system

Abstract

We provide the system. [Solution] A means of collecting energy consumption data in real time, A means for analyzing collected energy consumption data and generating an optimal energy use schedule, A means of notifying the user terminal of energy usage suggestions based on the analysis results, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, individuals and enterprises are required to promote energy consumption reduction, proper recycling of waste, and environmentally friendly actions. However, there is a lack of specific support systems for consciously implementing these in daily life. For this reason, it is often difficult to always perform conscious eco-friendly activities, and there is a problem that the realization of a sustainable society is delayed.

Means for Solving the Problems

[0005] Therefore, this invention provides a system that collects and analyzes energy consumption data in real time and proposes an optimal usage schedule to the user. Furthermore, it has a function to acquire images of waste, classify them using image recognition technology, provide information on recycling facilities, and reward the user when recycling is carried out. In addition, it calculates the carbon footprint from the user's behavior data, recommends environmentally friendly actions, and records their execution, thereby improving the user's environmental contribution. This makes it possible to provide a system that allows users to easily engage in continuous eco-friendly activities.

[0006] "Energy consumption data" refers to information that records the amount of electricity, gas, water, etc., used in homes and businesses in real time.

[0007] "Means of real-time data collection" refers to equipment or methods for continuously monitoring energy consumption occurring within a specific environment and rapidly incorporating that data into a system.

[0008] "Means of analysis" refers to algorithms or systems that use collected data to reveal its characteristics and patterns and generate suggestions for improving efficiency.

[0009] An "energy use schedule" is a plan or timetable for the efficient use of energy.

[0010] "Means of notification" refer to methods for informing users of generated information or suggestions, such as alerts or messages via smartphones or computers.

[0011] "Waste images" refer to visual information obtained by recording objects being considered for recycling or disposal using cameras or other means.

[0012] "Image recognition technology" is a technology that uses computer vision to automatically identify objects and attributes within an image.

[0013] "Recycling facility information" refers to data on the geographical location and usage methods of recycling centers and facilities that can process specific types of waste.

[0014] "Means of providing rewards" refer to point systems and reward programs that reward and incentivize user behavior.

[0015] "Carbon footprint" refers to the total amount of carbon dioxide emissions associated with a particular activity or operation.

[0016] "Means of recommending environmentally friendly behavior" refers to methods of presenting users with specific actions and options to improve their energy consumption and waste management. [Brief explanation of the drawing]

[0017] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 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 Embodiment 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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single 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.

[0021] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] 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.

[0023] 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).

[0024] 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."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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".

[0038] This invention provides a comprehensive system for optimizing energy consumption, encouraging recycling, and promoting environmentally friendly behavior.

[0039] First, the device acquires energy consumption data, such as electricity, gas, and water, from smart meters and various sensors installed in homes and businesses. This makes it possible to understand real-time energy usage in daily life.

[0040] Next, the server uses an AI agent to analyze the energy data sent from the terminal and identify usage patterns. For example, if energy consumption spikes during specific times in a household, the server generates an optimal energy usage schedule to level out those peaks. This schedule helps save on energy costs and reduce carbon emissions.

[0041] The device notifies the user of the generated schedule and prompts them to take action. Specifically, it displays suggestions such as "avoid using the air conditioner at night" or "use sunlight during the day to run the washing machine."

[0042] In waste management, users upload images of waste items to their devices using their smartphone cameras. These images are sent to servers and analyzed by AI agents using image recognition technology. For example, if an item is identified as plastic, the system provides information on the nearest recycling facility.

[0043] Furthermore, when a user recycles, the device reports the achievement to the server, and the user is awarded points or rewards. This helps to raise users' awareness of recycling and contributes to promoting a circular economy in the region.

[0044] Furthermore, the server calculates the carbon footprint based on the user's daily behavior data and uses this to recommend environmentally friendly behaviors to the user. For example, it sends notifications via the device such as, "We recommend cycling to work today," or "Reduce your meat consumption and incorporate more vegetarian options."

[0045] In this way, users can implement sustainable actions in their daily lives, achieving reductions in energy consumption and increased environmental awareness. The diverse information and incentives provided by the system facilitate users' participation in environmental actions and support their long-term continuation.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] The device acquires real-time energy consumption data from smart meters and sensors within the home or business. This data includes electricity, gas, and water usage.

[0049] Step 2:

[0050] The device sends energy consumption data it acquires to the server at regular intervals. For example, it is set to send data every 15 minutes.

[0051] Step 3:

[0052] The server receives energy data and passes it to an AI agent for analysis of consumption patterns. This includes identifying peak consumption times and detecting wasteful energy use.

[0053] Step 4:

[0054] The server generates an energy usage optimization schedule for the user based on the analysis results. This schedule includes suggestions on when and which appliances should be used.

[0055] Step 5:

[0056] The device notifies the user of energy usage suggestions. Specifically, it suggests actions such as "turn off the lights at night" or "use the washing machine during the day."

[0057] Step 6:

[0058] When users dispose of waste, they take photos of the waste with a device such as a smartphone and upload them to the application.

[0059] Step 7:

[0060] The device sends images of the waste to a server, where an AI agent analyzes the images to identify the type of waste.

[0061] Step 8:

[0062] Based on the analysis results, the server provides the user with information on the nearest recycling facility that can process the waste in question.

[0063] Step 9:

[0064] Once a user completes the recycling process at a facility, the terminal reports that information to the server, and points are added to the user's account.

[0065] Step 10:

[0066] The server analyzes the user's daily behavior data and calculates their carbon footprint. Based on this, it recommends environmentally friendly behaviors to the user via their device.

[0067] Step 11:

[0068] The device notifies the user with specific action suggestions. These suggestions include examples such as "commuting by bicycle instead of train" and "making use of Nikko (a natural area)."

[0069] (Example 1)

[0070] 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."

[0071] In modern society, the inefficient use of energy resources and wasteful carbon emissions are major problems. Furthermore, while promoting resource recovery through proper waste disposal is expected, there is a lack of effective incentives to change user awareness and behavior. Therefore, in addition to optimal energy resource management and carbon emission reduction, promoting sustainable actions, including resource recovery, is a crucial challenge.

[0072] 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.

[0073] In this invention, the server includes means for dynamically collecting energy resource data, means for analyzing the collected energy resource data to generate an appropriate energy use plan, and means for acquiring users' daily behavioral data and calculating carbon emissions based on this data. This makes it possible to improve the efficiency of energy use and reduce carbon emissions, and encourage users to recycle resources and engage in environmentally conscious behavior.

[0074] "Energy resource data" refers to information regarding the usage of various energy sources such as electricity, gas, and water.

[0075] "Dynamic collection" means acquiring data in real time and continuously.

[0076] An "appropriate energy use plan" refers to a schedule or strategy developed to promote efficient energy use.

[0077] "Daily behavioral data" refers to information about the activities and habits that individual users engage in in their daily lives.

[0078] "Calculating carbon emissions" means quantifying how much carbon dioxide a particular action or activity releases.

[0079] A "resource recovery facility" refers to equipment used to properly process waste for the purpose of recycling.

[0080] "Visual recognition technology" refers to computer vision technology that analyzes image data to identify its content.

[0081] "Rewarding" means giving users points, prizes, or other incentives for specific actions.

[0082] "Environmental protection actions" refer to specific proposals and activities aimed at encouraging actions that take the natural environment into consideration.

[0083] This invention is a system for optimizing energy consumption and promoting environmentally friendly behavior. The system dynamically collects energy data, such as electricity, gas, and water, via smart meters and sensors installed in homes and businesses. The terminals instantly transmit the data to a server via Wi-Fi or Bluetooth.

[0084] The server utilizes machine learning frameworks (e.g., TENSORFLOW® and PyTorch) to analyze the collected energy data. This allows it to analyze past consumption patterns and generate efficient energy use plans. The generated plans are then notified to the user via their terminal, thereby encouraging more efficient energy use.

[0085] Furthermore, in waste management, users take pictures of waste using their smartphone cameras and upload them to their devices. The server uses an AI agent to analyze the images and identifies the type of waste using visual recognition technology (e.g., OpenCV). Information on the relevant resource recovery facilities is provided to the user, and they are rewarded for carrying out recycling.

[0086] Furthermore, the server calculates carbon emissions based on the user's daily behavioral data and suggests actions for environmental improvement. These suggestions are also communicated to the user via their device.

[0087] For example, when suggesting commuting by bicycle, the server generates a prompt such as: "You can contribute even more to the environment by commuting by bicycle today instead of using your car."

[0088] By implementing this system, users can improve energy efficiency and raise environmental awareness in their daily lives.

[0089] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0090] Step 1:

[0091] The device collects energy data such as electricity, gas, and water in real time from smart meters and sensors installed in homes and businesses. It receives data from sensors as input and collects it via Wi-Fi or Bluetooth. The collected energy data is then generated as output.

[0092] Step 2:

[0093] The terminal transmits the collected energy data to the server. The input is the energy data collected by the terminal, which is transmitted to the server over the internet using an encryption protocol such as SSL / TLS. The output is the energy data as it was sent to the server.

[0094] Step 3:

[0095] The server analyzes the received energy data using a machine learning framework. The server receives energy data as input and compares and analyzes it with historical consumption data stored in a database. The output extracts energy consumption patterns, forming the basis for an optimal energy plan.

[0096] Step 4:

[0097] The server generates an optimal energy usage plan based on the analysis results. The input is consumption pattern data obtained from the analysis, which is processed by a Python script to generate an energy plan that considers peak shifting and demand response. The output is the energy usage plan.

[0098] Step 5:

[0099] The terminal notifies the user of the generated energy usage plan. The input is the energy plan sent from the server, which is delivered to the user as a push notification through an application installed on the terminal. The output is the energy usage suggestion the user receives.

[0100] Step 6:

[0101] The user takes a picture of the waste using their smartphone camera and uploads it to their device. The input is the captured image of the waste, and the data is uploaded through the app on the device. The output is the visual data sent to the server.

[0102] Step 7:

[0103] The server analyzes uploaded images using visual recognition technology. The input is images of waste sent from the terminal, and the server extracts features using visual recognition technology (e.g., OpenCV) to identify the type of waste. The output is the waste classification result.

[0104] Step 8:

[0105] The server provides the user with information on the nearest resource recovery facility based on the classification results. The input is the waste classification result, which is used to retrieve facility information by referring to a database of recycling facilities. The output is detailed information about the resource recovery facility sent to the user.

[0106] Step 9:

[0107] The terminal sends a report to the server indicating that the user has performed resource recovery. The input is the user's record of resource recovery, which is transmitted to the server as digital data via the terminal. The output is the performance record stored on the server.

[0108] Step 10:

[0109] The server analyzes the user's daily behavior data, calculates carbon emissions, and suggests environmentally friendly actions to the user. The input is the user's behavioral history data, which is analyzed and generated using an AI model to quantify carbon emissions. The output is a suggestion of environmentally conscious actions sent to the user.

[0110] (Application Example 1)

[0111] 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."

[0112] In recent years, improving energy efficiency and proper waste management in urban areas have become increasingly important. However, it is unclear how individual residents can contribute, and there is a lack of mechanisms to encourage concrete action. As a result, unified environmental protection activities by citizens are not progressing.

[0113] 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.

[0114] In this invention, the server includes a device for collecting energy consumption information in real time, a device for analyzing the collected energy consumption information and generating an optimal energy use plan, and a device for providing residents with information to optimize the energy consumption of the entire city. This makes it possible for residents to easily contribute to optimizing energy consumption and waste management, thereby enabling the realization of a sustainable city.

[0115] "Energy consumption information" refers to data on resource usage such as electricity, gas, and water in homes and businesses.

[0116] "Real-time data collection" refers to the process of instantly acquiring information about the current situation, gathering information without delay.

[0117] A "device" refers to equipment or a system designed for a specific purpose.

[0118] An "energy use plan" is an optimized schedule and procedure for the efficient use of energy resources.

[0119] "Analysis" is the process of examining data and understanding its structure and patterns.

[0120] "Information that optimizes energy consumption across an entire city" refers to data and knowledge used to improve the efficiency of resource use in a specific region.

[0121] "Providing to residents" means distributing information and services to people in the community and making them available to them.

[0122] "Waste images" refer to photographs or visual data of items that are scheduled to be disposed of as garbage or unwanted items.

[0123] "Image recognition technology" is a technique that uses computer vision to identify objects and patterns within image data.

[0124] "Recycling facility information" refers to details about the location and services of facilities for reusing waste.

[0125] "To reward" means to give encouragement in order to show appreciation for a series of actions or achievements.

[0126] "User environmental contribution" refers to the degree to which individual people have taken actions for environmental protection and sustainability.

[0127] A "smartphone application" is a software program that runs on a portable computer device.

[0128] "Carbon dioxide emissions" refer to the amount of CO2 released by an individual or organization as a result of their activities.

[0129] "Environmentally friendly behavior" refers to actions and choices that minimize the burden on the natural environment.

[0130] This invention is a comprehensive system for optimizing energy consumption and promoting environmentally conscious behavior. A server collects energy consumption information in real time from smart meters and various sensors in each home and business. The collected information is analyzed using Python and machine learning frameworks (such as TensorFlow and PyTorch). Based on the analysis results, an AI model generates an optimal energy usage plan, which is then notified to residents' smartphones. This notification is provided in an intuitive and actionable format, enabling users to easily improve their energy efficiency.

[0131] The terminal also uses its camera to acquire images of the waste and sends these images to a cloud server. The server analyzes the type of waste using AWS® Rekognition or similar image recognition technology and provides the user with information on the nearest recycling facility. When the waste is recycled, the server records the process and awards points or rewards to the user.

[0132] Furthermore, the server calculates carbon dioxide emissions based on the user's location and behavioral patterns. Based on this information, it provides residents with specific suggestions to promote environmentally friendly behavior through a smartphone application. For example, it might notify them with messages like, "We recommend cycling to work today," or "Why not try a vegetarian diet?"

[0133] As a concrete example, suppose a user is planning a picnic on Sunday. In this case, the application can check the weather forecast for that day and, if it is sunny, send a notification saying, "Why not use sunlight to cook your picnic food? We recommend using solar cooking equipment." By effectively implementing this invention, it is possible to support residents' sustainable lifestyles and contribute to energy management and environmental protection throughout the city.

[0134] A concrete example of a prompt message would be: "Design an application to optimize energy consumption and promote recycling. Include features that provide users with energy usage schedules and recycling information, and encourage environmentally conscious behavior."

[0135] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0136] Step 1:

[0137] The terminal collects energy consumption information, such as electricity, gas, and water, in real time from smart meters and various sensors installed in homes and businesses. This input information is organized within the terminal and prepared for transmission to the server.

