system

Abstract

Provide a system. 【Solution means】 Means for collecting in real time the energy usage information within a household, Means for analyzing the energy usage information to extract energy consumption rules, Means for generating an instruction for optimizing energy usage based on the energy consumption rules, Means for notifying the user of the instruction, Means for collecting the user's behavior information based on the instruction and evaluating the results, Means for collecting regional eco-activities and event information, Means for screening the eco-activities and event information based on the user's interests, Means for notifying the user of the screened information, Means for providing an information sharing platform for promoting participation in eco-activities, Means for receiving responses from users and continuously improving the effectiveness of the instruction, Means for storing the history of instructions generated to support a sustainable lifestyle, Means for providing an individualized energy usage plan for users using the stored instruction history, Means for collaborating with an energy management system at the regional level and contributing to the overall energy efficiency of the region, Means for providing an automatic registration function for the eco-activities and event information, A system including the above.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 recent years, although environmental awareness has been increasing, there is a problem that it is unclear how an individual should specifically manage energy consumption and participate in regional environmental protection activities. In particular, there is a lack of means to efficiently optimize energy consumption in daily life and strengthen the connection with the local community. Therefore, there is a need for a system to improve awareness of individual energy use and raise the overall environmental awareness of the region.

Means for Solving the Problems

[0005] This invention provides a means for collecting and analyzing household energy usage data in real time, thereby extracting energy consumption patterns and proposing an optimal usage schedule. It also includes a means for collecting local eco-activity information, filtering it based on user interests, and appropriately notifying users of useful information. Furthermore, it continuously improves the effectiveness of collected data by utilizing user feedback, saves a history of suggestions, and provides personalized strategies. This system promotes efficient energy management and participation in environmental activities at both the individual and community levels.

[0006] "Household energy usage data" refers to information obtained through meters, smart appliances, and other means regarding the consumption of energy such as electricity and gas used in a household.

[0007] An "energy consumption pattern" is a collection of data that shows fluctuations and trends in energy consumption over a certain period of time.

[0008] "Suggestions for optimizing energy use" refer to specific action plans and schedules provided to help users use energy efficiently and without waste.

[0009] "Local eco-activities and event information" refers to information about activities and events aimed at environmental protection that take place within a specific region.

[0010] "Filtering" is the process of selecting necessary elements from collected information according to specific criteria.

[0011] A "communication platform" is an online environment or tool that serves as a foundation for users to share information and exchange opinions.

[0012] "Proposal history" refers to a collection of data that records, in chronological order, the energy usage suggestions presented to the user in the past.

[0013] A "personalized energy use strategy" involves creating an optimal energy usage method tailored to each individual user, based on their energy usage habits and preferences. [Brief explanation of the drawing]

[0014] [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] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] 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.

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

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

[0018] 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.

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

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

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

[0022] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0035] This invention is a system that efficiently manages household energy consumption and promotes participation in local eco-activities. Its form is described below.

[0036] Analysis of energy patterns

[0037] The server acquires energy usage data from smart meters and connected smart appliances within the home. This data is updated in real time and stored chronologically. The server analyzes the collected data to extract energy consumption patterns, identifying peak times for electricity consumption in each household and areas where energy waste is occurring.

[0038] Optimization of energy use

[0039] The server suggests effective energy usage methods based on the analysis results. These suggestions include specific details, such as using certain appliances during off-peak hours or utilizing energy-saving modes. The terminal notifies the user of these suggestions and encourages them to act on them. The user receives this notification and adjusts the usage schedule of their appliances to improve energy efficiency.

[0040] Providing information on eco-friendly activities

[0041] The server collects information on local eco-activities and events from the internet. This information gathering utilizes local news sites and social media. The server filters the collected information based on the user's registration data and past participation history. The terminal notifies the user of the filtered information and suggests highly relevant activities and events. Based on this information, the user can participate in eco-activities that interest them.

[0042] User feedback and personalized strategies

[0043] The server collects and analyzes user behavior and feedback as data. Based on this, it evaluates the effectiveness of suggestions and continuously improves future suggestions. Past suggestion history is stored in a database, and this is used to build personalized energy use strategies for users. The terminal presents this personalized strategy to the user, supporting the realization of a sustainable lifestyle.

[0044] Specific example

[0045] For example, if a home's electricity peak is concentrated in the evening, the server might suggest using appliances like washing machines and dishwashers at night. The user can then reschedule these activities to the evening, reducing energy costs. Similarly, if the server gathers information about an eco-festival in the area, the terminal will notify the user of the details and encourage participation. By participating in these activities, users can interact with their local eco-community.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] The server collects real-time energy usage data from connected smart meters and smart appliances within the home. This includes electricity consumption and usage history for each appliance.

[0049] Step 2:

[0050] The server analyzes the collected energy usage data and extracts consumption patterns over time. In particular, it identifies peak consumption times and areas where energy is being wasted.

[0051] Step 3:

[0052] The server generates suggestions for optimizing energy use based on the extracted consumption patterns. For example, it recommends using appliances outside of peak hours and utilizing energy-saving modes.

[0053] Step 4:

[0054] The terminal notifies the user of suggestions from the server. These notifications may include specific changes to the usage time of home appliances or the selection of energy-saving modes.

[0055] Step 5:

[0056] Users receive notifications from their devices and adjust their appliance usage schedules accordingly. For example, they might set their washing machine to run at night, taking action based on the suggestions.

[0057] Step 6:

[0058] The server collects user behavior data again and evaluates how effective the suggestions were. Based on this evaluation, it improves the suggestions for future use.

[0059] Step 7:

[0060] The server collects local eco-activities and event information from online resources and filters it based on the user's interests.

[0061] Step 8:

[0062] The device notifies the user of filtered eco-friendly activities and event information. The notification includes details such as the date, time, and location of participation.

[0063] Step 9:

[0064] Users can participate in eco-friendly activities that interest them based on the information they receive, contributing to raising environmental awareness in their local communities. They can also exchange opinions with other participants through the platform.

[0065] (Example 1)

[0066] 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."

[0067] While modern households need to optimize energy use, there are insufficient means to identify and streamline wasteful consumption. In particular, it is difficult for individual households to develop energy-saving strategies suited to their lifestyles, and participation in local environmental activities is not encouraged. Furthermore, the lack of suitable options and suggestions for users hinders sustainable lifestyles.

[0068] 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.

[0069] In this invention, the server includes means for collecting energy usage data from measuring devices and communication equipment within the home, means for analyzing the energy usage data to identify energy consumption trends, and means for using generative AI technology to generate options for improving energy efficiency based on the consumption trends. This makes it possible to identify energy waste in each household and provide effective energy usage strategies on a personalized basis. It also provides information to promote participation in local environmental activities and supports the realization of a sustainable lifestyle.

[0070] "Measuring devices" refer to equipment used to acquire household energy usage data in real time, and include smart meters and communication-enabled home appliances.

[0071] "Communication equipment" refers to devices used to transmit energy usage data acquired from measuring devices to a server, and which can connect to the internet or a local network.

[0072] "Energy consumption trends" refer to information that shows the time-based consumption patterns and efficiency extracted from analyzed energy usage data.

[0073] "Generative AI technology" refers to machine learning algorithms and artificial intelligence technologies used to automatically generate suggestions and options for improving energy efficiency.

[0074] "Options" refer to specific actions and strategies proposed to optimize the user's energy use, including using appliances during off-peak hours and utilizing energy-saving modes.

[0075] A "communication platform" refers to a communication platform provided to make it easier for users to participate in local environmental activities, and includes online forums and information sharing sites.

[0076] "Historical data" refers to information that records the implementation status of choices and suggestions that a user has made so far, and is used to improve individual strategies in the future.

[0077] This invention is a system in which a server, terminal, and user work together to improve the efficiency of energy use within the home.

[0078] The server collects household energy usage data in real time through measuring devices and communication equipment. To do this, the server acquires data from smart meters and communicable home appliances and stores it in a database. For data analysis, the Python pandas library and scikit-learn library are used to identify energy consumption trends.

[0079] Next, generative AI technology is used to generate options for improving energy efficiency. The generative AI model analyzes data using machine learning algorithms and builds individually optimized suggestions. For example, the server sends the prompt "How should a user distribute their energy consumption if it peaks between 6 PM and 9 PM?" to the generative AI model and obtains suggestions.

[0080] The suggestions are communicated to the user via a device, such as a smartphone or PC, and the notifications are sent through the app's push notification function. This encourages users to take specific actions, such as using certain appliances during off-peak hours, enabling more efficient energy use.

[0081] Furthermore, the server collects local environmental activity and event information and filters it based on user preferences. This information is then communicated to the user via their device, encouraging participation in activities that interest them.

[0082] User feedback is sent to the server via the terminal. The server analyzes the effectiveness of the suggestions based on this data and improves the options to support sustainable lifestyles. Historical data is used to continuously provide users with personalized energy usage plans. In this way, the present invention highly streamlines energy management and promotes contributions to the local community.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] The server collects energy usage data in real time from measuring devices and communication equipment within the home. Inputs include data from smart meters and smart home appliances. This data is stored in a database in JSON format. Specifically, the server retrieves power consumption data from each device every minute and saves it as time-series data.

[0086] Step 2:

[0087] The server analyzes the collected data to identify energy consumption trends. The input is the time-series data obtained in step 1. Using the Python pandas library, it analyzes peak times and trends in consumption. The output includes peak times of the day and warnings about wasteful energy consumption. Specifically, the server aggregates the data and identifies time periods when consumption exceeds 20% of the average.

[0088] Step 3:

[0089] The server uses generative AI technology to generate options for improving energy efficiency. The input is the analysis results from step 2. The generative AI model receives a prompt and generates suggestions such as operating specific appliances during off-peak hours. The output is an optimized set of action proposals. Specifically, the server sends a prompt to the generative AI asking "how to shift energy consumption to off-peak hours" and receives specific suggestions.

[0090] Step 4:

[0091] The device notifies the user of suggestions from the server. The input is the suggestions generated in step 3. Using the notification function, the suggestions are delivered to the user as push notifications. As output, the suggestions are displayed on the user's smartphone or PC. Specifically, the device sends a message to the user via the app, such as "We recommend using the washing machine at night."

[0092] Step 5:

[0093] The server collects and filters local environmental activity and event information from the internet. Input information comes from local news sites and social media. NLP (Neuro-Linguistic Programming) technology is used to select relevant information based on user interests. The output is highly relevant event information. Specifically, the server retrieves events containing keywords such as "eco-festival" via web scraping and selects them considering the user's past participation history.

[0094] Step 6:

[0095] The user takes action based on the suggestion and sends feedback to the server via their device. The input is the user's actual behavioral data. This data is used to evaluate the effectiveness of the suggestion. As output, improved behavioral patterns are stored in a database. Specifically, the user changes the air conditioner settings according to the suggestion and inputs the result as an evaluation on their device.

[0096] Step 7:

[0097] The server updates the personalized energy use strategy based on the collected feedback. The input is the evaluation data obtained in step 6. This provides a means to improve the quality of the proposal. The output is the improved proposal that will be presented next time. Specifically, the server further optimizes future proposals based on data such as "successfully reduced power consumption through nighttime use."

[0098] (Application Example 1)

[0099] 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."

[0100] There are still challenges in optimizing energy use at the household and community levels, and in promoting participation in eco-friendly activities. The current system optimizes energy use only at the individual household level, and further improvements are needed to promote energy efficiency improvements and participation in eco-friendly activities throughout the community.

[0101] 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.

[0102] In this invention, the server includes means for collecting energy usage information within a household in real time, means for generating instructions for optimizing energy use based on energy consumption regulations, and means for coordinating with a regional energy management system to contribute to the overall energy efficiency of the region. This makes it possible to improve energy efficiency at both the household and the region, and to encourage residents to actively participate in eco-friendly activities.

[0103] "Household energy usage information" refers to data on the amount and patterns of energy consumption, such as electricity and gas, used within a household.

[0104] "Collecting data in real time" means acquiring data immediately when it occurs and keeping it up-to-date at all times for analysis and use.

[0105] "Energy consumption regulations" are laws and standards derived from analyzing energy usage trends and patterns under specific conditions.

[0106] "Energy use optimization instructions" are guidelines that provide users with specific methods and timing for efficiently consuming energy.

[0107] A "regional energy management system" is an integrated management system designed to improve the efficiency of energy consumption within a specific region.

[0108] "Regional energy efficiency" refers to a comprehensive effort to reduce overall energy use in a specific region and promote a sustainable environment.

[0109] "Eco-activities" refer to individual and community activities undertaken to protect the natural environment, conserve resources, and realize a sustainable society.

[0110] The system for implementing this invention aims to improve the efficiency of household energy management and encourage participation in local eco-activities. Its specific form is described below.

[0111] First, the server collects energy usage information in real time from smart meters and connected smart appliances. The collected information is stored in a database and analyzed as time-series data. As a result of the analysis, peak energy consumption and areas of wasteful use are identified, and energy consumption rules are derived.

[0112] The server generates optimization instructions for the user based on the analyzed energy consumption rules. For example, it can instruct the user to change the use of certain appliances to off-peak times. This allows the user to reduce energy costs and use energy more efficiently.

[0113] Furthermore, the server retrieves local eco-activities and event information from the internet and filters it to match the user's interests. For example, it can retrieve information about eco-festivals from local news sites and social media feeds and notify users. Users can also automatically register to participate in eco-activities based on this information.

[0114] This system is implemented using programming languages ​​such as Python, and employs machine learning algorithms for data acquisition and analysis. A SQL-based system is used for database management, enabling real-time data processing.

[0115] As a concrete example, let's assume a user was instructed to use their washing machine at night, resulting in a reduction in energy consumption. Another example is a user being notified about a local eco-festival and being able to easily register to participate.

[0116] An example of a prompt message is: "Please describe in detail a system that generates suggestions for optimizing household energy consumption and notifies users of local eco-activities."

