system

Abstract

Provide a system. 【Solution means】 Means for accumulating energy data in real time, Means for preprocessing the accumulated data and converting it into a format suitable for analysis, Means for predicting energy consumption using the preprocessed data, Means for generating energy-saving measures and recommendations for the use of renewable energy based on the analysis results, Means for notifying the members of the advice content and responding to interactive inquiries from the members, Means for collaborating with the weather situation and providing real-time proposals for efficient energy use, Means for generating instructions for optimizing the energy consumption of members at the city scale, 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, 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 enterprises and public institutions, problems such as rising energy costs and increasing environmental burdens have become apparent. However, many organizations are unable to implement effective energy management due to a lack of expertise. Therefore, effective means to promote the optimization of energy consumption and the introduction of renewable energy are required. In particular, there is a need for a system that can process data in real time and lead to specific energy-saving proposals.

Means for Solving the Problems

[0005] This invention provides a system that collects energy data in real time and converts it into a format suitable for analysis through preprocessing. This system includes means for predicting energy consumption using the preprocessed data, and further includes means for generating energy-saving measures and renewable energy utilization proposals based on the analysis results. The proposals are notified to the user, and the system can respond to interactive inquiries from the user. This enables organizations to achieve efficient energy management, reduce costs, and lessen environmental impact.

[0006] "Real-time data" refers to data that a system processes at all times with minimal delay, making it immediately available for use.

[0007] "Preprocessing" refers to a series of operations performed before data analysis, such as removing noise from raw data or organizing the data into a standard format.

[0008] "A format suitable for analysis" refers to a state in which data is organized into a systematic format so that data analysis methods can be applied effectively.

[0009] "Energy consumption forecasting" is the process of estimating future energy consumption based on past usage patterns and external conditions.

[0010] "Energy-saving measures" refer to means and strategies for efficiently reducing energy consumption and eliminating waste.

[0011] A "proposal for the use of renewable energy" refers to specific guidelines and plans for the efficient use of renewable energy resources such as solar and wind power.

[0012] "Notification to the user" refers to the act of concisely and effectively informing the user of important information or suggestions from the system.

[0013] An "interactive inquiry" is a process of two-way communication in which the user gives questions or instructions to the system. [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 the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] 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, when an emotion engine is combined. [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 provides a system for efficient energy management. This system effectively reduces energy consumption and supports the optimal use of renewable energy. The following describes in detail the configurations for implementing this system.

[0036] The entire system consists of four main functions: data collection, data analysis, proposal generation, and interactive response. First, the server collects data in real time from various sensors and existing energy management systems. This allows for monitoring of energy usage for each piece of equipment and facility.

[0037] Next, the server analyzes the collected data to identify energy waste. This analysis includes demand forecasting that takes into account past usage data and weather forecasts. Based on the analysis results, the system also proposes specific energy-saving measures that contribute to peak shifting and cost reduction.

[0038] As a concrete example, analysis revealed that a certain factory had extremely high daytime electricity consumption. Based on the system's suggestion, the user installed solar power panels and stored the generated electricity in batteries for use at night, resulting in a 10% reduction in overall electricity consumption.

[0039] Furthermore, users can interact with the system to resolve questions immediately. For example, if a user is curious about the effectiveness of introducing new energy-saving equipment, they can ask the system a question, and the server can present simulation results on the spot.

[0040] In this way, the system of the present invention provides an efficient and sustainable solution to the energy management challenges faced by companies and public institutions.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server acquires real-time energy data from sensors placed in each piece of equipment and facility, as well as from existing energy management systems. This data includes information such as power consumption, temperature, and equipment operating status. Furthermore, it also acquires weather data from weather forecasting systems.

[0044] Step 2:

[0045] The server preprocesses the acquired data. Specifically, it ensures data accuracy by correcting missing values ​​and removing outliers. To prepare the data for easier analysis, it organizes and smooths it as time-series data.

[0046] Step 3:

[0047] The server inputs pre-processed data into an AI model. Here, short-term and long-term forecasts of energy demand are made, taking into account historical data and weather conditions. The forecast results are used to identify energy consumption trends and peak demand.

[0048] Step 4:

[0049] Based on the prediction results, the server generates proposals regarding energy-saving measures and the feasibility of introducing renewable energy. These proposals include expected cost reductions and reductions in environmental impact. For example, it can create proposals for shifting the load to meet peak demand or introducing solar power generation.

[0050] Step 5:

[0051] The server notifies the user's device of the generated suggestions. The user can review the suggestions on the dashboard and evaluate the proposed actions. Important suggestions may be sent as alerts.

[0052] Step 6:

[0053] Users can ask the system questions about the proposals and receive interactive responses. Based on the user's inquiries, the server performs additional simulations and data analysis, providing detailed results immediately. This allows users to further understand the feasibility and effectiveness of the proposals.

[0054] (Example 1)

[0055] 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."