[0138] Step 2:

[0139] The server, upon receiving energy consumption information sent from the terminal, performs analysis using Python and a machine learning framework such as TensorFlow or PyTorch. The input for this step is energy consumption information, and the output is the generation of an optimal energy use plan.

[0140] Step 3:

[0141] The server notifies residents of the generated energy usage plan on their smartphones. The devices receive this information and present it to the users as actionable and concrete suggestions.

[0142] Step 4:

[0143] The device uses its camera to capture images of the user's waste and sends them to a cloud server. This image data becomes input information, preparing the server for image analysis.

[0144] Step 5:

[0145] The server analyzes the transmitted waste images using image recognition technologies such as AWS Rekognition. In this step, based on the input image data, the waste is classified and information about the recycling facility is generated as output.

[0146] Step 6:

[0147] The server records recycling activity and awards points or rewards to users based on the results. The terminal displays reward information to users, rewarding their eco-friendly efforts.

[0148] Step 7:

[0149] The device collects the user's location information and behavioral patterns and sends them to the server. The server uses this data to calculate the user's carbon dioxide emissions and generates suggestions for environmentally friendly actions as output.

[0150] Step 8:

[0151] The server provides the generated action suggestions to the user via a smartphone application. The user then uses this output information to carry out environmental contribution activities.

[0152] 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.

[0153] This invention is a system that optimizes energy consumption in homes and businesses and promotes environmentally conscious behavior that takes into account the user's emotional state. Based on energy consumption data acquired from smart meters and various sensors, the system provides an optimized energy usage schedule. Furthermore, in waste disposal, an AI agent analyzes images of waste taken by the user with their smartphone and recommends appropriate recycling methods.

[0154] By incorporating an emotion engine, this system can analyze the user's emotional state in real time and provide energy usage suggestions adapted to the user's emotions. For example, the device acquires the user's facial expressions and voice through the camera and voice input, and this data is analyzed by the emotion engine on the server. Based on the results of this analysis, the server improves the acceptability of energy usage suggestions by making more restrained suggestions when the user is feeling stressed and more proactive suggestions when the user is in a positive state.

[0155] Furthermore, the emotion engine monitors users' emotional responses to positive environmental actions and provides rewards and feedback at appropriate times. This enables interaction based on users' emotions, naturally encouraging participation in sustainable eco-activities. For example, users who show a positive response to recycling activities are given further rewards by the server to maintain their motivation. Through these responses based on emotion analysis, the entire system promotes long-term user cooperation and contributes to building a more sustainable society.

[0156] The following describes the processing flow.

[0157] Step 1:

[0158] The device acquires energy consumption data in real time via smart meters and sensors. This data includes electricity, gas, and water usage.

[0159] Step 2:

[0160] The device collects energy data and sends it to the server at regular intervals. The transmission interval is, for example, every 15 minutes.

[0161] Step 3:

[0162] The server receives data and passes it to an AI agent to analyze consumption patterns. This identifies peak power consumption and wasteful energy use.

[0163] Step 4:

[0164] The server generates an energy usage optimization schedule based on the analysis results and sends it to the terminal.

[0165] Step 5:

[0166] Users review suggested schedules on their devices and determine whether they can implement them in their daily lives.

[0167] Step 6:

[0168] The device uses its camera to record the user's facial expressions and voice, and sends the emotional data to a server.

[0169] Step 7:

[0170] The server uses an emotion engine to analyze the user's emotional state in real time. This analysis detects stress and positive emotions.

[0171] Step 8:

[0172] The server adjusts the content and timing of energy usage suggestions based on the emotion analysis results. For example, if the user is feeling stressed, it will make restrained suggestions, and if they are feeling positive, it will make more proactive suggestions.

[0173] Step 9:

[0174] When a user disposes of waste, they take a picture of the waste with their smartphone camera and upload that data to the server via their device.

[0175] Step 10:

[0176] The server uses image recognition technology to classify waste and notifies the user of the appropriate recycling method. Furthermore, it provides detailed information about recycling facilities.

[0177] Step 11:

[0178] The device monitors the user's emotional response to recycling activities, and if it shows a positive response, the server awards reward points and provides feedback.

[0179] Through this process, energy consumption is optimized and environmentally friendly behavior is promoted while taking user emotions into consideration.

[0180] (Example 2)

[0181] 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".

[0182] In modern society, efficient energy use in homes and businesses is required, but uniform energy use proposals that ignore consumers' emotional states are unlikely to be accepted, making it difficult to promote sustainable energy conservation activities. Similarly, in waste management, while users are required to accurately identify and properly recycle waste, a lack of motivation for this is a challenge.

[0183] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0184] In this invention, the server includes means for collecting energy consumption information in real time, means for analyzing the collected energy consumption information and generating an optimal energy usage plan, and means for analyzing the user's emotional state and adjusting energy usage suggestions based on that emotional state. This enables flexible energy usage suggestions that are adapted to the user's emotional state, thereby improving the acceptability of the suggestions.

[0185] "Energy consumption information" is a general term for data related to the use of electricity, gas, water, etc., in households, businesses, and other organizations.

[0186] A "real-time data collection processing device" refers to a machine or software that instantly acquires energy consumption information based on the actual passage of time.

[0187] A "processing device for generating an optimal energy use plan" is a machine or software that analyzes collected energy consumption information and devises effective and efficient energy use methods.

[0188] "Information processing terminal" refers to the user's device, such as a smartphone or personal computer, which is used to receive energy usage suggestions.

[0189] "User emotional state" refers to the psychological or emotional state of a user as recognized by an analysis system on the terminal or server.

[0190] A "processing device that adjusts suggestions based on emotional state" is a machine or software that optimizes energy usage suggestions by taking into account the analyzed emotional state of the user and provides them to the user.

[0191] "Images of waste" refers to image data of waste captured by devices such as smartphones and digital cameras.

[0192] "Image recognition technology" is a technology that identifies specific objects from captured video data and analyzes their characteristics.

[0193] A "waste disposal agency" refers to a facility or organization responsible for the proper processing or recycling of collected waste.

[0194] A "reward-granting processing device" is a machine or software that provides rewards based on a user's energy usage and waste disposal practices.

[0195] This system promotes sustainable energy activities by improving the efficiency of energy consumption in homes and businesses and by providing energy use suggestions that take into account the user's emotional state.

[0196] The server first collects energy consumption information in real time from smart meters and various sensors installed in homes and businesses. This includes consumption data for commonly used resources such as electricity, gas, and water. The collected information is stored in a database on the server and analyzed by analytical algorithms. The analysis includes identifying peak energy consumption times and inefficient usage patterns, and based on this, an optimized energy use plan is generated.

[0197] The terminal refers to the user's PC or smartphone, and the server notifies these terminals with energy usage suggestions. Furthermore, the terminal uses its built-in camera and microphone to collect the user's facial expressions and voice, and transmits this data to the server. The emotion engine on the server analyzes this data and determines the user's emotional state in real time. Based on the analyzed emotional state, the energy usage suggestions are adjusted to suit the user, and efforts are made to improve the acceptability of the suggestions.

[0198] Furthermore, users use their devices to take photos of waste and send this video data to a server. The server uses a generative AI model to analyze the video and identify the type of waste. Based on the identification results, the user is provided with information on appropriate recycling methods and facilities. The system records the user's recycling activities and provides rewards for them. Through these rewards, users can be motivated to engage in sustainable waste management activities.

[0199] For example, if a user actively engages in recycling activities, the server can sense the user's positive emotional state and encourage further activity by providing appropriate rewards. This application allows the system to support sustainable eco-friendly practices in the long term.

[0200] Examples of prompts generated using AI models include the following:

[0201] "List the recommended energy optimization strategies when the user's emotional state is positive."

[0202] "How can I offer gentle suggestions to users who are experiencing stress?"

[0203] In this way, the system provides energy usage suggestions and reward systems adapted to the user's emotional state, promoting sustainable environmental protection.

[0204] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0205] Step 1:

[0206] The terminal uses smart meters and sensors to collect energy consumption information from homes and businesses in real time. This data includes electricity, gas, and water usage. The collected data is sent to a server using a protocol and recorded in a database.

[0207] Step 2:

[0208] The server analyzes the collected energy consumption information using data analysis algorithms. Specifically, it performs time-series analysis to identify peak usage times and inefficient usage patterns. The output of this analysis is patterns and statistics extracted from the data.

[0209] Step 3:

[0210] The server generates an optimized energy usage plan based on the analysis results. The plan generation algorithm refers to the user's past usage data and current consumption patterns to propose specific actions for cost reduction and efficiency improvement. This generated plan is sent to the user's device as a notification.

[0211] Step 4:

[0212] The device uses its built-in camera and microphone to collect the user's facial expressions and voice. This data is sent to a server for analysis of their emotional state. This makes it possible to capture the user's instantaneous emotional responses.

[0213] Step 5:

[0214] The server analyzes the received emotional data to infer the user's emotional state. The emotional analysis engine uses facial recognition technology and voice analysis to identify stress levels and positive emotions. The output of this process is the result of the assessment of the user's emotional state.

[0215] Step 6:

[0216] The server adjusts the energy usage plan based on the user's emotional state. Specifically, it makes conservative suggestions if the user is stressed and more positive suggestions if the user is feeling positive. These adjusted suggestions are then sent back to the user's device.

[0217] Step 7:

[0218] Users film waste with their smartphones and send the footage to a server via their devices. The video data is then analyzed by an AI model to identify the type of waste.

[0219] Step 8:

[0220] The server uses a generation AI model to analyze video data of waste and identify its characteristics. As a result, the recycling method for each type of waste is determined and the user is notified with appropriate recycling information.

[0221] Step 9:

[0222] The server rewards users for recycling activities. This reward information is recorded in a database and used to encourage future user behavior.

[0223] (Application Example 2)

[0224] 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".

[0225] In modern society, efficient energy management and optimized waste disposal are crucial issues. Furthermore, it is necessary to promote sustainable environmental behavior by providing suggestions and feedback that consider the emotional state of users. However, effectively integrating these elements and enabling users to use energy efficiently and recycle without experiencing stress is a challenging task.

[0226] 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.

[0227] In this invention, the server includes means for collecting energy consumption information in real time, means for analyzing the collected energy consumption information and generating an optimal energy use plan, and means for evaluating the user's emotional state using emotion analysis means. This makes it possible to adjust energy use suggestions based on the user's emotional state and promote participation in sustainable environmental actions.

[0228] "Energy consumption information" refers to data on the usage of energy such as electricity and gas by households and businesses.

[0229] "Means of real-time collection" refers to a mechanism for continuously acquiring energy consumption information at the current time without interruption.

[0230] "Means for generating energy usage plans" refers to a system that automatically determines the optimal energy consumption schedule based on collected energy consumption information.

[0231] An "information terminal" refers to an electronic device used to transmit energy usage suggestions and recycling information to users.

[0232] "Emotional analysis methods" refer to technologies and systems that analyze information such as a user's facial expressions and voice to understand their emotional state.

[0233] "Means for acquiring images of waste" refers to methods and devices that acquire visual data of waste using cameras or sensing technology.

[0234] "Image analysis technology" refers to techniques for processing acquired image data and extracting specific information or features from it.

[0235] "Waste treatment facility information" refers to data about the location and methods used to properly process specific types of waste.

[0236] A "means of providing rewards" refers to a system that offers rewards or points as an incentive when users take desirable actions.

[0237] A "positive response" refers to a state in which users show a proactive and positive attitude towards suggestions and feedback.

[0238] "Environmental impact" is a measure that indicates the degree to which the activities of individuals and organizations have an impact on the global environment.

[0239] "Sustainable behavior" refers to actions that are kind to the Earth's environment and the ecosystems that comprise it, and that have characteristics that make them sustainable for the future.

[0240] "Feedback" is the process of providing information to communicate the next course of action or evaluation based on the user's behavior and reactions.

[0241] To implement this invention, it is necessary to construct a system combining several key components. The server collects energy consumption information in real time, analyzes it, and generates an optimal energy usage plan. The server is also equipped with emotion analysis means, which evaluates the user's emotional state using facial expressions and voice data. This makes it possible to suggest energy usage tailored to the user's emotions.

[0242] Specifically, devices such as smartphones and tablets use cameras and microphones to input the user's facial expressions and voice, and send this data to a server. The server runs a generative AI model for emotion analysis to determine the user's emotional state. Using this analysis, the server makes suggestions to optimize energy use in a way that minimizes stress for the user.

[0243] Furthermore, the terminal acquires images of the waste, and the server analyzes these images to identify the type of waste. Based on the identification results, information about recycling facilities is provided to the user, and rewards are given at the appropriate time.

[0244] As a concrete example, when a user takes a picture of a specific type of waste with their smartphone, the image is sent to a server. The server uses image analysis technology to classify the waste and suggests the optimal recycling method based on the user's emotional state. Furthermore, if positive user behavior is detected, the system can encourage sustained eco-friendly activities through rewards such as bonus points.

[0245] Example prompt to input to the generating AI model: "What energy-saving suggestions would be effective when the user's stress level is high?"

[0246] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0247] Step 1:

[0248] The device uses its camera and microphone to collect the user's facial expressions and voice data. This allows the device to obtain input data that indicates the user's current emotional state. This data forms the basis for perceiving and analyzing customer emotions in real time.

[0249] Step 2:

[0250] The device sends the collected facial and audio data to the server. The server receives this data and analyzes the user's emotions using a generative AI model. By processing the facial and audio information received as input data with the AI ​​model, the user's emotional state (e.g., stress or happiness) is output.

[0251] Step 3:

[0252] The server generates optimal energy usage suggestions for the user based on the results of emotion analysis and learning. Using the analyzed emotional state as input, the server outputs optimized suggestions that advise the user on how to expend energy. In cases where the user's stress level is high, it selects suggestions that are more acceptable to them.

[0253] Step 4:

[0254] The server sends energy usage suggestions to the terminal. The terminal notifies the user of the received suggestions. The user can review the energy usage suggestions from the server via the terminal and choose an action based on their content.

[0255] Step 5:

[0256] The user takes a picture of the waste with their device. The device sends this image to the server as input, providing initial information so that the user can learn how to proceed with recycling.

[0257] Step 6:

[0258] The server analyzes images of the received waste and identifies the type of waste using image analysis technology. It takes image data as input and outputs waste classification information. Based on this classification, the server determines the optimal recycling method.

[0259] Step 7:

[0260] The server sends information and suggestions about recycling facilities to the terminal based on the waste classification results, notifying the user. This allows the user to obtain detailed information necessary for recycling activities and take action.

[0261] Step 8:

[0262] The user performs the suggested eco-activities and sends a report from their device to the server. The server receives this report, evaluates the user's activities, and rewards positive responses. At this time, the server obtains the user's level of achievement in eco-activities as input and outputs reward points based on the result.