[0117] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0118] Step 1:

[0119] The server acquires energy usage information in real time from smart meters and smart home appliances. This input data is stored in a time-series database and serves as the source data representing each household's energy usage patterns.

[0120] Step 2:

[0121] The server extracts energy consumption patterns from time-series data. This process uses machine learning algorithms to identify peak consumption trends and wasteful energy use, and then identifies these patterns. The output provides specific energy consumption trends and optimization points.

[0122] Step 3:

[0123] The server generates energy use optimization instructions based on the analysis results. In this step, it suggests shifting the usage of specific appliances to off-peak hours. The generated instructions are provided to the user as specific guidelines for improving energy efficiency.

[0124] Step 4:

[0125] The device notifies the user of the generated instructions. The notification is displayed on the screen of a smartphone or tablet and functions as an output prompting the user to take specific action.

[0126] Step 5:

[0127] The server collects local eco-activities and event information via the internet. It uses information from news sites and social media as input, and employs natural language processing to filter the information based on user interests. The output is filtered to include information on eco-events that are of interest.

[0128] Step 6:

[0129] The device notifies users of filtered eco-activity information and encourages participation. In this step, it guides users who wish to participate in local eco-activities so that they can easily register.

[0130] Step 7:

[0131] Users provide feedback on energy usage instructions and eco-activity information provided by the system. This feedback is used as input data for the server to evaluate the effectiveness of the suggestions and continuously improve them. As output, users are provided with a personalized energy usage strategy.

[0132] 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.

[0133] This invention combines a system for optimizing energy consumption by collecting and analyzing household energy usage data in real time with an emotion engine that recognizes the user's emotional state.

[0134] Energy pattern analysis and optimization

[0135] The server acquires energy usage data from smart meters and home appliances and analyzes consumption patterns in real time. This data includes seasonal fluctuations and the usage history of individual appliances. Based on the analysis results, the server generates suggestions for optimal energy usage and uses an emotion engine to adjust the suggestions according to the user's emotional state.

[0136] Understanding user state using an emotion engine

[0137] The device uses information entered through the user interface and other sensor data to estimate the user's emotional state using an emotion engine. This emotional state indicates, for example, stress levels, relaxation levels, and levels of interest, and is used to understand the user's current psychological state. Based on the estimated emotional data, the server adjusts the content of energy usage suggestions and notification methods according to the user's level of acceptance or resistance. This makes it possible to reduce stress on the user and promote cooperative behavior.

[0138] Providing information on eco-friendly activities

[0139] The server collects local eco-activity information from online resources and filters it based on the user's interests, past participation history, and emotional state. The filtered information is then notified to the user via their device. The way the information is presented and the wording of invitations to participate are adjusted according to the user's emotional state.

[0140] Improving feedback and suggestions

[0141] Users provide feedback on their energy usage and participation in eco-friendly activities. The server receives this feedback and analyzes it along with emotional state and behavioral data to improve the effectiveness of future suggestions and notifications. By using suggestion history and emotional data, personalized energy usage strategies are built for each user, supporting the realization of a sustainable lifestyle.

[0142] Specific example

[0143] For example, if a user is sensitive to energy consumption and experiencing stress, the server uses an emotion engine to offer more flexible suggestions. For instance, it might introduce positive feedback, such as, "If you meet today's energy consumption goal, there will be a small celebration," to encourage user engagement. Furthermore, for users in a positive mood, it might send notifications encouraging participation in new eco-friendly activities, thereby revitalizing the local community.

[0144] The following describes the processing flow.

[0145] Step 1:

[0146] The server collects real-time power consumption data from smart meters and smart appliances within the home. This data includes power consumption and details of appliances being used during each time period.

[0147] Step 2:

[0148] The server analyzes the collected data to extract patterns in daily energy use. This analysis identifies peak times and areas where wasteful consumption is occurring.

[0149] Step 3:

[0150] The emotion engine infers the user's emotional state based on information obtained from the user's device and sensor inputs. This includes determining how stressed or relaxed the user is.

[0151] Step 4:

[0152] The server combines analysis results and emotional state data to generate suggestions for energy usage optimization best suited to the user's situation. These suggestions are then adjusted to a tone that respects the user's emotions.

[0153] Step 5:

[0154] The device presents the generated suggestions to the user as notifications. The notification messages may include encouragement or flexible suggestions depending on the user's emotional state.

[0155] Step 6:

[0156] The user receives a notification, reviews the suggestion, and adjusts their appliance usage schedule based on the suggestion. If the adjustment is made correctly, the system detects this and provides further feedback.

[0157] Step 7:

[0158] The server records the user's adjustment behavior and emotional responses, analyzing this data to improve the effectiveness of future suggestions. This enhances the personalized approach to the user.

[0159] Step 8:

[0160] The server collects local eco-activity information from online sources and filters the information based on the user's interests, participation history, and emotional state.

[0161] Step 9:

[0162] The device notifies users of filtered eco-friendly activity information and sends emotionally sensitive messages to encourage participation. Users can register to participate in events that interest them.

[0163] (Example 2)

[0164] 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".

[0165] In modern households, excessive energy consumption and insufficient participation in local environmental activities are significant problems. Furthermore, providing appropriate energy use strategies that consider users' emotional states is difficult, and the lack of acceptable suggestions and notifications results in insufficient means to effectively promote behavioral change among users. To address this challenge, a system is needed that considers user emotions and proposes effective energy use and participation in eco-friendly activities.

[0166] 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.

[0167] In this invention, the server includes means for collecting household resource usage data in real time, means for analyzing consumption patterns and generating suggestions for optimizing energy use, and means for inferring the user's emotional state using a generating AI model and adjusting the suggestions in consideration of the emotional state. This enables energy use suggestions that are appropriate to the user's specific emotional state, allowing for sustainable resource management and active participation in the local community.

[0168] "Resource usage data" refers to information about the amount and patterns of resources consumed within a household, such as energy and water.

[0169] A "generative AI model" refers to artificial intelligence technology trained to analyze data and infer human emotional states.

[0170] An "emotion engine" refers to software or a system that analyzes a user's voice, facial expressions, and behavior to determine their emotional state.

[0171] "Consumption patterns" refer to data characteristics that show usage trends for specific resources, broken down by time of day or season.

[0172] "Suggestions" refer to advice provided to users based on analyzed data, aimed at promoting efficient resource use and behavioral change.

[0173] "Feedback" refers to the information that users provide in response to suggestions or to the results of their use, and is used to improve the system.

[0174] A "personalized energy use strategy" refers to an energy usage plan customized for each individual user, created based on their lifestyle and emotional state.

[0175] "Communication methods" refer to the methods or interfaces by which users and systems exchange information with each other.

[0176] This invention is a system that optimizes energy use in the home and provides suggestions based on the user's emotional state. The system collects data from various smart devices and home appliances in the home and analyzes this data on a server in the cloud. The server runs on a general cloud computing platform and recognizes consumption patterns in real time using a database and data analysis software. The analysis uses a generative AI model that implements machine learning algorithms, which detects anomalies in consumption and points where optimization is possible.

[0177] The server generates specific energy usage suggestions, taking into account appliance usage history and external environmental information. These suggestions are adjusted as needed, based on emotion engine data transmitted from the device, such as the user's stress level and relaxation level. The device collects emotion data from sensors and user interfaces via commercially available computer devices such as the user's smartphone or tablet. Furthermore, the method and content of user notifications are flexibly customized, taking the user's emotional state into consideration.

[0178] For example, if the system analyzes that a user is experiencing stress, the server can provide positive feedback using a prompt such as, "If you meet today's energy goal, we'll play your favorite music." This encourages cooperative behavior from the user and effectively optimizes energy consumption within the home.

[0179] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0180] Step 1:

[0181] The server acquires resource usage data from smart devices and appliances within the home. It receives real-time usage data transmitted from smart meters and appliances as input. This data includes electricity consumption, usage time, device ID, and other information. The server stores this data in a database and organizes it by date and device.

[0182] Step 2:

[0183] The server analyzes collected resource usage data and extracts consumption patterns. It uses machine learning algorithms to analyze the data based on the input. This analysis highlights consumption trends by time of day and season, enabling anomaly detection. The output provides a list of energy consumption trends and saving points.

[0184] Step 3:

[0185] The device collects emotional data from the user. Inputs include text input via the user interface, voice commands, and information from other smartphone sensors. The emotion engine analyzes this data, quantifies the user's current emotional state, such as stress and happiness levels, and sends the results to the server.

[0186] Step 4:

[0187] The server generates resource usage optimization suggestions based on consumption patterns and emotional data. The input consists of analyzed consumption patterns and emotional data. A generative AI model is used to create flexible suggestions tailored to the user's emotional state. Specific action suggestions are generated as output and sent to the terminal.

[0188] Step 5:

[0189] The server notifies the user via the terminal. Input includes generated suggestions and prompts. The notification method is adjusted according to the user's emotional state, using prompts that emphasize encouragement and positive feedback. Output is a notification sent to the user.

[0190] Step 6:

[0191] Based on the suggested actions, users attempt actual resource usage and provide feedback. The input consists of the user's actions and their results, which are sent back to the server via the terminal as feedback. The server analyzes this feedback and uses it to improve future suggestions.

[0192] (Application Example 2)

[0193] 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".

[0194] Energy consumption in modern cities is influenced by a variety of complex factors. Optimizing energy consumption by taking into account individual energy use patterns and emotional states contributes to the efficient use of energy resources and the reduction of environmental impact. However, optimizing energy use and gaining user cooperation requires effective user suggestions and encouragement of participation in emotion-based eco-activities, which has been difficult to achieve with conventional systems.

[0195] 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.

[0196] In this invention, the server includes means for collecting energy usage data in homes and public facilities in real time, means for generating personalized suggestions for optimizing energy use based on the user's emotional state, and means for filtering and notifying the user of eco-activities and event information based on the user's emotional state and interests. This enables the optimization of energy consumption tailored to the user's living environment and the realization of a sustainable lifestyle.

[0197] "Energy usage data" refers to data that shows the amount of energy consumed in homes and public facilities, as well as its temporal fluctuations.

[0198] "Means of collecting data in real time" refers to methods and technologies for instantly obtaining current energy usage data.

[0199] An "energy consumption pattern" is the result of analyzing a series of data that shows the trends and characteristics of energy consumption.

[0200] "Emotional state" refers to the user's psychological or emotional condition, including stress, relaxation, and interest.

[0201] "Personalized suggestions" refer to proposals for optimal energy usage tailored to the user's characteristics and circumstances.

[0202] "Eco-activities and event information" refers to data on community events and participation opportunities related to environmental protection and sustainable activities.

[0203] "Filtering" is the process of selecting information based on specific criteria and extracting the necessary data.

[0204] A "communication platform" is a system or infrastructure that enables the sending and receiving of information.

[0205] This invention provides a system for optimizing energy consumption in homes and public facilities in smart cities. The central server of the system collects energy usage data in real time from smart meters and various IoT devices and processes it using a cloud platform (for example, AWS® or Google® Cloud Platform). Python's pandas library is used for data analysis, and natural language processing libraries such as TENSORFLOW® and spaCy are used for analyzing users' emotional states.

[0206] The server generates personalized energy usage suggestions based on each user's energy usage patterns and emotional state. It notifies the user of these suggestions via their device (such as a smartphone or smart glasses) and continuously collects user feedback and behavioral data. This feedback data is used for further analysis to improve the effectiveness of the suggestions.

[0207] Furthermore, the system collects information on local eco-activities and events, and notifies users of filtered information based on their interests and emotional state. This encourages active participation in activities and events that interest the target users.

[0208] For example, one system might display a notification on a user's smart glasses while they are at work, asking, "Your home's air conditioning settings are optimized, is this okay?" Furthermore, on weekends, emotion-based notifications might be sent, such as, "Why not participate in the eco-fair being held in the city?", thereby supporting increased awareness of sustainable lifestyles.

[0209] An example of a prompt message to a generative AI model is, "Generate optimal energy consumption suggestions based on the user's current emotional state and energy data."

[0210] In this way, we provide an implementation that effectively manages users' energy usage and enables a sustainable lifestyle in a smart city environment.

[0211] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0212] Step 1:

[0213] The server collects real-time energy usage data from homes and public facilities through smart meters and IoT devices. This data includes power consumption, usage time, and date for each device. The collected data is stored on a cloud platform.

[0214] Step 2:

[0215] The server analyzes the collected energy usage data. It uses Python's pandas library to clean and format the data and analyze trends in electricity consumption. The analysis results extract the energy consumption patterns of each household and facility. Output includes data visualizations and statistical trend results.

[0216] Step 3:

[0217] The device analyzes the user's emotional state based on input such as camera images and audio. It uses a generative AI model and natural language processing library to identify emotional states such as stress levels and happiness. The analysis results are then sent to a server.

[0218] Step 4:

[0219] The server generates personalized energy usage suggestions based on energy consumption patterns and the user's emotional state. This process utilizes a generative AI model to construct optimal suggestions and action-oriented messages tailored to the user's emotions. The generated results are output as specific suggestions and are ready to be notified to the user.

[0220] Step 5:

[0221] The device receives suggestions from the server and notifies the user via a smartphone or smart glasses. The device also considers the user's location and schedule to ensure timely notifications. The output displays specific suggestions to encourage user action.

[0222] Step 6:

[0223] Users respond to the suggestions they receive and send the results as feedback from their device to the server. This feedback includes their satisfaction with the suggestion and the results of their implementation. The server uses this feedback to perform data analysis to improve the effectiveness of future suggestions.

[0224] Step 7:

[0225] The server retrieves local eco-activities and event information from an external database and filters it based on the user's interests and emotional state. The filtered information is sent to the device as a message inviting participation. Ultimately, the user is notified of the event information, promoting participation in eco-activities.

[0226] 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.

