection actions taken by users (e.g., recycling, water conservation).

[0654] "Evaluation points" are values ​​used to quantitatively evaluate a user's environmental behavior and are indicators of the user's contribution.

[0655] A "generating artificial intelligence model" is a machine learning model or algorithm used to generate suggestions for users based on collected data.

[0656] A "suggestion" is information that indicates specific actions the user should take (e.g., adjusting the air conditioner's temperature settings).

[0657] "Means of input" refers to the interface (e.g., a smartphone application) that users use to input data or feedback.

[0658] "Feedback" is information used to report the results of a user taking a suggested action.

[0659] "Integrated management" is a method of centrally managing electricity usage data and environmental behavior data, and analyzing them in relation to each other.

[0660] "Means of promotion" refers to the methods and mechanisms for executing a plan and achieving its goals.

[0661] This invention is a system that collects and analyzes household electricity usage data and environmental behavior data, and proposes optimal decarbonization actions to the user. Specifically, it includes a terminal installed in the home, a server that analyzes the data and generates proposals, and an interface for notifying the user of the proposals. A specific embodiment of this system is described below.

[0662] System Configuration

[0663] 1. Terminal

[0664] The system collects electricity usage data from smart meters and environmental sensors installed in the user's home.

[0665] It provides an interface for users to input their environmental actions. Specifically, it uses a smartphone application or a web interface.

[0666] Receives suggestions from the server and notifies the user.

[0667] 2. Server

[0668] It is equipped with a database for receiving and storing power usage data and environmental behavior data transmitted from terminals.

[0669] The received data is analyzed to identify the electricity consumption patterns of each household. For example, it is possible to analyze increases and decreases in consumption during specific time periods.

[0670] It has an evaluation algorithm that assesses users' environmental behavior and assigns points (green score).

[0671] Using an AI model generated based on electricity data and environmental behavior data, we propose specific decarbonization actions to users.

[0672] The proposal and feedback will be saved in the database and used for future proposals.

[0673] 3. Interface

[0674] An interface for users to review and take suggested actions. Specifically, this can be achieved through application push notifications, email, or a web-based dashboard.

[0675] It also provides an interface for users to perform suggested actions and input the results as feedback.

[0676] Specific example

[0677] For example, if a household uses its air conditioner day and night, its power usage data is sent to a server. The server analyzes the received data and discovers that power consumption is particularly high at night. At the same time, it also considers the user's green score for environmental actions taken. Based on this, a generative AI model generates a specific suggestion, such as "raise the air conditioner's temperature setting by 1 degree at night." This suggestion is notified to the user's device, and feedback on whether the user has taken action is also collected to improve the accuracy of future suggestions.

[0678] Examples of prompts for generative AI models

[0679] "Based on household electricity usage data and user environmental behavior data, please propose effective decarbonization actions to users. Specifically, please provide detailed suggestions regarding adjustments to air conditioner settings and usage times."

[0680] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0681] Step 1:

[0682] Terminal: Acquires daily electricity usage data from home smart meters and environmental sensors.

[0683] Input: Power usage data from smart meters and environmental sensors.

[0684] Output: Acquired power usage data

[0685] Specific operation: Collects power consumption information transmitted from smart meters at regular time intervals (e.g., every 30 minutes). Similarly, collects data such as temperature and humidity from environmental sensors.

[0686] Step 2:

[0687] Terminal: Transmits acquired power usage data to the server at regular intervals.

[0688] Input: Acquired power usage data

[0689] Output: Power usage data sent to the server

[0690] Specific operation: Collected power usage data is sent to a server in batch processing or in real time. Data is typically transmitted via Wi-Fi or a wired network.

[0691] Step 3:

[0692] Server: Receives transmitted power usage data and stores it in the database.

[0693] Input: Power usage data transmitted from the terminal.

[0694] Output: Power usage data stored in the database

[0695] Specific operation: After initial processing of received data (e.g., data cleansing, normalization), it is saved to the database as time-series data.

[0696] Step 4:

[0697] Server: Analyzes stored power usage data to identify power consumption patterns for each household.

[0698] Input: Power usage data stored in the database

[0699] Output: Analyzed power consumption patterns

[0700] Specific operation: Use data analysis algorithms to identify significant fluctuations in power consumption during specific time periods (e.g., daytime and nighttime). Extract peak consumption levels and distinct consumption patterns.

[0701] Step 5:

[0702] User: Inputs their daily environmental actions (e.g., recycled, water-saving) into the application on their device.

[0703] Input: User-generated environmental behavior data

[0704] Output: Environmental behavior data entered into the terminal

[0705] Specific operation: The user enters the environmental actions they have taken into a form using a smartphone application or web interface.

[0706] Step 6:

[0707] Terminal: Sends the entered environmental behavior data to the server.

[0708] Input: User-generated environmental behavior data

[0709] Output: Environmental behavior data sent to the server

[0710] Specific operation: Input data is sent to the server in real time. Data is usually transferred securely using an API.

[0711] Step 7:

[0712] Server: Stores the transmitted environmental behavior data in a database and assigns points (green score) based on the behavior.

[0713] Input: Environmental behavior data sent to the server

[0714] Output: Environmental behavior data saved in the database and assigned points.

[0715] Specific operation: Received environmental behavior data is stored in a database, and an algorithm is applied to calculate a green score based on the importance of each behavior.

[0716] Step 8:

[0717] Server: Uses an AI model generated based on power consumption patterns and environmental behavior data to suggest effective decarbonization actions to users.

[0718] Input: Power consumption patterns and environmental behavior data

[0719] Output: Generated proposals

[0720] Specific operation: Using a generative AI model, the system generates optimal action suggestions for the user from accumulated data (e.g., adjusting the air conditioner temperature setting, changing the usage time).

[0721] Step 9:

[0722] Server: Sends the generated suggestions to the user's terminal.

[0723] Input: Generated proposal

[0724] Output: Suggestions sent to the user's terminal

[0725] Specific actions: The generated suggestions are sent to the user's device via the API. They are displayed as push notifications or in-app messages.

[0726] Step 10:

[0727] User: Perform the suggested action and input the result as feedback into the device.

[0728] Input: Result of the proposed action

[0729] Output: Feedback data entered into the terminal

[0730] Specific action: The user performs the suggested action and inputs the result (e.g., increased the air conditioner temperature by 1 degree) as feedback to the application.

[0731] Step 11:

[0732] Terminal: Sends the input feedback to the server.

[0733] Input: Feedback data

[0734] Output: Feedback data sent to the server

[0735] Specific action: Send feedback data to the server in real time.

[0736] Step 12:

[0737] Server: Saves feedback data and uses it for future proposals.

[0738] Input: Feedback data sent to the server

[0739] Output: Next proposals utilizing feedback data

[0740] Specific operation: Feedback data is saved to a database and reflected in the next proposal generation algorithm to improve the accuracy of the proposals.

[0741] Step 13:

[0742] Server: Shares collected data with companies and power companies to promote decarbonization measures.

[0743] Input: Power usage data and environmental activity data stored on the server

[0744] Output: Shared data

[0745] Specific actions: Statistically process data and share it with businesses and power companies. This will provide foundational data to promote widespread decarbonization measures.

[0746] (Application Example 1)

[0747] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0748] Modern energy efficiency issues involve challenges in both optimizing electricity consumption in homes and stores and promoting environmentally conscious behavior. In particular, brick-and-mortar stores often have high electricity consumption, hindering cost reduction and environmental impact mitigation. Furthermore, there is a lack of concrete and effective methods to raise employee environmental awareness and translate it into concrete actions. Therefore, these challenges need to be addressed by integrating and managing store electricity usage data and environmental behavior data, and by providing specific suggestions to employees.

[0749] 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.

[0750] In this invention, the server includes means for acquiring store power usage data and proposing actions to reduce power consumption, means for evaluating employee environmental behavior data and awarding points, and means for analyzing the acquired data and identifying power consumption patterns. This makes it possible to optimize power consumption in stores and propose ways to raise employees' environmental awareness.

[0751] Definitions of important words

[0752] "Household electricity usage data"

[0753] This refers to numerical information that shows the usage status of all electrical appliances and equipment used in a household.

[0754] "Means of acquisition"

[0755] This refers to devices and technologies used to measure and collect data.

[0756] "server"

[0757] A computer system is a computer system that receives, stores, and analyzes data, and then transmits it to other devices.

[0758] "Power consumption patterns"

[0759] This refers to the periodic fluctuations or trends in electricity usage in a particular household or store.

[0760] "Environmental behavior data"

[0761] This refers to records and reported data of environmentally friendly actions taken by users and employees.

[0762] "Means of awarding points"

[0763] This refers to systems and technologies that evaluate the environmental behavior of users and employees and award points as incentives.

[0764] “Generation AI”

[0765] This refers to artificial intelligence that automatically generates appropriate suggestions for users and employees based on acquired data.

[0766] "Means for generating proposals"

[0767] This refers to a technology that analyzes acquired data and creates suggestions to encourage effective actions from users and employees.

[0768] "Method for sending proposals"

[0769] This refers to communication technologies and systems used to notify users' or employees' terminals of proposals from a server.

[0770] "Methods for providing feedback"

[0771] This refers to an interface or system for users or employees to report the results of performing suggested actions.

[0772] "Data sharing"

[0773] This refers to coordinating and communicating collected data with other relevant systems and organizations.

[0774] "Environmentally friendly products"

[0775] This refers to products that are energy-efficient and designed to reduce their environmental impact.

[0776] "Actions to reduce electricity consumption"

[0777] This refers to specific actions or operations taken to reduce electricity consumption.

[0778] "Store electricity usage data"

[0779] This refers to numerical information that shows the usage status of all electrical equipment and facilities in a store.

[0780] "Employee environmental behavior data"

[0781] This refers to records and reports of environmentally friendly actions taken by store employees.

[0782] Modes for carrying out the invention

[0783] This invention is a system that integrates and manages electricity usage data and environmental behavior data in homes and stores, and proposes effective environmental actions. The embodiments for carrying out this invention are described below.

[0784] System Configuration

[0785] 1. Terminal

[0786] Hardware: Smartphones, tablets

[0787] Software: Applications for data acquisition and input

[0788] Features: Acquisition of electricity usage data for homes and stores, input of user and employee environmental behavior data, receiving and displaying suggestions, input of feedback.

[0789] 2. Sensor

[0790] Smart meter: Real-time acquisition of electricity usage data

[0791] Environmental sensors: Acquisition of ambient environmental data (temperature, humidity, etc.)

[0792] 3. Server

[0793] Hardware: Cloud servers (e.g., AWS, Google Cloud)

[0794] Software: Data analysis platform, database (Firebase Realtime Database), generative AI (GPT-3, OpenAI)

[0795] Functions: Receiving, storing, analyzing, generating suggestions, and sending data.

[0796] Program processing

[0797] Data collection and transmission

[0798] Terminal: Acquires real-time electricity usage data from homes and stores using smart meters and environmental sensors, and transmits it to a cloud server.

[0799] Server: Receives transmitted power usage data and stores it in a database. Analyzes the retrieved data to identify consumption patterns.

[0800] Input and evaluation of environmental behavioral data

[0801] Users and employees: Input their daily environmental actions (e.g., recycling, energy saving) into a terminal application.

[0802] Terminal: Sends the entered environmental behavior data to the server.

[0803] Server: Stores the transmitted environmental behavior data in a database and assigns points (green score).

[0804] Proposal generation and notification

[0805] Server: Based on power consumption patterns and environmental behavior data, it uses generated AI to propose specific decarbonization actions.

[0806] Server: Generates suggestions and sends them to user or employee terminals. For example, it generates specific suggestions such as "raise the air conditioner temperature by 1 degree."

[0807] Terminal: Receives generated proposals and notifies users and employees.

[0808] Gathering feedback and sharing data

[0809] Users and employees: Perform the suggested action and enter the result into the terminal.

[0810] Terminal: Sends the input feedback to the server.

[0811] Server: The server stores feedback data and uses it to inform future proposals. It also shares the collected data with relevant companies and service providers to promote decarbonization measures.

[0812] Specific example

[0813] For example, electricity usage data from a store is sent to a server, and analysis reveals peak consumption during specific time periods. The server uses AI to generate suggestions for employees, such as "reduce daytime lighting by 50%", and notifies them via their terminals. When employees implement the suggestions and report the results via their terminals, the server receives feedback and incorporates it into future suggestions. This optimizes the store's electricity consumption and reduces its environmental impact.

[0814] Examples of input prompts for a generative AI model

[0815] "Based on store electricity usage data and environmental behavior data, propose ways to optimize store operations. Specifically, generate suggestions for actions to reduce electricity consumption (e.g., adjusting air conditioner temperatures, dimming lights, etc.) and notify employees accordingly."

[0816] In this way, the present invention provides integrated management and proposals for electricity use and environmental behavior in homes and stores, and supports users and employees in effectively implementing decarbonization actions.

[0817] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0818] Program processing steps

[0819] Step 1:

[0820] Collecting power data

[0821] The terminal acquires real-time electricity usage data from homes and stores using smart meters and environmental sensors.

[0822] Input: Measurements from smart meters and environmental sensors.

[0823] Output: Acquired power data.

[0824] Specific operation: The smart meter measures electricity usage every minute and sends that data to the terminal. The terminal then sends this data to the cloud server in the appropriate format.

[0825] Step 2:

[0826] Analysis and storage of power data

[0827] The server receives power data transmitted from the terminal and stores it in a database. It then analyzes the stored data to identify power consumption patterns.

[0828] Input: Acquired power data.

[0829] Output: Analyzed power consumption pattern.

[0830] Specific operation: The server stores power usage data in a database and runs an analysis algorithm to identify daily consumption patterns. For example, it calculates the average consumption for each time period to extract peak hours.

[0831] Step 3:

[0832] Input of environmental behavioral data

[0833] Users and employees input their daily environmental behaviors into a terminal application.

[0834] Input: Environmental behavior data (e.g., recycling, energy-saving behavior).

[0835] Output: Input environmental behavior data.

[0836] Specific operation: Users and employees select and input specific environmental actions using smartphone or tablet apps. This data is transmitted to a cloud server in real time.

[0837] Step 4:

[0838] Evaluation and point allocation of environmental behavior data

[0839] The server receives the transmitted environmental behavior data, stores it in a database, and then assigns points (green scores) based on the content of the behavior.

[0840] Input: Entered environmental behavior data.

[0841] Output: Points (Green Score).

[0842] Specific operation: The server scores environmental behavior data based on an evaluation algorithm and stores the results in a database. For example, points are added based on the frequency of recycling.

[0843] Step 5:

[0844] Proposal generation

[0845] The server uses generative AI to propose specific decarbonization actions based on power consumption patterns and environmental behavior data.

[0846] Input: Power consumption patterns, environmental behavior data.

[0847] Output: Generated proposals.

[0848] Specific operation: The server inputs the analyzed data into the generating AI and generates optimal suggestions based on the prompt. For example, the prompt might be, "Please suggest ways to optimize store operations based on store power usage data and environmental behavior data."

[0849] Step 6:

[0850] Notification of proposal

[0851] The server sends the generated suggestions to the user's or employee's device and provides notifications.

[0852] Input: Generated suggestions.

[0853] Output: Notification to the terminal.

[0854] Specific operation: The server sends the suggestion as a push notification to the device. The device receives the notification and displays it in the app.

[0855] Step 7:

[0856] Gathering feedback

[0857] Users and employees perform the suggested actions and input the results into the terminal.

[0858] Input: Feedback data (results of action execution).

[0859] Output: Input feedback data.

[0860] Specific operation: After performing the suggested actions, users and employees input the results and effects into a terminal application. This data is sent to a cloud server in real time.

[0861] Step 8:

[0862] Feedback analysis and data sharing

[0863] The server analyzes the feedback data and uses it to improve the accuracy of future suggestions. It also shares the collected data with relevant companies and service providers.

[0864] Input: The input feedback data.

[0865] Output: Analysis results and shared data.

[0866] Specific operation: The server analyzes the feedback data and runs an algorithm to evaluate the effectiveness of the suggestions. The analysis results are stored in a database, and data for sharing is generated.

[0867] 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.

[0868] This invention is a system that combines a generative AI and an emotion engine using household electricity data and environmental behavior data to propose optimal decarbonization actions to the user. The system includes the following main components:

[0869] System Configuration

[0870] 1. Terminal

[0871] Electricity usage data is collected from smart meters and environmental sensors installed in homes.

[0872] It provides an interface for users to input environmental actions they have taken.

[0873] It provides an emotion input interface for recognizing the user's emotions.

[0874] Receives suggestions from the server and notifies the user.

[0875] 2. Server

[0876] It receives and stores power usage data, environmental behavior data, and emotional data transmitted from the device.

[0877] The received data is analyzed to identify household electricity consumption patterns.

[0878] The system evaluates users' environmental behavior and assigns them points (green score).

[0879] Using AI generated from electricity usage data and environmental behavior data, we propose specific decarbonization actions to users.

[0880] The emotion engine analyzes user emotions and optimizes suggestions accordingly.

[0881] We will save the feedback in the database and use it to improve future proposals.