[0263] 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.

[0264] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0265] 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.

[0266] [Second Embodiment]

[0267] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0268] 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.

[0269] 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).

[0270] 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.

[0271] 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.

[0272] 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).

[0273] 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.

[0274] 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.

[0275] 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.

[0276] 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.

[0277] 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.

[0278] 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".

[0279] This invention provides a comprehensive system for optimizing energy consumption, encouraging recycling, and promoting environmentally friendly behavior.

[0280] First, the device acquires energy consumption data, such as electricity, gas, and water, from smart meters and various sensors installed in homes and businesses. This makes it possible to understand real-time energy usage in daily life.

[0281] Next, the server analyzes the energy data transmitted from the terminal by the AI agent to identify usage patterns. For example, if there is a sharp increase in energy consumption at a specific time zone within a household, an optimal energy usage schedule is generated to level off the peak. This schedule helps save on energy costs and reduce carbon dioxide emissions.

[0282] The terminal notifies the user of the generated schedule and prompts for execution. Specifically, it is in the form of displaying suggestions such as "Avoid using the air conditioner at night" and "Operate the washing machine using sunlight during the day".

[0283] In waste management, the user uploads an image of the waste to the terminal using the camera of the smartphone. This image is sent to the server and analyzed by the AI agent using image recognition technology. For example, if an item is recognized as plastic, information on the nearest recycling facility is provided.

[0284] Furthermore, when the user performs recycling, the terminal reports the achievement to the server, and there is a function to give points and rewards to the user. This can enhance the user's recycling awareness and contribute to promoting the circular economy in the region.

[0285] Also, the server calculates the carbon footprint based on the user's daily behavior data and recommends environmentally friendly actions to the user based on this. For example, notifications such as "It is recommended to commute by bicycle today" and "Reduce meat days and incorporate a vegetarian diet" are sent through the terminal.

[0286] In this way, users can carry out sustainable actions in their daily lives, achieving reduction in energy consumption and improvement in environmental awareness. It is a form that facilitates the user's participation in environmental actions and supports long-term continuation through the diverse information and incentives provided by the system.

[0287] The following explains the process flow.

[0288] Step 1:

[0289] The device acquires real-time energy consumption data from smart meters and sensors within the home or business. This data includes electricity, gas, and water usage.

[0290] Step 2:

[0291] The device sends energy consumption data it acquires to the server at regular intervals. For example, it is set to send data every 15 minutes.

[0292] Step 3:

[0293] The server receives energy data and passes it to an AI agent for analysis of consumption patterns. This includes identifying peak consumption times and detecting wasteful energy use.

[0294] Step 4:

[0295] The server generates an energy usage optimization schedule for the user based on the analysis results. This schedule includes suggestions on when and which appliances should be used.

[0296] Step 5:

[0297] The device notifies the user of energy usage suggestions. Specifically, it suggests actions such as "turn off the lights at night" or "use the washing machine during the day."

[0298] Step 6:

[0299] When users dispose of waste, they take photos of the waste with a device such as a smartphone and upload them to the application.

[0300] Step 7:

[0301] The terminal sends the image of the waste taken to the server, and the AI agent analyzes the image to identify the type of waste.

[0302] Step 8:

[0303] Based on the analysis result, the server provides the user with information on the nearest recycling facility where the corresponding waste can be processed.

[0304] Step 9:

[0305] When the user completes recycling at the facility, the terminal reports the information to the server, and points are added to the user account.

[0306] Step 10:

[0307] The server analyzes the user's daily behavior data and calculates the carbon footprint. Based on this, it recommends environmentally friendly actions to the user via the terminal.

[0308] Step 11:

[0309] The terminal notifies the user of specific action proposals. The proposed content includes examples of actions such as "commuting by bicycle instead of by train" and "utilizing sunlight".

[0310] (Example 1)

[0311] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0312] In modern society, the inefficient use of energy resources and wasteful carbon emissions have become major problems. Also, although promoting resource recovery through proper treatment of waste is expected, there is a lack of effective incentives to change the awareness and behavior of users. Therefore, in addition to the optimal management of energy resources and the reduction of carbon emissions, promoting sustainable behaviors including resource recovery is an important issue.

[0313] 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.

[0314] In this invention, the server includes means for dynamically collecting energy resource data, means for analyzing the collected energy resource data to generate an appropriate energy use plan, and means for acquiring users' daily behavioral data and calculating carbon emissions based on this data. This makes it possible to improve the efficiency of energy use and reduce carbon emissions, and encourage users to recycle resources and engage in environmentally conscious behavior.

[0315] "Energy resource data" refers to information regarding the usage of various energy sources such as electricity, gas, and water.

[0316] "Dynamic collection" means acquiring data in real time and continuously.

[0317] An "appropriate energy use plan" refers to a schedule or strategy developed to promote efficient energy use.

[0318] "Daily behavioral data" refers to information about the activities and habits that individual users engage in in their daily lives.

[0319] "Calculating carbon emissions" means quantifying how much carbon dioxide a particular action or activity releases.

[0320] A "resource recovery facility" refers to equipment used to properly process waste for the purpose of recycling.

[0321] "Visual recognition technology" refers to computer vision technology that analyzes image data to identify its content.

[0322] "Rewarding" means giving users points, prizes, or other incentives for specific actions.

[0323] "Environmental protection actions" refer to specific proposals and activities aimed at encouraging actions that take the natural environment into consideration.

[0324] This invention is a system for optimizing energy consumption and promoting environmentally friendly behavior. The system dynamically collects energy data, such as electricity, gas, and water, via smart meters and sensors installed in homes and businesses. The terminals instantly transmit the data to a server via Wi-Fi or Bluetooth.

[0325] The server utilizes machine learning frameworks (e.g., TensorFlow and PyTorch) to analyze the collected energy data. This allows it to analyze past consumption patterns and generate efficient energy use plans. The generated plans are then communicated to the user via their terminal, encouraging more efficient energy use.

[0326] Furthermore, in waste management, users take pictures of waste using their smartphone cameras and upload them to their devices. The server uses an AI agent to analyze the images and identifies the type of waste using visual recognition technology (e.g., OpenCV). Information on the relevant resource recovery facilities is provided to the user, and they are rewarded for carrying out recycling.

[0327] Furthermore, the server calculates carbon emissions based on the user's daily behavioral data and suggests actions for environmental improvement. These suggestions are also communicated to the user via their device.

[0328] For example, when suggesting commuting by bicycle, the server generates a prompt such as: "You can contribute even more to the environment by commuting by bicycle today instead of using your car."

[0329] By implementing this system, users can improve energy efficiency and raise environmental awareness in their daily lives.

[0330] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0331] Step 1:

[0332] The device collects energy data such as electricity, gas, and water in real time from smart meters and sensors installed in homes and businesses. It receives data from sensors as input and collects it via Wi-Fi or Bluetooth. The collected energy data is then generated as output.

[0333] Step 2:

[0334] The terminal transmits the collected energy data to the server. The input is the energy data collected by the terminal, which is transmitted to the server over the internet using an encryption protocol such as SSL / TLS. The output is the energy data as it was sent to the server.

[0335] Step 3:

[0336] The server analyzes the received energy data using a machine learning framework. The server receives energy data as input and compares and analyzes it with historical consumption data stored in a database. The output extracts energy consumption patterns, forming the basis for an optimal energy plan.

[0337] Step 4:

[0338] The server generates an optimal energy usage plan based on the analysis results. The input is consumption pattern data obtained from the analysis, which is processed by a Python script to generate an energy plan that considers peak shifting and demand response. The output is the energy usage plan.

[0339] Step 5:

[0340] The terminal notifies the user of the generated energy usage plan. The input is the energy plan sent from the server, which is delivered to the user as a push notification through an application installed on the terminal. The output is the energy usage suggestion the user receives.

[0341] Step 6:

[0342] The user takes a picture of the waste using their smartphone camera and uploads it to their device. The input is the captured image of the waste, and the data is uploaded through the app on the device. The output is the visual data sent to the server.

[0343] Step 7:

[0344] The server analyzes uploaded images using visual recognition technology. The input is images of waste sent from the terminal, and the server extracts features using visual recognition technology (e.g., OpenCV) to identify the type of waste. The output is the waste classification result.

[0345] Step 8:

[0346] The server provides the user with information on the nearest resource recovery facility based on the classification results. The input is the waste classification result, which is used to retrieve facility information by referring to a database of recycling facilities. The output is detailed information about the resource recovery facility sent to the user.

[0347] Step 9:

[0348] The terminal sends a report to the server indicating that the user has performed resource recovery. The input is the user's record of resource recovery, which is transmitted to the server as digital data via the terminal. The output is the performance record stored on the server.

[0349] Step 10:

[0350] The server analyzes the user's daily behavior data, calculates carbon emissions, and suggests environmentally friendly actions to the user. The input is the user's behavioral history data, which is analyzed and generated using an AI model to quantify carbon emissions. The output is a suggestion of environmentally conscious actions sent to the user.

[0351] (Application Example 1)

[0352] 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."

[0353] In recent years, improving energy efficiency and proper waste management in urban areas have become increasingly important. However, it is unclear how individual residents can contribute, and there is a lack of mechanisms to encourage concrete action. As a result, unified environmental protection activities by citizens are not progressing.

[0354] 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.

[0355] In this invention, the server includes a device for collecting energy consumption information in real time, a device for analyzing the collected energy consumption information and generating an optimal energy use plan, and a device for providing residents with information to optimize the energy consumption of the entire city. This makes it possible for residents to easily contribute to optimizing energy consumption and waste management, thereby enabling the realization of a sustainable city.

[0356] "Energy consumption information" refers to data on resource usage such as electricity, gas, and water in homes and businesses.

[0357] "Real-time data collection" refers to the process of instantly acquiring information about the current situation, gathering information without delay.

[0358] A "device" refers to equipment or a system designed for a specific purpose.

[0359] An "energy use plan" is an optimized schedule and procedure for the efficient use of energy resources.

[0360] "Analysis" is the process of examining data and understanding its structure and patterns.

[0361] "Information that optimizes energy consumption across an entire city" refers to data and knowledge used to improve the efficiency of resource use in a specific region.

[0362] "Providing to residents" means distributing information and services to people in the community and making them available to them.

[0363] "Waste images" refer to photographs or visual data of items that are scheduled to be disposed of as garbage or unwanted items.

[0364] "Image recognition technology" is a technique that uses computer vision to identify objects and patterns within image data.

[0365] "Recycling facility information" refers to details about the location and services of facilities for reusing waste.

[0366] "To reward" means to give encouragement in order to show appreciation for a series of actions or achievements.

[0367] "User environmental contribution" refers to the degree to which individual people have taken actions for environmental protection and sustainability.

[0368] A "smartphone application" is a software program that runs on a portable computer device.

[0369] "Carbon dioxide emissions" refer to the amount of CO2 released by an individual or organization as a result of their activities.

[0370] "Environmentally friendly behavior" refers to actions and choices that minimize the burden on the natural environment.

[0371] This invention is a comprehensive system for optimizing energy consumption and promoting environmentally conscious behavior. A server collects energy consumption information in real time from smart meters and various sensors in each home and business. The collected information is analyzed using Python and machine learning frameworks (such as TensorFlow and PyTorch). Based on the analysis results, an AI model generates an optimal energy usage plan, which is then notified to residents' smartphones. This notification is provided in an intuitive and actionable format, enabling users to easily improve their energy efficiency.

[0372] The device also uses its camera to acquire images of the waste and sends these images to a cloud server. The server analyzes the type of waste using AWS Rekognition or similar image recognition technology and provides the user with information on the nearest recycling facility. When the waste is recycled, the server records the process and awards points or rewards to the user.

[0373] Furthermore, the server calculates carbon dioxide emissions based on the user's location and behavioral patterns. Based on this information, it provides residents with specific suggestions to promote environmentally friendly behavior through a smartphone application. For example, it might notify them with messages like, "We recommend cycling to work today," or "Why not try a vegetarian diet?"

[0374] As a concrete example, suppose a user is planning a picnic on Sunday. In this case, the application can check the weather forecast for that day and, if it is sunny, send a notification saying, "Why not use sunlight to cook your picnic food? We recommend using solar cooking equipment." By effectively implementing this invention, it is possible to support residents' sustainable lifestyles and contribute to energy management and environmental protection throughout the city.

[0375] A concrete example of a prompt message would be: "Design an application to optimize energy consumption and promote recycling. Include features that provide users with energy usage schedules and recycling information, and encourage environmentally conscious behavior."

[0376] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0377] Step 1:

[0378] The terminal collects energy consumption information, such as electricity, gas, and water, in real time from smart meters and various sensors installed in homes and businesses. This input information is organized within the terminal and prepared for transmission to the server.

[0379] Step 2:

[0380] The server, upon receiving energy consumption information sent from the terminal, performs analysis using Python and a machine learning framework such as TensorFlow or PyTorch. The input for this step is energy consumption information, and the output is the generation of an optimal energy use plan.

[0381] Step 3:

[0382] The server notifies residents of the generated energy usage plan on their smartphones. The devices receive this information and present it to the users as actionable and concrete suggestions.

[0383] Step 4:

[0384] The device uses its camera to capture images of the user's waste and sends them to a cloud server. This image data becomes input information, preparing the server for image analysis.

[0385] Step 5:

[0386] The server analyzes the transmitted waste images using image recognition technologies such as AWS Rekognition. In this step, based on the input image data, the waste is classified and information about the recycling facility is generated as output.

[0387] Step 6:

[0388] The server records recycling activity and awards points or rewards to users based on the results. The terminal displays reward information to users, rewarding their eco-friendly efforts.

[0389] Step 7:

[0390] The device collects the user's location information and behavioral patterns and sends them to the server. The server uses this data to calculate the user's carbon dioxide emissions and generates suggestions for environmentally friendly actions as output.

[0391] Step 8:

[0392] The server provides the generated action suggestions to the user via a smartphone application. The user then uses this output information to carry out environmental contribution activities.

[0393] 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.

[0394] This invention is a system that optimizes energy consumption in homes and businesses and promotes environmentally conscious behavior that takes into account the user's emotional state. Based on energy consumption data acquired from smart meters and various sensors, the system provides an optimized energy usage schedule. Furthermore, in waste disposal, an AI agent analyzes images of waste taken by the user with their smartphone and recommends appropriate recycling methods.

[0395] By incorporating an emotion engine, this system can analyze the user's emotional state in real time and provide energy usage suggestions adapted to the user's emotions. For example, the device acquires the user's facial expressions and voice through the camera and voice input, and this data is analyzed by the emotion engine on the server. Based on the results of this analysis, the server improves the acceptability of energy usage suggestions by making more restrained suggestions when the user is feeling stressed and more proactive suggestions when the user is in a positive state.