[0056] Modern energy management demands efficient energy consumption reduction and optimal utilization of renewable energy. However, conventional systems struggle with real-time data collection, and data analysis and proposal generation often involve manual operations, hindering rapid response. Furthermore, there is a need for technology that automatically generates effective proposals from the perspectives of environmental impact and cost, while ensuring the reliability of the data and analysis results that form the basis of the proposals.

[0057] 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.

[0058] In this invention, the server includes information gathering means for acquiring energy data in real time, processing means for processing the acquired data and converting it into a format suitable for analysis, analysis means for predicting energy demand using the processed data, and a proposal generation device for recommending energy-saving measures and the use of renewable energy based on the analysis results, which includes means for constructing proposals using prompt sentences based on a generated AI model, and bidirectional communication means for communicating the proposal content to the user and responding to responses from the user. This makes it possible to consistently perform everything from real-time data collection to automatic proposal generation and interaction with the user.

[0059] "Information gathering means for acquiring energy data in real time" refers to devices or methods that have the function of instantly acquiring various data related to energy use.

[0060] A "processing device that processes acquired data and converts it into a format suitable for analysis" is a component within a system that converts raw data into a format that can be easily analyzed.

[0061] An "analytical device for predicting energy demand using processed data" is a device that has the function of predicting future energy consumption based on converted data.

[0062] A "device that constructs proposals using prompt sentences based on a generative AI model" is a device that uses generative artificial intelligence technology to automatically create proposals related to energy management from specific inputs (prompts).

[0063] A "two-way communication device for conveying proposals to users and responding to user responses" is a device that has communication functions to inform users of proposals and to respond dynamically to user questions and requests.

[0064] This system is a comprehensive platform for achieving efficient energy management. The server collects data in real time to monitor energy consumption. Hardware-wise, it acquires data using various sensors and energy meters. For example, it aggregates electricity meter data from the entire building and temperature data from temperature sensors to the server via the network. The software utilizes an IoT platform for efficient data collection and management.

[0065] After data collection, the server analyzes the collected data using data analysis software such as Python or R. The collected data is preprocessed using libraries such as Pandas and NumPy to remove outliers and interpolate time series. Subsequently, energy demand is predicted using statistical models, taking into account past energy usage data and weather forecasts. Based on these analysis results, the server generates energy-saving suggestions using a generative AI model.

[0066] For example, if analysis reveals that a factory has extremely high daytime electricity consumption, the server will generate a prompt message such as, "Consider installing a solar power generation system to reduce daytime electricity peaks." The user can then use this suggestion to decide whether or not to invest in actual energy facilities.

[0067] Furthermore, users can obtain additional information from the system by making interactive inquiries. For example, if a user wants to know the energy reduction effect of introducing new equipment immediately, they can prompt the server with "Please simulate the effect of energy cost reduction from introducing new energy-saving equipment." In response to this prompt, the server immediately runs the simulation and provides the results to the user.

[0068] In this way, the server supports efficient and sustainable energy management, addressing the energy challenges faced by businesses and public institutions.

[0069] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0070] Step 1:

[0071] The server collects energy data in real time from various sensors and energy meters. Inputs include temperature sensor readings and power meter consumption data. The server aggregates this data via the network and stores it in a database. This allows for monitoring the energy usage of each piece of equipment.

[0072] Step 2:

[0073] The server processes the collected data and converts it into a format suitable for analysis. The input data includes raw sensor data. Specific data processing steps include conversion to a Pandas dataframe, removal of outliers, and imputation of time-series gaps. The output is a clean, analyzable dataset.

[0074] Step 3:

[0075] The server performs analysis to predict energy demand based on processed data. Inputs include pre-processed data and statistical models. Specifically, it performs regression analysis and time series analysis, and also considers weather forecast data. The output provides predicted future energy consumption patterns.

[0076] Step 4:

[0077] The server generates energy-saving suggestions using a generative AI model based on the prediction results. The input is the analysis results, and a prompt is provided. Specifically, the prompt "Please provide the optimal energy reduction suggestion under the following conditions" is passed to the generative AI model. The output is an energy-saving suggestion that the user can implement.

[0078] Step 5:

[0079] The user receives suggestions from the server and asks additional questions as needed. The input is the generated suggestions, and the output provides further analysis or simulation results. Specifically, interactive inquiries such as "Please simulate the effects of introducing new equipment" are possible, and the server responds dynamically accordingly.

[0080] (Application Example 1)

[0081] 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."

[0082] To optimize energy consumption in cities and efficiently utilize renewable energy, residents need to manage their own energy use and take energy-saving actions at the optimal time. However, existing technologies have limitations in providing residents with real-time, appropriate energy use suggestions and comprehensively optimizing energy consumption across the entire city.

[0083] 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.