[0882] Program processing (explained in natural language)

[0883] Collection and analysis of power data

[0884] 1. Terminal: Periodically acquires daily electricity usage data from the home's smart meter. For example, it measures and stores electricity usage every 10 minutes.

[0885] 2. Terminal: Sends acquired power usage data to the server. A secure communication protocol (e.g., HTTPS) is used for transmission.

[0886] 3. Server: Stores the transmitted power usage data in a database. A timestamp is added to the data during storage to maintain data integrity.

[0887] 4. Server: Analyzes stored data to identify household power consumption patterns. Specifically, it extracts peak hours and high-consumption periods on specific days.

[0888] Input and evaluation of environmental behavioral data

[0889] 1. User: Inputs daily environmental actions (e.g., waste sorting, water conservation, etc.) into the terminal's interface. Touchscreen or voice input can be used for input.

[0890] 2. Terminal: Sends the entered environmental behavior data to the server. Immediately after sending, a data confirmation screen is displayed to allow the user to reconfirm the entered information.

[0891] 3. Server: Stores the submitted environmental behavior data in a database and assigns points (green score) based on the behavior. For example, 50 points are awarded for recycling.

[0892] Proposal generation and notification

[0893] 1. Server: Uses AI generated based on power consumption patterns and environmental behavior data to suggest effective decarbonization actions to users. Suggestions are customized based on context, time of day, and consumption levels.

[0894] 2. Server: For example, it generates specific suggestions such as, "Raise the air conditioner's temperature setting by 1 degree to reduce power consumption at night."

[0895] 3. Server: Sends the generated suggestions to the user's terminal.

[0896] 4. Device: Receives proposals and notifies the user. Notifications are sent via push notifications or alerts.

[0897] Collection and analysis of emotional data

[0898] 1. User: Enter your daily emotional state (e.g., happy, sad, stressed, etc.) into the device's emotion input interface.

[0899] 2. Terminal: Sends emotional data to the server. Encrypted communication is used for transmission to protect user privacy.

[0900] 3. Server: Stores emotion data in a database and analyzes it using an emotion engine. Based on the analysis results, it generates suggestions tailored to the user's state.

[0901] Gathering feedback and sharing data

[0902] 1. User: Perform the suggested action and input the result into the terminal. For example, input "Raised the air conditioner temperature by 1 degree."

[0903] 2. Terminal: Sends the entered feedback to the server. The feedback includes the date and time the action was performed and its specific details.

[0904] 3. Server: Saves feedback data and uses it to improve future proposals. For example, it evaluates implementation rates and effectiveness to optimize the next proposal.

[0905] 4. Server: The collected data is shared with partner companies and power companies. The data is processed into statistical data and shared while ensuring security.

[0906] Specific example

[0907] For example, if a household uses its air conditioner day and night, its power consumption data is sent to a server. The server identifies peak times and, taking sentiment data into consideration, suggests raising the air conditioner's temperature setting by one degree at night. This suggestion is notified to the user's device, and feedback on whether the user acted on it is also collected to improve the accuracy of future suggestions.

[0908] If emotional data is analyzed as indicating "fatigue" or "stress," the suggestion will be customized to best suit the user's condition, such as "set the room temperature to a comfortable level so you can go to bed earlier," rather than simply "raise the air conditioner temperature."

[0909] In this way, the system integrates and manages household electricity consumption and environmental behavior, utilizes emotional data to provide users with optimized suggestions, and supports effective decarbonization actions.

[0910] The following describes the processing flow.

[0911] Step 1:

[0912] Terminal: Acquires electricity usage data from the home's smart meter every 10 minutes. For example, it records the amount of electricity consumed during each time period.

[0913] Step 2:

[0914] Terminal: Sends acquired power usage data to the server at regular intervals. A secure communication protocol (HTTPS) is used for transmission.

[0915] Step 3:

[0916] Server: Stores transmitted power usage data in a database. The data is stored with a timestamp to maintain consistency.

[0917] Step 4:

[0918] Server: Analyzes stored data to identify household power consumption patterns. Specifically, it extracts peak hours and high-consumption periods on specific days.

[0919] Step 5:

[0920] User: Inputs daily environmental actions (e.g., waste sorting, water conservation, energy saving) into the terminal's interface. Touchscreen or voice recognition can be used for input.

[0921] Step 6:

[0922] Terminal: Transmits entered environmental behavior data to the server in real time. A confirmation screen is displayed immediately after user input, allowing for reconfirmation.

[0923] Step 7:

[0924] Server: Stores the submitted environmental action data in a database and assigns points (green score) to each action. For example, 50 points are awarded for recycling.

[0925] Step 8:

[0926] User: Enter your current emotional state (e.g., happy, sad, stressed) into the device's emotion input interface. This can also utilize facial recognition or voice input.

[0927] Step 9:

[0928] Device: Sends emotional data to the server. Encrypted communication is used to send emotional data and protect user privacy.

[0929] Step 10:

[0930] Server: Stores emotional data in a database and analyzes it using an emotion engine. Based on the analysis results, it identifies the user's current emotional state.

[0931] Step 11:

[0932] Server: Uses AI generated based on power consumption patterns, environmental behavior data, and emotional data to suggest effective decarbonization actions to users. The suggestions are customized according to the user's situation and emotions.

[0933] Step 12:

[0934] Server: For example, it generates specific suggestions such as "raise the air conditioner's temperature by one degree to reduce nighttime power consumption" or "take a walk in a green space to reduce stress."

[0935] Step 13:

[0936] Server: Sends the generated suggestions to the user's terminal.

[0937] Step 14:

[0938] Terminal: Receives proposals and notifies the user. Notifications are displayed as push notifications or alerts.

[0939] Step 15:

[0940] User: Perform the suggested action and input the result into the terminal. For example, report, "Raised the air conditioner temperature by 1 degree."

[0941] Step 16:

[0942] Terminal: Sends the entered feedback to the server. The feedback includes the date and time the action was performed and its specific details.

[0943] Step 17:

[0944] Server: Saves and analyzes feedback data to improve future suggestions. For example, it evaluates the implementation rate and effectiveness of actions to optimize the next suggestion.

[0945] Step 18:

[0946] Server: Shares collected data with companies and power companies. The data is processed into statistical information and provided while ensuring security.

[0947] Step 19:

[0948] Terminal: Lists and displays environmentally friendly products based on their DecaVo score to the user. Products include detailed descriptions and user ratings.

[0949] Step 20:

[0950] User: Select an environmentally friendly product from the list and proceed with the purchase. The purchase process is conducted through secure online payment.

[0951] Step 21:

[0952] Server: Saves purchase data to the database and updates the Decabo score. Contribution from product purchases is reflected in the Green score.

[0953] Through the steps described above, this system considers the user's power consumption patterns and emotional state, proposes optimal and effective decarbonization actions, and supports the realization of a sustainable society.

[0954] (Example 2)

[0955] 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".

[0956] While conventional home energy management systems collected and analyzed electricity usage data, they were unable to integrate environmental behavior and emotional data to provide optimal recommendations. Furthermore, providing personalized feedback and suggestions to users in real time was difficult, resulting in insufficient efforts to improve user motivation and encourage continuous environmental improvement behaviors.

[0957] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for storing household power usage data with a timestamp and identifying household power consumption patterns, means for analyzing the user's environmental behavior data and assigning points, and means for proposing optimal decarbonization actions using an AI model generated based on power usage data and environmental behavior data. This enables the integrated analysis and evaluation of the user's power consumption patterns, environmental behavior, and emotional data, and makes it possible to propose specific decarbonization actions optimized for the user.

[0958] "Electricity usage data" refers to data that records household electricity consumption on an hourly basis.

[0959] A "secure communication protocol" is a means of communication used to ensure the security of data transmission. Specifically, HTTPS is an example of such a protocol.

[0960] A "timestamp" is information that records the date and time when data was acquired.

[0961] "Power consumption patterns" refer to data that shows trends and characteristics of power usage during specific time periods or dates.

[0962] "Environmental behavior data" refers to data that records users' daily actions taken to protect the environment. Specifically, this includes information such as waste sorting and water conservation practices.

[0963] A "generative AI model" is a system that generates optimal suggestions or predictions from specific input data based on artificial intelligence algorithms.

[0964] "Feedback" refers to information about the results and circumstances of a user's actions after performing a suggested action.

[0965] "Emotional data" refers to data that records a user's emotional state (e.g., happy, sad, stressed, etc.).

[0966] "Points" are rewards or scores awarded to users for their environmental actions.

[0967] A "notification" is a means of communicating information from a server to a user, and is usually done in the form of push notifications or alerts on the device.

[0968] "Data sharing" refers to the act of securely sharing collected data as statistical information with partner companies, power companies, and other relevant parties.

[0969] "Listing" refers to displaying products or information in a list format based on specific criteria.

[0970] This invention is a system that utilizes household electricity usage data and environmental behavior data, and combines a generative AI model and an emotion engine to propose optimal decarbonization actions to users. This system includes a smart meter for acquiring household electricity usage data, a terminal for inputting the user's environmental behavior, and a server for analyzing and storing the data. The following describes each component of this invention and its operation.

[0971] Components

[0972] 1. Smart meters and sensors: These are installed in homes and periodically collect electricity usage data every 10 minutes. For example, they collect data such as "10:00 AM - 0.45kWh" and "10:10 AM - 0.50kWh".

[0973] 2. Terminal: An interface for users to input environmental actions within the home (such as waste sorting and water conservation). It can use a touchscreen or voice input. For example, one might input, "Today I sorted my trash."

[0974] 3. Server: Stores and analyzes collected data and generates suggestions. The server stores the data in a database with timestamps and identifies power consumption patterns. For example, it stores the data with a timestamp such as "2023-10-05 10:10:00 - 0.50kWh". Furthermore, it uses a secure communication protocol (HTTPS) to exchange data.

[0975] 4. Generative AI Model: The generative AI model on the server proposes optimal decarbonization actions based on power consumption data and environmental behavior data. For example, it generates suggestions such as "Raise the air conditioner's temperature setting by 1 degree to reduce power consumption at night."

[0976] 5. Emotion Engine: Analyzes user emotion data and optimizes suggestions. Emotion data is transmitted to the server via encrypted communication and analyzed. For example, if a user inputs "I'm stressed," the system will make suggestions such as "Adjust the temperature setting for relaxation."

[0977] Specific example

[0978] For example, if a household uses its air conditioner day and night, its power consumption data is sent from the smart meter to a server. The server identifies peak times and, taking emotional data into consideration, suggests "raising the air conditioner's temperature setting by one degree at night." This suggestion is notified to the user's device, and feedback is collected on whether the user has taken action. Furthermore, if the user's emotional data is analyzed as "fatigue" or "stress," the suggestion is customized from "raising the air conditioner's temperature setting" to "setting the room temperature to a comfortable level so you can go to bed earlier."

[0979] Example of a prompt

[0980] Examples of prompt statements to input into a generative AI model include the following:

[0981] "Based on household electricity consumption data and environmental behavior data, propose the optimal decarbonization actions. If the user's emotional state is 'fatigue,' the proposal should be especially considerate of the user's physical condition."

[0982] With the above configuration and operation, the present invention is a system that comprehensively manages the user's power consumption patterns and environmental behavior, and utilizes emotional data to support effective and personalized decarbonization actions.

[0983] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0984] Step 1:

[0985] Terminal: Acquires electricity usage data from smart meters. For example, it acquires data such as "10:00 AM - 0.45kWh" and "10:10 AM - 0.50kWh" as electricity consumption every 10 minutes. The input is real-time data from the smart meter, and the output is electricity usage data temporarily stored in the terminal.

[0986] Step 2:

[0987] Terminal: Sends acquired power usage data to the server. A secure communication protocol (e.g., HTTPS) is used for this transmission. The input is temporarily stored power usage data, and the output is the data sent to the server. Specifically, the terminal encodes the data and sends it to the server using the HTTPS protocol.

[0988] Step 3:

[0989] Server: Stores transmitted power usage data in a database. During the storage process, a timestamp is added to the data. The input is the raw power usage data sent to the server, and the output is the timestamped data stored in the database. Specifically, the server uses a database management system to organize and store the data.

[0990] Step 4:

[0991] Server: Analyzes data stored in the database to identify power consumption patterns. Input is time-stamped power usage data, and output is power consumption patterns identified based on the analysis (e.g., peak hours and high-consumption periods). Specifically, the server uses statistical analysis software to extract consumption patterns.

[0992] Step 5:

[0993] User: Inputs daily environmental actions (e.g., waste sorting, water conservation) into the terminal's interface. Input can be done using a touchscreen or voice input. The input is raw environmental action data from the user, and the output is environmental action data temporarily stored as input data within the terminal.

[0994] Step 6:

[0995] Terminal: Sends the entered environmental behavior data to the server. A data confirmation screen is displayed to allow the user to reconfirm the entered information. The input is temporarily stored environmental behavior data, and the output is the data sent to the server. Specifically, the terminal encodes the data and sends it to the server using a secure communication protocol.

[0996] Step 7:

[0997] Server: Stores the submitted environmental behavior data in a database and assigns points (green scores). The input is the raw environmental behavior data sent to the server, and the output is the data with points attached, stored in the database. Specifically, the server calculates the score using a point-assigning algorithm and adds it to the database.

[0998] Step 8:

[0999] Server: Uses a generative AI model based on power consumption patterns and environmental behavior data to suggest effective decarbonization actions to the user. The input is power consumption pattern data and environmental behavior data stored on the server, and the output is specific suggestions generated by the generative AI model. Specifically, the server sends the prompt example "Please tell me effective actions to reduce tonight's peak power consumption" to the generative AI model and receives suggestions.

[1000] Step 9:

[1001] Server: Sends the generated proposals to the user's terminal. The input is the generated decarbonization action proposals, and the output is the content of the proposals sent to the user's terminal. Specifically, the server encodes the proposals and sends them to the terminal using a secure communication protocol.

[1002] Step 10:

[1003] Terminal: Receives suggestions and notifies the user. Notifications are made via push notifications or alerts. Input is suggestion data received from the server, and output is a notification message displayed to the user. Specifically, the terminal uses a notification API to display suggestions to the user.

[1004] Step 11:

[1005] User: Inputs their daily emotional state (e.g., happy, sad, stressed) into the device's emotion input interface. The input is the user's raw emotional data, and the output is the emotional data temporarily stored as input data within the device.

[1006] Step 12:

[1007] The terminal sends emotion data to the server. Encrypted communication is used for transmission to protect user privacy. The input is temporarily stored emotion data, and the output is the data sent to the server. Specifically, the terminal encodes the data and sends it to the server using an encryption protocol.

[1008] Step 13:

[1009] Server: Stores emotional data in a database and analyzes it using an emotion engine. The input is raw emotional data sent to the server, and the output is the analysis result. Specifically, the server uses an emotion analysis algorithm to obtain data corresponding to the user's state.

[1010] Step 14:

[1011] Server: Executes the proposed action and inputs the result into the terminal. The input is user feedback data, and the output is the feedback data temporarily stored as input data within the terminal.

[1012] Step 15:

[1013] Terminal: Sends the input feedback to the server. The input is temporarily stored feedback data, and the output is the data sent to the server. Specifically, the terminal encodes the data and sends it to the server using a secure communication protocol.

[1014] Step 16:

[1015] Server: Stores feedback data and uses it for future proposals. Input is raw feedback data sent to the server, and output is evaluation data stored in the database. Specifically, the server uses an evaluation algorithm to analyze the data in order to optimize the next proposal.

[1016] (Application Example 2)

[1017] 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."

[1018] In recent years, systems that optimize household electricity consumption and promote environmentally friendly behavior have become indispensable. However, conventional systems have been limited to collecting and analyzing electricity consumption data and environmental behavior data, and have suffered from low accuracy in recommendations because they do not adequately consider the user's emotional state or real-time feedback. Furthermore, if the suggested products or services do not match the user's emotional state, they are not well accepted, and this does not lead to effective decarbonization behavior.

[1019] 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. In this invention, the server includes means for acquiring household electricity usage data, means for analyzing environmental behavior data and assigning points, means for acquiring and analyzing user emotion data, means for generating suggestions according to the emotional state using a generating AI, means for transmitting the suggestions to the user's terminal, means for the user to perform the suggested actions and input feedback, and means for sharing data and listing environmentally friendly products and displaying them to the user. This makes it possible to propose optimal decarbonization actions that take into account the user's electricity consumption patterns and emotional state.

[1020] "Household electricity usage data" refers to data that shows the amount and patterns of electricity consumption used within a household.

[1021] A "server" is a computer system that collects, analyzes, stores, and notifies users of data.

[1022] "Generative AI" is an artificial intelligence model that generates optimal suggestions for users based on electricity usage data and environmental behavior data.

[1023] "Emotional data" refers to data that indicates a user's emotional state (for example, happy, sad, stressed, etc.).

[1024] An "emotion engine" is an algorithm that analyzes emotional data and evaluates the user's emotional state.

[1025] "Environmental behavior data" refers to data that shows environmentally friendly actions taken by users (e.g., recycling, waste sorting, energy saving, etc.).