[0227] Data generation model 58 is a type of 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.

[0228] 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.

[0229] [Second Embodiment]

[0230] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0231] 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.

[0232] 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).

[0233] 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.

[0234] 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.

[0235] 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).

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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".

[0242] This invention is a system that efficiently manages household energy consumption and promotes participation in local eco-activities. Its form is described below.

[0243] Analysis of energy patterns

[0244] The server acquires energy usage data from smart meters and connected smart appliances within the home. This data is updated in real time and stored chronologically. The server analyzes the collected data to extract energy consumption patterns, identifying peak times for electricity consumption in each household and areas where energy waste is occurring.

[0245] Optimization of energy use

[0246] The server suggests effective energy usage methods based on the analysis results. These suggestions include specific details, such as using certain appliances during off-peak hours or utilizing energy-saving modes. The terminal notifies the user of these suggestions and encourages them to act on them. The user receives this notification and adjusts the usage schedule of their appliances to improve energy efficiency.

[0247] Providing information on eco-friendly activities

[0248] The server collects information on local eco-activities and events from the internet. This information gathering utilizes local news sites and social media. The server filters the collected information based on the user's registration data and past participation history. The terminal notifies the user of the filtered information and suggests highly relevant activities and events. Based on this information, the user can participate in eco-activities that interest them.

[0249] User feedback and personalized strategies

[0250] The server collects and analyzes user behavior and feedback as data. Based on this, it evaluates the effectiveness of suggestions and continuously improves future suggestions. Past suggestion history is stored in a database, and this is used to build personalized energy use strategies for users. The terminal presents this personalized strategy to the user, supporting the realization of a sustainable lifestyle.

[0251] Specific example

[0252] For example, if a home's electricity peak is concentrated in the evening, the server might suggest using appliances like washing machines and dishwashers at night. The user can then reschedule these activities to the evening, reducing energy costs. Similarly, if the server gathers information about an eco-festival in the area, the terminal will notify the user of the details and encourage participation. By participating in these activities, users can interact with their local eco-community.

[0253] The following describes the processing flow.

[0254] Step 1:

[0255] The server collects real-time energy usage data from connected smart meters and smart appliances within the home. This includes electricity consumption and usage history for each appliance.

[0256] Step 2:

[0257] The server analyzes the collected energy usage data and extracts consumption patterns over time. In particular, it identifies peak consumption times and areas where energy is being wasted.

[0258] Step 3:

[0259] The server generates suggestions for optimizing energy use based on the extracted consumption patterns. For example, it recommends using appliances outside of peak hours and utilizing energy-saving modes.

[0260] Step 4:

[0261] The terminal notifies the user of suggestions from the server. These notifications may include specific changes to the usage time of home appliances or the selection of energy-saving modes.

[0262] Step 5:

[0263] Users receive notifications from their devices and adjust their appliance usage schedules accordingly. For example, they might set their washing machine to run at night, taking action based on the suggestions.

[0264] Step 6:

[0265] The server collects user behavior data again and evaluates how effective the suggestions were. Based on this evaluation, it improves the suggestions for future use.

[0266] Step 7:

[0267] The server collects local eco-activities and event information from online resources and filters it based on the user's interests.

[0268] Step 8:

[0269] The device notifies the user of filtered eco-friendly activities and event information. The notification includes details such as the date, time, and location of participation.

[0270] Step 9:

[0271] Users can participate in eco-friendly activities that interest them based on the information they receive, contributing to raising environmental awareness in their local communities. They can also exchange opinions with other participants through the platform.

[0272] (Example 1)

[0273] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0274] While modern households need to optimize energy use, there are insufficient means to identify and streamline wasteful consumption. In particular, it is difficult for individual households to develop energy-saving strategies suited to their lifestyles, and participation in local environmental activities is not encouraged. Furthermore, the lack of suitable options and suggestions for users hinders sustainable lifestyles.

[0275] 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.

[0276] In this invention, the server includes means for collecting energy usage data from measuring devices and communication equipment within the home, means for analyzing the energy usage data to identify energy consumption trends, and means for using generative AI technology to generate options for improving energy efficiency based on the consumption trends. This makes it possible to identify energy waste in each household and provide effective energy usage strategies on a personalized basis. It also provides information to promote participation in local environmental activities and supports the realization of a sustainable lifestyle.

[0277] "Measuring devices" refer to equipment used to acquire household energy usage data in real time, and include smart meters and communication-enabled home appliances.

[0278] "Communication equipment" refers to devices used to transmit energy usage data acquired from measuring devices to a server, and which can connect to the internet or a local network.

[0279] "Energy consumption trends" refer to information that shows the consumption patterns and efficiency over time, extracted from analyzed energy usage data.

[0280] "Generative AI technology" refers to machine learning algorithms and artificial intelligence technologies used to automatically generate suggestions and options for improving energy efficiency.

[0281] The "option" refers to a specific action plan or strategy proposed to optimize the user's energy use, including using household appliances during off-peak hours and activating energy-saving modes.

[0282] The "communication infrastructure" refers to a communication platform provided to facilitate the user's participation in regional environmental activities, including online forums and information sharing sites.

[0283] The "historical data" refers to information recording the options and implementation status of proposals executed by the user so far, which is used to improve future individual strategies.

[0284] The present invention is a system in which a server, a terminal, and a user cooperate to improve the efficiency of energy use within a household.

[0285] The server collects in real time the energy use data within the household through a measuring device and a communication device. For this purpose, the server obtains data from a smart meter and communicable household appliances and accumulates it in a database. For data analysis, the pandas library and scikit-learn library of Python are used to identify energy consumption trends.

[0286] Next, the generative AI technology is utilized to generate options for improving energy use efficiency. The generative AI model analyzes data using a machine learning algorithm and constructs individually optimized proposals. For example, the server sends the prompt sentence "When the user's energy consumption peaks from 6 pm to 9 pm, how can the consumption be dispersed?" to the generative AI model to obtain a proposal.

[0287] The proposal is notified to the user via the terminal. The terminal is a smartphone or a PC, and the notification is made through the push notification function of the app. As a result, the user is prompted to take specific actions such as using a specific household appliance during off-peak hours, enabling efficient energy utilization.

[0288] Furthermore, the server collects local environmental activity and event information and filters it based on user preferences. This information is then communicated to the user via their device, encouraging participation in activities that interest them.

[0289] User feedback is sent to the server via the terminal. The server analyzes the effectiveness of the suggestions based on this data and improves the options to support sustainable lifestyles. Historical data is used to continuously provide users with personalized energy usage plans. In this way, the present invention highly streamlines energy management and promotes contributions to the local community.

[0290] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0291] Step 1:

[0292] The server collects energy usage data in real time from measuring devices and communication equipment within the home. Inputs include data from smart meters and smart home appliances. This data is stored in a database in JSON format. Specifically, the server retrieves power consumption data from each device every minute and saves it as time-series data.

[0293] Step 2:

[0294] The server analyzes the collected data to identify energy consumption trends. The input is the time-series data obtained in step 1. Using the Python pandas library, it analyzes peak times and trends in consumption. The output includes peak times of the day and warnings about wasteful energy consumption. Specifically, the server aggregates the data and identifies time periods when consumption exceeds 20% of the average.

[0295] Step 3:

[0296] The server uses generative AI technology to generate options for improving energy efficiency. The input is the analysis results from step 2. The generative AI model receives a prompt and generates suggestions such as operating specific appliances during off-peak hours. The output is an optimized set of action proposals. Specifically, the server sends a prompt to the generative AI asking "how to shift energy consumption to off-peak hours" and receives specific suggestions.

[0297] Step 4:

[0298] The device notifies the user of suggestions from the server. The input is the suggestions generated in step 3. Using the notification function, the suggestions are delivered to the user as push notifications. As output, the suggestions are displayed on the user's smartphone or PC. Specifically, the device sends a message to the user via the app, such as "We recommend using the washing machine at night."

[0299] Step 5:

[0300] The server collects and filters local environmental activity and event information from the internet. Input information comes from local news sites and social media. NLP (Neuro-Linguistic Programming) technology is used to select relevant information based on user interests. The output is highly relevant event information. Specifically, the server retrieves events containing keywords such as "eco-festival" via web scraping and selects them considering the user's past participation history.

[0301] Step 6:

[0302] The user takes action based on the suggestion and sends feedback to the server via their device. The input is the user's actual behavioral data. This data is used to evaluate the effectiveness of the suggestion. As output, improved behavioral patterns are stored in a database. Specifically, the user changes the air conditioner settings according to the suggestion and inputs the result as an evaluation on their device.

[0303] Step 7:

[0304] Based on the collected feedback, the server updates the individualized energy usage strategy. The input is the evaluation data obtained in Step 6. This provides a means to improve the quality of the proposal. The output is the improved proposal to be presented next. As a specific operation, the server further optimizes the content of future proposals based on data such as "successfully reduced power consumption during nighttime use".

[0305] (Application Example 1)

[0306] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0307] There is an issue that the efficiency of energy use in homes and regions and the promotion of participation in eco activities are still insufficient. The current system only optimizes energy use in individual homes, and further improvement is needed to promote energy efficiency improvement and participation in eco activities across the region.

[0308] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0309] In this invention, the server includes means for collecting in real-time energy usage information within a home, means for generating an instruction for optimizing energy use based on energy consumption rules, and means for collaborating with an energy management system at the regional level to contribute to improving the energy efficiency of the entire region. This makes it possible to enhance energy efficiency both at home and in the region and to promote the active participation of residents in eco activities.

[0310] "Energy usage information within a home" refers to data regarding the consumption amount and consumption pattern of energy such as electricity and gas used within a home.

[0311] "Collecting data in real time" means acquiring data immediately when it occurs and keeping it up-to-date at all times for analysis and use.

[0312] "Energy consumption regulations" are laws and standards derived from analyzing energy usage trends and patterns under specific conditions.

[0313] "Energy use optimization instructions" are guidelines that provide users with specific methods and timing for efficiently consuming energy.

[0314] A "regional energy management system" is an integrated management system designed to improve the efficiency of energy consumption within a specific region.

[0315] "Regional energy efficiency" refers to a comprehensive effort to reduce overall energy use in a specific region and promote a sustainable environment.

[0316] "Eco-activities" refer to individual and community activities undertaken to protect the natural environment, conserve resources, and realize a sustainable society.

[0317] The system for implementing this invention aims to improve the efficiency of household energy management and encourage participation in local eco-activities. Its specific form is described below.

[0318] First, the server collects energy usage information in real time from smart meters and connected smart appliances. The collected information is stored in a database and analyzed as time-series data. As a result of the analysis, peak energy consumption and areas of wasteful use are identified, and energy consumption rules are derived.

[0319] The server generates optimization instructions for the user based on the analyzed energy consumption rules. For example, it can instruct the user to change the use of certain appliances to off-peak times. This allows the user to reduce energy costs and use energy more efficiently.

[0320] Furthermore, the server retrieves local eco-activities and event information from the internet and filters it to match the user's interests. For example, it can retrieve information about eco-festivals from local news sites and social media feeds and notify users. Users can also automatically register to participate in eco-activities based on this information.

[0321] This system is implemented using programming languages ​​such as Python, and employs machine learning algorithms for data acquisition and analysis. A SQL-based system is used for database management, enabling real-time data processing.

[0322] As a concrete example, let's assume a user was instructed to use their washing machine at night, resulting in a reduction in energy consumption. Another example is a user being notified about a local eco-festival and being able to easily register to participate.

[0323] An example of a prompt message is: "Please describe in detail a system that generates suggestions for optimizing household energy consumption and notifies users of local eco-activities."

[0324] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0325] Step 1:

[0326] The server acquires energy usage information in real time from smart meters and smart home appliances. This input data is stored in a time-series database and serves as the source data representing each household's energy usage patterns.

[0327] Step 2:

[0328] The server extracts energy consumption patterns from time-series data. This process uses machine learning algorithms to identify peak consumption trends and wasteful energy use, and then identifies these patterns. The output provides specific energy consumption trends and optimization points.

[0329] Step 3:

[0330] The server generates energy use optimization instructions based on the analysis results. In this step, it suggests shifting the usage of specific appliances to off-peak hours. The generated instructions are provided to the user as specific guidelines for improving energy efficiency.

[0331] Step 4:

[0332] The device notifies the user of the generated instructions. The notification is displayed on the screen of a smartphone or tablet and functions as an output prompting the user to take specific action.

[0333] Step 5:

[0334] The server collects local eco-activities and event information via the internet. It uses information from news sites and social media as input, and employs natural language processing to filter the information based on user interests. The output is filtered to include information on eco-events that are of interest.

[0335] Step 6:

[0336] The device notifies users of filtered eco-activity information and encourages participation. In this step, it guides users who wish to participate in local eco-activities so that they can easily register.

[0337] Step 7:

[0338] Users provide feedback on energy usage instructions and eco-activity information provided by the system. This feedback is used as input data for the server to evaluate the effectiveness of the suggestions and continuously improve them. As output, users are provided with a personalized energy usage strategy.

[0339] 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.

[0340] This invention combines a system for optimizing energy consumption by collecting and analyzing household energy usage data in real time with an emotion engine that recognizes the user's emotional state.

[0341] Energy pattern analysis and optimization

[0342] The server acquires energy usage data from smart meters and home appliances and analyzes consumption patterns in real time. This data includes seasonal fluctuations and the usage history of individual appliances. Based on the analysis results, the server generates suggestions for optimal energy usage and uses an emotion engine to adjust the suggestions according to the user's emotional state.

[0343] Understanding user state using an emotion engine

[0344] The device uses information entered through the user interface and other sensor data to estimate the user's emotional state using an emotion engine. This emotional state indicates, for example, stress levels, relaxation levels, and levels of interest, and is used to understand the user's current psychological state. Based on the estimated emotional data, the server adjusts the content of energy usage suggestions and notification methods according to the user's level of acceptance or resistance. This makes it possible to reduce stress on the user and promote cooperative behavior.