[0396] Furthermore, the emotion engine monitors users' emotional responses to positive environmental actions and provides rewards and feedback at appropriate times. This enables interaction based on users' emotions, naturally encouraging participation in sustainable eco-activities. For example, users who show a positive response to recycling activities are given further rewards by the server to maintain their motivation. Through these responses based on emotion analysis, the entire system promotes long-term user cooperation and contributes to building a more sustainable society.

[0397] The following describes the processing flow.

[0398] Step 1:

[0399] The device acquires energy consumption data in real time via smart meters and sensors. This data includes electricity, gas, and water usage.

[0400] Step 2:

[0401] The device collects energy data and sends it to the server at regular intervals. The transmission interval is, for example, every 15 minutes.

[0402] Step 3:

[0403] The server receives data and passes it to an AI agent to analyze consumption patterns. This identifies peak power consumption and wasteful energy use.

[0404] Step 4:

[0405] The server generates an energy usage optimization schedule based on the analysis results and sends it to the terminal.

[0406] Step 5:

[0407] Users review suggested schedules on their devices and determine whether they can implement them in their daily lives.

[0408] Step 6:

[0409] The device uses its camera to record the user's facial expressions and voice, and sends the emotional data to a server.

[0410] Step 7:

[0411] The server uses an emotion engine to analyze the user's emotional state in real time. This analysis detects stress and positive emotions.

[0412] Step 8:

[0413] The server adjusts the content and timing of energy usage suggestions based on the emotion analysis results. For example, if the user is feeling stressed, it will make restrained suggestions, and if they are feeling positive, it will make more proactive suggestions.

[0414] Step 9:

[0415] When a user disposes of waste, they take a picture of the waste with their smartphone camera and upload that data to the server via their device.

[0416] Step 10:

[0417] The server uses image recognition technology to classify waste and notifies the user of the appropriate recycling method. Furthermore, it provides detailed information about recycling facilities.

[0418] Step 11:

[0419] The device monitors the user's emotional response to recycling activities, and if it shows a positive response, the server awards reward points and provides feedback.

[0420] Through this process, energy consumption is optimized and environmentally friendly behavior is promoted while taking user emotions into consideration.

[0421] (Example 2)

[0422] 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".

[0423] In modern society, efficient energy use in homes and businesses is required, but uniform energy use proposals that ignore consumers' emotional states are unlikely to be accepted, making it difficult to promote sustainable energy conservation activities. Similarly, in waste management, while users are required to accurately identify and properly recycle waste, a lack of motivation for this is a challenge.

[0424] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0425] In this invention, the server includes means for collecting energy consumption information in real time, means for analyzing the collected energy consumption information and generating an optimal energy usage plan, and means for analyzing the user's emotional state and adjusting energy usage suggestions based on that emotional state. This enables flexible energy usage suggestions that are adapted to the user's emotional state, thereby improving the acceptability of the suggestions.

[0426] "Energy consumption information" is a general term for data related to the use of electricity, gas, water, etc., in households, businesses, and other organizations.

[0427] A "real-time data collection processing device" refers to a machine or software that instantly acquires energy consumption information based on the actual passage of time.

[0428] A "processing device for generating an optimal energy use plan" is a machine or software that analyzes collected energy consumption information and devises effective and efficient energy use methods.

[0429] "Information processing terminal" refers to the user's device, such as a smartphone or personal computer, which is used to receive energy usage suggestions.

[0430] "User emotional state" refers to the psychological or emotional state of a user as recognized by an analysis system on the terminal or server.

[0431] A "processing device that adjusts suggestions based on emotional state" is a machine or software that optimizes energy usage suggestions by taking into account the analyzed emotional state of the user and provides them to the user.

[0432] "Images of waste" refers to image data of waste captured by devices such as smartphones and digital cameras.

[0433] "Image recognition technology" is a technology that identifies specific objects from captured video data and analyzes their characteristics.

[0434] A "waste disposal agency" refers to a facility or organization responsible for the proper processing or recycling of collected waste.

[0435] A "reward-granting processing device" is a machine or software that provides rewards based on a user's energy usage and waste disposal practices.

[0436] This system promotes sustainable energy activities by improving the efficiency of energy consumption in homes and businesses and by providing energy use suggestions that take into account the user's emotional state.

[0437] The server first collects energy consumption information in real time from smart meters and various sensors installed in homes and businesses. This includes consumption data for commonly used resources such as electricity, gas, and water. The collected information is stored in a database on the server and analyzed by analytical algorithms. The analysis includes identifying peak energy consumption times and inefficient usage patterns, and based on this, an optimized energy use plan is generated.

[0438] The terminal refers to the user's PC or smartphone, and the server notifies these terminals with energy usage suggestions. Furthermore, the terminal uses its built-in camera and microphone to collect the user's facial expressions and voice, and transmits this data to the server. The emotion engine on the server analyzes this data and determines the user's emotional state in real time. Based on the analyzed emotional state, the energy usage suggestions are adjusted to suit the user, and efforts are made to improve the acceptability of the suggestions.

[0439] Furthermore, users use their devices to take photos of waste and send this video data to a server. The server uses a generative AI model to analyze the video and identify the type of waste. Based on the identification results, the user is provided with information on appropriate recycling methods and facilities. The system records the user's recycling activities and provides rewards for them. Through these rewards, users can be motivated to engage in sustainable waste management activities.

[0440] For example, if a user actively engages in recycling activities, the server can sense the user's positive emotional state and encourage further activity by providing appropriate rewards. This application allows the system to support sustainable eco-friendly practices in the long term.

[0441] Examples of prompts generated using AI models include the following:

[0442] "List the recommended energy optimization strategies when the user's emotional state is positive."

[0443] "How can I offer gentle suggestions to users who are experiencing stress?"

[0444] In this way, the system provides energy usage suggestions and reward systems adapted to the user's emotional state, promoting sustainable environmental protection.

[0445] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0446] Step 1:

[0447] The terminal uses smart meters and sensors to collect energy consumption information from homes and businesses in real time. This data includes electricity, gas, and water usage. The collected data is sent to a server using a protocol and recorded in a database.

[0448] Step 2:

[0449] The server analyzes the collected energy consumption information using data analysis algorithms. Specifically, it performs time-series analysis to identify peak usage times and inefficient usage patterns. The output of this analysis is patterns and statistics extracted from the data.

[0450] Step 3:

[0451] The server generates an optimized energy usage plan based on the analysis results. The plan generation algorithm refers to the user's past usage data and current consumption patterns to propose specific actions for cost reduction and efficiency improvement. This generated plan is sent to the user's device as a notification.

[0452] Step 4:

[0453] The device uses its built-in camera and microphone to collect the user's facial expressions and voice. This data is sent to a server for analysis of their emotional state. This makes it possible to capture the user's instantaneous emotional responses.

[0454] Step 5:

[0455] The server analyzes the received emotional data to infer the user's emotional state. The emotional analysis engine uses facial recognition technology and voice analysis to identify stress levels and positive emotions. The output of this process is the result of the assessment of the user's emotional state.

[0456] Step 6:

[0457] The server adjusts the energy usage plan based on the user's emotional state. Specifically, it makes conservative suggestions if the user is stressed and more positive suggestions if the user is feeling positive. These adjusted suggestions are then sent back to the user's device.

[0458] Step 7:

[0459] Users film waste with their smartphones and send the footage to a server via their devices. The video data is then analyzed by an AI model to identify the type of waste.

[0460] Step 8:

[0461] The server uses a generation AI model to analyze video data of waste and identify its characteristics. As a result, the recycling method for each type of waste is determined and the user is notified with appropriate recycling information.

[0462] Step 9:

[0463] The server rewards users for recycling activities. This reward information is recorded in a database and used to encourage future user behavior.

[0464] (Application Example 2)

[0465] 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."

[0466] In modern society, efficient energy management and optimized waste disposal are crucial issues. Furthermore, it is necessary to promote sustainable environmental behavior by providing suggestions and feedback that consider the emotional state of users. However, effectively integrating these elements and enabling users to use energy efficiently and recycle without experiencing stress is a challenging task.

[0467] 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.

[0468] In this invention, the server includes means for collecting energy consumption information in real time, means for analyzing the collected energy consumption information and generating an optimal energy use plan, and means for evaluating the user's emotional state using emotion analysis means. This makes it possible to adjust energy use suggestions based on the user's emotional state and promote participation in sustainable environmental actions.

[0469] "Energy consumption information" refers to data on the usage of energy such as electricity and gas by households and businesses.

[0470] "Means of real-time collection" refers to a mechanism for continuously acquiring energy consumption information at the current time without interruption.

[0471] "Means for generating energy usage plans" refers to a system that automatically determines the optimal energy consumption schedule based on collected energy consumption information.

[0472] An "information terminal" refers to an electronic device used to transmit energy usage suggestions and recycling information to users.

[0473] "Emotional analysis methods" refer to technologies and systems that analyze information such as a user's facial expressions and voice to understand their emotional state.

[0474] "Means for acquiring images of waste" refers to methods and devices that acquire visual data of waste using cameras or sensing technology.

[0475] "Image analysis technology" refers to techniques for processing acquired image data and extracting specific information or features from it.

[0476] "Waste treatment facility information" refers to data about the location and methods used to properly process specific types of waste.

[0477] A "means of providing rewards" refers to a system that offers rewards or points as an incentive when users take desirable actions.

[0478] A "positive response" refers to a state in which users show a proactive and positive attitude towards suggestions and feedback.

[0479] "Environmental impact" is a measure that indicates the degree to which the activities of individuals and organizations have an impact on the global environment.

[0480] "Sustainable behavior" refers to actions that are kind to the Earth's environment and the ecosystems that comprise it, and that have characteristics that make them sustainable for the future.

[0481] "Feedback" is the process of providing information to communicate the next course of action or evaluation based on the user's behavior and reactions.

[0482] To implement this invention, it is necessary to construct a system combining several key components. The server collects energy consumption information in real time, analyzes it, and generates an optimal energy usage plan. The server is also equipped with emotion analysis means, which evaluates the user's emotional state using facial expressions and voice data. This makes it possible to suggest energy usage tailored to the user's emotions.

[0483] Specifically, devices such as smartphones and tablets use cameras and microphones to input the user's facial expressions and voice, and send this data to a server. The server runs a generative AI model for emotion analysis to determine the user's emotional state. Using this analysis, the server makes suggestions to optimize energy use in a way that minimizes stress for the user.

[0484] Furthermore, the terminal acquires images of the waste, and the server analyzes these images to identify the type of waste. Based on the identification results, information about recycling facilities is provided to the user, and rewards are given at the appropriate time.

[0485] As a concrete example, when a user takes a picture of a specific type of waste with their smartphone, the image is sent to a server. The server uses image analysis technology to classify the waste and suggests the optimal recycling method based on the user's emotional state. Furthermore, if positive user behavior is detected, the system can encourage sustained eco-friendly activities through rewards such as bonus points.

[0486] Example prompt to input to the generating AI model: "What energy-saving suggestions would be effective when the user's stress level is high?"

[0487] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0488] Step 1:

[0489] The device uses its camera and microphone to collect the user's facial expressions and voice data. This allows the device to obtain input data that indicates the user's current emotional state. This data forms the basis for perceiving and analyzing customer emotions in real time.

[0490] Step 2:

[0491] The device sends the collected facial and audio data to the server. The server receives this data and analyzes the user's emotions using a generative AI model. By processing the facial and audio information received as input data with the AI ​​model, the user's emotional state (e.g., stress or happiness) is output.

[0492] Step 3:

[0493] The server generates optimal energy usage suggestions for the user based on the results of emotion analysis and learning. Using the analyzed emotional state as input, the server outputs optimized suggestions that advise the user on how to expend energy. In cases where the user's stress level is high, it selects suggestions that are more acceptable to them.

[0494] Step 4:

[0495] The server sends energy usage suggestions to the terminal. The terminal notifies the user of the received suggestions. The user can review the energy usage suggestions from the server via the terminal and choose an action based on their content.

[0496] Step 5:

[0497] The user takes a picture of the waste with their device. The device sends this image to the server as input, providing initial information so that the user can learn how to proceed with recycling.

[0498] Step 6:

[0499] The server analyzes images of the received waste and identifies the type of waste using image analysis technology. It takes image data as input and outputs waste classification information. Based on this classification, the server determines the optimal recycling method.

[0500] Step 7:

[0501] The server sends information and suggestions about recycling facilities to the terminal based on the waste classification results, notifying the user. This allows the user to obtain detailed information necessary for recycling activities and take action.

[0502] Step 8:

[0503] The user performs the suggested eco-activities and sends a report from their device to the server. The server receives this report, evaluates the user's activities, and rewards positive responses. At this time, the server obtains the user's level of achievement in eco-activities as input and outputs reward points based on the result.

[0504] 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.

[0505] 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.

[0506] 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.

[0507] [Third Embodiment]

[0508] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0509] 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.

[0510] 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).

[0511] 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.

[0512] 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.

[0513] 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).

[0514] 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.

[0515] 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.

[0516] 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.

[0517] 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.

[0518] 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.

[0519] 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".

[0520] This invention provides a comprehensive system for optimizing energy consumption, encouraging recycling, and promoting environmentally friendly behavior.

[0521] First, the device acquires energy consumption data, such as electricity, gas, and water, from smart meters and various sensors installed in homes and businesses. This makes it possible to understand real-time energy usage in daily life.

[0522] Next, the server uses an AI agent to analyze the energy data sent from the terminal and identify usage patterns. For example, if energy consumption spikes during specific times in a household, the server generates an optimal energy usage schedule to level out those peaks. This schedule helps save on energy costs and reduce carbon emissions.

[0523] The device notifies the user of the generated schedule and prompts them to take action. Specifically, it displays suggestions such as "avoid using the air conditioner at night" or "use sunlight during the day to run the washing machine."

[0524] In waste management, users upload images of waste items to their devices using their smartphone cameras. These images are sent to servers and analyzed by AI agents using image recognition technology. For example, if an item is identified as plastic, the system provides information on the nearest recycling facility.

[0525] Furthermore, when a user recycles, the device reports the achievement to the server, and the user is awarded points or rewards. This helps to raise users' awareness of recycling and contributes to promoting a circular economy in the region.

[0526] Furthermore, the server calculates the carbon footprint based on the user's daily behavior data and uses this to recommend environmentally friendly behaviors to the user. For example, it sends notifications via the device such as, "We recommend cycling to work today," or "Reduce your meat consumption and incorporate more vegetarian options."

[0527] In this way, users can implement sustainable actions in their daily lives, achieving reductions in energy consumption and increased environmental awareness. The diverse information and incentives provided by the system facilitate users' participation in environmental actions and support their long-term continuation.

[0528] The following describes the processing flow.