[1026] "Means of awarding points" refers to a function that evaluates a user's environmental behavior and awards the user points (green score) commensurate with that behavior.

[1027] A "terminal" is a device used by a user (e.g., a smartphone, tablet, smart glasses, etc.) that provides an interface with the system.

[1028] "Feedback" refers to the process of a user performing an action suggested by the system and providing feedback on the results.

[1029] Modes for carrying out the invention

[1030] This invention is a system that analyzes household electricity data, environmental behavior data, and emotional data, and combines a generative AI and an emotion engine to propose optimal decarbonization actions to the user. This system will be implemented as a virtual store application called "Green Shopping Assistant" using smart glasses or a head-mounted display.

[1031] Key components of the system

[1032] 1. Terminal:

[1033] Get:

[1034] Daily electricity usage data is regularly acquired from home smart meters and environmental sensors. For example, electricity usage is measured and stored every 10 minutes.

[1035] Data entry:

[1036] It provides an interface for users to input environmental actions they have taken (e.g., waste sorting, water conservation). Input can be done via a touchscreen or voice input.

[1037] Acquiring emotion data:

[1038] Real-time acquisition of emotional data using devices equipped with an emotion engine (e.g., cameras with facial recognition software).

[1039] Proposal notification:

[1040] The server notifies the user of suggestions sent from the server. The notifications are made in real time and displayed on the user's smart glasses or head-mounted display.

[1041] 2. Server:

[1042] Data management:

[1043] Household electricity usage data, environmental behavior data, and emotional data are received using the HTTPS communication protocol and stored in a database.

[1044] Data analysis:

[1045] The system analyzes the received data to identify household electricity consumption patterns. Points (green score) are assigned based on environmental behavior data.

[1046] Suggestion generation:

[1047] Using a generative AI model (e.g., GPT-4), the system proposes optimal decarbonization actions based on the user's power consumption patterns and emotional data. For example: "Raise the air conditioner's temperature setting by 1 degree to reduce nighttime power consumption."

[1048] Emotion analysis:

[1049] An emotion engine is used to analyze emotional data and optimize suggestions based on the user's emotions. For example, if the user is tired, the suggestion might be "create a relaxing environment" rather than "raise the air conditioner temperature."

[1050] 3. Feedback function:

[1051] input:

[1052] When a user performs a suggested action, feedback is entered into the device. For example, "I raised the air conditioner temperature by 1 degree."

[1053] evaluation:

[1054] The server optimizes its next proposal based on the feedback data. The collected data is processed into statistical data and securely shared with partner companies and power companies.

[1055] Specific example

[1056] For example, if a household uses its air conditioner day and night, its power consumption data is sent to a server. The server identifies peak times and, taking sentiment data into consideration, generates specific suggestions such as "raise the air conditioner's temperature by one degree at night." This suggestion is notified to the user's smart glasses or head-mounted display in real time. If the user accepts the suggestion, their feedback is collected to improve the accuracy of future suggestions.

[1057] Example of a prompt

[1058] "Consider the user's household electricity data and environmental behavior data, and generate a proposal for the most optimal decarbonization actions under the following conditions."

[1059] Power Consumption Patterns: Based on current data, make suggestions regarding energy-saving home appliances.

[1060] Emotional state: If the user is tired, suggest products with relaxation effects.

[1061] Proposal details: Please specify the product you are proposing and the reasons for your proposal.

[1062] In this way, the system integrates and manages household electricity consumption and environmental behavior, utilizes emotional data to provide users with optimized suggestions, and supports effective decarbonization actions.

[1063] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1064] Step 1:

[1065] The device acquires electricity usage data from the home's smart meter and environmental sensors. This data includes the household's electricity consumption and is recorded every 10 minutes.

[1066] Step 2:

[1067] The terminal sends power usage data it acquires to the server using the HTTPS communication protocol. The transmitted data includes a timestamp and is encrypted to ensure security.

[1068] Step 3:

[1069] The server saves the received power usage data to a database. A timestamp is added to maintain data integrity during saving, and data backups are also performed.

[1070] Step 4:

[1071] The terminal provides an interface for users to input environmental behavior data. Users input behavior data (e.g., recycling, water conservation) using a touchscreen or voice input.

[1072] Step 5:

[1073] The device sends the entered environmental behavior data to the server. The user can reconfirm the sent data on a confirmation screen.

[1074] Step 6:

[1075] The server stores the received environmental behavior data in a database and performs analysis. The analysis includes a process of assigning points (green scores) based on the behavior. For example, 50 points might be awarded for recycling.

[1076] Step 7:

[1077] The device acquires user emotion data using a device equipped with an emotion engine. This emotion data includes the user's emotional state (e.g., happy, sad, stressed, etc.).

[1078] Step 8:

[1079] The device sends the acquired emotion data to the server. Encrypted communication is used for transmission to protect user privacy.

[1080] Step 9:

[1081] The server stores the received emotional data in a database and analyzes it using an emotion engine. Based on the analysis results, the user's emotional state is identified.

[1082] Step 10:

[1083] The server uses an AI model generated based on power consumption patterns, environmental behavior data, and emotional data to suggest optimal decarbonization actions to the user. For example, it might generate a specific suggestion such as "raise the air conditioner temperature by 1 degree to reduce power consumption at night."

[1084] Step 11:

[1085] The server generates suggestions and sends them to the user's device. The user is then notified in real time via their smart glasses or head-mounted display.

[1086] Step 12:

[1087] The user performs the suggested action and inputs feedback into the device. For example, they might input, "I raised the air conditioner temperature by 1 degree."

[1088] Step 13:

[1089] The device sends the input feedback to the server. The feedback includes the date and time the action was performed and its result.

[1090] Step 14:

[1091] The server stores the feedback data in a database and uses it to optimize future proposals. The collected feedback data is shared with partner companies and power companies as statistical data.

[1092] At each step, it is clearly explained what data the server, terminal, and user input, what data processing is performed, and what output is obtained. This processing flow enables a system that comprehensively manages the user's power consumption, environmental behavior, and emotional state, supporting optimal decarbonization actions.

[1093] 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.

[1094] 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.

[1095] 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.

[1096] [Third Embodiment]

[1097] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1098] 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.

[1099] 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).

[1100] 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.

[1101] 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.

[1102] 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).

[1103] 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.

[1104] 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.

[1105] 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.

[1106] 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.

[1107] 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.

[1108] 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".

[1109] This invention is a system that uses household electricity data and environmental behavior data to propose optimal decarbonization actions to users. The system consists of a terminal installed in the user's home, a server that analyzes the data and generates proposals, and an interface for notifying the user of the proposals. A specific embodiment of this system is described below.

[1110] System Configuration

[1111] 1. Terminal

[1112] It collects electricity usage data from smart meters and environmental sensors within the user's home.

[1113] It provides an interface for users to input environmental actions they have taken.

[1114] Receives suggestions from the server and notifies the user.

[1115] 2. Server

[1116] It receives and stores power usage data and environmental behavior data transmitted from the terminal.

[1117] The received data is analyzed to identify household electricity consumption patterns.

[1118] The system evaluates users' environmental behavior and assigns them points (green score).

[1119] Using AI generated from electricity data and environmental behavior data, we propose specific decarbonization actions to users.

[1120] We will save the feedback in the database and use it to improve future proposals.

[1121] Program processing (explained in natural language)

[1122] Collection and analysis of power data

[1123] 1. Terminal: Obtains daily electricity usage data from the home's smart meter.

[1124] 2. Terminal: Sends the acquired data to the server.

[1125] 3. Server: Stores the transmitted power usage data in the database.

[1126] 4. Server: Analyzes stored data to identify household power consumption patterns. For example, it extracts peak hours and periods of high consumption.

[1127] Input and evaluation of environmental behavioral data

[1128] 1. User: Inputs their daily environmental actions (e.g., recycled, water-saving, etc.) into the terminal.

[1129] 2. Terminal: Sends the entered data to the server.

[1130] 3. Server: Stores the transmitted environmental behavior data in a database and assigns points (green score) based on the behavior.

[1131] Proposal generation and notification

[1132] 1. Server: Uses AI generated based on power consumption patterns and environmental behavior data to suggest effective decarbonization actions to users.

[1133] 2. Server: For example, it generates specific suggestions such as, "Raise the air conditioner's temperature setting by 1 degree to reduce power consumption at night."

[1134] 3. Server: Sends the generated suggestions to the user's terminal.

[1135] 4. Terminal: Receives proposals and notifies the user.

[1136] Gathering feedback and sharing data

[1137] 1. User: Perform the suggested action and input the result into the terminal.

[1138] 2. Terminal: Sends the input feedback to the server.

[1139] 3. Server: Saves feedback data and uses it for future proposals.

[1140] 4. Server: Shares collected data with companies and power companies to promote decarbonization measures.

[1141] Specific example

[1142] For example, if a household uses its air conditioner day and night, its power consumption data is sent to a server. The server identifies peak times and, using the green score as a reference, suggests raising the air conditioner's temperature setting by one degree at night. This suggestion is notified to the user's device, and feedback on whether the user has taken action is collected to improve the accuracy of future suggestions.

[1143] In this way, the system integrates and manages household electricity consumption and environmental behavior, providing support for users to effectively implement decarbonization actions.

[1144] The following describes the processing flow.

[1145] Step 1:

[1146] Terminal: Periodically acquires electricity usage data from smart meters installed in homes. For example, it measures and stores electricity usage every 10 minutes.

[1147] Step 2:

[1148] Terminal: Sends acquired power usage data to the server. A secure communication protocol (e.g., HTTPS) is used for transmission.

[1149] Step 3:

[1150] Server: Stores the transmitted power usage data in the database. A timestamp is added to the data during storage to maintain data integrity.

[1151] Step 4:

[1152] Server: Analyzes stored data to identify household power consumption patterns. Specifically, it extracts peak hours and high-consumption periods on specific days.

[1153] Step 5:

[1154] User: Inputs daily environmental actions (e.g., waste sorting, water conservation, etc.) into the terminal's interface. Input can be done via touchscreen or voice input.

[1155] Step 6:

[1156] Terminal: Sends the entered environmental behavior data to the server. Immediately after sending, a data confirmation screen is displayed to allow the user to reconfirm the entered information.

[1157] Step 7:

[1158] Server: Stores the submitted environmental action data in a database and assigns points (green score) to each action. For example, 50 points are awarded for recycling.

[1159] Step 8:

[1160] Server: Uses AI generated from power consumption patterns and environmental behavior data to suggest decarbonization actions suitable for the user. The suggestions are customized based on context, time of day, and consumption levels.

[1161] Step 9:

[1162] Server: Sends the generated suggestion (e.g., "Raise the air conditioner's temperature by 1 degree to reduce nighttime power consumption") to the user's terminal.

[1163] Step 10:

[1164] Terminal: Receives proposals and notifies the user. Notifications are delivered via push notifications or alerts.

[1165] Step 11:

[1166] User: Perform the suggested action and input the result into the terminal. For example, input "Raised the air conditioner temperature by 1 degree."

[1167] Step 12:

[1168] Terminal: Sends the entered feedback to the server. The feedback includes the date and time the action was performed and its specific details.

[1169] Step 13:

[1170] Server: Saves feedback data and uses it to improve future proposals. For example, it evaluates implementation rates and effectiveness to optimize the next proposal.

[1171] Step 14:

[1172] Server: The collected data is shared with partner companies and power companies. The data is processed into statistical data and shared while ensuring security.

[1173] Step 15:

[1174] User's device: Displays a list of environmentally friendly products based on their DecaVo score, allowing the user to select from them. Each product includes a detailed description and rating.

[1175] Step 16:

[1176] User: Select an environmentally friendly product from the list and proceed with the purchase. The purchase process is conducted via the device, and the purchase data is sent to the server after confirmation.

[1177] Step 17:

[1178] Server: Saves purchase data to the database and updates the Decabo score. Contribution from product purchases is reflected in the Green score.

[1179] Through the steps outlined above, this system supports users in effectively implementing decarbonization actions.

[1180] (Example 1)

[1181] 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."

[1182] There is a need for a means to integrate and manage household electricity usage data and environmental behavior data to propose effective decarbonization actions to users. Conventional systems often only collect and analyze electricity usage data, lacking sufficient integration with environmental behavior data, resulting in limited concrete suggestions to users. Furthermore, it has been difficult to utilize the results of these suggestions as feedback, making it challenging to improve the accuracy of the suggestions.

[1183] 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.

[1184] In this invention, the server includes means for acquiring household electricity usage data, means for transmitting the acquired electricity usage data to the server, means for analyzing the data acquired by the server and identifying electricity consumption patterns, means for inputting and transmitting user environmental behavior data to the server, means for analyzing the user's environmental behavior data and assigning evaluation points, means for generating suggestions using an artificial intelligence model generated based on electricity usage data and environmental behavior data, means for transmitting suggestions from the server to the user's terminal, means for the user to perform the suggested actions and input feedback, means for collecting and accumulating data and reflecting the analysis results as feedback in the next suggestion, and means for sharing the collected data and promoting decarbonization measures. This makes it possible to integrate and manage electricity usage data and environmental behavior data and propose individually optimized decarbonization actions to users. Furthermore, by utilizing the results of the suggestions as feedback, it is possible to improve the accuracy of the suggestions.

[1185] "Household electricity usage data" refers to information that shows the amount of electricity consumed within a household and its usage patterns by time of day.

[1186] "Means of acquisition" refers to devices or methods for automatically collecting data using sensors, smart meters, etc.

[1187] A "server" refers to a computer system used to receive, analyze, and store power data and environmental behavior data.

[1188] "Means of analysis" refer to algorithms and software that process collected data to identify power consumption patterns.

[1189] "Environmental behavior data" refers to information about environmental protection actions taken by users (e.g., recycling, water conservation).

[1190] "Evaluation points" are values ​​used to quantitatively evaluate a user's environmental behavior and are indicators of the user's contribution.

[1191] A "generating artificial intelligence model" is a machine learning model or algorithm used to generate suggestions for users based on collected data.

[1192] A "suggestion" is information that indicates specific actions the user should take (e.g., adjusting the air conditioner's temperature settings).

[1193] "Means of input" refers to the interface (e.g., a smartphone application) that users use to input data or feedback.

[1194] "Feedback" is information used to report the results of a user taking a suggested action.

[1195] "Integrated management" is a method of centrally managing electricity usage data and environmental behavior data, and analyzing them in relation to each other.

[1196] "Means of promotion" refers to the methods and mechanisms for executing a plan and achieving its goals.

[1197] This invention is a system that collects and analyzes household electricity usage data and environmental behavior data, and proposes optimal decarbonization actions to the user. Specifically, it includes a terminal installed in the home, a server that analyzes the data and generates proposals, and an interface for notifying the user of the proposals. A specific embodiment of this system is described below.

[1198] System Configuration

[1199] 1. Terminal

[1200] The system collects electricity usage data from smart meters and environmental sensors installed in the user's home.

[1201] It provides an interface for users to input their environmental actions. Specifically, it uses a smartphone application or a web interface.

[1202] Receives suggestions from the server and notifies the user.

[1203] 2. Server

[1204] It is equipped with a database for receiving and storing power usage data and environmental behavior data transmitted from terminals.

[1205] The received data is analyzed to identify the electricity consumption patterns of each household. For example, it is possible to analyze increases and decreases in consumption during specific time periods.

[1206] It has an evaluation algorithm that assesses users' environmental behavior and assigns points (green score).

[1207] Using an AI model generated based on electricity data and environmental behavior data, we propose specific decarbonization actions to users.

[1208] The proposal and feedback will be saved in the database and used for future proposals.

[1209] 3. Interface

[1210] An interface for users to review and take suggested actions. Specifically, this can be achieved through application push notifications, email, or a web-based dashboard.

[1211] It also provides an interface for users to perform suggested actions and input the results as feedback.

[1212] Specific example

[1213] For example, if a household uses its air conditioner day and night, its power usage data is sent to a server. The server analyzes the received data and discovers that power consumption is particularly high at night. At the same time, it also considers the user's green score for environmental actions taken. Based on this, a generative AI model generates a specific suggestion, such as "raise the air conditioner's temperature setting by 1 degree at night." This suggestion is notified to the user's device, and feedback on whether the user has taken action is also collected to improve the accuracy of future suggestions.

[1214] Examples of prompts for generative AI models

[1215] "Based on household electricity usage data and user environmental behavior data, please propose effective decarbonization actions to users. Specifically, please provide detailed suggestions regarding adjustments to air conditioner settings and usage times."

[1216] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1217] Step 1:

[1218] Terminal: Acquires daily electricity usage data from home smart meters and environmental sensors.

[1219] Input: Power usage data from smart meters and environmental sensors.

[1220] Output: Acquired power usage data

[1221] Specific operation: Collects power consumption information transmitted from smart meters at regular time intervals (e.g., every 30 minutes). Similarly, collects data such as temperature and humidity from environmental sensors.

[1222] Step 2:

[1223] Terminal: Transmits acquired power usage data to the server at regular intervals.