[0345] Providing information on eco-friendly activities

[0346] The server collects local eco-activity information from online resources and filters it based on the user's interests, past participation history, and emotional state. The filtered information is then notified to the user via their device. The way the information is presented and the wording of invitations to participate are adjusted according to the user's emotional state.

[0347] Improving feedback and suggestions

[0348] Users provide feedback on their energy usage and participation in eco-friendly activities. The server receives this feedback and analyzes it along with emotional state and behavioral data to improve the effectiveness of future suggestions and notifications. By using suggestion history and emotional data, personalized energy usage strategies are built for each user, supporting the realization of a sustainable lifestyle.

[0349] Specific example

[0350] For example, if a user is sensitive to energy consumption and experiencing stress, the server uses an emotion engine to offer more flexible suggestions. For instance, it might introduce positive feedback, such as, "If you meet today's energy consumption goal, there will be a small celebration," to encourage user engagement. Furthermore, for users in a positive mood, it might send notifications encouraging participation in new eco-friendly activities, thereby revitalizing the local community.

[0351] The following describes the processing flow.

[0352] Step 1:

[0353] The server collects real-time power consumption data from smart meters and smart appliances within the home. This data includes power consumption and details of appliances being used during each time period.

[0354] Step 2:

[0355] The server analyzes the collected data to extract patterns in daily energy use. This analysis identifies peak times and areas where wasteful consumption is occurring.

[0356] Step 3:

[0357] The emotion engine infers the user's emotional state based on information obtained from the user's device and sensor inputs. This includes determining how stressed or relaxed the user is.

[0358] Step 4:

[0359] The server combines analysis results and emotional state data to generate suggestions for energy usage optimization best suited to the user's situation. These suggestions are then adjusted to a tone that respects the user's emotions.

[0360] Step 5:

[0361] The device presents the generated suggestions to the user as notifications. The notification messages may include encouragement or flexible suggestions depending on the user's emotional state.

[0362] Step 6:

[0363] The user receives a notification, reviews the suggestion, and adjusts their appliance usage schedule based on the suggestion. If the adjustment is made correctly, the system detects this and provides further feedback.

[0364] Step 7:

[0365] The server records the user's adjustment behavior and emotional responses, analyzing this data to improve the effectiveness of future suggestions. This enhances the personalized approach to the user.

[0366] Step 8:

[0367] The server collects local eco-activity information from online sources and filters the information based on the user's interests, participation history, and emotional state.

[0368] Step 9:

[0369] The device notifies users of filtered eco-friendly activity information and sends emotionally sensitive messages to encourage participation. Users can register to participate in events that interest them.

[0370] (Example 2)

[0371] 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".

[0372] In modern households, excessive energy consumption and insufficient participation in local environmental activities are significant problems. Furthermore, providing appropriate energy use strategies that consider users' emotional states is difficult, and the lack of acceptable suggestions and notifications results in insufficient means to effectively promote behavioral change among users. To address this challenge, a system is needed that considers user emotions and proposes effective energy use and participation in eco-friendly activities.

[0373] 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.

[0374] In this invention, the server includes means for collecting household resource usage data in real time, means for analyzing consumption patterns and generating suggestions for optimizing energy use, and means for inferring the user's emotional state using a generating AI model and adjusting the suggestions in consideration of the emotional state. This enables energy use suggestions that are appropriate to the user's specific emotional state, allowing for sustainable resource management and active participation in the local community.

[0375] "Resource usage data" refers to information about the amount and patterns of resources consumed within a household, such as energy and water.

[0376] A "generative AI model" refers to artificial intelligence technology trained to analyze data and infer human emotional states.

[0377] An "emotion engine" refers to software or a system that analyzes a user's voice, facial expressions, and behavior to determine their emotional state.

[0378] "Consumption patterns" refer to data characteristics that show usage trends for specific resources, broken down by time of day or season.

[0379] "Suggestions" refer to advice provided to users based on analyzed data, aimed at promoting efficient resource use and behavioral change.

[0380] "Feedback" refers to the information that users provide in response to suggestions or to the results of their use, and is used to improve the system.

[0381] A "personalized energy use strategy" refers to an energy usage plan customized for each individual user, created based on their lifestyle and emotional state.

[0382] "Communication methods" refer to the methods or interfaces by which users and systems exchange information with each other.

[0383] This invention is a system that optimizes energy use in the home and provides suggestions based on the user's emotional state. The system collects data from various smart devices and home appliances in the home and analyzes this data on a server in the cloud. The server runs on a general cloud computing platform and recognizes consumption patterns in real time using a database and data analysis software. The analysis uses a generative AI model that implements machine learning algorithms, which detects anomalies in consumption and points where optimization is possible.

[0384] The server generates specific energy usage suggestions, taking into account appliance usage history and external environmental information. These suggestions are adjusted as needed, based on emotion engine data transmitted from the device, such as the user's stress level and relaxation level. The device collects emotion data from sensors and user interfaces via commercially available computer devices such as the user's smartphone or tablet. Furthermore, the method and content of user notifications are flexibly customized, taking the user's emotional state into consideration.

[0385] For example, if the system analyzes that a user is experiencing stress, the server can provide positive feedback using a prompt such as, "If you meet today's energy goal, we'll play your favorite music." This encourages cooperative behavior from the user and effectively optimizes energy consumption within the home.

[0386] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0387] Step 1:

[0388] The server acquires resource usage data from smart devices and appliances within the home. It receives real-time usage data transmitted from smart meters and appliances as input. This data includes electricity consumption, usage time, device ID, and other information. The server stores this data in a database and organizes it by date and device.

[0389] Step 2:

[0390] The server analyzes collected resource usage data and extracts consumption patterns. It uses machine learning algorithms to analyze the data based on the input. This analysis highlights consumption trends by time of day and season, enabling anomaly detection. The output provides a list of energy consumption trends and saving points.

[0391] Step 3:

[0392] The device collects emotional data from the user. Inputs include text input via the user interface, voice commands, and information from other smartphone sensors. The emotion engine analyzes this data, quantifies the user's current emotional state, such as stress and happiness levels, and sends the results to the server.

[0393] Step 4:

[0394] The server generates resource usage optimization suggestions based on consumption patterns and emotional data. The input consists of analyzed consumption patterns and emotional data. A generative AI model is used to create flexible suggestions tailored to the user's emotional state. Specific action suggestions are generated as output and sent to the terminal.

[0395] Step 5:

[0396] The server notifies the user via the terminal. Input includes generated suggestions and prompts. The notification method is adjusted according to the user's emotional state, using prompts that emphasize encouragement and positive feedback. Output is a notification sent to the user.

[0397] Step 6:

[0398] Based on the suggested actions, users attempt actual resource usage and provide feedback. The input consists of the user's actions and their results, which are sent back to the server via the terminal as feedback. The server analyzes this feedback and uses it to improve future suggestions.

[0399] (Application Example 2)

[0400] 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 as the "terminal".

[0401] Energy consumption in modern cities is influenced by a variety of complex factors. Optimizing energy consumption by taking into account individual energy use patterns and emotional states contributes to the efficient use of energy resources and the reduction of environmental impact. However, optimizing energy use and gaining user cooperation requires effective user suggestions and encouragement of participation in emotion-based eco-activities, which has been difficult to achieve with conventional systems.

[0402] 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.

[0403] In this invention, the server includes means for collecting energy usage data in homes and public facilities in real time, means for generating personalized suggestions for optimizing energy use based on the user's emotional state, and means for filtering and notifying the user of eco-activities and event information based on the user's emotional state and interests. This enables the optimization of energy consumption tailored to the user's living environment and the realization of a sustainable lifestyle.

[0404] "Energy usage data" refers to data that shows the amount of energy consumed in homes and public facilities, as well as its temporal fluctuations.

[0405] "Means of collecting data in real time" refers to methods and technologies for instantly obtaining current energy usage data.

[0406] An "energy consumption pattern" is the result of analyzing a series of data that shows the trends and characteristics of energy consumption.

[0407] "Emotional state" refers to the user's psychological or emotional condition, including stress, relaxation, and interest.

[0408] "Personalized suggestions" refer to proposals for optimal energy usage tailored to the user's characteristics and circumstances.

[0409] "Eco-activities and event information" refers to data on community events and participation opportunities related to environmental protection and sustainable activities.

[0410] "Filtering" is the process of selecting information based on specific criteria and extracting the necessary data.

[0411] A "communication platform" is a system or infrastructure that enables the sending and receiving of information.

[0412] This invention provides a system for optimizing energy consumption in homes and public facilities in smart cities. The central server of the system collects energy usage data in real time from smart meters and various IoT devices and processes it using a cloud platform (e.g., AWS or Google Cloud Platform). Python's pandas library is used for data analysis, and natural language processing libraries such as TensorFlow and spaCy are used for analyzing users' emotional states.

[0413] The server generates personalized energy usage suggestions based on each user's energy usage patterns and emotional state. It notifies the user of these suggestions via their device (such as a smartphone or smart glasses) and continuously collects user feedback and behavioral data. This feedback data is used for further analysis to improve the effectiveness of the suggestions.

[0414] Furthermore, the system collects information on local eco-activities and events, and notifies users of filtered information based on their interests and emotional state. This encourages active participation in activities and events that interest the target users.

[0415] For example, one system might display a notification on a user's smart glasses while they are at work, asking, "Your home's air conditioning settings are optimized, is this okay?" Furthermore, on weekends, emotion-based notifications might be sent, such as, "Why not participate in the eco-fair being held in the city?", thereby supporting increased awareness of sustainable lifestyles.

[0416] An example of a prompt message for a generative AI model is, "Generate optimal energy consumption suggestions based on the user's current emotional state and energy data."

[0417] In this way, we provide an implementation that effectively manages users' energy usage and enables a sustainable lifestyle in a smart city environment.

[0418] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0419] Step 1:

[0420] The server collects real-time energy usage data from homes and public facilities through smart meters and IoT devices. This data includes power consumption, usage time, and date for each device. The collected data is stored on a cloud platform.

[0421] Step 2:

[0422] The server analyzes the collected energy usage data. It uses Python's pandas library to clean and format the data and analyze trends in electricity consumption. The analysis results extract the energy consumption patterns of each household and facility. Output includes data visualizations and statistical trend results.

[0423] Step 3:

[0424] The device analyzes the user's emotional state based on input such as camera images and audio. It uses a generative AI model and natural language processing library to identify emotional states such as stress levels and happiness. The analysis results are then sent to a server.

[0425] Step 4:

[0426] The server generates personalized energy usage suggestions based on energy consumption patterns and the user's emotional state. This process utilizes a generative AI model to construct optimal suggestions and action-oriented messages tailored to the user's emotions. The generated results are output as specific suggestions and are ready to be notified to the user.

[0427] Step 5:

[0428] The device receives suggestions from the server and notifies the user via a smartphone or smart glasses. The device also considers the user's location and schedule to ensure timely notifications. The output displays specific suggestions to encourage user action.

[0429] Step 6:

[0430] Users respond to the suggestions they receive and send the results as feedback from their device to the server. This feedback includes satisfaction with the suggestion and the results of its implementation. The server uses this feedback to perform data analysis to improve the effectiveness of future suggestions.

[0431] Step 7:

[0432] The server retrieves local eco-activities and event information from an external database and filters it based on the user's interests and emotional state. The filtered information is sent to the device as a message inviting participation. Ultimately, the user is notified of the event information, promoting participation in eco-activities.

[0433] 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.

[0434] 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.

[0435] 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.

[0436] [Third Embodiment]

[0437] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0438] 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.

[0439] 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).

[0440] 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.

[0441] 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.

[0442] 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).

[0443] 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.

[0444] 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.

[0445] 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.

[0446] 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.

[0447] 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.

[0448] 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".

[0449] This invention is a system that efficiently manages household energy consumption and promotes participation in local eco-activities. Its form is described below.

[0450] Analysis of energy patterns

[0451] The server acquires energy usage data from smart meters and connected smart appliances within the home. This data is updated in real time and stored chronologically. The server analyzes the collected data to extract energy consumption patterns, identifying peak times for electricity consumption in each household and areas where energy waste is occurring.

[0452] Optimization of energy use

[0453] The server suggests effective energy usage methods based on the analysis results. These suggestions include specific details, such as using certain appliances during off-peak hours or utilizing energy-saving modes. The terminal notifies the user of these suggestions and encourages them to act on them. The user receives this notification and adjusts the usage schedule of their appliances to improve energy efficiency.

[0454] Providing information on eco-friendly activities

[0455] The server collects information on local eco-activities and events from the internet. This information gathering utilizes local news sites and social media. The server filters the collected information based on the user's registration data and past participation history. The terminal notifies the user of the filtered information and suggests highly relevant activities and events. Based on this information, the user can participate in eco-activities that interest them.

[0456] User feedback and personalized strategies

[0457] The server collects and analyzes user behavior and feedback as data. Based on this, it evaluates the effectiveness of suggestions and continuously improves future suggestions. Past suggestion history is stored in a database, and this is used to build personalized energy use strategies for users. The terminal presents this personalized strategy to the user, supporting the realization of a sustainable lifestyle.

[0458] Specific example

[0459] For example, if a home's electricity peak is concentrated in the evening, the server might suggest using appliances like washing machines and dishwashers at night. The user can then reschedule these activities to the evening, reducing energy costs. Similarly, if the server gathers information about an eco-festival in the area, the terminal will notify the user of the details and encourage participation. By participating in these activities, users can interact with their local eco-community.

[0460] The following describes the processing flow.

[0461] Step 1:

[0462] The server collects real-time energy usage data from connected smart meters and smart appliances within the home. This includes electricity consumption and usage history for each appliance.

[0463] Step 2:

[0464] The server analyzes the collected energy usage data and extracts consumption patterns over time. In particular, it identifies peak consumption times and areas where energy is being wasted.