[0529] Step 1:

[0530] The device acquires real-time energy consumption data from smart meters and sensors within the home or business. This data includes electricity, gas, and water usage.

[0531] Step 2:

[0532] The device sends energy consumption data it acquires to the server at regular intervals. For example, it is set to send data every 15 minutes.

[0533] Step 3:

[0534] The server receives energy data and passes it to an AI agent for analysis of consumption patterns. This includes identifying peak consumption times and detecting wasteful energy use.

[0535] Step 4:

[0536] The server generates an energy usage optimization schedule for the user based on the analysis results. This schedule includes suggestions on when and which appliances should be used.

[0537] Step 5:

[0538] The device notifies the user of energy usage suggestions. Specifically, it suggests actions such as "turn off the lights at night" or "use the washing machine during the day."

[0539] Step 6:

[0540] When users dispose of waste, they take photos of the waste with a device such as a smartphone and upload them to the application.

[0541] Step 7:

[0542] The device sends images of the waste to a server, where an AI agent analyzes the images to identify the type of waste.

[0543] Step 8:

[0544] Based on the analysis results, the server provides the user with information on the nearest recycling facility that can process the waste in question.

[0545] Step 9:

[0546] Once a user completes the recycling process at a facility, the terminal reports that information to the server, and points are added to the user's account.

[0547] Step 10:

[0548] The server analyzes the user's daily behavior data and calculates their carbon footprint. Based on this, it recommends environmentally friendly behaviors to the user via their device.

[0549] Step 11:

[0550] The device notifies the user with specific action suggestions. These suggestions include examples such as "commuting by bicycle instead of train" and "making use of Nikko (a natural area)."

[0551] (Example 1)

[0552] 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."

[0553] In modern society, the inefficient use of energy resources and wasteful carbon emissions are major problems. Furthermore, while promoting resource recovery through proper waste disposal is expected, there is a lack of effective incentives to change user awareness and behavior. Therefore, in addition to optimal energy resource management and carbon emission reduction, promoting sustainable actions, including resource recovery, is a crucial challenge.

[0554] 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.

[0555] In this invention, the server includes means for dynamically collecting energy resource data, means for analyzing the collected energy resource data to generate an appropriate energy use plan, and means for acquiring users' daily behavioral data and calculating carbon emissions based on this data. This makes it possible to improve the efficiency of energy use and reduce carbon emissions, and encourage users to recycle resources and engage in environmentally conscious behavior.

[0556] "Energy resource data" refers to information regarding the usage of various energy sources such as electricity, gas, and water.

[0557] "Dynamic collection" means acquiring data in real time and continuously.

[0558] An "appropriate energy use plan" refers to a schedule or strategy developed to promote efficient energy use.

[0559] "Daily behavioral data" refers to information about the activities and habits that individual users engage in in their daily lives.

[0560] "Calculating carbon emissions" means quantifying how much carbon dioxide a particular action or activity releases.

[0561] A "resource recovery facility" refers to equipment used to properly process waste for the purpose of recycling.

[0562] "Visual recognition technology" refers to computer vision technology that analyzes image data to identify its content.

[0563] "Rewarding" means giving users points, prizes, or other incentives for specific actions.

[0564] "Environmental protection actions" refer to specific proposals and activities aimed at encouraging actions that take the natural environment into consideration.

[0565] This invention is a system for optimizing energy consumption and promoting environmentally friendly behavior. The system dynamically collects energy data, such as electricity, gas, and water, via smart meters and sensors installed in homes and businesses. The terminals instantly transmit the data to a server via Wi-Fi or Bluetooth.

[0566] The server utilizes machine learning frameworks (e.g., TensorFlow and PyTorch) to analyze the collected energy data. This allows it to analyze past consumption patterns and generate efficient energy use plans. The generated plans are then communicated to the user via their terminal, encouraging more efficient energy use.

[0567] Furthermore, in waste management, users take pictures of waste using their smartphone cameras and upload them to their devices. The server uses an AI agent to analyze the images and identifies the type of waste using visual recognition technology (e.g., OpenCV). Information on the relevant resource recovery facilities is provided to the user, and they are rewarded for carrying out recycling.

[0568] Furthermore, the server calculates carbon emissions based on the user's daily behavioral data and suggests actions for environmental improvement. These suggestions are also communicated to the user via their device.

[0569] For example, when suggesting commuting by bicycle, the server generates a prompt such as: "You can contribute even more to the environment by commuting by bicycle today instead of using your car."

[0570] By implementing this system, users can improve energy efficiency and raise environmental awareness in their daily lives.

[0571] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0572] Step 1:

[0573] The device collects energy data such as electricity, gas, and water in real time from smart meters and sensors installed in homes and businesses. It receives data from sensors as input and collects it via Wi-Fi or Bluetooth. The collected energy data is then generated as output.

[0574] Step 2:

[0575] The terminal transmits the collected energy data to the server. The input is the energy data collected by the terminal, which is transmitted to the server over the internet using an encryption protocol such as SSL / TLS. The output is the energy data as it was sent to the server.

[0576] Step 3:

[0577] The server analyzes the received energy data using a machine learning framework. The server receives energy data as input and compares and analyzes it with historical consumption data stored in a database. The output extracts energy consumption patterns, forming the basis for an optimal energy plan.

[0578] Step 4:

[0579] The server generates an optimal energy usage plan based on the analysis results. The input is consumption pattern data obtained from the analysis, which is processed by a Python script to generate an energy plan that considers peak shifting and demand response. The output is the energy usage plan.

[0580] Step 5:

[0581] The terminal notifies the user of the generated energy usage plan. The input is the energy plan sent from the server, which is delivered to the user as a push notification through an application installed on the terminal. The output is the energy usage suggestion the user receives.

[0582] Step 6:

[0583] The user takes a picture of the waste using their smartphone camera and uploads it to their device. The input is the captured image of the waste, and the data is uploaded through the app on the device. The output is the visual data sent to the server.

[0584] Step 7:

[0585] The server analyzes uploaded images using visual recognition technology. The input is images of waste sent from the terminal, and the server extracts features using visual recognition technology (e.g., OpenCV) to identify the type of waste. The output is the waste classification result.

[0586] Step 8:

[0587] The server provides the user with information on the nearest resource recovery facility based on the classification results. The input is the waste classification result, which is used to retrieve facility information by referring to a database of recycling facilities. The output is detailed information about the resource recovery facility sent to the user.

[0588] Step 9:

[0589] The terminal sends a report to the server indicating that the user has performed resource recovery. The input is the user's record of resource recovery, which is transmitted to the server as digital data via the terminal. The output is the performance record stored on the server.

[0590] Step 10:

[0591] The server analyzes the user's daily behavior data, calculates carbon emissions, and suggests environmentally friendly actions to the user. The input is the user's behavioral history data, which is analyzed and generated using an AI model to quantify carbon emissions. The output is a suggestion of environmentally conscious actions sent to the user.

[0592] (Application Example 1)

[0593] 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."

[0594] In recent years, improving energy efficiency and proper waste management in urban areas have become increasingly important. However, it is unclear how individual residents can contribute, and there is a lack of mechanisms to encourage concrete action. As a result, unified environmental protection activities by citizens are not progressing.

[0595] 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.

[0596] In this invention, the server includes a device for collecting energy consumption information in real time, a device for analyzing the collected energy consumption information and generating an optimal energy use plan, and a device for providing residents with information to optimize the energy consumption of the entire city. This makes it possible for residents to easily contribute to optimizing energy consumption and waste management, thereby enabling the realization of a sustainable city.

[0597] "Energy consumption information" refers to data on resource usage such as electricity, gas, and water in homes and businesses.

[0598] "Real-time data collection" refers to the process of instantly acquiring information about the current situation, gathering information without delay.

[0599] A "device" refers to equipment or a system designed for a specific purpose.

[0600] An "energy use plan" is an optimized schedule and procedure for the efficient use of energy resources.

[0601] "Analysis" is the process of examining data and understanding its structure and patterns.

[0602] "Information that optimizes energy consumption across an entire city" refers to data and knowledge used to improve the efficiency of resource use in a specific region.

[0603] "Providing to residents" means distributing information and services to people in the community and making them available to them.

[0604] "Waste images" refer to photographs or visual data of items that are scheduled to be disposed of as garbage or unwanted items.

[0605] "Image recognition technology" is a technique that uses computer vision to identify objects and patterns within image data.

[0606] "Recycling facility information" refers to details about the location and services of facilities for reusing waste.

[0607] "To reward" means to give encouragement in order to show appreciation for a series of actions or achievements.

[0608] "User environmental contribution" refers to the degree to which individual people have taken actions for environmental protection and sustainability.

[0609] A "smartphone application" is a software program that runs on a portable computer device.

[0610] "Carbon dioxide emissions" refer to the amount of CO2 released by an individual or organization as a result of their activities.

[0611] "Environmentally friendly behavior" refers to actions and choices that minimize the burden on the natural environment.

[0612] This invention is a comprehensive system for optimizing energy consumption and promoting environmentally conscious behavior. A server collects energy consumption information in real time from smart meters and various sensors in each home and business. The collected information is analyzed using Python and machine learning frameworks (such as TensorFlow and PyTorch). Based on the analysis results, an AI model generates an optimal energy usage plan, which is then notified to residents' smartphones. This notification is provided in an intuitive and actionable format, enabling users to easily improve their energy efficiency.

[0613] The device also uses its camera to acquire images of the waste and sends these images to a cloud server. The server analyzes the type of waste using AWS Rekognition or similar image recognition technology and provides the user with information on the nearest recycling facility. When the waste is recycled, the server records the process and awards points or rewards to the user.

[0614] Furthermore, the server calculates carbon dioxide emissions based on the user's location and behavioral patterns. Based on this information, it provides residents with specific suggestions to promote environmentally friendly behavior through a smartphone application. For example, it might notify them with messages like, "We recommend cycling to work today," or "Why not try a vegetarian diet?"

[0615] As a concrete example, suppose a user is planning a picnic on Sunday. In this case, the application can check the weather forecast for that day and, if it is sunny, send a notification saying, "Why not use sunlight to cook your picnic food? We recommend using solar cooking equipment." By effectively implementing this invention, it is possible to support residents' sustainable lifestyles and contribute to energy management and environmental protection throughout the city.

[0616] A concrete example of a prompt message would be: "Design an application to optimize energy consumption and promote recycling. Include features that provide users with energy usage schedules and recycling information, and encourage environmentally conscious behavior."

[0617] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0618] Step 1:

[0619] The terminal collects energy consumption information, such as electricity, gas, and water, in real time from smart meters and various sensors installed in homes and businesses. This input information is organized within the terminal and prepared for transmission to the server.

[0620] Step 2:

[0621] The server, upon receiving energy consumption information sent from the terminal, performs analysis using Python and a machine learning framework such as TensorFlow or PyTorch. The input for this step is energy consumption information, and the output is the generation of an optimal energy use plan.

[0622] Step 3:

[0623] The server notifies residents of the generated energy usage plan on their smartphones. The devices receive this information and present it to the users as actionable and concrete suggestions.

[0624] Step 4:

[0625] The device uses its camera to capture images of the user's waste and sends them to a cloud server. This image data becomes input information, preparing the server for image analysis.

[0626] Step 5:

[0627] The server analyzes the transmitted waste images using image recognition technologies such as AWS Rekognition. In this step, based on the input image data, the waste is classified and information about the recycling facility is generated as output.

[0628] Step 6:

[0629] The server records recycling activity and awards points or rewards to users based on the results. The terminal displays reward information to users, rewarding their eco-friendly efforts.

[0630] Step 7:

[0631] The device collects the user's location information and behavioral patterns and sends them to the server. The server uses this data to calculate the user's carbon dioxide emissions and generates suggestions for environmentally friendly actions as output.

[0632] Step 8:

[0633] The server provides the generated action suggestions to the user via a smartphone application. The user then uses this output information to carry out environmental contribution activities.

[0634] 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.

[0635] This invention is a system that optimizes energy consumption in homes and businesses and promotes environmentally conscious behavior that takes into account the user's emotional state. Based on energy consumption data acquired from smart meters and various sensors, the system provides an optimized energy usage schedule. Furthermore, in waste disposal, an AI agent analyzes images of waste taken by the user with their smartphone and recommends appropriate recycling methods.

[0636] By incorporating an emotion engine, this system can analyze the user's emotional state in real time and provide energy usage suggestions adapted to the user's emotions. For example, the device acquires the user's facial expressions and voice through the camera and voice input, and this data is analyzed by the emotion engine on the server. Based on the results of this analysis, the server improves the acceptability of energy usage suggestions by making more restrained suggestions when the user is feeling stressed and more proactive suggestions when the user is in a positive state.

[0637] Furthermore, the emotion engine monitors users' emotional responses to positive environmental actions and provides rewards and feedback at appropriate times. This enables interaction based on users' emotions, naturally encouraging participation in sustainable eco-activities. For example, users who show a positive response to recycling activities are given further rewards by the server to maintain their motivation. Through these responses based on emotion analysis, the entire system promotes long-term user cooperation and contributes to building a more sustainable society.

[0638] The following describes the processing flow.

[0639] Step 1:

[0640] The device acquires energy consumption data in real time via smart meters and sensors. This data includes electricity, gas, and water usage.

[0641] Step 2:

[0642] The device collects energy data and sends it to the server at regular intervals. The transmission interval is, for example, every 15 minutes.

[0643] Step 3:

[0644] The server receives data and passes it to an AI agent to analyze consumption patterns. This identifies peak power consumption and wasteful energy use.

[0645] Step 4:

[0646] The server generates an energy usage optimization schedule based on the analysis results and sends it to the terminal.

[0647] Step 5:

[0648] Users review suggested schedules on their devices and determine whether they can implement them in their daily lives.

[0649] Step 6:

[0650] The device uses its camera to record the user's facial expressions and voice, and sends the emotional data to a server.

[0651] Step 7:

[0652] The server uses an emotion engine to analyze the user's emotional state in real time. This analysis detects stress and positive emotions.

[0653] Step 8:

[0654] The server adjusts the content and timing of energy usage suggestions based on the emotion analysis results. For example, if the user is feeling stressed, it will make restrained suggestions, and if they are feeling positive, it will make more proactive suggestions.

[0655] Step 9:

[0656] When a user disposes of waste, they take a picture of the waste with their smartphone camera and upload that data to the server via their device.

[0657] Step 10:

[0658] The server uses image recognition technology to classify waste and notifies the user of the appropriate recycling method. Furthermore, it provides detailed information about recycling facilities.

[0659] Step 11:

[0660] The device monitors the user's emotional response to recycling activities, and if it shows a positive response, the server awards reward points and provides feedback.

[0661] Through this process, energy consumption is optimized and environmentally friendly behavior is promoted while taking user emotions into consideration.