[1224] Input: Acquired power usage data

[1225] Output: Power usage data sent to the server

[1226] Specific operation: Collected power usage data is sent to a server in batch processing or in real time. Data is typically transmitted via Wi-Fi or a wired network.

[1227] Step 3:

[1228] Server: Receives transmitted power usage data and stores it in the database.

[1229] Input: Power usage data transmitted from the terminal.

[1230] Output: Power usage data stored in the database

[1231] Specific operation: After initial processing of received data (e.g., data cleansing, normalization), it is saved to the database as time-series data.

[1232] Step 4:

[1233] Server: Analyzes stored power usage data to identify power consumption patterns for each household.

[1234] Input: Power usage data stored in the database

[1235] Output: Analyzed power consumption patterns

[1236] Specific operation: Use data analysis algorithms to identify significant fluctuations in power consumption during specific time periods (e.g., daytime and nighttime). Extract peak consumption levels and distinct consumption patterns.

[1237] Step 5:

[1238] User: Inputs their daily environmental actions (e.g., recycled, water-saving) into the application on their device.

[1239] Input: User-generated environmental behavior data

[1240] Output: Environmental behavior data entered into the terminal

[1241] Specific operation: The user enters the environmental actions they have taken into a form using a smartphone application or web interface.

[1242] Step 6:

[1243] Terminal: Sends the entered environmental behavior data to the server.

[1244] Input: User-generated environmental behavior data

[1245] Output: Environmental behavior data sent to the server

[1246] Specific operation: Input data is sent to the server in real time. Data is usually transferred securely using an API.

[1247] Step 7:

[1248] Server: Stores the transmitted environmental behavior data in a database and assigns points (green score) based on the behavior.

[1249] Input: Environmental behavior data sent to the server

[1250] Output: Environmental behavior data saved in the database and assigned points.

[1251] Specific operation: Received environmental behavior data is stored in a database, and an algorithm is applied to calculate a green score based on the importance of each behavior.

[1252] Step 8:

[1253] Server: Uses an AI model generated based on power consumption patterns and environmental behavior data to suggest effective decarbonization actions to users.

[1254] Input: Power consumption patterns and environmental behavior data

[1255] Output: Generated proposals

[1256] Specific operation: Using a generative AI model, the system generates optimal action suggestions for the user from accumulated data (e.g., adjusting the air conditioner temperature setting, changing the usage time).

[1257] Step 9:

[1258] Server: Sends the generated suggestions to the user's terminal.

[1259] Input: Generated proposal

[1260] Output: Suggestions sent to the user's terminal

[1261] Specific actions: The generated suggestions are sent to the user's device via the API. They are displayed as push notifications or in-app messages.

[1262] Step 10:

[1263] User: Perform the suggested action and input the result as feedback into the device.

[1264] Input: Result of the proposed action

[1265] Output: Feedback data entered into the terminal

[1266] Specific action: The user performs the suggested action and inputs the result (e.g., increased the air conditioner temperature by 1 degree) as feedback to the application.

[1267] Step 11:

[1268] Terminal: Sends the input feedback to the server.

[1269] Input: Feedback data

[1270] Output: Feedback data sent to the server

[1271] Specific action: Send feedback data to the server in real time.

[1272] Step 12:

[1273] Server: Saves feedback data and uses it for future proposals.

[1274] Input: Feedback data sent to the server

[1275] Output: Next proposals utilizing feedback data

[1276] Specific operation: Feedback data is saved to a database and reflected in the next proposal generation algorithm to improve the accuracy of the proposals.

[1277] Step 13:

[1278] Server: Shares collected data with companies and power companies to promote decarbonization measures.

[1279] Input: Power usage data and environmental activity data stored on the server

[1280] Output: Shared data

[1281] Specific actions: Statistically process data and share it with businesses and power companies. This will provide foundational data to promote widespread decarbonization measures.

[1282] (Application Example 1)

[1283] 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."

[1284] Modern energy efficiency issues involve challenges in both optimizing electricity consumption in homes and stores and promoting environmentally conscious behavior. In particular, brick-and-mortar stores often have high electricity consumption, hindering cost reduction and environmental impact mitigation. Furthermore, there is a lack of concrete and effective methods to raise employee environmental awareness and translate it into concrete actions. Therefore, these challenges need to be addressed by integrating and managing store electricity usage data and environmental behavior data, and by providing specific suggestions to employees.

[1285] 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.

[1286] In this invention, the server includes means for acquiring store power usage data and proposing actions to reduce power consumption, means for evaluating employee environmental behavior data and awarding points, and means for analyzing the acquired data and identifying power consumption patterns. This makes it possible to optimize power consumption in stores and propose ways to raise employees' environmental awareness.

[1287] Definitions of important words

[1288] "Household electricity usage data"

[1289] This refers to numerical information that shows the usage status of all electrical appliances and equipment used in a household.

[1290] "Means of acquisition"

[1291] This refers to devices and technologies used to measure and collect data.

[1292] "server"

[1293] A computer system is a computer system that receives, stores, and analyzes data, and then transmits it to other devices.

[1294] "Power consumption patterns"

[1295] This refers to the periodic fluctuations or trends in electricity usage in a particular household or store.

[1296] "Environmental behavior data"

[1297] This refers to records and reported data of environmentally friendly actions taken by users and employees.

[1298] "Means of awarding points"

[1299] This refers to systems and technologies that evaluate the environmental behavior of users and employees and award points as incentives.

[1300] “Generation AI”

[1301] This refers to artificial intelligence that automatically generates appropriate suggestions for users and employees based on acquired data.

[1302] "Means for generating proposals"

[1303] This refers to a technology that analyzes acquired data and creates suggestions to encourage effective actions from users and employees.

[1304] "Method for sending proposals"

[1305] This refers to communication technologies and systems used to notify users' or employees' terminals of proposals from a server.

[1306] "Methods for providing feedback"

[1307] This refers to an interface or system for users or employees to report the results of performing suggested actions.

[1308] "Data sharing"

[1309] This refers to coordinating and communicating collected data with other relevant systems and organizations.

[1310] "Environmentally friendly products"

[1311] This refers to products that are energy-efficient and designed to reduce their environmental impact.

[1312] "Actions to reduce electricity consumption"

[1313] This refers to specific actions or operations taken to reduce electricity consumption.

[1314] "Store electricity usage data"

[1315] This refers to numerical information that shows the usage status of all electrical equipment and facilities in a store.

[1316] "Employee environmental behavior data"

[1317] This refers to records and reports of environmentally friendly actions taken by store employees.

[1318] Modes for carrying out the invention

[1319] This invention is a system that integrates and manages electricity usage data and environmental behavior data in homes and stores, and proposes effective environmental actions. The embodiments for carrying out this invention are described below.

[1320] System Configuration

[1321] 1. Terminal

[1322] Hardware: Smartphones, tablets

[1323] Software: Applications for data acquisition and input

[1324] Features: Acquisition of electricity usage data for homes and stores, input of user and employee environmental behavior data, receiving and displaying suggestions, input of feedback.

[1325] 2. Sensor

[1326] Smart meter: Real-time acquisition of electricity usage data

[1327] Environmental sensors: Acquisition of ambient environmental data (temperature, humidity, etc.)

[1328] 3. Server

[1329] Hardware: Cloud servers (e.g., AWS, Google Cloud)

[1330] Software: Data analysis platform, database (Firebase Realtime Database), generative AI (GPT-3, OpenAI)

[1331] Functions: Receiving, storing, analyzing, generating suggestions, and sending data.

[1332] Program processing

[1333] Data collection and transmission

[1334] Terminal: Acquires real-time electricity usage data from homes and stores using smart meters and environmental sensors, and transmits it to a cloud server.

[1335] Server: Receives transmitted power usage data and stores it in a database. Analyzes the retrieved data to identify consumption patterns.

[1336] Input and evaluation of environmental behavioral data

[1337] Users and employees: Input their daily environmental actions (e.g., recycling, energy saving) into a terminal application.

[1338] Terminal: Sends the entered environmental behavior data to the server.

[1339] Server: Stores the transmitted environmental behavior data in a database and assigns points (green score).

[1340] Proposal generation and notification

[1341] Server: Based on power consumption patterns and environmental behavior data, it uses generated AI to propose specific decarbonization actions.

[1342] Server: Generates suggestions and sends them to user or employee terminals. For example, it generates specific suggestions such as "raise the air conditioner temperature by 1 degree."

[1343] Terminal: Receives generated proposals and notifies users and employees.

[1344] Gathering feedback and sharing data

[1345] Users and employees: Perform the suggested action and enter the result into the terminal.

[1346] Terminal: Sends the input feedback to the server.

[1347] Server: The server stores feedback data and uses it to inform future proposals. It also shares the collected data with relevant companies and service providers to promote decarbonization measures.

[1348] Specific example

[1349] For example, electricity usage data from a store is sent to a server, and analysis reveals peak consumption during specific time periods. The server uses AI to generate suggestions for employees, such as "reduce daytime lighting by 50%", and notifies them via their terminals. When employees implement the suggestions and report the results via their terminals, the server receives feedback and incorporates it into future suggestions. This optimizes the store's electricity consumption and reduces its environmental impact.

[1350] Examples of input prompts for a generative AI model

[1351] "Based on store electricity usage data and environmental behavior data, propose ways to optimize store operations. Specifically, generate suggestions for actions to reduce electricity consumption (e.g., adjusting air conditioner temperatures, dimming lights, etc.) and notify employees accordingly."

[1352] In this way, the present invention provides integrated management and proposals for electricity use and environmental behavior in homes and stores, and supports users and employees in effectively implementing decarbonization actions.

[1353] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1354] Program processing steps

[1355] Step 1:

[1356] Collecting power data

[1357] The terminal acquires real-time electricity usage data from homes and stores using smart meters and environmental sensors.

[1358] Input: Measurements from smart meters and environmental sensors.

[1359] Output: Acquired power data.

[1360] Specific operation: The smart meter measures electricity usage every minute and sends that data to the terminal. The terminal then sends this data to the cloud server in the appropriate format.

[1361] Step 2:

[1362] Analysis and storage of power data

[1363] The server receives power data transmitted from the terminal and stores it in a database. It then analyzes the stored data to identify power consumption patterns.

[1364] Input: Acquired power data.

[1365] Output: Analyzed power consumption pattern.

[1366] Specific operation: The server stores power usage data in a database and runs an analysis algorithm to identify daily consumption patterns. For example, it calculates the average consumption for each time period to extract peak hours.

[1367] Step 3:

[1368] Input of environmental behavioral data

[1369] Users and employees input their daily environmental behaviors into a terminal application.

[1370] Input: Environmental behavior data (e.g., recycling, energy-saving behavior).

[1371] Output: Input environmental behavior data.

[1372] Specific operation: Users and employees select and input specific environmental actions using smartphone or tablet apps. This data is transmitted to a cloud server in real time.

[1373] Step 4:

[1374] Evaluation and point allocation of environmental behavior data

[1375] The server receives the transmitted environmental behavior data, stores it in a database, and then assigns points (green scores) based on the content of the behavior.

[1376] Input: Entered environmental behavior data.

[1377] Output: Points (Green Score).

[1378] Specific operation: The server scores environmental behavior data based on an evaluation algorithm and stores the results in a database. For example, points are added based on the frequency of recycling.

[1379] Step 5:

[1380] Proposal generation

[1381] The server uses generative AI to propose specific decarbonization actions based on power consumption patterns and environmental behavior data.

[1382] Input: Power consumption patterns, environmental behavior data.

[1383] Output: Generated proposals.

[1384] Specific operation: The server inputs the analyzed data into the generating AI and generates optimal suggestions based on the prompt. For example, the prompt might be, "Please suggest ways to optimize store operations based on store power usage data and environmental behavior data."

[1385] Step 6:

[1386] Notification of proposal

[1387] The server sends the generated suggestions to the user's or employee's device and provides notifications.

[1388] Input: Generated suggestions.

[1389] Output: Notification to the terminal.

[1390] Specific operation: The server sends the suggestion as a push notification to the device. The device receives the notification and displays it in the app.

[1391] Step 7:

[1392] Gathering feedback

[1393] Users and employees perform the suggested actions and input the results into the terminal.

[1394] Input: Feedback data (results of action execution).

[1395] Output: Input feedback data.

[1396] Specific operation: After performing the suggested actions, users and employees input the results and effects into a terminal application. This data is sent to a cloud server in real time.

[1397] Step 8:

[1398] Feedback analysis and data sharing

[1399] The server analyzes the feedback data and uses it to improve the accuracy of future suggestions. It also shares the collected data with relevant companies and service providers.

[1400] Input: The input feedback data.

[1401] Output: Analysis results and shared data.

[1402] Specific operation: The server analyzes the feedback data and runs an algorithm to evaluate the effectiveness of the suggestions. The analysis results are stored in a database, and data for sharing is generated.

[1403] 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.

[1404] This invention is a system that combines a generative AI and an emotion engine using household electricity data and environmental behavior data to propose optimal decarbonization actions to the user. The system includes the following main components:

[1405] System Configuration

[1406] 1. Terminal

[1407] Electricity usage data is collected from smart meters and environmental sensors installed in homes.

[1408] It provides an interface for users to input environmental actions they have taken.

[1409] It provides an emotion input interface for recognizing the user's emotions.

[1410] Receives suggestions from the server and notifies the user.

[1411] 2. Server

[1412] It receives and stores power usage data, environmental behavior data, and emotional data transmitted from the device.

[1413] The received data is analyzed to identify household electricity consumption patterns.

[1414] The system evaluates users' environmental behavior and assigns them points (green score).

[1415] Using AI generated from electricity usage data and environmental behavior data, we propose specific decarbonization actions to users.

[1416] The emotion engine analyzes user emotions and optimizes suggestions accordingly.

[1417] We will save the feedback in the database and use it to improve future proposals.

[1418] Program processing (explained in natural language)

[1419] Collection and analysis of power data

[1420] 1. Terminal: Periodically acquires daily electricity usage data from the home's smart meter. For example, it measures and stores electricity usage every 10 minutes.

[1421] 2. Terminal: Sends acquired power usage data to the server. A secure communication protocol (e.g., HTTPS) is used for transmission.

[1422] 3. Server: Stores the transmitted power usage data in a database. A timestamp is added to the data during storage to maintain data integrity.

[1423] 4. Server: Analyzes stored data to identify household power consumption patterns. Specifically, it extracts peak hours and high-consumption periods on specific days.

[1424] Input and evaluation of environmental behavioral data

[1425] 1. User: Inputs daily environmental actions (e.g., waste sorting, water conservation, etc.) into the terminal's interface. Touchscreen or voice input can be used for input.

[1426] 2. Terminal: Sends the entered environmental behavior data to the server. Immediately after sending, a data confirmation screen is displayed to allow the user to reconfirm the entered information.

[1427] 3. Server: Stores the submitted environmental behavior data in a database and assigns points (green score) based on the behavior. For example, 50 points are awarded for recycling.

[1428] Proposal generation and notification

[1429] 1. Server: Uses AI generated based on power consumption patterns and environmental behavior data to suggest effective decarbonization actions to users. Suggestions are customized based on context, time of day, and consumption levels.

[1430] 2. Server: For example, it generates specific suggestions such as, "Raise the air conditioner's temperature setting by 1 degree to reduce power consumption at night."

[1431] 3. Server: Sends the generated suggestions to the user's terminal.

[1432] 4. Device: Receives proposals and notifies the user. Notifications are sent via push notifications or alerts.

[1433] Collection and analysis of emotional data

[1434] 1. User: Enter your daily emotional state (e.g., happy, sad, stressed, etc.) into the device's emotion input interface.

[1435] 2. Terminal: Sends emotional data to the server. Encrypted communication is used for transmission to protect user privacy.

[1436] 3. Server: Stores emotion data in a database and analyzes it using an emotion engine. Based on the analysis results, it generates suggestions tailored to the user's state.

[1437] Gathering feedback and sharing data

[1438] 1. User: Perform the suggested action and input the result into the terminal. For example, input "Raised the air conditioner temperature by 1 degree."

[1439] 2. Terminal: Sends the entered feedback to the server. The feedback includes the date and time the action was performed and its specific details.

[1440] 3. Server: Saves feedback data and uses it to improve future proposals. For example, it evaluates implementation rates and effectiveness to optimize the next proposal.

[1441] 4. Server: The collected data is shared with partner companies and power companies. The data is processed into statistical data and shared while ensuring security.

[1442] Specific example

[1443] For example, if a household uses its air conditioner day and night, its power consumption data is sent to a server. The server identifies peak times and, taking sentiment data into consideration, suggests raising the air conditioner's temperature setting by one degree at night. This suggestion is notified to the user's device, and feedback on whether the user acted on it is also collected to improve the accuracy of future suggestions.

[1444] If emotional data is analyzed as indicating "fatigue" or "stress," the suggestion will be customized to best suit the user's condition, such as "set the room temperature to a comfortable level so you can go to bed earlier," rather than simply "raise the air conditioner temperature."

[1445] In this way, the system integrates and manages household electricity consumption and environmental behavior, utilizes emotional data to provide users with optimized suggestions, and supports effective decarbonization actions.

[1446] The following describes the processing flow.