[0465] Step 3:

[0466] The server generates suggestions for optimizing energy use based on the extracted consumption patterns. For example, it recommends using appliances outside of peak hours and utilizing energy-saving modes.

[0467] Step 4:

[0468] The terminal notifies the user of suggestions from the server. These notifications may include specific changes to the usage time of home appliances or the selection of energy-saving modes.

[0469] Step 5:

[0470] Users receive notifications from their devices and adjust their appliance usage schedules accordingly. For example, they might set their washing machine to run at night, taking action based on the suggestions.

[0471] Step 6:

[0472] The server collects user behavior data again and evaluates how effective the suggestions were. Based on this evaluation, it improves the suggestions for future use.

[0473] Step 7:

[0474] The server collects local eco-activities and event information from online resources and filters it based on the user's interests.

[0475] Step 8:

[0476] The device notifies the user of filtered eco-friendly activities and event information. The notification includes details such as the date, time, and location of participation.

[0477] Step 9:

[0478] Users can participate in eco-friendly activities that interest them based on the information they receive, contributing to raising environmental awareness in their local communities. They can also exchange opinions with other participants through the platform.

[0479] (Example 1)

[0480] 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."

[0481] While modern households need to optimize energy use, there are insufficient means to identify and streamline wasteful consumption. In particular, it is difficult for individual households to develop energy-saving strategies suited to their lifestyles, and participation in local environmental activities is not encouraged. Furthermore, the lack of suitable options and suggestions for users hinders sustainable lifestyles.

[0482] 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.

[0483] In this invention, the server includes means for collecting energy usage data from measuring devices and communication equipment within the home, means for analyzing the energy usage data to identify energy consumption trends, and means for using generative AI technology to generate options for improving energy efficiency based on the consumption trends. This makes it possible to identify energy waste in each household and provide effective energy usage strategies on a personalized basis. It also provides information to promote participation in local environmental activities and supports the realization of a sustainable lifestyle.

[0484] "Measuring devices" refer to equipment used to acquire household energy usage data in real time, and include smart meters and communication-enabled home appliances.

[0485] "Communication equipment" refers to devices used to transmit energy usage data acquired from measuring devices to a server, and which can connect to the internet or a local network.

[0486] "Energy consumption trends" refer to information that shows the consumption patterns and efficiency over time, extracted from analyzed energy usage data.

[0487] "Generative AI technology" refers to machine learning algorithms and artificial intelligence technologies used to automatically generate suggestions and options for improving energy efficiency.

[0488] "Options" refer to specific actions and strategies proposed to optimize the user's energy use, including using appliances during off-peak hours and utilizing energy-saving modes.

[0489] A "communication platform" refers to a communication platform provided to make it easier for users to participate in local environmental activities, and includes online forums and information sharing sites.

[0490] "Historical data" refers to information that records the implementation status of choices and suggestions that a user has made so far, and is used to improve individual strategies in the future.

[0491] This invention is a system in which a server, terminal, and user work together to improve the efficiency of energy use within the home.

[0492] The server collects household energy usage data in real time through measuring devices and communication equipment. To do this, the server acquires data from smart meters and communicable home appliances and stores it in a database. For data analysis, the Python pandas library and scikit-learn library are used to identify energy consumption trends.

[0493] Next, generative AI technology is used to generate options for improving energy efficiency. The generative AI model analyzes data using machine learning algorithms and builds individually optimized suggestions. For example, the server sends the prompt "How should a user distribute their energy consumption if it peaks between 6 PM and 9 PM?" to the generative AI model and obtains suggestions.

[0494] The suggestions are communicated to the user via a device, such as a smartphone or PC, and the notifications are sent through the app's push notification function. This encourages users to take specific actions, such as using certain appliances during off-peak hours, enabling more efficient energy use.

[0495] Furthermore, the server collects local environmental activity and event information and filters it based on user preferences. This information is then communicated to the user via their device, encouraging participation in activities that interest them.

[0496] User feedback is sent to the server via the terminal. The server analyzes the effectiveness of the suggestions based on this data and improves the options to support sustainable lifestyles. Historical data is used to continuously provide users with personalized energy usage plans. In this way, the present invention highly streamlines energy management and promotes contributions to the local community.

[0497] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0498] Step 1:

[0499] The server collects energy usage data in real time from measuring devices and communication equipment within the home. Inputs include data from smart meters and smart home appliances. This data is stored in a database in JSON format. Specifically, the server retrieves power consumption data from each device every minute and saves it as time-series data.

[0500] Step 2:

[0501] The server analyzes the collected data to identify energy consumption trends. The input is the time-series data obtained in step 1. Using the Python pandas library, it analyzes peak times and trends in consumption. The output includes peak times of the day and warnings about wasteful energy consumption. Specifically, the server aggregates the data and identifies time periods when consumption exceeds 20% of the average.

[0502] Step 3:

[0503] The server uses generative AI technology to generate options for improving energy efficiency. The input is the analysis results from step 2. The generative AI model receives a prompt and generates suggestions such as operating specific appliances during off-peak hours. The output is an optimized set of action proposals. Specifically, the server sends a prompt to the generative AI asking "how to shift energy consumption to off-peak hours" and receives specific suggestions.

[0504] Step 4:

[0505] The device notifies the user of suggestions from the server. The input is the suggestions generated in step 3. Using the notification function, the suggestions are delivered to the user as push notifications. As output, the suggestions are displayed on the user's smartphone or PC. Specifically, the device sends a message to the user via the app, such as "We recommend using the washing machine at night."

[0506] Step 5:

[0507] The server collects and filters local environmental activity and event information from the internet. Input information comes from local news sites and social media. NLP (Neuro-Linguistic Programming) technology is used to select relevant information based on user interests. The output is highly relevant event information. Specifically, the server retrieves events containing keywords such as "eco-festival" via web scraping and selects them considering the user's past participation history.

[0508] Step 6:

[0509] The user takes action based on the suggestion and sends feedback to the server via their device. The input is the user's actual behavioral data. This data is used to evaluate the effectiveness of the suggestion. As output, improved behavioral patterns are stored in a database. Specifically, the user changes the air conditioner settings according to the suggestion and inputs the result as an evaluation on their device.

[0510] Step 7:

[0511] The server updates the personalized energy use strategy based on the collected feedback. The input is the evaluation data obtained in step 6. This provides a means to improve the quality of the proposal. The output is the improved proposal that will be presented next time. Specifically, the server further optimizes future proposals based on data such as "successfully reduced power consumption through nighttime use."

[0512] (Application Example 1)

[0513] 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."

[0514] There are still challenges in optimizing energy use at the household and community levels, and in promoting participation in eco-friendly activities. The current system optimizes energy use only at the individual household level, and further improvements are needed to promote energy efficiency improvements and participation in eco-friendly activities throughout the community.

[0515] 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.

[0516] In this invention, the server includes means for collecting energy usage information within a household in real time, means for generating instructions for optimizing energy use based on energy consumption regulations, and means for coordinating with a regional energy management system to contribute to the overall energy efficiency of the region. This makes it possible to improve energy efficiency at both the household and the region, and to encourage residents to actively participate in eco-friendly activities.

[0517] "Household energy usage information" refers to data on the amount and patterns of energy consumption, such as electricity and gas, used within a household.

[0518] "Collecting data in real time" means acquiring data immediately when it occurs and keeping it up-to-date at all times for analysis and use.

[0519] "Energy consumption regulations" are laws and standards derived from analyzing energy usage trends and patterns under specific conditions.

[0520] "Energy use optimization instructions" are guidelines that provide users with specific methods and timing for efficiently consuming energy.

[0521] A "regional energy management system" is an integrated management system designed to improve the efficiency of energy consumption within a specific region.

[0522] "Regional energy efficiency" refers to a comprehensive effort to reduce overall energy use in a specific region and promote a sustainable environment.

[0523] "Eco-activities" refer to individual and community activities undertaken to protect the natural environment, conserve resources, and realize a sustainable society.

[0524] The system for implementing this invention aims to improve the efficiency of household energy management and encourage participation in local eco-activities. Its specific form is described below.

[0525] First, the server collects energy usage information in real time from smart meters and connected smart appliances. The collected information is stored in a database and analyzed as time-series data. As a result of the analysis, peak energy consumption and areas of wasteful use are identified, and energy consumption rules are derived.

[0526] The server generates optimization instructions for the user based on the analyzed energy consumption rules. For example, it can instruct the user to change the use of certain appliances to off-peak times. This allows the user to reduce energy costs and use energy more efficiently.

[0527] Furthermore, the server retrieves local eco-activities and event information from the internet and filters it to match the user's interests. For example, it can retrieve information about eco-festivals from local news sites and social media feeds and notify users. Users can also automatically register to participate in eco-activities based on this information.

[0528] This system is implemented using programming languages ​​such as Python, and employs machine learning algorithms for data acquisition and analysis. A SQL-based system is used for database management, enabling real-time data processing.

[0529] As a concrete example, let's assume a user was instructed to use their washing machine at night, resulting in a reduction in energy consumption. Another example is a user being notified about a local eco-festival and being able to easily register to participate.

[0530] An example of a prompt message is: "Please describe in detail a system that generates suggestions for optimizing household energy consumption and notifies users of local eco-activities."

[0531] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0532] Step 1:

[0533] The server acquires energy usage information in real time from smart meters and smart home appliances. This input data is stored in a time-series database and serves as the source data representing each household's energy usage patterns.

[0534] Step 2:

[0535] The server extracts energy consumption patterns from time-series data. This process uses machine learning algorithms to identify peak consumption trends and wasteful energy use, and then identifies these patterns. The output provides specific energy consumption trends and optimization points.

[0536] Step 3:

[0537] The server generates energy use optimization instructions based on the analysis results. In this step, it suggests shifting the usage of specific appliances to off-peak hours. The generated instructions are provided to the user as specific guidelines for improving energy efficiency.

[0538] Step 4:

[0539] The device notifies the user of the generated instructions. The notification is displayed on the screen of a smartphone or tablet and functions as an output prompting the user to take specific action.

[0540] Step 5:

[0541] The server collects local eco-activities and event information via the internet. It uses information from news sites and social media as input, and employs natural language processing to filter the information based on user interests. The output is filtered to include information on eco-events that are of interest.

[0542] Step 6:

[0543] The device notifies users of filtered eco-activity information and encourages participation. In this step, it guides users who wish to participate in local eco-activities so that they can easily register.

[0544] Step 7:

[0545] Users provide feedback on energy usage instructions and eco-activity information provided by the system. This feedback is used as input data for the server to evaluate the effectiveness of the suggestions and continuously improve them. As output, users are provided with a personalized energy usage strategy.

[0546] 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.

[0547] This invention combines a system for optimizing energy consumption by collecting and analyzing household energy usage data in real time with an emotion engine that recognizes the user's emotional state.

[0548] Energy pattern analysis and optimization

[0549] The server acquires energy usage data from smart meters and home appliances and analyzes consumption patterns in real time. This data includes seasonal fluctuations and the usage history of individual appliances. Based on the analysis results, the server generates suggestions for optimal energy usage and uses an emotion engine to adjust the suggestions according to the user's emotional state.

[0550] Understanding user state using an emotion engine

[0551] The device uses information entered through the user interface and other sensor data to estimate the user's emotional state using an emotion engine. This emotional state indicates, for example, stress levels, relaxation levels, and levels of interest, and is used to understand the user's current psychological state. Based on the estimated emotional data, the server adjusts the content of energy usage suggestions and notification methods according to the user's level of acceptance or resistance. This makes it possible to reduce stress on the user and promote cooperative behavior.

[0552] Providing information on eco-friendly activities

[0553] The server collects local eco-activity information from online resources and filters it based on the user's interests, past participation history, and emotional state. The filtered information is then notified to the user via their device. The way the information is presented and the wording of invitations to participate are adjusted according to the user's emotional state.

[0554] Improving feedback and suggestions

[0555] Users provide feedback on their energy usage and participation in eco-friendly activities. The server receives this feedback and analyzes it along with emotional state and behavioral data to improve the effectiveness of future suggestions and notifications. By using suggestion history and emotional data, personalized energy usage strategies are built for each user, supporting the realization of a sustainable lifestyle.

[0556] Specific example

[0557] For example, if a user is sensitive to energy consumption and experiencing stress, the server uses an emotion engine to offer more flexible suggestions. For instance, it might introduce positive feedback, such as, "If you meet today's energy consumption goal, there will be a small celebration," to encourage user engagement. Furthermore, for users in a positive mood, it might send notifications encouraging participation in new eco-friendly activities, thereby revitalizing the local community.

[0558] The following describes the processing flow.

[0559] Step 1:

[0560] The server collects real-time power consumption data from smart meters and smart appliances within the home. This data includes power consumption and details of appliances being used during each time period.

[0561] Step 2:

[0562] The server analyzes the collected data to extract patterns in daily energy use. This analysis identifies peak times and areas where wasteful consumption is occurring.

[0563] Step 3:

[0564] The emotion engine infers the user's emotional state based on information obtained from the user's device and sensor inputs. This includes determining how stressed or relaxed the user is.

[0565] Step 4:

[0566] The server combines analysis results and emotional state data to generate suggestions for energy usage optimization best suited to the user's situation. These suggestions are then adjusted to a tone that respects the user's emotions.

[0567] Step 5:

[0568] The device presents the generated suggestions to the user as notifications. The notification messages may include encouragement or flexible suggestions depending on the user's emotional state.

[0569] Step 6:

[0570] The user receives a notification, reviews the suggestion, and adjusts their appliance usage schedule based on the suggestion. If the adjustment is made correctly, the system detects this and provides further feedback.

[0571] Step 7:

[0572] The server records the user's adjustment behavior and emotional responses, analyzing this data to improve the effectiveness of future suggestions. This enhances the personalized approach to the user.