[0662] (Example 2)

[0663] 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."

[0664] In modern society, efficient energy use in homes and businesses is required, but uniform energy use proposals that ignore consumers' emotional states are unlikely to be accepted, making it difficult to promote sustainable energy conservation activities. Similarly, in waste management, while users are required to accurately identify and properly recycle waste, a lack of motivation for this is a challenge.

[0665] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0666] In this invention, the server includes means for collecting energy consumption information in real time, means for analyzing the collected energy consumption information and generating an optimal energy usage plan, and means for analyzing the user's emotional state and adjusting energy usage suggestions based on that emotional state. This enables flexible energy usage suggestions that are adapted to the user's emotional state, thereby improving the acceptability of the suggestions.

[0667] "Energy consumption information" is a general term for data related to the use of electricity, gas, water, etc., in households, businesses, and other organizations.

[0668] A "real-time data collection processing device" refers to a machine or software that instantly acquires energy consumption information based on the actual passage of time.

[0669] A "processing device for generating an optimal energy use plan" is a machine or software that analyzes collected energy consumption information and devises effective and efficient energy use methods.

[0670] "Information processing terminal" refers to the user's device, such as a smartphone or personal computer, which is used to receive energy usage suggestions.

[0671] "User emotional state" refers to the psychological or emotional state of a user as recognized by an analysis system on the terminal or server.

[0672] A "processing device that adjusts suggestions based on emotional state" is a machine or software that optimizes energy usage suggestions by taking into account the analyzed emotional state of the user and provides them to the user.

[0673] "Images of waste" refers to image data of waste captured by devices such as smartphones and digital cameras.

[0674] "Image recognition technology" is a technology that identifies specific objects from captured video data and analyzes their characteristics.

[0675] A "waste disposal agency" refers to a facility or organization responsible for the proper processing or recycling of collected waste.

[0676] A "reward-granting processing device" is a machine or software that provides rewards based on a user's energy usage and waste disposal practices.

[0677] This system promotes sustainable energy activities by improving the efficiency of energy consumption in homes and businesses and by providing energy use suggestions that take into account the user's emotional state.

[0678] The server first collects energy consumption information in real time from smart meters and various sensors installed in homes and businesses. This includes consumption data for commonly used resources such as electricity, gas, and water. The collected information is stored in a database on the server and analyzed by analytical algorithms. The analysis includes identifying peak energy consumption times and inefficient usage patterns, and based on this, an optimized energy use plan is generated.

[0679] The terminal refers to the user's PC or smartphone, and the server notifies these terminals with energy usage suggestions. Furthermore, the terminal uses its built-in camera and microphone to collect the user's facial expressions and voice, and transmits this data to the server. The emotion engine on the server analyzes this data and determines the user's emotional state in real time. Based on the analyzed emotional state, the energy usage suggestions are adjusted to suit the user, and efforts are made to improve the acceptability of the suggestions.

[0680] Furthermore, users use their devices to take photos of waste and send this video data to a server. The server uses a generative AI model to analyze the video and identify the type of waste. Based on the identification results, the user is provided with information on appropriate recycling methods and facilities. The system records the user's recycling activities and provides rewards for them. Through these rewards, users can be motivated to engage in sustainable waste management activities.

[0681] For example, if a user actively engages in recycling activities, the server can sense the user's positive emotional state and encourage further activity by providing appropriate rewards. This application allows the system to support sustainable eco-friendly practices in the long term.

[0682] Examples of prompts generated using AI models include the following:

[0683] "List the recommended energy optimization strategies when the user's emotional state is positive."

[0684] "How can I offer gentle suggestions to users who are experiencing stress?"

[0685] In this way, the system provides energy usage suggestions and reward systems adapted to the user's emotional state, promoting sustainable environmental protection.

[0686] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0687] Step 1:

[0688] The terminal uses smart meters and sensors to collect energy consumption information from homes and businesses in real time. This data includes electricity, gas, and water usage. The collected data is sent to a server using a protocol and recorded in a database.

[0689] Step 2:

[0690] The server analyzes the collected energy consumption information using data analysis algorithms. Specifically, it performs time-series analysis to identify peak usage times and inefficient usage patterns. The output of this analysis is patterns and statistics extracted from the data.

[0691] Step 3:

[0692] The server generates an optimized energy usage plan based on the analysis results. The plan generation algorithm refers to the user's past usage data and current consumption patterns to propose specific actions for cost reduction and efficiency improvement. This generated plan is sent to the user's device as a notification.

[0693] Step 4:

[0694] The device uses its built-in camera and microphone to collect the user's facial expressions and voice. This data is sent to a server for analysis of their emotional state. This makes it possible to capture the user's instantaneous emotional responses.

[0695] Step 5:

[0696] The server analyzes the received emotional data to infer the user's emotional state. The emotional analysis engine uses facial recognition technology and voice analysis to identify stress levels and positive emotions. The output of this process is the result of the assessment of the user's emotional state.

[0697] Step 6:

[0698] The server adjusts the energy usage plan based on the user's emotional state. Specifically, it makes conservative suggestions if the user is stressed and more positive suggestions if the user is feeling positive. These adjusted suggestions are then sent back to the user's device.

[0699] Step 7:

[0700] Users film waste with their smartphones and send the footage to a server via their devices. The video data is then analyzed by an AI model to identify the type of waste.

[0701] Step 8:

[0702] The server uses a generation AI model to analyze video data of waste and identify its characteristics. As a result, the recycling method for each type of waste is determined and the user is notified with appropriate recycling information.

[0703] Step 9:

[0704] The server rewards users for recycling activities. This reward information is recorded in a database and used to encourage future user behavior.

[0705] (Application Example 2)

[0706] 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."

[0707] In modern society, efficient energy management and optimized waste disposal are crucial issues. Furthermore, it is necessary to promote sustainable environmental behavior by providing suggestions and feedback that consider the emotional state of users. However, effectively integrating these elements and enabling users to use energy efficiently and recycle without experiencing stress is a challenging task.

[0708] 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.

[0709] In this invention, the server includes means for collecting energy consumption information in real time, means for analyzing the collected energy consumption information and generating an optimal energy use plan, and means for evaluating the user's emotional state using emotion analysis means. This makes it possible to adjust energy use suggestions based on the user's emotional state and promote participation in sustainable environmental actions.

[0710] "Energy consumption information" refers to data on the usage of energy such as electricity and gas by households and businesses.

[0711] "Means of real-time collection" refers to a mechanism for continuously acquiring energy consumption information at the current time without interruption.

[0712] "Means for generating energy usage plans" refers to a system that automatically determines the optimal energy consumption schedule based on collected energy consumption information.

[0713] An "information terminal" refers to an electronic device used to transmit energy usage suggestions and recycling information to users.

[0714] "Emotional analysis methods" refer to technologies and systems that analyze information such as a user's facial expressions and voice to understand their emotional state.

[0715] "Means for acquiring images of waste" refers to methods and devices that acquire visual data of waste using cameras or sensing technology.

[0716] "Image analysis technology" refers to techniques for processing acquired image data and extracting specific information or features from it.

[0717] "Waste treatment facility information" refers to data about the location and methods used to properly process specific types of waste.

[0718] A "means of providing rewards" refers to a system that offers rewards or points as an incentive when users take desirable actions.

[0719] A "positive response" refers to a state in which users show a proactive and positive attitude towards suggestions and feedback.

[0720] "Environmental impact" is a measure that indicates the degree to which the activities of individuals and organizations have an impact on the global environment.

[0721] "Sustainable behavior" refers to actions that are kind to the Earth's environment and the ecosystems that comprise it, and that have characteristics that make them sustainable for the future.

[0722] "Feedback" is the process of providing information to communicate the next course of action or evaluation based on the user's behavior and reactions.

[0723] To implement this invention, it is necessary to construct a system combining several key components. The server collects energy consumption information in real time, analyzes it, and generates an optimal energy usage plan. The server is also equipped with emotion analysis means, which evaluates the user's emotional state using facial expressions and voice data. This makes it possible to suggest energy usage tailored to the user's emotions.

[0724] Specifically, devices such as smartphones and tablets use cameras and microphones to input the user's facial expressions and voice, and send this data to a server. The server runs a generative AI model for emotion analysis to determine the user's emotional state. Using this analysis, the server makes suggestions to optimize energy use in a way that minimizes stress for the user.

[0725] Furthermore, the terminal acquires images of the waste, and the server analyzes these images to identify the type of waste. Based on the identification results, information about recycling facilities is provided to the user, and rewards are given at the appropriate time.

[0726] As a concrete example, when a user takes a picture of a specific type of waste with their smartphone, the image is sent to a server. The server uses image analysis technology to classify the waste and suggests the optimal recycling method based on the user's emotional state. Furthermore, if positive user behavior is detected, the system can encourage sustained eco-friendly activities through rewards such as bonus points.

[0727] Example prompt to input to the generating AI model: "What energy-saving suggestions would be effective when the user's stress level is high?"

[0728] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0729] Step 1:

[0730] The device uses its camera and microphone to collect the user's facial expressions and voice data. This allows the device to obtain input data that indicates the user's current emotional state. This data forms the basis for perceiving and analyzing customer emotions in real time.

[0731] Step 2:

[0732] The device sends the collected facial and audio data to the server. The server receives this data and analyzes the user's emotions using a generative AI model. By processing the facial and audio information received as input data with the AI ​​model, the user's emotional state (e.g., stress or happiness) is output.

[0733] Step 3:

[0734] The server generates optimal energy usage suggestions for the user based on the results of emotion analysis and learning. Using the analyzed emotional state as input, the server outputs optimized suggestions that advise the user on how to expend energy. In cases where the user's stress level is high, it selects suggestions that are more acceptable to them.

[0735] Step 4:

[0736] The server sends energy usage suggestions to the terminal. The terminal notifies the user of the received suggestions. The user can review the energy usage suggestions from the server via the terminal and choose an action based on their content.

[0737] Step 5:

[0738] The user takes a picture of the waste with their device. The device sends this image to the server as input, providing initial information so that the user can learn how to proceed with recycling.

[0739] Step 6:

[0740] The server analyzes images of the received waste and identifies the type of waste using image analysis technology. It takes image data as input and outputs waste classification information. Based on this classification, the server determines the optimal recycling method.

[0741] Step 7:

[0742] The server sends information and suggestions about recycling facilities to the terminal based on the waste classification results, notifying the user. This allows the user to obtain detailed information necessary for recycling activities and take action.

[0743] Step 8:

[0744] The user performs the suggested eco-activities and sends a report from their device to the server. The server receives this report, evaluates the user's activities, and rewards positive responses. At this time, the server obtains the user's level of achievement in eco-activities as input and outputs reward points based on the result.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] [Fourth Embodiment]

[0749] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0750] 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.

[0751] 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).

[0752] 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.

[0753] 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.

[0754] 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).

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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".

[0762] This invention provides a comprehensive system for optimizing energy consumption, encouraging recycling, and promoting environmentally friendly behavior.

[0763] First, the device acquires energy consumption data, such as electricity, gas, and water, from smart meters and various sensors installed in homes and businesses. This makes it possible to understand real-time energy usage in daily life.

[0764] Next, the server uses an AI agent to analyze the energy data sent from the terminal and identify usage patterns. For example, if energy consumption spikes during specific times in a household, the server generates an optimal energy usage schedule to level out those peaks. This schedule helps save on energy costs and reduce carbon emissions.

[0765] The device notifies the user of the generated schedule and prompts them to take action. Specifically, it displays suggestions such as "avoid using the air conditioner at night" or "use sunlight during the day to run the washing machine."

[0766] In waste management, users upload images of waste items to their devices using their smartphone cameras. These images are sent to servers and analyzed by AI agents using image recognition technology. For example, if an item is identified as plastic, the system provides information on the nearest recycling facility.

[0767] Furthermore, when a user recycles, the device reports the achievement to the server, and the user is awarded points or rewards. This helps to raise users' awareness of recycling and contributes to promoting a circular economy in the region.

[0768] Furthermore, the server calculates the carbon footprint based on the user's daily behavior data and uses this to recommend environmentally friendly behaviors to the user. For example, it sends notifications via the device such as, "We recommend cycling to work today," or "Reduce your meat consumption and incorporate more vegetarian options."

[0769] In this way, users can implement sustainable actions in their daily lives, achieving reductions in energy consumption and increased environmental awareness. The diverse information and incentives provided by the system facilitate users' participation in environmental actions and support their long-term continuation.

[0770] The following describes the processing flow.

[0771] Step 1:

[0772] The device acquires real-time energy consumption data from smart meters and sensors within the home or business. This data includes electricity, gas, and water usage.

[0773] Step 2:

[0774] The device sends energy consumption data it acquires to the server at regular intervals. For example, it is set to send data every 15 minutes.

[0775] Step 3:

[0776] The server receives energy data and passes it to an AI agent for analysis of consumption patterns. This includes identifying peak consumption times and detecting wasteful energy use.

[0777] Step 4:

[0778] The server generates an energy usage optimization schedule for the user based on the analysis results. This schedule includes suggestions on when and which appliances should be used.

[0779] Step 5:

[0780] The device notifies the user of energy usage suggestions. Specifically, it suggests actions such as "turn off the lights at night" or "use the washing machine during the day."

[0781] Step 6:

[0782] When users dispose of waste, they take photos of the waste with a device such as a smartphone and upload them to the application.

[0783] Step 7:

[0784] The device sends images of the waste to a server, where an AI agent analyzes the images to identify the type of waste.

[0785] Step 8:

[0786] Based on the analysis results, the server provides the user with information on the nearest recycling facility that can process the waste in question.

[0787] Step 9:

[0788] Once a user completes the recycling process at a facility, the terminal reports that information to the server, and points are added to the user's account.

[0789] Step 10:

[0790] The server analyzes the user's daily behavior data and calculates their carbon footprint. Based on this, it recommends environmentally friendly behaviors to the user via their device.

[0791] Step 11:

[0792] The device notifies the user with specific action suggestions. These suggestions include examples such as "commuting by bicycle instead of train" and "making use of Nikko (a natural area)."

[0793] (Example 1)

[0794] 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".

[0795] In modern society, the inefficient use of energy resources and wasteful carbon emissions are major problems. Furthermore, while promoting resource recovery through proper waste disposal is expected, there is a lack of effective incentives to change user awareness and behavior. Therefore, in addition to optimal energy resource management and carbon emission reduction, promoting sustainable actions, including resource recovery, is a crucial challenge.

[0796] 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.

[0797] In this invention, the server includes means for dynamically collecting energy resource data, means for analyzing the collected energy resource data to generate an appropriate energy use plan, and means for acquiring users' daily behavioral data and calculating carbon emissions based on this data. This makes it possible to improve the efficiency of energy use and reduce carbon emissions, and encourage users to recycle resources and engage in environmentally conscious behavior.