[1447] Step 1:

[1448] Terminal: Acquires electricity usage data from the home's smart meter every 10 minutes. For example, it records the amount of electricity consumed during each time period.

[1449] Step 2:

[1450] Terminal: Sends acquired power usage data to the server at regular intervals. A secure communication protocol (HTTPS) is used for transmission.

[1451] Step 3:

[1452] Server: Stores transmitted power usage data in a database. The data is stored with a timestamp to maintain consistency.

[1453] Step 4:

[1454] Server: Analyzes stored data to identify household power consumption patterns. Specifically, it extracts peak hours and high-consumption periods on specific days.

[1455] Step 5:

[1456] User: Inputs daily environmental actions (e.g., waste sorting, water conservation, energy saving) into the terminal's interface. Touchscreen or voice recognition can be used for input.

[1457] Step 6:

[1458] Terminal: Transmits entered environmental behavior data to the server in real time. A confirmation screen is displayed immediately after user input, allowing for reconfirmation.

[1459] Step 7:

[1460] Server: Stores the submitted environmental action data in a database and assigns points (green score) to each action. For example, 50 points are awarded for recycling.

[1461] Step 8:

[1462] User: Enter your current emotional state (e.g., happy, sad, stressed) into the device's emotion input interface. This can also utilize facial recognition or voice input.

[1463] Step 9:

[1464] Device: Sends emotional data to the server. Encrypted communication is used to send emotional data and protect user privacy.

[1465] Step 10:

[1466] Server: Stores emotional data in a database and analyzes it using an emotion engine. Based on the analysis results, it identifies the user's current emotional state.

[1467] Step 11:

[1468] Server: Uses AI generated based on power consumption patterns, environmental behavior data, and emotional data to suggest effective decarbonization actions to users. The suggestions are customized according to the user's situation and emotions.

[1469] Step 12:

[1470] Server: For example, it generates specific suggestions such as "raise the air conditioner's temperature by one degree to reduce nighttime power consumption" or "take a walk in a green space to reduce stress."

[1471] Step 13:

[1472] Server: Sends the generated suggestions to the user's terminal.

[1473] Step 14:

[1474] Terminal: Receives proposals and notifies the user. Notifications are displayed as push notifications or alerts.

[1475] Step 15:

[1476] User: Perform the suggested action and input the result into the terminal. For example, report, "Raised the air conditioner temperature by 1 degree."

[1477] Step 16:

[1478] Terminal: Sends the entered feedback to the server. The feedback includes the date and time the action was performed and its specific details.

[1479] Step 17:

[1480] Server: Saves and analyzes feedback data to improve future suggestions. For example, it evaluates the implementation rate and effectiveness of actions to optimize the next suggestion.

[1481] Step 18:

[1482] Server: Shares collected data with companies and power companies. The data is processed into statistical information and provided while ensuring security.

[1483] Step 19:

[1484] Terminal: Lists and displays environmentally friendly products based on their DecaVo score to the user. Products include detailed descriptions and user ratings.

[1485] Step 20:

[1486] User: Select an environmentally friendly product from the list and proceed with the purchase. The purchase process is conducted through secure online payment.

[1487] Step 21:

[1488] Server: Saves purchase data to the database and updates the Decabo score. Contribution from product purchases is reflected in the Green score.

[1489] Through the steps described above, this system considers the user's power consumption patterns and emotional state, proposes optimal and effective decarbonization actions, and supports the realization of a sustainable society.

[1490] (Example 2)

[1491] 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."

[1492] While conventional home energy management systems collected and analyzed electricity usage data, they were unable to integrate environmental behavior and emotional data to provide optimal recommendations. Furthermore, providing personalized feedback and suggestions to users in real time was difficult, resulting in insufficient efforts to improve user motivation and encourage continuous environmental improvement behaviors.

[1493] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for storing household power usage data with a timestamp and identifying household power consumption patterns, means for analyzing the user's environmental behavior data and assigning points, and means for proposing optimal decarbonization actions using an AI model generated based on power usage data and environmental behavior data. This enables the integrated analysis and evaluation of the user's power consumption patterns, environmental behavior, and emotional data, and makes it possible to propose specific decarbonization actions optimized for the user.

[1494] "Electricity usage data" refers to data that records household electricity consumption on an hourly basis.

[1495] A "secure communication protocol" is a means of communication used to ensure the security of data transmission. Specifically, HTTPS is an example of such a protocol.

[1496] A "timestamp" is information that records the date and time when data was acquired.

[1497] "Power consumption patterns" refer to data that shows trends and characteristics of power usage during specific time periods or dates.

[1498] "Environmental behavior data" refers to data that records users' daily actions taken to protect the environment. Specifically, this includes information such as waste sorting and water conservation practices.

[1499] A "generative AI model" is a system that generates optimal suggestions or predictions from specific input data based on artificial intelligence algorithms.

[1500] "Feedback" refers to information about the results and circumstances of a user's actions after performing a suggested action.

[1501] "Emotional data" refers to data that records a user's emotional state (e.g., happy, sad, stressed, etc.).

[1502] "Points" are rewards or scores awarded to users for their environmental actions.

[1503] A "notification" is a means of communicating information from a server to a user, and is usually done in the form of push notifications or alerts on the device.

[1504] "Data sharing" refers to the act of securely sharing collected data as statistical information with partner companies, power companies, and other relevant parties.

[1505] "Listing" refers to displaying products or information in a list format based on specific criteria.

[1506] This invention is a system that utilizes household electricity usage data and environmental behavior data, and combines a generative AI model and an emotion engine to propose optimal decarbonization actions to users. This system includes a smart meter for acquiring household electricity usage data, a terminal for inputting the user's environmental behavior, and a server for analyzing and storing the data. The following describes each component of this invention and its operation.

[1507] Components

[1508] 1. Smart meters and sensors: These are installed in homes and periodically collect electricity usage data every 10 minutes. For example, they collect data such as "10:00 AM - 0.45kWh" and "10:10 AM - 0.50kWh".

[1509] 2. Terminal: An interface for users to input environmental actions within the home (such as waste sorting and water conservation). It can use a touchscreen or voice input. For example, one might input, "Today I sorted my trash."

[1510] 3. Server: Stores and analyzes collected data and generates suggestions. The server stores the data in a database with timestamps and identifies power consumption patterns. For example, it stores the data with a timestamp such as "2023-10-05 10:10:00 - 0.50kWh". Furthermore, it uses a secure communication protocol (HTTPS) to exchange data.

[1511] 4. Generative AI Model: The generative AI model on the server proposes optimal decarbonization actions based on power consumption data and environmental behavior data. For example, it generates suggestions such as "Raise the air conditioner's temperature setting by 1 degree to reduce power consumption at night."

[1512] 5. Emotion Engine: Analyzes user emotion data and optimizes suggestions. Emotion data is transmitted to the server via encrypted communication and analyzed. For example, if a user inputs "I'm stressed," the system will make suggestions such as "Adjust the temperature setting for relaxation."

[1513] Specific example

[1514] For example, if a household uses its air conditioner day and night, its power consumption data is sent from the smart meter to a server. The server identifies peak times and, taking emotional data into consideration, suggests "raising the air conditioner's temperature setting by one degree at night." This suggestion is notified to the user's device, and feedback is collected on whether the user has taken action. Furthermore, if the user's emotional data is analyzed as "fatigue" or "stress," the suggestion is customized from "raising the air conditioner's temperature setting" to "setting the room temperature to a comfortable level so you can go to bed earlier."

[1515] Example of a prompt

[1516] Examples of prompt statements to input into a generative AI model include the following:

[1517] "Based on household electricity consumption data and environmental behavior data, propose the optimal decarbonization actions. If the user's emotional state is 'fatigue,' the proposal should be especially considerate of the user's physical condition."

[1518] With the above configuration and operation, the present invention is a system that comprehensively manages the user's power consumption patterns and environmental behavior, and utilizes emotional data to support effective and personalized decarbonization actions.

[1519] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1520] Step 1:

[1521] Terminal: Acquires electricity usage data from smart meters. For example, it acquires data such as "10:00 AM - 0.45kWh" and "10:10 AM - 0.50kWh" as electricity consumption every 10 minutes. The input is real-time data from the smart meter, and the output is electricity usage data temporarily stored in the terminal.

[1522] Step 2:

[1523] Terminal: Sends acquired power usage data to the server. A secure communication protocol (e.g., HTTPS) is used for this transmission. The input is temporarily stored power usage data, and the output is the data sent to the server. Specifically, the terminal encodes the data and sends it to the server using the HTTPS protocol.

[1524] Step 3:

[1525] Server: Stores transmitted power usage data in a database. During the storage process, a timestamp is added to the data. The input is the raw power usage data sent to the server, and the output is the timestamped data stored in the database. Specifically, the server uses a database management system to organize and store the data.

[1526] Step 4:

[1527] Server: Analyzes data stored in the database to identify power consumption patterns. Input is time-stamped power usage data, and output is power consumption patterns identified based on the analysis (e.g., peak hours and high-consumption periods). Specifically, the server uses statistical analysis software to extract consumption patterns.

[1528] Step 5:

[1529] User: Inputs daily environmental actions (e.g., waste sorting, water conservation) into the terminal's interface. Input can be done using a touchscreen or voice input. The input is raw environmental action data from the user, and the output is environmental action data temporarily stored as input data within the terminal.

[1530] Step 6:

[1531] Terminal: Sends the entered environmental behavior data to the server. A data confirmation screen is displayed to allow the user to reconfirm the entered information. The input is temporarily stored environmental behavior data, and the output is the data sent to the server. Specifically, the terminal encodes the data and sends it to the server using a secure communication protocol.

[1532] Step 7:

[1533] Server: Stores the submitted environmental behavior data in a database and assigns points (green scores). The input is the raw environmental behavior data sent to the server, and the output is the data with points attached, stored in the database. Specifically, the server calculates the score using a point-assigning algorithm and adds it to the database.

[1534] Step 8:

[1535] Server: Uses a generative AI model based on power consumption patterns and environmental behavior data to suggest effective decarbonization actions to the user. The input is power consumption pattern data and environmental behavior data stored on the server, and the output is specific suggestions generated by the generative AI model. Specifically, the server sends the prompt example "Please tell me effective actions to reduce tonight's peak power consumption" to the generative AI model and receives suggestions.

[1536] Step 9:

[1537] Server: Sends the generated proposals to the user's terminal. The input is the generated decarbonization action proposals, and the output is the content of the proposals sent to the user's terminal. Specifically, the server encodes the proposals and sends them to the terminal using a secure communication protocol.

[1538] Step 10:

[1539] Terminal: Receives suggestions and notifies the user. Notifications are made via push notifications or alerts. Input is suggestion data received from the server, and output is a notification message displayed to the user. Specifically, the terminal uses a notification API to display suggestions to the user.

[1540] Step 11:

[1541] User: Inputs their daily emotional state (e.g., happy, sad, stressed) into the device's emotion input interface. The input is the user's raw emotional data, and the output is the emotional data temporarily stored as input data within the device.

[1542] Step 12:

[1543] The terminal sends emotion data to the server. Encrypted communication is used for transmission to protect user privacy. The input is temporarily stored emotion data, and the output is the data sent to the server. Specifically, the terminal encodes the data and sends it to the server using an encryption protocol.

[1544] Step 13:

[1545] Server: Stores emotional data in a database and analyzes it using an emotion engine. The input is raw emotional data sent to the server, and the output is the analysis result. Specifically, the server uses an emotion analysis algorithm to obtain data corresponding to the user's state.

[1546] Step 14:

[1547] Server: Executes the proposed action and inputs the result into the terminal. The input is user feedback data, and the output is the feedback data temporarily stored as input data within the terminal.

[1548] Step 15:

[1549] Terminal: Sends the input feedback to the server. The input is temporarily stored feedback data, and the output is the data sent to the server. Specifically, the terminal encodes the data and sends it to the server using a secure communication protocol.

[1550] Step 16:

[1551] Server: Stores feedback data and uses it for future proposals. Input is raw feedback data sent to the server, and output is evaluation data stored in the database. Specifically, the server uses an evaluation algorithm to analyze the data in order to optimize the next proposal.

[1552] (Application Example 2)

[1553] 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."

[1554] In recent years, systems that optimize household electricity consumption and promote environmentally friendly behavior have become indispensable. However, conventional systems have been limited to collecting and analyzing electricity consumption data and environmental behavior data, and have suffered from low accuracy in recommendations because they do not adequately consider the user's emotional state or real-time feedback. Furthermore, if the suggested products or services do not match the user's emotional state, they are not well accepted, and this does not lead to effective decarbonization behavior.

[1555] 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. In this invention, the server includes means for acquiring household electricity usage data, means for analyzing environmental behavior data and assigning points, means for acquiring and analyzing user emotion data, means for generating suggestions according to the emotional state using a generating AI, means for transmitting the suggestions to the user's terminal, means for the user to perform the suggested actions and input feedback, and means for sharing data and listing environmentally friendly products and displaying them to the user. This makes it possible to propose optimal decarbonization actions that take into account the user's electricity consumption patterns and emotional state.

[1556] "Household electricity usage data" refers to data that shows the amount and patterns of electricity consumption used within a household.

[1557] A "server" is a computer system that collects, analyzes, stores, and notifies users of data.

[1558] "Generative AI" is an artificial intelligence model that generates optimal suggestions for users based on electricity usage data and environmental behavior data.

[1559] "Emotional data" refers to data that indicates a user's emotional state (for example, happy, sad, stressed, etc.).

[1560] An "emotion engine" is an algorithm that analyzes emotional data and evaluates the user's emotional state.

[1561] "Environmental behavior data" refers to data that shows environmentally friendly actions taken by users (e.g., recycling, waste sorting, energy saving, etc.).

[1562] "Means of awarding points" refers to a function that evaluates a user's environmental behavior and awards the user points (green score) commensurate with that behavior.

[1563] A "terminal" is a device used by a user (e.g., a smartphone, tablet, smart glasses, etc.) that provides an interface with the system.

[1564] "Feedback" refers to the process of a user performing an action suggested by the system and providing feedback on the results.

[1565] Modes for carrying out the invention

[1566] This invention is a system that analyzes household electricity data, environmental behavior data, and emotional data, and combines a generative AI and an emotion engine to propose optimal decarbonization actions to the user. This system will be implemented as a virtual store application called "Green Shopping Assistant" using smart glasses or a head-mounted display.

[1567] Key components of the system

[1568] 1. Terminal:

[1569] Get:

[1570] Daily electricity usage data is regularly acquired from home smart meters and environmental sensors. For example, electricity usage is measured and stored every 10 minutes.

[1571] Data entry:

[1572] It provides an interface for users to input environmental actions they have taken (e.g., waste sorting, water conservation). Input can be done via a touchscreen or voice input.

[1573] Acquiring emotion data:

[1574] Real-time acquisition of emotional data using devices equipped with an emotion engine (e.g., cameras with facial recognition software).

[1575] Proposal notification:

[1576] The server notifies the user of suggestions sent from the server. The notifications are made in real time and displayed on the user's smart glasses or head-mounted display.

[1577] 2. Server:

[1578] Data management:

[1579] Household electricity usage data, environmental behavior data, and emotional data are received using the HTTPS communication protocol and stored in a database.

[1580] Data analysis:

[1581] The system analyzes the received data to identify household electricity consumption patterns. Points (green score) are assigned based on environmental behavior data.

[1582] Suggestion generation:

[1583] Using a generative AI model (e.g., GPT-4), the system proposes optimal decarbonization actions based on the user's power consumption patterns and emotional data. For example: "Raise the air conditioner's temperature setting by 1 degree to reduce nighttime power consumption."

[1584] Emotion analysis:

[1585] An emotion engine is used to analyze emotional data and optimize suggestions based on the user's emotions. For example, if the user is tired, the suggestion might be "create a relaxing environment" rather than "raise the air conditioner temperature."

[1586] 3. Feedback function:

[1587] input:

[1588] When a user performs a suggested action, feedback is entered into the device. For example, "I raised the air conditioner temperature by 1 degree."

[1589] evaluation:

[1590] The server optimizes its next proposal based on the feedback data. The collected data is processed into statistical data and securely shared with partner companies and power companies.

[1591] Specific example

[1592] For example, if a household uses its air conditioner day and night, its power consumption data is sent to a server. The server identifies peak times and, taking sentiment data into consideration, generates specific suggestions such as "raise the air conditioner's temperature by one degree at night." This suggestion is notified to the user's smart glasses or head-mounted display in real time. If the user accepts the suggestion, their feedback is collected to improve the accuracy of future suggestions.

[1593] Example of a prompt

[1594] "Consider the user's household electricity data and environmental behavior data, and generate a proposal for the most optimal decarbonization actions under the following conditions."

[1595] Power Consumption Patterns: Based on current data, make suggestions regarding energy-saving home appliances.

[1596] Emotional state: If the user is tired, suggest products with relaxation effects.

[1597] Proposal details: Please specify the product you are proposing and the reasons for your proposal.

[1598] In this way, the system integrates and manages household electricity consumption and environmental behavior, utilizes emotional data to provide users with optimized suggestions, and supports effective decarbonization actions.