[0573] Step 8:

[0574] The server collects local eco-activity information from online sources and filters the information based on the user's interests, participation history, and emotional state.

[0575] Step 9:

[0576] The device notifies users of filtered eco-friendly activity information and sends emotionally sensitive messages to encourage participation. Users can register to participate in events that interest them.

[0577] (Example 2)

[0578] 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."

[0579] In modern households, excessive energy consumption and insufficient participation in local environmental activities are significant problems. Furthermore, providing appropriate energy use strategies that consider users' emotional states is difficult, and the lack of acceptable suggestions and notifications results in insufficient means to effectively promote behavioral change among users. To address this challenge, a system is needed that considers user emotions and proposes effective energy use and participation in eco-friendly activities.

[0580] 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.

[0581] In this invention, the server includes means for collecting household resource usage data in real time, means for analyzing consumption patterns and generating suggestions for optimizing energy use, and means for inferring the user's emotional state using a generating AI model and adjusting the suggestions in consideration of the emotional state. This enables energy use suggestions that are appropriate to the user's specific emotional state, allowing for sustainable resource management and active participation in the local community.

[0582] "Resource usage data" refers to information about the amount and patterns of resources consumed within a household, such as energy and water.

[0583] A "generative AI model" refers to artificial intelligence technology trained to analyze data and infer human emotional states.

[0584] An "emotion engine" refers to software or a system that analyzes a user's voice, facial expressions, and behavior to determine their emotional state.

[0585] "Consumption patterns" refer to data characteristics that show usage trends for specific resources, broken down by time of day or season.

[0586] "Suggestions" refer to advice provided to users based on analyzed data, aimed at promoting efficient resource use and behavioral change.

[0587] "Feedback" refers to the information that users provide in response to suggestions or to the results of their use, and is used to improve the system.

[0588] A "personalized energy use strategy" refers to an energy usage plan customized for each individual user, created based on their lifestyle and emotional state.

[0589] "Communication methods" refer to the methods or interfaces by which users and systems exchange information with each other.

[0590] This invention is a system that optimizes energy use in the home and provides suggestions based on the user's emotional state. The system collects data from various smart devices and home appliances in the home and analyzes this data on a server in the cloud. The server runs on a general cloud computing platform and recognizes consumption patterns in real time using a database and data analysis software. The analysis uses a generative AI model that implements machine learning algorithms, which detects anomalies in consumption and points where optimization is possible.

[0591] The server generates specific energy usage suggestions, taking into account appliance usage history and external environmental information. These suggestions are adjusted as needed, based on emotion engine data transmitted from the device, such as the user's stress level and relaxation level. The device collects emotion data from sensors and user interfaces via commercially available computer devices such as the user's smartphone or tablet. Furthermore, the method and content of user notifications are flexibly customized, taking the user's emotional state into consideration.

[0592] For example, if the system analyzes that a user is experiencing stress, the server can provide positive feedback using a prompt such as, "If you meet today's energy goal, we'll play your favorite music." This encourages cooperative behavior from the user and effectively optimizes energy consumption within the home.

[0593] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0594] Step 1:

[0595] The server acquires resource usage data from smart devices and appliances within the home. It receives real-time usage data transmitted from smart meters and appliances as input. This data includes electricity consumption, usage time, device ID, and other information. The server stores this data in a database and organizes it by date and device.

[0596] Step 2:

[0597] The server analyzes collected resource usage data and extracts consumption patterns. It uses machine learning algorithms to analyze the data based on the input. This analysis highlights consumption trends by time of day and season, enabling anomaly detection. The output provides a list of energy consumption trends and saving points.

[0598] Step 3:

[0599] The device collects emotional data from the user. Inputs include text input via the user interface, voice commands, and information from other smartphone sensors. The emotion engine analyzes this data, quantifies the user's current emotional state, such as stress and happiness levels, and sends the results to the server.

[0600] Step 4:

[0601] The server generates resource usage optimization suggestions based on consumption patterns and emotional data. The input consists of analyzed consumption patterns and emotional data. A generative AI model is used to create flexible suggestions tailored to the user's emotional state. Specific action suggestions are generated as output and sent to the terminal.

[0602] Step 5:

[0603] The server notifies the user via the terminal. Input includes generated suggestions and prompts. The notification method is adjusted according to the user's emotional state, using prompts that emphasize encouragement and positive feedback. Output is a notification sent to the user.

[0604] Step 6:

[0605] Based on the suggested actions, users attempt actual resource usage and provide feedback. The input consists of the user's actions and their results, which are sent back to the server via the terminal as feedback. The server analyzes this feedback and uses it to improve future suggestions.

[0606] (Application Example 2)

[0607] 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."

[0608] Energy consumption in modern cities is influenced by a variety of complex factors. Optimizing energy consumption by taking into account individual energy use patterns and emotional states contributes to the efficient use of energy resources and the reduction of environmental impact. However, optimizing energy use and gaining user cooperation requires effective user suggestions and encouragement of participation in emotion-based eco-activities, which has been difficult to achieve with conventional systems.

[0609] 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.

[0610] In this invention, the server includes means for collecting energy usage data in homes and public facilities in real time, means for generating personalized suggestions for optimizing energy use based on the user's emotional state, and means for filtering and notifying the user of eco-activities and event information based on the user's emotional state and interests. This enables the optimization of energy consumption tailored to the user's living environment and the realization of a sustainable lifestyle.

[0611] "Energy usage data" refers to data that shows the amount of energy consumed in homes and public facilities, as well as its temporal fluctuations.

[0612] "Means of collecting data in real time" refers to methods and technologies for instantly obtaining current energy usage data.

[0613] An "energy consumption pattern" is the result of analyzing a series of data that shows the trends and characteristics of energy consumption.

[0614] "Emotional state" refers to the user's psychological or emotional condition, including stress, relaxation, and interest.

[0615] "Personalized suggestions" refer to proposals for optimal energy usage tailored to the user's characteristics and circumstances.

[0616] "Eco-activities and event information" refers to data on community events and participation opportunities related to environmental protection and sustainable activities.

[0617] "Filtering" is the process of selecting information based on specific criteria and extracting the necessary data.

[0618] A "communication platform" is a system or infrastructure that enables the sending and receiving of information.

[0619] This invention provides a system for optimizing energy consumption in homes and public facilities in smart cities. The central server of the system collects energy usage data in real time from smart meters and various IoT devices and processes it using a cloud platform (e.g., AWS or Google Cloud Platform). Python's pandas library is used for data analysis, and natural language processing libraries such as TensorFlow and spaCy are used for analyzing users' emotional states.

[0620] The server generates personalized energy usage suggestions based on each user's energy usage patterns and emotional state. It notifies the user of these suggestions via their device (such as a smartphone or smart glasses) and continuously collects user feedback and behavioral data. This feedback data is used for further analysis to improve the effectiveness of the suggestions.

[0621] Furthermore, the system collects information on local eco-activities and events, and notifies users of filtered information based on their interests and emotional state. This encourages active participation in activities and events that interest the target users.

[0622] For example, one system might display a notification on a user's smart glasses while they are at work, asking, "Your home's air conditioning settings are optimized, is this okay?" Furthermore, on weekends, emotion-based notifications might be sent, such as, "Why not participate in the eco-fair being held in the city?", thereby supporting increased awareness of sustainable lifestyles.

[0623] An example of a prompt message for a generative AI model is, "Generate optimal energy consumption suggestions based on the user's current emotional state and energy data."

[0624] In this way, we provide an implementation that effectively manages users' energy usage and enables a sustainable lifestyle in a smart city environment.

[0625] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0626] Step 1:

[0627] The server collects real-time energy usage data from homes and public facilities through smart meters and IoT devices. This data includes power consumption, usage time, and date for each device. The collected data is stored on a cloud platform.

[0628] Step 2:

[0629] The server analyzes the collected energy usage data. It uses Python's pandas library to clean and format the data and analyze trends in electricity consumption. The analysis results extract the energy consumption patterns of each household and facility. Output includes data visualizations and statistical trend results.

[0630] Step 3:

[0631] The device analyzes the user's emotional state based on input such as camera images and audio. It uses a generative AI model and natural language processing library to identify emotional states such as stress levels and happiness. The analysis results are then sent to a server.

[0632] Step 4:

[0633] The server generates personalized energy usage suggestions based on energy consumption patterns and the user's emotional state. This process utilizes a generative AI model to construct optimal suggestions and action-oriented messages tailored to the user's emotions. The generated results are output as specific suggestions and are ready to be notified to the user.

[0634] Step 5:

[0635] The device receives suggestions from the server and notifies the user via a smartphone or smart glasses. The device also considers the user's location and schedule to ensure timely notifications. The output displays specific suggestions to encourage user action.

[0636] Step 6:

[0637] Users respond to the suggestions they receive and send the results as feedback from their device to the server. This feedback includes satisfaction with the suggestion and the results of its implementation. The server uses this feedback to perform data analysis to improve the effectiveness of future suggestions.

[0638] Step 7:

[0639] The server retrieves local eco-activities and event information from an external database and filters it based on the user's interests and emotional state. The filtered information is sent to the device as a message inviting participation. Ultimately, the user is notified of the event information, promoting participation in eco-activities.

[0640] 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.

[0641] 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.

[0642] 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.

[0643] [Fourth Embodiment]

[0644] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0645] 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.

[0646] 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).

[0647] 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.

[0648] 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.

[0649] 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).

[0650] 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.

[0651] 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.

[0652] 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.

[0653] 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.

[0654] 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.

[0655] 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.

[0656] 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".

[0657] This invention is a system that efficiently manages household energy consumption and promotes participation in local eco-activities. Its form is described below.

[0658] Analysis of energy patterns

[0659] The server acquires energy usage data from smart meters and connected smart appliances within the home. This data is updated in real time and stored chronologically. The server analyzes the collected data to extract energy consumption patterns, identifying peak times for electricity consumption in each household and areas where energy waste is occurring.

[0660] Optimization of energy use

[0661] The server suggests effective energy usage methods based on the analysis results. These suggestions include specific details, such as using certain appliances during off-peak hours or utilizing energy-saving modes. The terminal notifies the user of these suggestions and encourages them to act on them. The user receives this notification and adjusts the usage schedule of their appliances to improve energy efficiency.

[0662] Providing information on eco-friendly activities

[0663] The server collects information on local eco-activities and events from the internet. This information gathering utilizes local news sites and social media. The server filters the collected information based on the user's registration data and past participation history. The terminal notifies the user of the filtered information and suggests highly relevant activities and events. Based on this information, the user can participate in eco-activities that interest them.

[0664] User feedback and personalized strategies

[0665] The server collects and analyzes user behavior and feedback as data. Based on this, it evaluates the effectiveness of suggestions and continuously improves future suggestions. Past suggestion history is stored in a database, and this is used to build personalized energy use strategies for users. The terminal presents this personalized strategy to the user, supporting the realization of a sustainable lifestyle.

[0666] Specific example

[0667] For example, if a home's electricity peak is concentrated in the evening, the server might suggest using appliances like washing machines and dishwashers at night. The user can then reschedule these activities to the evening, reducing energy costs. Similarly, if the server gathers information about an eco-festival in the area, the terminal will notify the user of the details and encourage participation. By participating in these activities, users can interact with their local eco-community.

[0668] The following describes the processing flow.

[0669] Step 1:

[0670] The server collects real-time energy usage data from connected smart meters and smart appliances within the home. This includes electricity consumption and usage history for each appliance.

[0671] Step 2:

[0672] The server analyzes the collected energy usage data and extracts consumption patterns over time. In particular, it identifies peak consumption times and areas where energy is being wasted.

[0673] Step 3:

[0674] The server generates suggestions for optimizing energy use based on the extracted consumption patterns. For example, it recommends using appliances outside of peak hours and utilizing energy-saving modes.

[0675] Step 4:

[0676] The terminal notifies the user of suggestions from the server. These notifications may include specific changes to the usage time of home appliances or the selection of energy-saving modes.

[0677] Step 5:

[0678] Users receive notifications from their devices and adjust their appliance usage schedules accordingly. For example, they might set their washing machine to run at night, taking action based on the suggestions.

[0679] Step 6:

[0680] The server collects user behavior data again and evaluates how effective the suggestions were. Based on this evaluation, it improves the suggestions for future use.

[0681] Step 7:

[0682] The server collects local eco-activities and event information from online resources and filters it based on the user's interests.

[0683] Step 8:

[0684] The device notifies the user of filtered eco-friendly activities and event information. The notification includes details such as the date, time, and location of participation.

[0685] Step 9:

[0686] Users can participate in eco-friendly activities that interest them based on the information they receive, contributing to raising environmental awareness in their local communities. They can also exchange opinions with other participants through the platform.

[0687] (Example 1)

[0688] 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".

[0689] While modern households need to optimize energy use, there are insufficient means to identify and streamline wasteful consumption. In particular, it is difficult for individual households to develop energy-saving strategies suited to their lifestyles, and participation in local environmental activities is not encouraged. Furthermore, the lack of suitable options and suggestions for users hinders sustainable lifestyles.

[0690] 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.

[0691] In this invention, the server includes means for collecting energy usage data from measuring devices and communication equipment within the home, means for analyzing the energy usage data to identify energy consumption trends, and means for using generative AI technology to generate options for improving energy efficiency based on the consumption trends. This makes it possible to identify energy waste in each household and provide effective energy usage strategies on a personalized basis. It also provides information to promote participation in local environmental activities and supports the realization of a sustainable lifestyle.

[0692] "Measuring devices" refer to equipment used to acquire household energy usage data in real time, and include smart meters and communication-enabled home appliances.

[0693] "Communication equipment" refers to devices used to transmit energy usage data acquired from measuring devices to a server, and which can connect to the internet or a local network.

[0694] "Energy consumption trends" refer to information that shows the consumption patterns and efficiency over time, extracted from analyzed energy usage data.