[0798] "Energy resource data" refers to information regarding the usage of various energy sources such as electricity, gas, and water.

[0799] "Dynamic collection" means acquiring data in real time and continuously.

[0800] An "appropriate energy use plan" refers to a schedule or strategy developed to promote efficient energy use.

[0801] "Daily behavioral data" refers to information about the activities and habits that individual users engage in in their daily lives.

[0802] "Calculating carbon emissions" means quantifying how much carbon dioxide a particular action or activity releases.

[0803] A "resource recovery facility" refers to equipment used to properly process waste for the purpose of recycling.

[0804] "Visual recognition technology" refers to computer vision technology that analyzes image data to identify its content.

[0805] "Rewarding" means giving users points, prizes, or other incentives for specific actions.

[0806] "Environmental protection actions" refer to specific proposals and activities aimed at encouraging actions that take the natural environment into consideration.

[0807] This invention is a system for optimizing energy consumption and promoting environmentally friendly behavior. The system dynamically collects energy data, such as electricity, gas, and water, via smart meters and sensors installed in homes and businesses. The terminals instantly transmit the data to a server via Wi-Fi or Bluetooth.

[0808] The server utilizes machine learning frameworks (e.g., TensorFlow and PyTorch) to analyze the collected energy data. This allows it to analyze past consumption patterns and generate efficient energy use plans. The generated plans are then communicated to the user via their terminal, encouraging more efficient energy use.

[0809] Furthermore, in waste management, users take pictures of waste using their smartphone cameras and upload them to their devices. The server uses an AI agent to analyze the images and identifies the type of waste using visual recognition technology (e.g., OpenCV). Information on the relevant resource recovery facilities is provided to the user, and they are rewarded for carrying out recycling.

[0810] Furthermore, the server calculates carbon emissions based on the user's daily behavioral data and suggests actions for environmental improvement. These suggestions are also communicated to the user via their device.

[0811] For example, when suggesting commuting by bicycle, the server generates a prompt such as: "You can contribute even more to the environment by commuting by bicycle today instead of using your car."

[0812] By implementing this system, users can improve energy efficiency and raise environmental awareness in their daily lives.

[0813] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0814] Step 1:

[0815] The device collects energy data such as electricity, gas, and water in real time from smart meters and sensors installed in homes and businesses. It receives data from sensors as input and collects it via Wi-Fi or Bluetooth. The collected energy data is then generated as output.

[0816] Step 2:

[0817] The terminal transmits the collected energy data to the server. The input is the energy data collected by the terminal, which is transmitted to the server over the internet using an encryption protocol such as SSL / TLS. The output is the energy data as it was sent to the server.

[0818] Step 3:

[0819] The server analyzes the received energy data using a machine learning framework. The server receives energy data as input and compares and analyzes it with historical consumption data stored in a database. The output extracts energy consumption patterns, forming the basis for an optimal energy plan.

[0820] Step 4:

[0821] The server generates an optimal energy usage plan based on the analysis results. The input is consumption pattern data obtained from the analysis, which is processed by a Python script to generate an energy plan that considers peak shifting and demand response. The output is the energy usage plan.

[0822] Step 5:

[0823] The terminal notifies the user of the generated energy usage plan. The input is the energy plan sent from the server, which is delivered to the user as a push notification through an application installed on the terminal. The output is the energy usage suggestion the user receives.

[0824] Step 6:

[0825] The user takes a picture of the waste using their smartphone camera and uploads it to their device. The input is the captured image of the waste, and the data is uploaded through the app on the device. The output is the visual data sent to the server.

[0826] Step 7:

[0827] The server analyzes uploaded images using visual recognition technology. The input is images of waste sent from the terminal, and the server extracts features using visual recognition technology (e.g., OpenCV) to identify the type of waste. The output is the waste classification result.

[0828] Step 8:

[0829] The server provides the user with information on the nearest resource recovery facility based on the classification results. The input is the waste classification result, which is used to retrieve facility information by referring to a database of recycling facilities. The output is detailed information about the resource recovery facility sent to the user.

[0830] Step 9:

[0831] The terminal sends a report to the server indicating that the user has performed resource recovery. The input is the user's record of resource recovery, which is transmitted to the server as digital data via the terminal. The output is the performance record stored on the server.

[0832] Step 10:

[0833] The server analyzes the user's daily behavior data, calculates carbon emissions, and suggests environmentally friendly actions to the user. The input is the user's behavioral history data, which is analyzed and generated using an AI model to quantify carbon emissions. The output is a suggestion of environmentally conscious actions sent to the user.

[0834] (Application Example 1)

[0835] 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".

[0836] In recent years, improving energy efficiency and proper waste management in urban areas have become increasingly important. However, it is unclear how individual residents can contribute, and there is a lack of mechanisms to encourage concrete action. As a result, unified environmental protection activities by citizens are not progressing.

[0837] 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.

[0838] In this invention, the server includes a device for collecting energy consumption information in real time, a device for analyzing the collected energy consumption information and generating an optimal energy use plan, and a device for providing residents with information to optimize the energy consumption of the entire city. This makes it possible for residents to easily contribute to optimizing energy consumption and waste management, thereby enabling the realization of a sustainable city.

[0839] "Energy consumption information" refers to data on resource usage such as electricity, gas, and water in homes and businesses.

[0840] "Real-time data collection" refers to the process of instantly acquiring information about the current situation, gathering information without delay.

[0841] A "device" refers to equipment or a system designed for a specific purpose.

[0842] An "energy use plan" is an optimized schedule and procedure for the efficient use of energy resources.

[0843] "Analysis" is the process of examining data and understanding its structure and patterns.

[0844] "Information that optimizes energy consumption across an entire city" refers to data and knowledge used to improve the efficiency of resource use in a specific region.

[0845] "Providing to residents" means distributing information and services to people in the community and making them available to them.

[0846] "Waste images" refer to photographs or visual data of items that are scheduled to be disposed of as garbage or unwanted items.

[0847] "Image recognition technology" is a technique that uses computer vision to identify objects and patterns within image data.

[0848] "Recycling facility information" refers to details about the location and services of facilities for reusing waste.

[0849] "To reward" means to give encouragement in order to show appreciation for a series of actions or achievements.

[0850] "User environmental contribution" refers to the degree to which individual people have taken actions for environmental protection and sustainability.

[0851] A "smartphone application" is a software program that runs on a portable computer device.

[0852] "Carbon dioxide emissions" refer to the amount of CO2 released by an individual or organization as a result of their activities.

[0853] "Environmentally friendly behavior" refers to actions and choices that minimize the burden on the natural environment.

[0854] This invention is a comprehensive system for optimizing energy consumption and promoting environmentally conscious behavior. A server collects energy consumption information in real time from smart meters and various sensors in each home and business. The collected information is analyzed using Python and machine learning frameworks (such as TensorFlow and PyTorch). Based on the analysis results, an AI model generates an optimal energy usage plan, which is then notified to residents' smartphones. This notification is provided in an intuitive and actionable format, enabling users to easily improve their energy efficiency.

[0855] The device also uses its camera to acquire images of the waste and sends these images to a cloud server. The server analyzes the type of waste using AWS Rekognition or similar image recognition technology and provides the user with information on the nearest recycling facility. When the waste is recycled, the server records the process and awards points or rewards to the user.

[0856] Furthermore, the server calculates carbon dioxide emissions based on the user's location and behavioral patterns. Based on this information, it provides residents with specific suggestions to promote environmentally friendly behavior through a smartphone application. For example, it might notify them with messages like, "We recommend cycling to work today," or "Why not try a vegetarian diet?"

[0857] As a concrete example, suppose a user is planning a picnic on Sunday. In this case, the application can check the weather forecast for that day and, if it is sunny, send a notification saying, "Why not use sunlight to cook your picnic food? We recommend using solar cooking equipment." By effectively implementing this invention, it is possible to support residents' sustainable lifestyles and contribute to energy management and environmental protection throughout the city.

[0858] A concrete example of a prompt message would be: "Design an application to optimize energy consumption and promote recycling. Include features that provide users with energy usage schedules and recycling information, and encourage environmentally conscious behavior."

[0859] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0860] Step 1:

[0861] The terminal collects energy consumption information, such as electricity, gas, and water, in real time from smart meters and various sensors installed in homes and businesses. This input information is organized within the terminal and prepared for transmission to the server.

[0862] Step 2:

[0863] The server, upon receiving energy consumption information sent from the terminal, performs analysis using Python and a machine learning framework such as TensorFlow or PyTorch. The input for this step is energy consumption information, and the output is the generation of an optimal energy use plan.

[0864] Step 3:

[0865] The server notifies residents of the generated energy usage plan on their smartphones. The devices receive this information and present it to the users as actionable and concrete suggestions.

[0866] Step 4:

[0867] The device uses its camera to capture images of the user's waste and sends them to a cloud server. This image data becomes input information, preparing the server for image analysis.

[0868] Step 5:

[0869] The server analyzes the transmitted waste images using image recognition technologies such as AWS Rekognition. In this step, based on the input image data, the waste is classified and information about the recycling facility is generated as output.

[0870] Step 6:

[0871] The server records recycling activity and awards points or rewards to users based on the results. The terminal displays reward information to users, rewarding their eco-friendly efforts.

[0872] Step 7:

[0873] The device collects the user's location information and behavioral patterns and sends them to the server. The server uses this data to calculate the user's carbon dioxide emissions and generates suggestions for environmentally friendly actions as output.

[0874] Step 8:

[0875] The server provides the generated action suggestions to the user via a smartphone application. The user then uses this output information to carry out environmental contribution activities.

[0876] 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.

[0877] This invention is a system that optimizes energy consumption in homes and businesses and promotes environmentally conscious behavior that takes into account the user's emotional state. Based on energy consumption data acquired from smart meters and various sensors, the system provides an optimized energy usage schedule. Furthermore, in waste disposal, an AI agent analyzes images of waste taken by the user with their smartphone and recommends appropriate recycling methods.

[0878] By incorporating an emotion engine, this system can analyze the user's emotional state in real time and provide energy usage suggestions adapted to the user's emotions. For example, the device acquires the user's facial expressions and voice through the camera and voice input, and this data is analyzed by the emotion engine on the server. Based on the results of this analysis, the server improves the acceptability of energy usage suggestions by making more restrained suggestions when the user is feeling stressed and more proactive suggestions when the user is in a positive state.

[0879] Furthermore, the emotion engine monitors users' emotional responses to positive environmental actions and provides rewards and feedback at appropriate times. This enables interaction based on users' emotions, naturally encouraging participation in sustainable eco-activities. For example, users who show a positive response to recycling activities are given further rewards by the server to maintain their motivation. Through these responses based on emotion analysis, the entire system promotes long-term user cooperation and contributes to building a more sustainable society.

[0880] The following describes the processing flow.

[0881] Step 1:

[0882] The device acquires energy consumption data in real time via smart meters and sensors. This data includes electricity, gas, and water usage.

[0883] Step 2:

[0884] The device collects energy data and sends it to the server at regular intervals. The transmission interval is, for example, every 15 minutes.

[0885] Step 3:

[0886] The server receives data and passes it to an AI agent to analyze consumption patterns. This identifies peak power consumption and wasteful energy use.

[0887] Step 4:

[0888] The server generates an energy usage optimization schedule based on the analysis results and sends it to the terminal.

[0889] Step 5:

[0890] Users review suggested schedules on their devices and determine whether they can implement them in their daily lives.

[0891] Step 6:

[0892] The device uses its camera to record the user's facial expressions and voice, and sends the emotional data to a server.

[0893] Step 7:

[0894] The server uses an emotion engine to analyze the user's emotional state in real time. This analysis detects stress and positive emotions.

[0895] Step 8:

[0896] The server adjusts the content and timing of energy usage suggestions based on the emotion analysis results. For example, if the user is feeling stressed, it will make restrained suggestions, and if they are feeling positive, it will make more proactive suggestions.

[0897] Step 9:

[0898] When a user disposes of waste, they take a picture of the waste with their smartphone camera and upload that data to the server via their device.

[0899] Step 10:

[0900] The server uses image recognition technology to classify waste and notifies the user of the appropriate recycling method. Furthermore, it provides detailed information about recycling facilities.

[0901] Step 11:

[0902] The device monitors the user's emotional response to recycling activities, and if it shows a positive response, the server awards reward points and provides feedback.

[0903] Through this process, energy consumption is optimized and environmentally friendly behavior is promoted while taking user emotions into consideration.

[0904] (Example 2)

[0905] 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".

[0906] In modern society, efficient energy use in homes and businesses is required, but uniform energy use proposals that ignore consumers' emotional states are unlikely to be accepted, making it difficult to promote sustainable energy conservation activities. Similarly, in waste management, while users are required to accurately identify and properly recycle waste, a lack of motivation for this is a challenge.

[0907] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0908] In this invention, the server includes means for collecting energy consumption information in real time, means for analyzing the collected energy consumption information and generating an optimal energy usage plan, and means for analyzing the user's emotional state and adjusting energy usage suggestions based on that emotional state. This enables flexible energy usage suggestions that are adapted to the user's emotional state, thereby improving the acceptability of the suggestions.

[0909] "Energy consumption information" is a general term for data related to the use of electricity, gas, water, etc., in households, businesses, and other organizations.

[0910] A "real-time data collection processing device" refers to a machine or software that instantly acquires energy consumption information based on the actual passage of time.

[0911] A "processing device for generating an optimal energy use plan" is a machine or software that analyzes collected energy consumption information and devises effective and efficient energy use methods.

[0912] "Information processing terminal" refers to the user's device, such as a smartphone or personal computer, which is used to receive energy usage suggestions.

[0913] "User emotional state" refers to the psychological or emotional state of a user as recognized by an analysis system on the terminal or server.

[0914] A "processing device that adjusts suggestions based on emotional state" is a machine or software that optimizes energy usage suggestions by taking into account the analyzed emotional state of the user and provides them to the user.

[0915] "Images of waste" refers to image data of waste captured by devices such as smartphones and digital cameras.

[0916] "Image recognition technology" is a technology that identifies specific objects from captured video data and analyzes their characteristics.

[0917] A "waste disposal agency" refers to a facility or organization responsible for the proper processing or recycling of collected waste.

[0918] A "reward-granting processing device" is a machine or software that provides rewards based on a user's energy usage and waste disposal practices.

[0919] This system promotes sustainable energy activities by improving the efficiency of energy consumption in homes and businesses and by providing energy use suggestions that take into account the user's emotional state.