[1599] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1600] Step 1:

[1601] The device acquires electricity usage data from the home's smart meter and environmental sensors. This data includes the household's electricity consumption and is recorded every 10 minutes.

[1602] Step 2:

[1603] The terminal sends power usage data it acquires to the server using the HTTPS communication protocol. The transmitted data includes a timestamp and is encrypted to ensure security.

[1604] Step 3:

[1605] The server saves the received power usage data to a database. A timestamp is added to maintain data integrity during saving, and data backups are also performed.

[1606] Step 4:

[1607] The terminal provides an interface for users to input environmental behavior data. Users input behavior data (e.g., recycling, water conservation) using a touchscreen or voice input.

[1608] Step 5:

[1609] The device sends the entered environmental behavior data to the server. The user can reconfirm the sent data on a confirmation screen.

[1610] Step 6:

[1611] The server stores the received environmental behavior data in a database and performs analysis. The analysis includes a process of assigning points (green scores) based on the behavior. For example, 50 points might be awarded for recycling.

[1612] Step 7:

[1613] The device acquires user emotion data using a device equipped with an emotion engine. This emotion data includes the user's emotional state (e.g., happy, sad, stressed, etc.).

[1614] Step 8:

[1615] The device sends the acquired emotion data to the server. Encrypted communication is used for transmission to protect user privacy.

[1616] Step 9:

[1617] The server stores the received emotional data in a database and analyzes it using an emotion engine. Based on the analysis results, the user's emotional state is identified.

[1618] Step 10:

[1619] The server uses an AI model generated based on power consumption patterns, environmental behavior data, and emotional data to suggest optimal decarbonization actions to the user. For example, it might generate a specific suggestion such as "raise the air conditioner temperature by 1 degree to reduce power consumption at night."

[1620] Step 11:

[1621] The server generates suggestions and sends them to the user's device. The user is then notified in real time via their smart glasses or head-mounted display.

[1622] Step 12:

[1623] The user performs the suggested action and inputs feedback into the device. For example, they might input, "I raised the air conditioner temperature by 1 degree."

[1624] Step 13:

[1625] The device sends the input feedback to the server. The feedback includes the date and time the action was performed and its result.

[1626] Step 14:

[1627] The server stores the feedback data in a database and uses it to optimize future proposals. The collected feedback data is shared with partner companies and power companies as statistical data.

[1628] At each step, it is clearly explained what data the server, terminal, and user input, what data processing is performed, and what output is obtained. This processing flow enables a system that comprehensively manages the user's power consumption, environmental behavior, and emotional state, supporting optimal decarbonization actions.

[1629] 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.

[1630] 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.

[1631] 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.

[1632] [Fourth Embodiment]

[1633] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1634] 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.

[1635] 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).

[1636] 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.

[1637] 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.

[1638] 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).

[1639] 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.

[1640] 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.

[1641] 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.

[1642] 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.

[1643] 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.

[1644] 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.

[1645] 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".

[1646] This invention is a system that uses household electricity data and environmental behavior data to propose optimal decarbonization actions to users. The system consists of a terminal installed in the user's home, a server that analyzes the data and generates proposals, and an interface for notifying the user of the proposals. A specific embodiment of this system is described below.

[1647] System Configuration

[1648] 1. Terminal

[1649] It collects electricity usage data from smart meters and environmental sensors within the user's home.

[1650] It provides an interface for users to input environmental actions they have taken.

[1651] Receives suggestions from the server and notifies the user.

[1652] 2. Server

[1653] It receives and stores power usage data and environmental behavior data transmitted from the terminal.

[1654] The received data is analyzed to identify household electricity consumption patterns.

[1655] The system evaluates users' environmental behavior and assigns them points (green score).

[1656] Using AI generated from electricity data and environmental behavior data, we propose specific decarbonization actions to users.

[1657] We will save the feedback in the database and use it to improve future proposals.

[1658] Program processing (explained in natural language)

[1659] Collection and analysis of power data

[1660] 1. Terminal: Obtains daily electricity usage data from the home's smart meter.

[1661] 2. Terminal: Sends the acquired data to the server.

[1662] 3. Server: Stores the transmitted power usage data in the database.

[1663] 4. Server: Analyzes stored data to identify household power consumption patterns. For example, it extracts peak hours and periods of high consumption.

[1664] Input and evaluation of environmental behavioral data

[1665] 1. User: Inputs their daily environmental actions (e.g., recycled, water-saving, etc.) into the terminal.

[1666] 2. Terminal: Sends the entered data to the server.

[1667] 3. Server: Stores the transmitted environmental behavior data in a database and assigns points (green score) based on the behavior.

[1668] Proposal generation and notification

[1669] 1. Server: Uses AI generated based on power consumption patterns and environmental behavior data to suggest effective decarbonization actions to users.

[1670] 2. Server: For example, it generates specific suggestions such as, "Raise the air conditioner's temperature setting by 1 degree to reduce power consumption at night."

[1671] 3. Server: Sends the generated suggestions to the user's terminal.

[1672] 4. Terminal: Receives proposals and notifies the user.

[1673] Gathering feedback and sharing data

[1674] 1. User: Perform the suggested action and input the result into the terminal.

[1675] 2. Terminal: Sends the input feedback to the server.

[1676] 3. Server: Saves feedback data and uses it for future proposals.

[1677] 4. Server: Shares collected data with companies and power companies to promote decarbonization measures.

[1678] Specific example

[1679] For example, if a household uses its air conditioner day and night, its power consumption data is sent to a server. The server identifies peak times and, using the green score as a reference, suggests raising the air conditioner's temperature setting by one degree at night. This suggestion is notified to the user's device, and feedback on whether the user has taken action is collected to improve the accuracy of future suggestions.

[1680] In this way, the system integrates and manages household electricity consumption and environmental behavior, providing support for users to effectively implement decarbonization actions.

[1681] The following describes the processing flow.

[1682] Step 1:

[1683] Terminal: Periodically acquires electricity usage data from smart meters installed in homes. For example, it measures and stores electricity usage every 10 minutes.

[1684] Step 2:

[1685] Terminal: Sends acquired power usage data to the server. A secure communication protocol (e.g., HTTPS) is used for transmission.

[1686] Step 3:

[1687] Server: Stores the transmitted power usage data in the database. A timestamp is added to the data during storage to maintain data integrity.

[1688] Step 4:

[1689] Server: Analyzes stored data to identify household power consumption patterns. Specifically, it extracts peak hours and high-consumption periods on specific days.

[1690] Step 5:

[1691] User: Inputs daily environmental actions (e.g., waste sorting, water conservation, etc.) into the terminal's interface. Input can be done via touchscreen or voice input.

[1692] Step 6:

[1693] Terminal: Sends the entered environmental behavior data to the server. Immediately after sending, a data confirmation screen is displayed to allow the user to reconfirm the entered information.

[1694] Step 7:

[1695] Server: Stores the submitted environmental action data in a database and assigns points (green score) to each action. For example, 50 points are awarded for recycling.

[1696] Step 8:

[1697] Server: Uses AI generated from power consumption patterns and environmental behavior data to suggest decarbonization actions suitable for the user. The suggestions are customized based on context, time of day, and consumption levels.

[1698] Step 9:

[1699] Server: Sends the generated suggestion (e.g., "Raise the air conditioner's temperature by 1 degree to reduce nighttime power consumption") to the user's terminal.

[1700] Step 10:

[1701] Terminal: Receives proposals and notifies the user. Notifications are delivered via push notifications or alerts.

[1702] Step 11:

[1703] User: Perform the suggested action and input the result into the terminal. For example, input "Raised the air conditioner temperature by 1 degree."

[1704] Step 12:

[1705] Terminal: Sends the entered feedback to the server. The feedback includes the date and time the action was performed and its specific details.

[1706] Step 13:

[1707] Server: Saves feedback data and uses it to improve future proposals. For example, it evaluates implementation rates and effectiveness to optimize the next proposal.

[1708] Step 14:

[1709] Server: The collected data is shared with partner companies and power companies. The data is processed into statistical data and shared while ensuring security.

[1710] Step 15:

[1711] User's device: Displays a list of environmentally friendly products based on their DecaVo score, allowing the user to select from them. Each product includes a detailed description and rating.

[1712] Step 16:

[1713] User: Select an environmentally friendly product from the list and proceed with the purchase. The purchase process is conducted via the device, and the purchase data is sent to the server after confirmation.

[1714] Step 17:

[1715] Server: Saves purchase data to the database and updates the Decabo score. Contribution from product purchases is reflected in the Green score.

[1716] Through the steps outlined above, this system supports users in effectively implementing decarbonization actions.

[1717] (Example 1)

[1718] 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".

[1719] There is a need for a means to integrate and manage household electricity usage data and environmental behavior data to propose effective decarbonization actions to users. Conventional systems often only collect and analyze electricity usage data, lacking sufficient integration with environmental behavior data, resulting in limited concrete suggestions to users. Furthermore, it has been difficult to utilize the results of these suggestions as feedback, making it challenging to improve the accuracy of the suggestions.

[1720] 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.

[1721] In this invention, the server includes means for acquiring household electricity usage data, means for transmitting the acquired electricity usage data to the server, means for analyzing the data acquired by the server and identifying electricity consumption patterns, means for inputting and transmitting user environmental behavior data to the server, means for analyzing the user's environmental behavior data and assigning evaluation points, means for generating suggestions using an artificial intelligence model generated based on electricity usage data and environmental behavior data, means for transmitting suggestions from the server to the user's terminal, means for the user to perform the suggested actions and input feedback, means for collecting and accumulating data and reflecting the analysis results as feedback in the next suggestion, and means for sharing the collected data and promoting decarbonization measures. This makes it possible to integrate and manage electricity usage data and environmental behavior data and propose individually optimized decarbonization actions to users. Furthermore, by utilizing the results of the suggestions as feedback, it is possible to improve the accuracy of the suggestions.

[1722] "Household electricity usage data" refers to information that shows the amount of electricity consumed within a household and its usage patterns by time of day.

[1723] "Means of acquisition" refers to devices or methods for automatically collecting data using sensors, smart meters, etc.

[1724] A "server" refers to a computer system used to receive, analyze, and store power data and environmental behavior data.

[1725] "Means of analysis" refer to algorithms and software that process collected data to identify power consumption patterns.

[1726] "Environmental behavior data" refers to information about environmental protection actions taken by users (e.g., recycling, water conservation).

[1727] "Evaluation points" are values ​​used to quantitatively evaluate a user's environmental behavior and are indicators of the user's contribution.

[1728] A "generating artificial intelligence model" is a machine learning model or algorithm used to generate suggestions for users based on collected data.

[1729] A "suggestion" is information that indicates specific actions the user should take (e.g., adjusting the air conditioner's temperature settings).

[1730] "Means of input" refers to the interface (e.g., a smartphone application) that users use to input data or feedback.

[1731] "Feedback" is information used to report the results of a user taking a suggested action.

[1732] "Integrated management" is a method of centrally managing electricity usage data and environmental behavior data, and analyzing them in relation to each other.

[1733] "Means of promotion" refers to the methods and mechanisms for executing a plan and achieving its goals.

[1734] This invention is a system that collects and analyzes household electricity usage data and environmental behavior data, and proposes optimal decarbonization actions to the user. Specifically, it includes a terminal installed in the home, a server that analyzes the data and generates proposals, and an interface for notifying the user of the proposals. A specific embodiment of this system is described below.

[1735] System Configuration

[1736] 1. Terminal

[1737] The system collects electricity usage data from smart meters and environmental sensors installed in the user's home.

[1738] It provides an interface for users to input their environmental actions. Specifically, it uses a smartphone application or a web interface.

[1739] Receives suggestions from the server and notifies the user.

[1740] 2. Server

[1741] It is equipped with a database for receiving and storing power usage data and environmental behavior data transmitted from terminals.

[1742] The received data is analyzed to identify the electricity consumption patterns of each household. For example, it is possible to analyze increases and decreases in consumption during specific time periods.

[1743] It has an evaluation algorithm that assesses users' environmental behavior and assigns points (green score).

[1744] Using an AI model generated based on electricity data and environmental behavior data, we propose specific decarbonization actions to users.

[1745] The proposal and feedback will be saved in the database and used for future proposals.

[1746] 3. Interface

[1747] An interface for users to review and take suggested actions. Specifically, this can be achieved through application push notifications, email, or a web-based dashboard.

[1748] It also provides an interface for users to perform suggested actions and input the results as feedback.

[1749] Specific example

[1750] For example, if a household uses its air conditioner day and night, its power usage data is sent to a server. The server analyzes the received data and discovers that power consumption is particularly high at night. At the same time, it also considers the user's green score for environmental actions taken. Based on this, a generative AI model generates a specific suggestion, such as "raise the air conditioner's temperature setting by 1 degree at night." This suggestion is notified to the user's device, and feedback on whether the user has taken action is also collected to improve the accuracy of future suggestions.

[1751] Examples of prompts for generative AI models

[1752] "Based on household electricity usage data and user environmental behavior data, please propose effective decarbonization actions to users. Specifically, please provide detailed suggestions regarding adjustments to air conditioner settings and usage times."

[1753] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1754] Step 1:

[1755] Terminal: Acquires daily electricity usage data from home smart meters and environmental sensors.

[1756] Input: Power usage data from smart meters and environmental sensors.

[1757] Output: Acquired power usage data

[1758] Specific operation: Collects power consumption information transmitted from smart meters at regular time intervals (e.g., every 30 minutes). Similarly, collects data such as temperature and humidity from environmental sensors.

[1759] Step 2:

[1760] Terminal: Transmits acquired power usage data to the server at regular intervals.

[1761] Input: Acquired power usage data

[1762] Output: Power usage data sent to the server

[1763] Specific operation: Collected power usage data is sent to a server in batch processing or in real time. Data is typically transmitted via Wi-Fi or a wired network.

[1764] Step 3:

[1765] Server: Receives transmitted power usage data and stores it in the database.

[1766] Input: Power usage data transmitted from the terminal.

[1767] Output: Power usage data stored in the database

[1768] Specific operation: After initial processing of received data (e.g., data cleansing, normalization), it is saved to the database as time-series data.

[1769] Step 4:

[1770] Server: Analyzes stored power usage data to identify power consumption patterns for each household.

[1771] Input: Power usage data stored in the database

[1772] Output: Analyzed power consumption patterns

[1773] Specific operation: Use data analysis algorithms to identify significant fluctuations in power consumption during specific time periods (e.g., daytime and nighttime). Extract peak consumption levels and distinct consumption patterns.

[1774] Step 5:

[1775] User: Inputs their daily environmental actions (e.g., recycled, water-saving) into the application on their device.

[1776] Input: User-generated environmental behavior data

[1777] Output: Environmental behavior data entered into the terminal

[1778] Specific operation: The user enters the environmental actions they have taken into a form using a smartphone application or web interface.

[1779] Step 6:

[1780] Terminal: Sends the entered environmental behavior data to the server.

[1781] Input: User-generated environmental behavior data

[1782] Output: Environmental behavior data sent to the server

[1783] Specific operation: Input data is sent to the server in real time. Data is usually transferred securely using an API.

[1784] Step 7:

[1785] Server: Stores the transmitted environmental behavior data in a database and assigns points (green score) based on the behavior.

[1786] Input: Environmental behavior data sent to the server

[1787] Output: Environmental behavior data saved in the database and assigned points.

[1788] Specific operation: Received environmental behavior data is stored in a database, and an algorithm is applied to calculate a green score based on the importance of each behavior.

[1789] Step 8:

[1790] Server: Uses an AI model generated based on power consumption patterns and environmental behavior data to suggest effective decarbonization actions to users.

[1791] Input: Power consumption patterns and environmental behavior data

[1792] Output: Generated proposals

[1793] Specific operation: Using a generative AI model, the system generates optimal action suggestions for the user from accumulated data (e.g., adjusting the air conditioner temperature setting, changing the usage time).

[1794] Step 9:

[1795] Server: Sends the generated suggestions to the user's terminal.

[1796] Input: Generated proposal

[1797] Output: Suggestions sent to the user's terminal

[1798] Specific actions: The generated suggestions are sent to the user's device via the API. They are displayed as push notifications or in-app messages.

[1799] Step 10:

[1800] User: Perform the suggested action and input the result as feedback into the device.

[1801] Input: Result of the proposed action

[1802] Output: Feedback data entered into the terminal

[1803] Specific action: The user performs the suggested action and inputs the result (e.g., increased the air conditioner temperature by 1 degree) as feedback to the application.

[1804] Step 11:

[1805] Terminal: Sends the input feedback to the server.

[1806] Input: Feedback data

[1807] Output: Feedback data sent to the server

[1808] Specific action: Send feedback data to the server in real time.

[1809] Step 12:

[1810] Server: Saves feedback data and uses it for future proposals.

[1811] Input: Feedback data sent to the server

[1812] Output: Next proposals utilizing feedback data

[1813] Specific operation: Feedback data is saved to a database and reflected in the next proposal generation algorithm to improve the accuracy of the proposals.

[1814] Step 13:

[1815] Server: Shares collected data with companies and power companies to promote decarbonization measures.