[0695] "Generative AI technology" refers to machine learning algorithms and artificial intelligence technologies used to automatically generate suggestions and options for improving energy efficiency.

[0696] "Options" refer to specific actions and strategies proposed to optimize the user's energy use, including using appliances during off-peak hours and utilizing energy-saving modes.

[0697] A "communication platform" refers to a communication platform provided to make it easier for users to participate in local environmental activities, and includes online forums and information sharing sites.

[0698] "Historical data" refers to information that records the implementation status of choices and suggestions that a user has made so far, and is used to improve individual strategies in the future.

[0699] This invention is a system in which a server, terminal, and user work together to improve the efficiency of energy use within the home.

[0700] The server collects household energy usage data in real time through measuring devices and communication equipment. To do this, the server acquires data from smart meters and communicable home appliances and stores it in a database. For data analysis, the Python pandas library and scikit-learn library are used to identify energy consumption trends.

[0701] Next, generative AI technology is used to generate options for improving energy efficiency. The generative AI model analyzes data using machine learning algorithms and builds individually optimized suggestions. For example, the server sends the prompt "How should a user distribute their energy consumption if it peaks between 6 PM and 9 PM?" to the generative AI model and obtains suggestions.

[0702] The suggestions are communicated to the user via a device, such as a smartphone or PC, and the notifications are sent through the app's push notification function. This encourages users to take specific actions, such as using certain appliances during off-peak hours, enabling more efficient energy use.

[0703] Furthermore, the server collects local environmental activity and event information and filters it based on user preferences. This information is then communicated to the user via their device, encouraging participation in activities that interest them.

[0704] User feedback is sent to the server via the terminal. The server analyzes the effectiveness of the suggestions based on this data and improves the options to support sustainable lifestyles. Historical data is used to continuously provide users with personalized energy usage plans. In this way, the present invention highly streamlines energy management and promotes contributions to the local community.

[0705] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0706] Step 1:

[0707] The server collects energy usage data in real time from measuring devices and communication equipment within the home. Inputs include data from smart meters and smart home appliances. This data is stored in a database in JSON format. Specifically, the server retrieves power consumption data from each device every minute and saves it as time-series data.

[0708] Step 2:

[0709] The server analyzes the collected data to identify energy consumption trends. The input is the time-series data obtained in step 1. Using the Python pandas library, it analyzes peak times and trends in consumption. The output includes peak times of the day and warnings about wasteful energy consumption. Specifically, the server aggregates the data and identifies time periods when consumption exceeds 20% of the average.

[0710] Step 3:

[0711] The server uses generative AI technology to generate options for improving energy efficiency. The input is the analysis results from step 2. The generative AI model receives a prompt and generates suggestions such as operating specific appliances during off-peak hours. The output is an optimized set of action proposals. Specifically, the server sends a prompt to the generative AI asking "how to shift energy consumption to off-peak hours" and receives specific suggestions.

[0712] Step 4:

[0713] The device notifies the user of suggestions from the server. The input is the suggestions generated in step 3. Using the notification function, the suggestions are delivered to the user as push notifications. As output, the suggestions are displayed on the user's smartphone or PC. Specifically, the device sends a message to the user via the app, such as "We recommend using the washing machine at night."

[0714] Step 5:

[0715] The server collects and filters local environmental activity and event information from the internet. Input information comes from local news sites and social media. NLP (Neuro-Linguistic Programming) technology is used to select relevant information based on user interests. The output is highly relevant event information. Specifically, the server retrieves events containing keywords such as "eco-festival" via web scraping and selects them considering the user's past participation history.

[0716] Step 6:

[0717] The user takes action based on the suggestion and sends feedback to the server via their device. The input is the user's actual behavioral data. This data is used to evaluate the effectiveness of the suggestion. As output, improved behavioral patterns are stored in a database. Specifically, the user changes the air conditioner settings according to the suggestion and inputs the result as an evaluation on their device.

[0718] Step 7:

[0719] The server updates the personalized energy use strategy based on the collected feedback. The input is the evaluation data obtained in step 6. This provides a means to improve the quality of the proposal. The output is the improved proposal that will be presented next time. Specifically, the server further optimizes future proposals based on data such as "successfully reduced power consumption through nighttime use."

[0720] (Application Example 1)

[0721] 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".

[0722] There are still challenges in optimizing energy use at the household and community levels, and in promoting participation in eco-friendly activities. The current system optimizes energy use only at the individual household level, and further improvements are needed to promote energy efficiency improvements and participation in eco-friendly activities throughout the community.

[0723] 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.

[0724] In this invention, the server includes means for collecting energy usage information within a household in real time, means for generating instructions for optimizing energy use based on energy consumption regulations, and means for coordinating with a regional energy management system to contribute to the overall energy efficiency of the region. This makes it possible to improve energy efficiency at both the household and the region, and to encourage residents to actively participate in eco-friendly activities.

[0725] "Household energy usage information" refers to data on the amount and patterns of energy consumption, such as electricity and gas, used within a household.

[0726] "Collecting data in real time" means acquiring data immediately when it occurs and keeping it up-to-date at all times for analysis and use.

[0727] "Energy consumption regulations" are laws and standards derived from analyzing energy usage trends and patterns under specific conditions.

[0728] "Energy use optimization instructions" are guidelines that provide users with specific methods and timing for efficiently consuming energy.

[0729] A "regional energy management system" is an integrated management system designed to improve the efficiency of energy consumption within a specific region.

[0730] "Regional energy efficiency" refers to a comprehensive effort to reduce overall energy use in a specific region and promote a sustainable environment.

[0731] "Eco-activities" refer to individual and community activities undertaken to protect the natural environment, conserve resources, and realize a sustainable society.

[0732] The system for implementing this invention aims to improve the efficiency of household energy management and encourage participation in local eco-activities. Its specific form is described below.

[0733] First, the server collects energy usage information in real time from smart meters and connected smart appliances. The collected information is stored in a database and analyzed as time-series data. As a result of the analysis, peak energy consumption and areas of wasteful use are identified, and energy consumption rules are derived.

[0734] The server generates optimization instructions for the user based on the analyzed energy consumption rules. For example, it can instruct the user to change the use of certain appliances to off-peak times. This allows the user to reduce energy costs and use energy more efficiently.

[0735] Furthermore, the server retrieves local eco-activities and event information from the internet and filters it to match the user's interests. For example, it can retrieve information about eco-festivals from local news sites and social media feeds and notify users. Users can also automatically register to participate in eco-activities based on this information.

[0736] This system is implemented using programming languages ​​such as Python, and employs machine learning algorithms for data acquisition and analysis. A SQL-based system is used for database management, enabling real-time data processing.

[0737] As a concrete example, let's assume a user was instructed to use their washing machine at night, resulting in a reduction in energy consumption. Another example is a user being notified about a local eco-festival and being able to easily register to participate.

[0738] An example of a prompt message is: "Please describe in detail a system that generates suggestions for optimizing household energy consumption and notifies users of local eco-activities."

[0739] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0740] Step 1:

[0741] The server acquires energy usage information in real time from smart meters and smart home appliances. This input data is stored in a time-series database and serves as the source data representing each household's energy usage patterns.

[0742] Step 2:

[0743] The server extracts energy consumption patterns from time-series data. This process uses machine learning algorithms to identify peak consumption trends and wasteful energy use, and then identifies these patterns. The output provides specific energy consumption trends and optimization points.

[0744] Step 3:

[0745] The server generates energy use optimization instructions based on the analysis results. In this step, it suggests shifting the usage of specific appliances to off-peak hours. The generated instructions are provided to the user as specific guidelines for improving energy efficiency.

[0746] Step 4:

[0747] The device notifies the user of the generated instructions. The notification is displayed on the screen of a smartphone or tablet and functions as an output prompting the user to take specific action.

[0748] Step 5:

[0749] The server collects local eco-activities and event information via the internet. It uses information from news sites and social media as input, and employs natural language processing to filter the information based on user interests. The output is filtered to include information on eco-events that are of interest.

[0750] Step 6:

[0751] The device notifies users of filtered eco-activity information and encourages participation. In this step, it guides users who wish to participate in local eco-activities so that they can easily register.

[0752] Step 7:

[0753] Users provide feedback on energy usage instructions and eco-activity information provided by the system. This feedback is used as input data for the server to evaluate the effectiveness of the suggestions and continuously improve them. As output, users are provided with a personalized energy usage strategy.

[0754] 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.

[0755] This invention combines a system for optimizing energy consumption by collecting and analyzing household energy usage data in real time with an emotion engine that recognizes the user's emotional state.

[0756] Energy pattern analysis and optimization

[0757] The server acquires energy usage data from smart meters and home appliances and analyzes consumption patterns in real time. This data includes seasonal fluctuations and the usage history of individual appliances. Based on the analysis results, the server generates suggestions for optimal energy usage and uses an emotion engine to adjust the suggestions according to the user's emotional state.

[0758] Understanding user state using an emotion engine

[0759] The device uses information entered through the user interface and other sensor data to estimate the user's emotional state using an emotion engine. This emotional state indicates, for example, stress levels, relaxation levels, and levels of interest, and is used to understand the user's current psychological state. Based on the estimated emotional data, the server adjusts the content of energy usage suggestions and notification methods according to the user's level of acceptance or resistance. This makes it possible to reduce stress on the user and promote cooperative behavior.

[0760] Providing information on eco-friendly activities

[0761] The server collects local eco-activity information from online resources and filters it based on the user's interests, past participation history, and emotional state. The filtered information is then notified to the user via their device. The way the information is presented and the wording of invitations to participate are adjusted according to the user's emotional state.

[0762] Improving feedback and suggestions

[0763] Users provide feedback on their energy usage and participation in eco-friendly activities. The server receives this feedback and analyzes it along with emotional state and behavioral data to improve the effectiveness of future suggestions and notifications. By using suggestion history and emotional data, personalized energy usage strategies are built for each user, supporting the realization of a sustainable lifestyle.

[0764] Specific example

[0765] For example, if a user is sensitive to energy consumption and experiencing stress, the server uses an emotion engine to offer more flexible suggestions. For instance, it might introduce positive feedback, such as, "If you meet today's energy consumption goal, there will be a small celebration," to encourage user engagement. Furthermore, for users in a positive mood, it might send notifications encouraging participation in new eco-friendly activities, thereby revitalizing the local community.

[0766] The following describes the processing flow.

[0767] Step 1:

[0768] The server collects real-time power consumption data from smart meters and smart appliances within the home. This data includes power consumption and details of appliances being used during each time period.

[0769] Step 2:

[0770] The server analyzes the collected data to extract patterns in daily energy use. This analysis identifies peak times and areas where wasteful consumption is occurring.

[0771] Step 3:

[0772] The emotion engine infers the user's emotional state based on information obtained from the user's device and sensor inputs. This includes determining how stressed or relaxed the user is.

[0773] Step 4:

[0774] The server combines analysis results and emotional state data to generate suggestions for energy usage optimization best suited to the user's situation. These suggestions are then adjusted to a tone that respects the user's emotions.

[0775] Step 5:

[0776] The device presents the generated suggestions to the user as notifications. The notification messages may include encouragement or flexible suggestions depending on the user's emotional state.

[0777] Step 6:

[0778] The user receives a notification, reviews the suggestion, and adjusts their appliance usage schedule based on the suggestion. If the adjustment is made correctly, the system detects this and provides further feedback.

[0779] Step 7:

[0780] The server records the user's adjustment behavior and emotional responses, analyzing this data to improve the effectiveness of future suggestions. This enhances the personalized approach to the user.

[0781] Step 8:

[0782] The server collects local eco-activity information from online sources and filters the information based on the user's interests, participation history, and emotional state.

[0783] Step 9:

[0784] The device notifies users of filtered eco-friendly activity information and sends emotionally sensitive messages to encourage participation. Users can register to participate in events that interest them.

[0785] (Example 2)

[0786] 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".

[0787] In modern households, excessive energy consumption and insufficient participation in local environmental activities are significant problems. Furthermore, providing appropriate energy use strategies that consider users' emotional states is difficult, and the lack of acceptable suggestions and notifications results in insufficient means to effectively promote behavioral change among users. To address this challenge, a system is needed that considers user emotions and proposes effective energy use and participation in eco-friendly activities.

[0788] 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.

[0789] In this invention, the server includes means for collecting household resource usage data in real time, means for analyzing consumption patterns and generating suggestions for optimizing energy use, and means for inferring the user's emotional state using a generating AI model and adjusting the suggestions in consideration of the emotional state. This enables energy use suggestions that are appropriate to the user's specific emotional state, allowing for sustainable resource management and active participation in the local community.

[0790] "Resource usage data" refers to information about the amount and patterns of resources consumed within a household, such as energy and water.

[0791] A "generative AI model" refers to artificial intelligence technology trained to analyze data and infer human emotional states.

[0792] An "emotion engine" refers to software or a system that analyzes a user's voice, facial expressions, and behavior to determine their emotional state.

[0793] "Consumption patterns" refer to data characteristics that show usage trends for specific resources, broken down by time of day or season.

[0794] "Suggestions" refer to advice provided to users based on analyzed data, aimed at promoting efficient resource use and behavioral change.

[0795] "Feedback" refers to the information that users provide in response to suggestions or to the results of their use, and is used to improve the system.

[0796] A "personalized energy use strategy" refers to an energy usage plan customized for each individual user, created based on their lifestyle and emotional state.

[0797] "Communication methods" refer to the methods or interfaces by which users and systems exchange information with each other.

[0798] This invention is a system that optimizes energy use in the home and provides suggestions based on the user's emotional state. The system collects data from various smart devices and home appliances in the home and analyzes this data on a server in the cloud. The server runs on a general cloud computing platform and recognizes consumption patterns in real time using a database and data analysis software. The analysis uses a generative AI model that implements machine learning algorithms, which detects anomalies in consumption and points where optimization is possible.