[0920] The server first collects energy consumption information in real time from smart meters and various sensors installed in homes and businesses. This includes consumption data for commonly used resources such as electricity, gas, and water. The collected information is stored in a database on the server and analyzed by analytical algorithms. The analysis includes identifying peak energy consumption times and inefficient usage patterns, and based on this, an optimized energy use plan is generated.

[0921] The terminal refers to the user's PC or smartphone, and the server notifies these terminals with energy usage suggestions. Furthermore, the terminal uses its built-in camera and microphone to collect the user's facial expressions and voice, and transmits this data to the server. The emotion engine on the server analyzes this data and determines the user's emotional state in real time. Based on the analyzed emotional state, the energy usage suggestions are adjusted to suit the user, and efforts are made to improve the acceptability of the suggestions.

[0922] Furthermore, users use their devices to take photos of waste and send this video data to a server. The server uses a generative AI model to analyze the video and identify the type of waste. Based on the identification results, the user is provided with information on appropriate recycling methods and facilities. The system records the user's recycling activities and provides rewards for them. Through these rewards, users can be motivated to engage in sustainable waste management activities.

[0923] For example, if a user actively engages in recycling activities, the server can sense the user's positive emotional state and encourage further activity by providing appropriate rewards. This application allows the system to support sustainable eco-friendly practices in the long term.

[0924] Examples of prompts generated using AI models include the following:

[0925] "List the recommended energy optimization strategies when the user's emotional state is positive."

[0926] "How can I offer gentle suggestions to users who are experiencing stress?"

[0927] In this way, the system provides energy usage suggestions and reward systems adapted to the user's emotional state, promoting sustainable environmental protection.

[0928] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0929] Step 1:

[0930] The terminal uses smart meters and sensors to collect energy consumption information from homes and businesses in real time. This data includes electricity, gas, and water usage. The collected data is sent to a server using a protocol and recorded in a database.

[0931] Step 2:

[0932] The server analyzes the collected energy consumption information using data analysis algorithms. Specifically, it performs time-series analysis to identify peak usage times and inefficient usage patterns. The output of this analysis is patterns and statistics extracted from the data.

[0933] Step 3:

[0934] The server generates an optimized energy usage plan based on the analysis results. The plan generation algorithm refers to the user's past usage data and current consumption patterns to propose specific actions for cost reduction and efficiency improvement. This generated plan is sent to the user's device as a notification.

[0935] Step 4:

[0936] The device uses its built-in camera and microphone to collect the user's facial expressions and voice. This data is sent to a server for analysis of their emotional state. This makes it possible to capture the user's instantaneous emotional responses.

[0937] Step 5:

[0938] The server analyzes the received emotional data to infer the user's emotional state. The emotional analysis engine uses facial recognition technology and voice analysis to identify stress levels and positive emotions. The output of this process is the result of the assessment of the user's emotional state.

[0939] Step 6:

[0940] The server adjusts the energy usage plan based on the user's emotional state. Specifically, it makes conservative suggestions if the user is stressed and more positive suggestions if the user is feeling positive. These adjusted suggestions are then sent back to the user's device.

[0941] Step 7:

[0942] Users film waste with their smartphones and send the footage to a server via their devices. The video data is then analyzed by an AI model to identify the type of waste.

[0943] Step 8:

[0944] The server uses a generation AI model to analyze video data of waste and identify its characteristics. As a result, the recycling method for each type of waste is determined and the user is notified with appropriate recycling information.

[0945] Step 9:

[0946] The server rewards users for recycling activities. This reward information is recorded in a database and used to encourage future user behavior.

[0947] (Application Example 2)

[0948] 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".

[0949] In modern society, efficient energy management and optimized waste disposal are crucial issues. Furthermore, it is necessary to promote sustainable environmental behavior by providing suggestions and feedback that consider the emotional state of users. However, effectively integrating these elements and enabling users to use energy efficiently and recycle without experiencing stress is a challenging task.

[0950] 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.

[0951] In this invention, the server includes means for collecting energy consumption information in real time, means for analyzing the collected energy consumption information and generating an optimal energy use plan, and means for evaluating the user's emotional state using emotion analysis means. This makes it possible to adjust energy use suggestions based on the user's emotional state and promote participation in sustainable environmental actions.

[0952] "Energy consumption information" refers to data on the usage of energy such as electricity and gas by households and businesses.

[0953] "Means of real-time collection" refers to a mechanism for continuously acquiring energy consumption information at the current time without interruption.

[0954] "Means for generating energy usage plans" refers to a system that automatically determines the optimal energy consumption schedule based on collected energy consumption information.

[0955] An "information terminal" refers to an electronic device used to transmit energy usage suggestions and recycling information to users.

[0956] "Emotional analysis methods" refer to technologies and systems that analyze information such as a user's facial expressions and voice to understand their emotional state.

[0957] "Means for acquiring images of waste" refers to methods and devices that acquire visual data of waste using cameras or sensing technology.

[0958] "Image analysis technology" refers to techniques for processing acquired image data and extracting specific information or features from it.

[0959] "Waste treatment facility information" refers to data about the location and methods used to properly process specific types of waste.

[0960] A "means of providing rewards" refers to a system that offers rewards or points as an incentive when users take desirable actions.

[0961] A "positive response" refers to a state in which users show a proactive and positive attitude towards suggestions and feedback.

[0962] "Environmental impact" is a measure that indicates the degree to which the activities of individuals and organizations have an impact on the global environment.

[0963] "Sustainable behavior" refers to actions that are kind to the Earth's environment and the ecosystems that comprise it, and that have characteristics that make them sustainable for the future.

[0964] "Feedback" is the process of providing information to communicate the next course of action or evaluation based on the user's behavior and reactions.

[0965] To implement this invention, it is necessary to construct a system combining several key components. The server collects energy consumption information in real time, analyzes it, and generates an optimal energy usage plan. The server is also equipped with emotion analysis means, which evaluates the user's emotional state using facial expressions and voice data. This makes it possible to suggest energy usage tailored to the user's emotions.

[0966] Specifically, devices such as smartphones and tablets use cameras and microphones to input the user's facial expressions and voice, and send this data to a server. The server runs a generative AI model for emotion analysis to determine the user's emotional state. Using this analysis, the server makes suggestions to optimize energy use in a way that minimizes stress for the user.

[0967] Furthermore, the terminal acquires images of the waste, and the server analyzes these images to identify the type of waste. Based on the identification results, information about recycling facilities is provided to the user, and rewards are given at the appropriate time.

[0968] As a concrete example, when a user takes a picture of a specific type of waste with their smartphone, the image is sent to a server. The server uses image analysis technology to classify the waste and suggests the optimal recycling method based on the user's emotional state. Furthermore, if positive user behavior is detected, the system can encourage sustained eco-friendly activities through rewards such as bonus points.

[0969] Example prompt to input to the generating AI model: "What energy-saving suggestions would be effective when the user's stress level is high?"

[0970] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0971] Step 1:

[0972] The device uses its camera and microphone to collect the user's facial expressions and voice data. This allows the device to obtain input data that indicates the user's current emotional state. This data forms the basis for perceiving and analyzing customer emotions in real time.

[0973] Step 2:

[0974] The device sends the collected facial and audio data to the server. The server receives this data and analyzes the user's emotions using a generative AI model. By processing the facial and audio information received as input data with the AI ​​model, the user's emotional state (e.g., stress or happiness) is output.

[0975] Step 3:

[0976] The server generates optimal energy usage suggestions for the user based on the results of emotion analysis and learning. Using the analyzed emotional state as input, the server outputs optimized suggestions that advise the user on how to expend energy. In cases where the user's stress level is high, it selects suggestions that are more acceptable to them.

[0977] Step 4:

[0978] The server sends energy usage suggestions to the terminal. The terminal notifies the user of the received suggestions. The user can review the energy usage suggestions from the server via the terminal and choose an action based on their content.

[0979] Step 5:

[0980] The user takes a picture of the waste with their device. The device sends this image to the server as input, providing initial information so that the user can learn how to proceed with recycling.

[0981] Step 6:

[0982] The server analyzes images of the received waste and identifies the type of waste using image analysis technology. It takes image data as input and outputs waste classification information. Based on this classification, the server determines the optimal recycling method.

[0983] Step 7:

[0984] The server sends information and suggestions about recycling facilities to the terminal based on the waste classification results, notifying the user. This allows the user to obtain detailed information necessary for recycling activities and take action.

[0985] Step 8:

[0986] The user performs the suggested eco-activities and sends a report from their device to the server. The server receives this report, evaluates the user's activities, and rewards positive responses. At this time, the server obtains the user's level of achievement in eco-activities as input and outputs reward points based on the result.

[0987] 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.

[0988] 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.

[0989] 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.

[0990] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0991] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0992] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0993] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0994] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0995] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0996] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0997] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0998] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0999] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1000] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1001] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1002] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1003] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1004] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1005] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1006] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1007] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1008] The following is further disclosed regarding the embodiments described above.

[1009] (Claim 1)

[1010] A means of collecting energy consumption data in real time,

[1011] A means for analyzing collected energy consumption data and generating an optimal energy use schedule,

[1012] A means of notifying the user terminal of energy usage suggestions based on the analysis results,

[1013] A system that includes this.

[1014] (Claim 2)

[1015] A means for acquiring images of waste and classifying the type of waste using image recognition technology,

[1016] A means of providing information on recycling facilities to user terminals according to the type of waste,

[1017] A means of rewarding users for completing recycling,

[1018] The system according to claim 1, including the following:

[1019] (Claim 3)

[1020] A means of calculating the carbon footprint based on user behavior data,

[1021] A means of recommending environmentally friendly actions to users based on their carbon footprint,

[1022] A means of recording the execution of recommended actions and improving the user's environmental contribution,

[1023] The system according to claim 1, including the following:

[1024] "Example 1"

[1025] (Claim 1)

[1026] Means for dynamically collecting energy resource data,

[1027] A means for analyzing collected energy resource data to generate an appropriate energy utilization plan,

[1028] A means of transmitting the plan to the user's terminal based on the analysis results,

[1029] A means of acquiring users' daily behavioral data and calculating carbon emissions based on this data,

[1030] A means of proposing environmental protection actions to users based on carbon emissions,

[1031] A means of recording the execution of proposed actions and enhancing users' environmental awareness,

[1032] A system that includes this.

[1033] (Claim 2)

[1034] A means for acquiring images of waste and identifying the type of waste using visual recognition technology,

[1035] A means of providing user terminals with information on resource recovery facilities that handle waste,

[1036] A means of rewarding users for carrying out resource collection,

[1037] The system according to claim 1, including the following:

[1038] (Claim 3)

[1039] A method for calculating carbon footprints based on user behavior data,

[1040] A means of encouraging environmentally conscious behavior among users based on carbon footprints,

[1041] A means of recording the performance of recommended actions and improving the environmental contribution of users,

[1042] The system according to claim 1, including the following:

[1043] "Application Example 1"

[1044] (Claim 1)

[1045] A device that collects energy consumption information in real time,

[1046] A device that analyzes collected energy consumption information and generates an optimal energy usage plan,

[1047] A device that notifies the user terminal of energy usage suggestions based on the analysis results,

[1048] A device that provides residents with information to optimize the energy consumption of the entire city,

[1049] A system that includes this.

[1050] (Claim 2)

[1051] A device that acquires images of waste and classifies the type of waste using image recognition technology,

[1052] A device that provides information on recycling facilities to user terminals according to the type of waste,

[1053] A device that rewards users for completing the recycling process,

[1054] The system according to claim 1, including the following:

[1055] (Claim 3)

[1056] A device that calculates carbon dioxide emissions based on user behavior information,

[1057] A device that recommends environmentally friendly behavior to users based on carbon dioxide emissions,

[1058] A device that records the execution of recommended actions and improves the user's contribution to the environment,

[1059] A device that provides information on eco-friendly activities within the city via a smartphone application,

[1060] The system according to claim 1, including the following:

[1061] "Example 2 of combining an emotion engine"

[1062] (Claim 1)

[1063] A processing unit that collects energy consumption information in real time,

[1064] A processing device that analyzes collected energy consumption information and generates an optimal energy usage plan,

[1065] A processing device that notifies an information processing terminal of energy usage suggestions based on the analysis results,

[1066] A processing device that analyzes the user's emotional state and adjusts energy usage suggestions based on that emotional state,

[1067] A system that includes this.

[1068] (Claim 2)

[1069] A processing device that acquires images of waste and classifies the type of waste using image recognition technology,

[1070] A processing device that provides information on waste disposal organizations to an information processing terminal according to the type of waste,

[1071] A processing device that provides compensation in accordance with the execution of waste disposal,

[1072] A processing unit that detects the user's emotional response and adjusts the timing of rewards based on that response,

[1073] The system according to claim 1, including the following:

[1074] (Claim 3)

[1075] A processing device that calculates environmental impact based on user behavior information,

[1076] A processing device that recommends environmental protection actions to users based on their environmental impact,

[1077] A processing device that records the implementation of recommended actions and improves the user's environmental contribution,

[1078] The system according to claim 1, including the following:

[1079] "Application example 2 when combining with an emotional engine"

[1080] (Claim 1)

[1081] A means of collecting energy consumption information in real time,

[1082] A means for analyzing collected energy consumption information and generating an optimal energy use plan,

[1083] A means of notifying an information terminal of energy usage suggestions based on the analysis results,

[1084] A means for evaluating a user's emotional state using emotion analysis tools,

[1085] A means of adjusting energy usage suggestions based on the user's emotional state,

[1086] A system that includes this.

[1087] (Claim 2)

[1088] A means of acquiring images of waste and classifying the type of waste using image analysis technology,

[1089] A means of providing information about waste treatment facilities to an information terminal according to the type of waste,

[1090] A means of rewarding users for completing recycling,

[1091] A means of monitoring the emotional state of users and providing further rewards in response to positive reactions,

[1092] The system according to claim 1, including the following:

[1093] (Claim 3)

[1094] A means of calculating environmental impact based on user behavior information,

[1095] A means of recommending sustainable actions to users based on their environmental impact,

[1096] A means of recording the execution of recommended actions, improving the user's environmental contribution, and providing feedback based on emotional state,

[1097] The system according to claim 1, including the following: [Explanation of symbols]

[1098] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting energy consumption data in real time, A means for analyzing collected energy consumption data and generating an optimal energy use schedule, A means of notifying the user terminal of energy usage suggestions based on the analysis results, A system that includes this.

2. A means for acquiring images of waste and classifying the type of waste using image recognition technology, A means of providing information on recycling facilities to user terminals according to the type of waste, A means of rewarding users for completing recycling, The system according to claim 1, including the following:

3. A means of calculating the carbon footprint based on user behavior data, A means of recommending environmentally friendly actions to users based on their carbon footprint, A means of recording the execution of recommended actions and improving the user's environmental contribution, The system according to claim 1, including the following:

Images