[1816] Input: Power usage data and environmental activity data stored on the server

[1817] Output: Shared data

[1818] Specific actions: Statistically process data and share it with businesses and power companies. This will provide foundational data to promote widespread decarbonization measures.

[1819] (Application Example 1)

[1820] 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".

[1821] Modern energy efficiency issues involve challenges in both optimizing electricity consumption in homes and stores and promoting environmentally conscious behavior. In particular, brick-and-mortar stores often have high electricity consumption, hindering cost reduction and environmental impact mitigation. Furthermore, there is a lack of concrete and effective methods to raise employee environmental awareness and translate it into concrete actions. Therefore, these challenges need to be addressed by integrating and managing store electricity usage data and environmental behavior data, and by providing specific suggestions to employees.

[1822] 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.

[1823] In this invention, the server includes means for acquiring store power usage data and proposing actions to reduce power consumption, means for evaluating employee environmental behavior data and awarding points, and means for analyzing the acquired data and identifying power consumption patterns. This makes it possible to optimize power consumption in stores and propose ways to raise employees' environmental awareness.

[1824] Definitions of important words

[1825] "Household electricity usage data"

[1826] This refers to numerical information that shows the usage status of all electrical appliances and equipment used in a household.

[1827] "Means of acquisition"

[1828] This refers to devices and technologies used to measure and collect data.

[1829] "server"

[1830] A computer system is a computer system that receives, stores, and analyzes data, and then transmits it to other devices.

[1831] "Power consumption patterns"

[1832] This refers to the periodic fluctuations or trends in electricity usage in a particular household or store.

[1833] "Environmental behavior data"

[1834] This refers to records and reported data of environmentally friendly actions taken by users and employees.

[1835] "Means of awarding points"

[1836] This refers to systems and technologies that evaluate the environmental behavior of users and employees and award points as incentives.

[1837] “Generation AI”

[1838] This refers to artificial intelligence that automatically generates appropriate suggestions for users and employees based on acquired data.

[1839] "Means for generating proposals"

[1840] This refers to a technology that analyzes acquired data and creates suggestions to encourage effective actions from users and employees.

[1841] "Method for sending proposals"

[1842] This refers to communication technologies and systems used to notify users' or employees' terminals of proposals from a server.

[1843] "Methods for providing feedback"

[1844] This refers to an interface or system for users or employees to report the results of performing suggested actions.

[1845] "Data sharing"

[1846] This refers to coordinating and communicating collected data with other relevant systems and organizations.

[1847] "Environmentally friendly products"

[1848] This refers to products that are energy-efficient and designed to reduce their environmental impact.

[1849] "Actions to reduce electricity consumption"

[1850] This refers to specific actions or operations taken to reduce electricity consumption.

[1851] "Store electricity usage data"

[1852] This refers to numerical information that shows the usage status of all electrical equipment and facilities in a store.

[1853] "Employee environmental behavior data"

[1854] This refers to records and reports of environmentally friendly actions taken by store employees.

[1855] Modes for carrying out the invention

[1856] This invention is a system that integrates and manages electricity usage data and environmental behavior data in homes and stores, and proposes effective environmental actions. The embodiments for carrying out this invention are described below.

[1857] System Configuration

[1858] 1. Terminal

[1859] Hardware: Smartphones, tablets

[1860] Software: Applications for data acquisition and input

[1861] Features: Acquisition of electricity usage data for homes and stores, input of user and employee environmental behavior data, receiving and displaying suggestions, input of feedback.

[1862] 2. Sensor

[1863] Smart meter: Real-time acquisition of electricity usage data

[1864] Environmental sensors: Acquisition of ambient environmental data (temperature, humidity, etc.)

[1865] 3. Server

[1866] Hardware: Cloud servers (e.g., AWS, Google Cloud)

[1867] Software: Data analysis platform, database (Firebase Realtime Database), generative AI (GPT-3, OpenAI)

[1868] Functions: Receiving, storing, analyzing, generating suggestions, and sending data.

[1869] Program processing

[1870] Data collection and transmission

[1871] Terminal: Acquires real-time electricity usage data from homes and stores using smart meters and environmental sensors, and transmits it to a cloud server.

[1872] Server: Receives transmitted power usage data and stores it in a database. Analyzes the retrieved data to identify consumption patterns.

[1873] Input and evaluation of environmental behavioral data

[1874] Users and employees: Input their daily environmental actions (e.g., recycling, energy saving) into a terminal application.

[1875] Terminal: Sends the entered environmental behavior data to the server.

[1876] Server: Stores the transmitted environmental behavior data in a database and assigns points (green score).

[1877] Proposal generation and notification

[1878] Server: Based on power consumption patterns and environmental behavior data, it uses generated AI to propose specific decarbonization actions.

[1879] Server: Generates suggestions and sends them to user or employee terminals. For example, it generates specific suggestions such as "raise the air conditioner temperature by 1 degree."

[1880] Terminal: Receives generated proposals and notifies users and employees.

[1881] Gathering feedback and sharing data

[1882] Users and employees: Perform the suggested action and enter the result into the terminal.

[1883] Terminal: Sends the input feedback to the server.

[1884] Server: The server stores feedback data and uses it to inform future proposals. It also shares the collected data with relevant companies and service providers to promote decarbonization measures.

[1885] Specific example

[1886] For example, electricity usage data from a store is sent to a server, and analysis reveals peak consumption during specific time periods. The server uses AI to generate suggestions for employees, such as "reduce daytime lighting by 50%", and notifies them via their terminals. When employees implement the suggestions and report the results via their terminals, the server receives feedback and incorporates it into future suggestions. This optimizes the store's electricity consumption and reduces its environmental impact.

[1887] Examples of input prompts for a generative AI model

[1888] "Based on store electricity usage data and environmental behavior data, propose ways to optimize store operations. Specifically, generate suggestions for actions to reduce electricity consumption (e.g., adjusting air conditioner temperatures, dimming lights, etc.) and notify employees accordingly."

[1889] In this way, the present invention provides integrated management and proposals for electricity use and environmental behavior in homes and stores, and supports users and employees in effectively implementing decarbonization actions.

[1890] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1891] Program processing steps

[1892] Step 1:

[1893] Collecting power data

[1894] The terminal acquires real-time electricity usage data from homes and stores using smart meters and environmental sensors.

[1895] Input: Measurements from smart meters and environmental sensors.

[1896] Output: Acquired power data.

[1897] Specific operation: The smart meter measures electricity usage every minute and sends that data to the terminal. The terminal then sends this data to the cloud server in the appropriate format.

[1898] Step 2:

[1899] Analysis and storage of power data

[1900] The server receives power data transmitted from the terminal and stores it in a database. It then analyzes the stored data to identify power consumption patterns.

[1901] Input: Acquired power data.

[1902] Output: Analyzed power consumption pattern.

[1903] Specific operation: The server stores power usage data in a database and runs an analysis algorithm to identify daily consumption patterns. For example, it calculates the average consumption for each time period to extract peak hours.

[1904] Step 3:

[1905] Input of environmental behavioral data

[1906] Users and employees input their daily environmental behaviors into a terminal application.

[1907] Input: Environmental behavior data (e.g., recycling, energy-saving behavior).

[1908] Output: Input environmental behavior data.

[1909] Specific operation: Users and employees select and input specific environmental actions using smartphone or tablet apps. This data is transmitted to a cloud server in real time.

[1910] Step 4:

[1911] Evaluation and point allocation of environmental behavior data

[1912] The server receives the transmitted environmental behavior data, stores it in a database, and then assigns points (green scores) based on the content of the behavior.

[1913] Input: Entered environmental behavior data.

[1914] Output: Points (Green Score).

[1915] Specific operation: The server scores environmental behavior data based on an evaluation algorithm and stores the results in a database. For example, points are added based on the frequency of recycling.

[1916] Step 5:

[1917] Proposal generation

[1918] The server uses generative AI to propose specific decarbonization actions based on power consumption patterns and environmental behavior data.

[1919] Input: Power consumption patterns, environmental behavior data.

[1920] Output: Generated proposals.

[1921] Specific operation: The server inputs the analyzed data into the generating AI and generates optimal suggestions based on the prompt. For example, the prompt might be, "Please suggest ways to optimize store operations based on store power usage data and environmental behavior data."

[1922] Step 6:

[1923] Notification of proposal

[1924] The server sends the generated suggestions to the user's or employee's device and provides notifications.

[1925] Input: Generated suggestions.

[1926] Output: Notification to the terminal.

[1927] Specific operation: The server sends the suggestion as a push notification to the device. The device receives the notification and displays it in the app.

[1928] Step 7:

[1929] Gathering feedback

[1930] Users and employees perform the suggested actions and input the results into the terminal.

[1931] Input: Feedback data (results of action execution).

[1932] Output: Input feedback data.

[1933] Specific operation: After performing the suggested actions, users and employees input the results and effects into a terminal application. This data is sent to a cloud server in real time.

[1934] Step 8:

[1935] Feedback analysis and data sharing

[1936] The server analyzes the feedback data and uses it to improve the accuracy of future suggestions. It also shares the collected data with relevant companies and service providers.

[1937] Input: The input feedback data.

[1938] Output: Analysis results and shared data.

[1939] Specific operation: The server analyzes the feedback data and runs an algorithm to evaluate the effectiveness of the suggestions. The analysis results are stored in a database, and data for sharing is generated.

[1940] 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.

[1941] This invention is a system that combines a generative AI and an emotion engine using household electricity data and environmental behavior data to propose optimal decarbonization actions to the user. The system includes the following main components:

[1942] System Configuration

[1943] 1. Terminal

[1944] Electricity usage data is collected from smart meters and environmental sensors installed in homes.

[1945] It provides an interface for users to input environmental actions they have taken.

[1946] It provides an emotion input interface for recognizing the user's emotions.

[1947] Receives suggestions from the server and notifies the user.

[1948] 2. Server

[1949] It receives and stores power usage data, environmental behavior data, and emotional data transmitted from the device.

[1950] The received data is analyzed to identify household electricity consumption patterns.

[1951] The system evaluates users' environmental behavior and assigns them points (green score).

[1952] Using AI generated from electricity usage data and environmental behavior data, we propose specific decarbonization actions to users.

[1953] The emotion engine analyzes user emotions and optimizes suggestions accordingly.

[1954] We will save the feedback in the database and use it to improve future proposals.

[1955] Program processing (explained in natural language)

[1956] Collection and analysis of power data

[1957] 1. Terminal: Periodically acquires daily electricity usage data from the home's smart meter. For example, it measures and stores electricity usage every 10 minutes.

[1958] 2. Terminal: Sends acquired power usage data to the server. A secure communication protocol (e.g., HTTPS) is used for transmission.

[1959] 3. Server: Stores the transmitted power usage data in a database. A timestamp is added to the data during storage to maintain data integrity.

[1960] 4. Server: Analyzes stored data to identify household power consumption patterns. Specifically, it extracts peak hours and high-consumption periods on specific days.

[1961] Input and evaluation of environmental behavioral data

[1962] 1. User: Inputs daily environmental actions (e.g., waste sorting, water conservation, etc.) into the terminal's interface. Touchscreen or voice input can be used for input.

[1963] 2. Terminal: Sends the entered environmental behavior data to the server. Immediately after sending, a data confirmation screen is displayed to allow the user to reconfirm the entered information.

[1964] 3. Server: Stores the submitted environmental behavior data in a database and assigns points (green score) based on the behavior. For example, 50 points are awarded for recycling.

[1965] Proposal generation and notification

[1966] 1. Server: Uses AI generated based on power consumption patterns and environmental behavior data to suggest effective decarbonization actions to users. Suggestions are customized based on context, time of day, and consumption levels.

[1967] 2. Server: For example, it generates specific suggestions such as, "Raise the air conditioner's temperature setting by 1 degree to reduce power consumption at night."

[1968] 3. Server: Sends the generated suggestions to the user's terminal.

[1969] 4. Device: Receives proposals and notifies the user. Notifications are sent via push notifications or alerts.

[1970] Collection and analysis of emotional data

[1971] 1. User: Enter your daily emotional state (e.g., happy, sad, stressed, etc.) into the device's emotion input interface.

[1972] 2. Terminal: Sends emotional data to the server. Encrypted communication is used for transmission to protect user privacy.

[1973] 3. Server: Stores emotion data in a database and analyzes it using an emotion engine. Based on the analysis results, it generates suggestions tailored to the user's state.

[1974] Gathering feedback and sharing data

[1975] 1. User: Perform the suggested action and input the result into the terminal. For example, input "Raised the air conditioner temperature by 1 degree."

[1976] 2. Terminal: Sends the entered feedback to the server. The feedback includes the date and time the action was performed and its specific details.

[1977] 3. Server: Saves feedback data and uses it to improve future proposals. For example, it evaluates implementation rates and effectiveness to optimize the next proposal.

[1978] 4. Server: The collected data is shared with partner companies and power companies. The data is processed into statistical data and shared while ensuring security.

[1979] Specific example

[1980] For example, if a household uses its air conditioner day and night, its power consumption data is sent to a server. The server identifies peak times and, taking sentiment data into consideration, suggests raising the air conditioner's temperature setting by one degree at night. This suggestion is notified to the user's device, and feedback on whether the user acted on it is also collected to improve the accuracy of future suggestions.

[1981] If emotional data is analyzed as indicating "fatigue" or "stress," the suggestion will be customized to best suit the user's condition, such as "set the room temperature to a comfortable level so you can go to bed earlier," rather than simply "raise the air conditioner temperature."

[1982] In this way, the system integrates and manages household electricity consumption and environmental behavior, utilizes emotional data to provide users with optimized suggestions, and supports effective decarbonization actions.

[1983] The following describes the processing flow.

[1984] Step 1:

[1985] Terminal: Acquires electricity usage data from the home's smart meter every 10 minutes. For example, it records the amount of electricity consumed during each time period.

[1986] Step 2:

[1987] Terminal: Sends acquired power usage data to the server at regular intervals. A secure communication protocol (HTTPS) is used for transmission.

[1988] Step 3:

[1989] Server: Stores transmitted power usage data in a database. The data is stored with a timestamp to maintain consistency.

[1990] Step 4:

[1991] Server: Analyzes stored data to identify household power consumption patterns. Specifically, it extracts peak hours and high-consumption periods on specific days.

[1992] Step 5:

[1993] User: Inputs daily environmental actions (e.g., waste sorting, water conservation, energy saving) into the terminal's interface. Touchscreen or voice recognition can be used for input.

[1994] Step 6:

[1995] Terminal: Transmits entered environmental behavior data to the server in real time. A confirmation screen is displayed immediately after user input, allowing for reconfirmation.

[1996] Step 7:

[1997] Server: Stores the submitted environmental action data in a database and assigns points (green score) to each action. For example, 50 points are awarded for recycling.

[1998] Step 8:

[1999] User: Enter your current emotional state (e.g., happy, sad, stressed) into the device's emotion input interface. This can also utilize facial recognition or voice input.

[2000] Step 9:

[2001] Device: Sends emotional data to the server. Encrypted communication is used to send emotional data and protect user privacy.

[2002] Step 10:

[2003] Server: Stores emotional data in a database and analyzes it using an emotion engine. Based on the analysis results, it identifies the user's current emotional state.

[2004] Step 11:

[2005] Server: Uses AI generated based on power consumption patterns, environmental behavior data, and emotional data to suggest effective decarbonization actions to users. The suggestions are customized according to the user's situation and emotions.

[2006] Step 12:

[2007] Server: For example, it generates specific suggestions such as "raise the air conditioner's temperature by one degree to reduce nighttime power consumption" or "take a walk in a green space to reduce stress."

[2008] Step 13:

[2009] Server: Sends the generated suggestions to the user's terminal.

[2010] Step 14:

[2011] Terminal: Receives proposals and notifies the user. Notifications are displayed as push notifications or alerts.

[2012] Step 15:

[2013] User: Perform the suggested action and input the result into the terminal. For example, report, "Raised the air conditioner temperature by 1 degree."

[2014] Step 16:

[2015] Terminal: Sends the entered feedback to the server. The feedback includes the date and time the action was performed and its specific details.

[2016] Step 17:

[2017] Server: Saves and analyzes feedback data to improve future suggestions. For example, it evaluates the implementation rate and effectiveness of actions to optimize the next suggestion.

[2018] Step 18:

[2019] Server: Shares collected data with companies and power companies. The data is processed into statistical information and provided while ensuring security.

[2020] Step 19:

[2021] Terminal: Lists and displays environmentally friendly products based on their DecaVo score to the user. Products include detailed descriptions and user ratings.

[2022] Step 20:

[2023] User: Select an environmentally friendly product from the list and proceed with the purchase. The purchase process is conducted through secure online payment.

[2024] Step 21:

[2025] Server: Saves purchase data to the database and updates the Decabo score. Contribution from product purchases is reflected in the Green score.

[2026] Through the steps described above, this system considers the user's power consumption patterns and emotional state, proposes optimal and effective decarbonization actions, and supports the realization of a sustainable society.

[2027] (Example 2)

[2028] 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".