[0799] The server generates specific energy usage suggestions, taking into account appliance usage history and external environmental information. These suggestions are adjusted as needed, based on emotion engine data transmitted from the device, such as the user's stress level and relaxation level. The device collects emotion data from sensors and user interfaces via commercially available computer devices such as the user's smartphone or tablet. Furthermore, the method and content of user notifications are flexibly customized, taking the user's emotional state into consideration.

[0800] For example, if the system analyzes that a user is experiencing stress, the server can provide positive feedback using a prompt such as, "If you meet today's energy goal, we'll play your favorite music." This encourages cooperative behavior from the user and effectively optimizes energy consumption within the home.

[0801] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0802] Step 1:

[0803] The server acquires resource usage data from smart devices and appliances within the home. It receives real-time usage data transmitted from smart meters and appliances as input. This data includes electricity consumption, usage time, device ID, and other information. The server stores this data in a database and organizes it by date and device.

[0804] Step 2:

[0805] The server analyzes collected resource usage data and extracts consumption patterns. It uses machine learning algorithms to analyze the data based on the input. This analysis highlights consumption trends by time of day and season, enabling anomaly detection. The output provides a list of energy consumption trends and saving points.

[0806] Step 3:

[0807] The device collects emotional data from the user. Inputs include text input via the user interface, voice commands, and information from other smartphone sensors. The emotion engine analyzes this data, quantifies the user's current emotional state, such as stress and happiness levels, and sends the results to the server.

[0808] Step 4:

[0809] The server generates resource usage optimization suggestions based on consumption patterns and emotional data. The input consists of analyzed consumption patterns and emotional data. A generative AI model is used to create flexible suggestions tailored to the user's emotional state. Specific action suggestions are generated as output and sent to the terminal.

[0810] Step 5:

[0811] The server notifies the user via the terminal. Input includes generated suggestions and prompts. The notification method is adjusted according to the user's emotional state, using prompts that emphasize encouragement and positive feedback. Output is a notification sent to the user.

[0812] Step 6:

[0813] Based on the suggested actions, users attempt actual resource usage and provide feedback. The input consists of the user's actions and their results, which are sent back to the server via the terminal as feedback. The server analyzes this feedback and uses it to improve future suggestions.

[0814] (Application Example 2)

[0815] 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".

[0816] Energy consumption in modern cities is influenced by a variety of complex factors. Optimizing energy consumption by taking into account individual energy use patterns and emotional states contributes to the efficient use of energy resources and the reduction of environmental impact. However, optimizing energy use and gaining user cooperation requires effective user suggestions and encouragement of participation in emotion-based eco-activities, which has been difficult to achieve with conventional systems.

[0817] 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.

[0818] In this invention, the server includes means for collecting energy usage data in homes and public facilities in real time, means for generating personalized suggestions for optimizing energy use based on the user's emotional state, and means for filtering and notifying the user of eco-activities and event information based on the user's emotional state and interests. This enables the optimization of energy consumption tailored to the user's living environment and the realization of a sustainable lifestyle.

[0819] "Energy usage data" refers to data that shows the amount of energy consumed in homes and public facilities, as well as its temporal fluctuations.

[0820] "Means of collecting data in real time" refers to methods and technologies for instantly obtaining current energy usage data.

[0821] An "energy consumption pattern" is the result of analyzing a series of data that shows the trends and characteristics of energy consumption.

[0822] "Emotional state" refers to the user's psychological or emotional condition, including stress, relaxation, and interest.

[0823] "Personalized suggestions" refer to proposals for optimal energy usage tailored to the user's characteristics and circumstances.

[0824] "Eco-activities and event information" refers to data on community events and participation opportunities related to environmental protection and sustainable activities.

[0825] "Filtering" is the process of selecting information based on specific criteria and extracting the necessary data.

[0826] A "communication platform" is a system or infrastructure that enables the sending and receiving of information.

[0827] This invention provides a system for optimizing energy consumption in homes and public facilities in smart cities. The central server of the system collects energy usage data in real time from smart meters and various IoT devices and processes it using a cloud platform (e.g., AWS or Google Cloud Platform). Python's pandas library is used for data analysis, and natural language processing libraries such as TensorFlow and spaCy are used for analyzing users' emotional states.

[0828] The server generates personalized energy usage suggestions based on each user's energy usage patterns and emotional state. It notifies the user of these suggestions via their device (such as a smartphone or smart glasses) and continuously collects user feedback and behavioral data. This feedback data is used for further analysis to improve the effectiveness of the suggestions.

[0829] Furthermore, the system collects information on local eco-activities and events, and notifies users of filtered information based on their interests and emotional state. This encourages active participation in activities and events that interest the target users.

[0830] For example, one system might display a notification on a user's smart glasses while they are at work, asking, "Your home's air conditioning settings are optimized, is this okay?" Furthermore, on weekends, emotion-based notifications might be sent, such as, "Why not participate in the eco-fair being held in the city?", thereby supporting increased awareness of sustainable lifestyles.

[0831] An example of a prompt message for a generative AI model is, "Generate optimal energy consumption suggestions based on the user's current emotional state and energy data."

[0832] In this way, we provide an implementation that effectively manages users' energy usage and enables a sustainable lifestyle in a smart city environment.

[0833] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0834] Step 1:

[0835] The server collects real-time energy usage data from homes and public facilities through smart meters and IoT devices. This data includes power consumption, usage time, and date for each device. The collected data is stored on a cloud platform.

[0836] Step 2:

[0837] The server analyzes the collected energy usage data. It uses Python's pandas library to clean and format the data and analyze trends in electricity consumption. The analysis results extract the energy consumption patterns of each household and facility. Output includes data visualizations and statistical trend results.

[0838] Step 3:

[0839] The device analyzes the user's emotional state based on input such as camera images and audio. It uses a generative AI model and natural language processing library to identify emotional states such as stress levels and happiness. The analysis results are then sent to a server.

[0840] Step 4:

[0841] The server generates personalized energy usage suggestions based on energy consumption patterns and the user's emotional state. This process utilizes a generative AI model to construct optimal suggestions and action-oriented messages tailored to the user's emotions. The generated results are output as specific suggestions and are ready to be notified to the user.

[0842] Step 5:

[0843] The device receives suggestions from the server and notifies the user via a smartphone or smart glasses. The device also considers the user's location and schedule to ensure timely notifications. The output displays specific suggestions to encourage user action.

[0844] Step 6:

[0845] Users respond to the suggestions they receive and send the results as feedback from their device to the server. This feedback includes satisfaction with the suggestion and the results of its implementation. The server uses this feedback to perform data analysis to improve the effectiveness of future suggestions.

[0846] Step 7:

[0847] The server retrieves local eco-activities and event information from an external database and filters it based on the user's interests and emotional state. The filtered information is sent to the device as a message inviting participation. Ultimately, the user is notified of the event information, promoting participation in eco-activities.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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."

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] The following is further disclosed regarding the embodiments described above.

[0870] (Claim 1)

[0871] A means of collecting household energy usage data in real time,

[0872] A means for analyzing the aforementioned energy usage data and extracting energy consumption patterns,

[0873] A means for generating proposals for optimizing energy use based on the aforementioned energy consumption pattern,

[0874] A means of notifying the user of the aforementioned proposal,

[0875] A means for collecting user behavior data based on the above proposal and evaluating its effectiveness,

[0876] A system that includes this.

[0877] (Claim 2)

[0878] A means of collecting information on local eco-activities and events,

[0879] A means for filtering the aforementioned eco-activities and event information based on user interests,

[0880] A means of notifying the user of filtered information,

[0881] A means of providing a communication platform to promote participation in eco-activities,

[0882] The system according to claim 1, including the following:

[0883] (Claim 3)

[0884] A means of receiving user feedback and continuously improving the effectiveness of the aforementioned proposal,

[0885] A means of saving a history of proposals generated to support sustainable lifestyles,

[0886] A means of providing personalized energy usage strategies to users using a saved suggestion history,

[0887] The system according to claim 1, including the following:

[0888] "Example 1"

[0889] (Claim 1)

[0890] A means of collecting energy usage data from household measuring devices and communication equipment,

[0891] A means for analyzing the aforementioned energy usage data to identify energy consumption trends,

[0892] Means for using generative AI technology to generate options for improving energy efficiency based on the aforementioned consumption trends,

[0893] A means of informing the user of the aforementioned options via a notification device,

[0894] A means for collecting and analyzing the impact of the aforementioned choices from user behavior,

[0895] A system that includes this.

[0896] (Claim 2)

[0897] A means of collecting and organizing local environmental activities and public events,

[0898] A means for selecting the aforementioned activity information according to the user's preferences,

[0899] A means of informing users of organized information and encouraging their participation,

[0900] A means of providing a platform for interaction to support participation in community activities,

[0901] The system according to claim 1, including the following:

[0902] (Claim 3)

[0903] A means to continuously improve the quality of the aforementioned options based on user feedback,

[0904] A means of recording the history of choices created to support environmentally conscious lifestyles,

[0905] A means of presenting a user-specific energy usage plan using recorded historical data,

[0906] The system according to claim 1, including the following:

[0907] "Application Example 1"

[0908] (Claim 1)

[0909] A means of collecting household energy usage information in real time,

[0910] A means for analyzing the aforementioned energy usage information and extracting energy consumption rules,

[0911] Means for generating instructions for optimizing energy use based on the aforementioned energy consumption rules,

[0912] Means for notifying the user of the aforementioned instructions,

[0913] A means for collecting user behavior information based on the aforementioned instructions and evaluating the results thereof,

[0914] A means of collecting information on local eco-activities and events,

[0915] A means for selecting the aforementioned eco-activities and event information based on the user's interests,

[0916] A means of notifying users of the selected information,

[0917] A means of providing an information sharing platform to encourage participation in eco-activities,

[0918] A means of receiving responses from users and continuously improving the effectiveness of the aforementioned instructions,

[0919] A means of saving a history of instructions generated to support sustainable lifestyles,

[0920] A means of providing an individualized energy usage plan for users using a saved instruction history,

[0921] A means of contributing to the overall energy efficiency of a region by linking with a regional energy management system,

[0922] A means for providing an automatic registration function for the aforementioned eco-activities and event information,

[0923] A system that includes this.

[0924] (Claim 2)

[0925] The system according to claim 1, which provides the user with an automatic registration function for energy efficiency activities.

[0926] (Claim 3)

[0927] The system according to claim 1, which synchronizes with a regional shared energy management network.

[0928] "Example 2 of combining an emotion engine"

[0929] (Claim 1)

[0930] A means of collecting household resource usage data in real time,

[0931] A means for analyzing the resource usage data and extracting consumption patterns,

[0932] A means for generating suggestions for optimizing usage based on the aforementioned consumption pattern,

[0933] A method for inferring a user's emotional state using a generative AI model,

[0934] A means of adjusting the aforementioned proposal in consideration of the emotional state and notifying the user,

[0935] A means for collecting user behavior data based on the above proposal and evaluating its effectiveness,

[0936] A system that includes this.

[0937] (Claim 2)

[0938] A means of collecting information on local environmental activities and events,

[0939] A means for filtering the aforementioned activity and event information based on user interests,

[0940] A means of notifying the user of filtered information,

[0941] The system according to claim 1, which provides a means of communication to promote participation in environmental activities using an emotion engine.

[0942] (Claim 3)

[0943] A means for receiving user feedback and continuously improving the effectiveness of the proposal using a generative AI model,

[0944] A means of saving a history of proposals generated to support sustainable lifestyles,

[0945] A means of providing personalized resource usage strategies to users using saved suggestion history and sentiment data,

[0946] The system according to claim 1, including the following:

[0947] "Application example 2 when combining with an emotional engine"

[0948] (Claim 1)

[0949] A means of collecting data on energy use in homes and public facilities in real time,

[0950] A means for analyzing the aforementioned energy usage data and extracting energy consumption patterns,

[0951] A means for generating personalized suggestions for optimizing energy use based on the user's emotional state,

[0952] The above proposal will be communicated to users as a means to promote energy consumption efficiency,

[0953] A means of filtering and notifying users of eco-friendly activities and event information based on their emotional state and interests,

[0954] A means of providing a communication platform to promote participation in eco-activities,

[0955] A system that includes this.

[0956] (Claim 2)

[0957] A means for collecting user behavior data and emotional state data, and for evaluating the effectiveness of the proposal based on that data,

[0958] A means of receiving user feedback and continuously improving the effectiveness of the aforementioned proposal,

[0959] The system according to claim 1, including the following:

[0960] (Claim 3)

[0961] A means of saving a history of proposals generated to support sustainable lifestyles,

[0962] A means of providing a personalized energy usage strategy to a user, taking into account the user's emotional state and using a saved suggestion history.

[0963] The system according to claim 1, including the following: [Explanation of Symbols]

[0964] 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 household energy usage information in real time, A means for analyzing the aforementioned energy usage information and extracting energy consumption rules, Means for generating instructions for optimizing energy use based on the aforementioned energy consumption rules, Means for notifying the user of the aforementioned instructions, A means for collecting user behavior information based on the aforementioned instructions and evaluating the results thereof, A means of collecting information on local eco-activities and events, A means for selecting the aforementioned eco-activities and event information based on the user's interests, A means of notifying users of the selected information, A means of providing an information sharing platform to encourage participation in eco-activities, A means of receiving responses from users and continuously improving the effectiveness of the aforementioned instructions, A means of saving a history of instructions generated to support sustainable lifestyles, A means of providing an individualized energy usage plan for users using a saved instruction history, A means of contributing to the overall energy efficiency of a region by linking with a regional energy management system, A means for providing an automatic registration function for the aforementioned eco-activities and event information, A system that includes this.

2. The system according to claim 1, which provides the user with an automatic registration function for energy efficiency activities.

3. The system according to claim 1, which synchronizes with a regional shared energy management network.

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