[2029] While conventional home energy management systems collected and analyzed electricity usage data, they were unable to integrate environmental behavior and emotional data to provide optimal recommendations. Furthermore, providing personalized feedback and suggestions to users in real time was difficult, resulting in insufficient efforts to improve user motivation and encourage continuous environmental improvement behaviors.

[2030] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for storing household power usage data with a timestamp and identifying household power consumption patterns, means for analyzing the user's environmental behavior data and assigning points, and means for proposing optimal decarbonization actions using an AI model generated based on power usage data and environmental behavior data. This enables the integrated analysis and evaluation of the user's power consumption patterns, environmental behavior, and emotional data, and makes it possible to propose specific decarbonization actions optimized for the user.

[2031] "Electricity usage data" refers to data that records household electricity consumption on an hourly basis.

[2032] A "secure communication protocol" is a means of communication used to ensure the security of data transmission. Specifically, HTTPS is an example of such a protocol.

[2033] A "timestamp" is information that records the date and time when data was acquired.

[2034] "Power consumption patterns" refer to data that shows trends and characteristics of power usage during specific time periods or dates.

[2035] "Environmental behavior data" refers to data that records users' daily actions taken to protect the environment. Specifically, this includes information such as waste sorting and water conservation practices.

[2036] A "generative AI model" is a system that generates optimal suggestions or predictions from specific input data based on artificial intelligence algorithms.

[2037] "Feedback" refers to information about the results and circumstances of a user's actions after performing a suggested action.

[2038] "Emotional data" refers to data that records a user's emotional state (e.g., happy, sad, stressed, etc.).

[2039] "Points" are rewards or scores awarded to users for their environmental actions.

[2040] A "notification" is a means of communicating information from a server to a user, and is usually done in the form of push notifications or alerts on the device.

[2041] "Data sharing" refers to the act of securely sharing collected data as statistical information with partner companies, power companies, and other relevant parties.

[2042] "Listing" refers to displaying products or information in a list format based on specific criteria.

[2043] This invention is a system that utilizes household electricity usage data and environmental behavior data, and combines a generative AI model and an emotion engine to propose optimal decarbonization actions to users. This system includes a smart meter for acquiring household electricity usage data, a terminal for inputting the user's environmental behavior, and a server for analyzing and storing the data. The following describes each component of this invention and its operation.

[2044] Components

[2045] 1. Smart meters and sensors: These are installed in homes and periodically collect electricity usage data every 10 minutes. For example, they collect data such as "10:00 AM - 0.45kWh" and "10:10 AM - 0.50kWh".

[2046] 2. Terminal: An interface for users to input environmental actions within the home (such as waste sorting and water conservation). It can use a touchscreen or voice input. For example, one might input, "Today I sorted my trash."

[2047] 3. Server: Stores and analyzes collected data and generates suggestions. The server stores the data in a database with timestamps and identifies power consumption patterns. For example, it stores the data with a timestamp such as "2023-10-05 10:10:00 - 0.50kWh". Furthermore, it uses a secure communication protocol (HTTPS) to exchange data.

[2048] 4. Generative AI Model: The generative AI model on the server proposes optimal decarbonization actions based on power consumption data and environmental behavior data. For example, it generates suggestions such as "Raise the air conditioner's temperature setting by 1 degree to reduce power consumption at night."

[2049] 5. Emotion Engine: Analyzes user emotion data and optimizes suggestions. Emotion data is transmitted to the server via encrypted communication and analyzed. For example, if a user inputs "I'm stressed," the system will make suggestions such as "Adjust the temperature setting for relaxation."

[2050] Specific example

[2051] For example, if a household uses its air conditioner day and night, its power consumption data is sent from the smart meter to a server. The server identifies peak times and, taking emotional data into consideration, suggests "raising the air conditioner's temperature setting by one degree at night." This suggestion is notified to the user's device, and feedback is collected on whether the user has taken action. Furthermore, if the user's emotional data is analyzed as "fatigue" or "stress," the suggestion is customized from "raising the air conditioner's temperature setting" to "setting the room temperature to a comfortable level so you can go to bed earlier."

[2052] Example of a prompt

[2053] Examples of prompt statements to input into a generative AI model include the following:

[2054] "Based on household electricity consumption data and environmental behavior data, propose the optimal decarbonization actions. If the user's emotional state is 'fatigue,' the proposal should be especially considerate of the user's physical condition."

[2055] With the above configuration and operation, the present invention is a system that comprehensively manages the user's power consumption patterns and environmental behavior, and utilizes emotional data to support effective and personalized decarbonization actions.

[2056] The flow of the specific processing in Example 2 will be explained using Figure 13.

[2057] Step 1:

[2058] Terminal: Acquires electricity usage data from smart meters. For example, it acquires data such as "10:00 AM - 0.45kWh" and "10:10 AM - 0.50kWh" as electricity consumption every 10 minutes. The input is real-time data from the smart meter, and the output is electricity usage data temporarily stored in the terminal.

[2059] Step 2:

[2060] Terminal: Sends acquired power usage data to the server. A secure communication protocol (e.g., HTTPS) is used for this transmission. The input is temporarily stored power usage data, and the output is the data sent to the server. Specifically, the terminal encodes the data and sends it to the server using the HTTPS protocol.

[2061] Step 3:

[2062] Server: Stores transmitted power usage data in a database. During the storage process, a timestamp is added to the data. The input is the raw power usage data sent to the server, and the output is the timestamped data stored in the database. Specifically, the server uses a database management system to organize and store the data.

[2063] Step 4:

[2064] Server: Analyzes data stored in the database to identify power consumption patterns. Input is time-stamped power usage data, and output is power consumption patterns identified based on the analysis (e.g., peak hours and high-consumption periods). Specifically, the server uses statistical analysis software to extract consumption patterns.

[2065] Step 5:

[2066] User: Inputs daily environmental actions (e.g., waste sorting, water conservation) into the terminal's interface. Input can be done using a touchscreen or voice input. The input is raw environmental action data from the user, and the output is environmental action data temporarily stored as input data within the terminal.

[2067] Step 6:

[2068] Terminal: Sends the entered environmental behavior data to the server. A data confirmation screen is displayed to allow the user to reconfirm the entered information. The input is temporarily stored environmental behavior data, and the output is the data sent to the server. Specifically, the terminal encodes the data and sends it to the server using a secure communication protocol.

[2069] Step 7:

[2070] Server: Stores the submitted environmental behavior data in a database and assigns points (green scores). The input is the raw environmental behavior data sent to the server, and the output is the data with points attached, stored in the database. Specifically, the server calculates the score using a point-assigning algorithm and adds it to the database.

[2071] Step 8:

[2072] Server: Uses a generative AI model based on power consumption patterns and environmental behavior data to suggest effective decarbonization actions to the user. The input is power consumption pattern data and environmental behavior data stored on the server, and the output is specific suggestions generated by the generative AI model. Specifically, the server sends the prompt example "Please tell me effective actions to reduce tonight's peak power consumption" to the generative AI model and receives suggestions.

[2073] Step 9:

[2074] Server: Sends the generated proposals to the user's terminal. The input is the generated decarbonization action proposals, and the output is the content of the proposals sent to the user's terminal. Specifically, the server encodes the proposals and sends them to the terminal using a secure communication protocol.

[2075] Step 10:

[2076] Terminal: Receives suggestions and notifies the user. Notifications are made via push notifications or alerts. Input is suggestion data received from the server, and output is a notification message displayed to the user. Specifically, the terminal uses a notification API to display suggestions to the user.

[2077] Step 11:

[2078] User: Inputs their daily emotional state (e.g., happy, sad, stressed) into the device's emotion input interface. The input is the user's raw emotional data, and the output is the emotional data temporarily stored as input data within the device.

[2079] Step 12:

[2080] The terminal sends emotion data to the server. Encrypted communication is used for transmission to protect user privacy. The input is temporarily stored emotion data, and the output is the data sent to the server. Specifically, the terminal encodes the data and sends it to the server using an encryption protocol.

[2081] Step 13:

[2082] Server: Stores emotional data in a database and analyzes it using an emotion engine. The input is raw emotional data sent to the server, and the output is the analysis result. Specifically, the server uses an emotion analysis algorithm to obtain data corresponding to the user's state.

[2083] Step 14:

[2084] Server: Executes the proposed action and inputs the result into the terminal. The input is user feedback data, and the output is the feedback data temporarily stored as input data within the terminal.

[2085] Step 15:

[2086] Terminal: Sends the input feedback to the server. The input is temporarily stored feedback data, and the output is the data sent to the server. Specifically, the terminal encodes the data and sends it to the server using a secure communication protocol.

[2087] Step 16:

[2088] Server: Stores feedback data and uses it for future proposals. Input is raw feedback data sent to the server, and output is evaluation data stored in the database. Specifically, the server uses an evaluation algorithm to analyze the data in order to optimize the next proposal.

[2089] (Application Example 2)

[2090] 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".

[2091] In recent years, systems that optimize household electricity consumption and promote environmentally friendly behavior have become indispensable. However, conventional systems have been limited to collecting and analyzing electricity consumption data and environmental behavior data, and have suffered from low accuracy in recommendations because they do not adequately consider the user's emotional state or real-time feedback. Furthermore, if the suggested products or services do not match the user's emotional state, they are not well accepted, and this does not lead to effective decarbonization behavior.

[2092] 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. In this invention, the server includes means for acquiring household electricity usage data, means for analyzing environmental behavior data and assigning points, means for acquiring and analyzing user emotion data, means for generating suggestions according to the emotional state using a generating AI, means for transmitting the suggestions to the user's terminal, means for the user to perform the suggested actions and input feedback, and means for sharing data and listing environmentally friendly products and displaying them to the user. This makes it possible to propose optimal decarbonization actions that take into account the user's electricity consumption patterns and emotional state.

[2093] "Household electricity usage data" refers to data that shows the amount and patterns of electricity consumption used within a household.

[2094] A "server" is a computer system that collects, analyzes, stores, and notifies users of data.

[2095] "Generative AI" is an artificial intelligence model that generates optimal suggestions for users based on electricity usage data and environmental behavior data.

[2096] "Emotional data" refers to data that indicates a user's emotional state (for example, happy, sad, stressed, etc.).

[2097] An "emotion engine" is an algorithm that analyzes emotional data and evaluates the user's emotional state.

[2098] "Environmental behavior data" refers to data that shows environmentally friendly actions taken by users (e.g., recycling, waste sorting, energy saving, etc.).

[2099] "Means of awarding points" refers to a function that evaluates a user's environmental behavior and awards the user points (green score) commensurate with that behavior.

[2100] A "terminal" is a device used by a user (e.g., a smartphone, tablet, smart glasses, etc.) that provides an interface with the system.

[2101] "Feedback" refers to the process of a user performing an action suggested by the system and providing feedback on the results.

[2102] Modes for carrying out the invention

[2103] This invention is a system that analyzes household electricity data, environmental behavior data, and emotional data, and combines a generative AI and an emotion engine to propose optimal decarbonization actions to the user. This system will be implemented as a virtual store application called "Green Shopping Assistant" using smart glasses or a head-mounted display.

[2104] Key components of the system

[2105] 1. Terminal:

[2106] Get:

[2107] Daily electricity usage data is regularly acquired from home smart meters and environmental sensors. For example, electricity usage is measured and stored every 10 minutes.

[2108] Data entry:

[2109] It provides an interface for users to input environmental actions they have taken (e.g., waste sorting, water conservation). Input can be done via a touchscreen or voice input.

[2110] Acquiring emotion data:

[2111] Real-time acquisition of emotional data using devices equipped with an emotion engine (e.g., cameras with facial recognition software).

[2112] Proposal notification:

[2113] The server notifies the user of suggestions sent from the server. The notifications are made in real time and displayed on the user's smart glasses or head-mounted display.

[2114] 2. Server:

[2115] Data management:

[2116] Household electricity usage data, environmental behavior data, and emotional data are received using the HTTPS communication protocol and stored in a database.

[2117] Data analysis:

[2118] The system analyzes the received data to identify household electricity consumption patterns. Points (green score) are assigned based on environmental behavior data.

[2119] Suggestion generation:

[2120] Using a generative AI model (e.g., GPT-4), the system proposes optimal decarbonization actions based on the user's power consumption patterns and emotional data. For example: "Raise the air conditioner's temperature setting by 1 degree to reduce nighttime power consumption."

[2121] Emotion analysis:

[2122] An emotion engine is used to analyze emotional data and optimize suggestions based on the user's emotions. For example, if the user is tired, the suggestion might be "create a relaxing environment" rather than "raise the air conditioner temperature."

[2123] 3. Feedback function:

[2124] input:

[2125] When a user performs a suggested action, feedback is entered into the device. For example, "I raised the air conditioner temperature by 1 degree."

[2126] evaluation:

[2127] The server optimizes its next proposal based on the feedback data. The collected data is processed into statistical data and securely shared with partner companies and power companies.

[2128] Specific example

[2129] For example, if a household uses its air conditioner day and night, its power consumption data is sent to a server. The server identifies peak times and, taking sentiment data into consideration, generates specific suggestions such as "raise the air conditioner's temperature by one degree at night." This suggestion is notified to the user's smart glasses or head-mounted display in real time. If the user accepts the suggestion, their feedback is collected to improve the accuracy of future suggestions.

[2130] Example of a prompt

[2131] "Consider the user's household electricity data and environmental behavior data, and generate a proposal for the most optimal decarbonization actions under the following conditions."

[2132] Power Consumption Patterns: Based on current data, make suggestions regarding energy-saving home appliances.

[2133] Emotional state: If the user is tired, suggest products with relaxation effects.

[2134] Proposal details: Please specify the product you are proposing and the reasons for your proposal.

[2135] In this way, the system integrates and manages household electricity consumption and environmental behavior, utilizes emotional data to provide users with optimized suggestions, and supports effective decarbonization actions.

[2136] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[2137] Step 1:

[2138] The device acquires electricity usage data from the home's smart meter and environmental sensors. This data includes the household's electricity consumption and is recorded every 10 minutes.

[2139] Step 2:

[2140] The terminal sends power usage data it acquires to the server using the HTTPS communication protocol. The transmitted data includes a timestamp and is encrypted to ensure security.

[2141] Step 3:

[2142] The server saves the received power usage data to a database. A timestamp is added to maintain data integrity during saving, and data backups are also performed.

[2143] Step 4:

[2144] The terminal provides an interface for users to input environmental behavior data. Users input behavior data (e.g., recycling, water conservation) using a touchscreen or voice input.

[2145] Step 5:

[2146] The device sends the entered environmental behavior data to the server. The user can reconfirm the sent data on a confirmation screen.

[2147] Step 6:

[2148] The server stores the received environmental behavior data in a database and performs analysis. The analysis includes a process of assigning points (green scores) based on the behavior. For example, 50 points might be awarded for recycling.

[2149] Step 7:

[2150] The device acquires user emotion data using a device equipped with an emotion engine. This emotion data includes the user's emotional state (e.g., happy, sad, stressed, etc.).

[2151] Step 8:

[2152] The device sends the acquired emotion data to the server. Encrypted communication is used for transmission to protect user privacy.

[2153] Step 9:

[2154] The server stores the received emotional data in a database and analyzes it using an emotion engine. Based on the analysis results, the user's emotional state is identified.

[2155] Step 10:

[2156] The server uses an AI model generated based on power consumption patterns, environmental behavior data, and emotional data to suggest optimal decarbonization actions to the user. For example, it might generate a specific suggestion such as "raise the air conditioner temperature by 1 degree to reduce power consumption at night."

[2157] Step 11:

[2158] The server generates suggestions and sends them to the user's device. The user is then notified in real time via their smart glasses or head-mounted display.

[2159] Step 12:

[2160] The user performs the suggested action and inputs feedback into the device. For example, they might input, "I raised the air conditioner temperature by 1 degree."

[2161] Step 13:

[2162] The device sends the input feedback to the server. The feedback includes the date and time the action was performed and its result.

[2163] Step 14:

[2164] The server stores the feedback data in a database and uses it to optimize future proposals. The collected feedback data is shared with partner companies and power companies as statistical data.

[2165] At each step, it is clearly explained what data the server, terminal, and user input, what data processing is performed, and what output is obtained. This processing flow enables a system that comprehensively manages the user's power consumption, environmental behavior, and emotional state, supporting optimal decarbonization actions.

[2166] 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.

[2167] 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.

[2168] 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.

[2169] 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 ide...

Claims

1. A means of obtaining household electricity usage data, A means of transmitting acquired power usage data to a server, A means of analyzing data acquired by a server to identify power consumption patterns, A means of inputting environmental behavior data by users and sending it to a server, A means of analyzing users' environmental behavior data and awarding points, A method for generating proposals using AI based on electricity usage data and environmental behavior data, A means of sending a proposal from the server to the user's terminal, A means for the user to perform the suggested action and input feedback, A means of sharing data and listing environmentally friendly products and displaying them to users, A system that includes this.

2. The system according to claim 1, further comprising means for visualizing household electricity usage data.

3. The system according to claim 1, further comprising means for evaluating a user's environmental behavior and calculating a green score.

Images