[0084] In this invention, the server includes means for collecting energy data in real time, means for preprocessing the collected data and converting it into a format suitable for analysis, means for generating energy-saving measures and recommendations for renewable energy use based on the analysis results, means for providing suggestions for efficient energy use in real time in conjunction with weather conditions, and means for generating instructions for optimizing the energy consumption of members on a city scale. This makes it possible to effectively suppress energy consumption throughout the city and maximize the use of renewable energy.

[0085] A "device for accumulating energy data in real time" is a device that instantly grasps the status of energy use and continuously collects the latest data.

[0086] A "device for preprocessing and converting data into a format suitable for analysis" is a device that processes collected data and prepares it in a format that facilitates analysis.

[0087] A "device for predicting energy consumption" is a system that estimates future energy usage based on collected data.

[0088] A "device for generating energy-saving measures and recommendations for renewable energy use" is a device that creates proposals for reducing energy consumption and effectively utilizing renewable energy based on analysis results.

[0089] A "device that notifies members and responds to interactive inquiries from members" is a system that sends generated energy-saving measures information to users and provides real-time answers to user questions and concerns.

[0090] A "device that provides real-time suggestions for efficient energy use in conjunction with weather conditions" is a device that uses weather data to instantly show users the optimal way to use energy.

[0091] A "device that generates instructions to optimize the energy consumption of its members on a city-wide scale" is a device that integrates energy data from multiple members and creates commands aimed at optimizing energy consumption throughout the entire city.

[0092] This invention provides a system that collects and analyzes vast amounts of energy data in real time and efficiently manages energy consumption on a city scale.

[0093] The server collects energy data in real time by receiving data from sensing devices and information from forecasting systems. This data is first preprocessed and converted into a format suitable for analysis. This process utilizes Python programs and Apache® Kafka. Next, the server analyzes the converted data and predicts future energy demand. This generates recommendations aimed at reducing costs and environmental impact.

[0094] The generated recommendations are notified to members' devices via the network. Members can review and implement the recommended energy-saving actions on devices such as smartphones and smart glasses. Real-time responses to interactive inquiries from members are also possible. AWS® is a platform likely to be used for this purpose.

[0095] For example, if the weather forecast for a given day is sunny, the server will generate a suggestion such as "We recommend doing your laundry in the morning tomorrow" and display it on the terminal. This allows members to easily select and perform actions that optimize their energy consumption.

[0096] An example of a prompt message is: "Generate specific suggestions for maximizing the use of renewable energy based on tomorrow's weather forecast."

[0097] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0098] Step 1:

[0099] The server receives data in real time from sensing devices and forecasting systems. Inputs include current energy consumption data from energy sensors and weather information. This data is collected and stored on the server.

[0100] Step 2:

[0101] The server preprocesses the collected energy data and converts it into a format suitable for analysis. The input is raw energy data, and the output is cleaned data. This process imputes missing values ​​and removes outliers.

[0102] Step 3:

[0103] The server uses pre-processed data to predict energy consumption. The input is pre-processed data, and the output is a prediction of future energy consumption. The server uses a generative AI model to make predictions from historical data and saves the results.

[0104] Step 4:

[0105] The server generates energy-saving measures and renewable energy utilization proposals based on the analysis results. The input is predictive data, and the output is energy-saving proposals. This includes data on cost reduction effects and environmental impact reduction effects.

[0106] Step 5:

[0107] The server notifies the member's terminal of the generated proposal and responds to interactive inquiries from the member. The input is the member's inquiry, and the output is the response message. The terminal displays the analysis results and proposal, and the member reviews the content.

[0108] Step 6:

[0109] The server works in conjunction with weather conditions to provide real-time suggestions for efficient energy use. The input is the latest weather forecast data, and the output is a suggestion for the optimal timing of energy use for each member. The server detects weather patterns and makes corresponding suggestions.

[0110] Step 7:

[0111] The terminal receives instructions to optimize the energy consumption of its members on a city-wide scale and prompts users to take action. Input is instruction data from the server, and output is specific action suggestions for the user. Users follow the terminal's guidance to implement energy-saving measures.

[0112] 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.

[0113] This invention aims to achieve more effective and user-friendly energy management by incorporating an emotion engine that recognizes the user's emotions into an energy management system. This system generates adaptive energy-saving suggestions based on the user's emotional state, thereby optimizing energy consumption.

[0114] The main components of this system are: "energy data collection," "data analysis and suggestion generation," "user emotion recognition using an emotion engine," and "notifications and interactive responses." First, the server acquires energy data in real time from sensors and existing forecasting systems. Based on this data, it analyzes energy usage trends and identifies peak demand.

[0115] Next, the server proposes energy-saving measures and renewable energy implementations based on the analysis. These proposals take into account the user's emotional state as recognized by the emotion engine. For example, if a user is in a high-stress state, the tone and priority of the proposals are adjusted, and energy-saving suggestions are provided to reduce stress.

[0116] For example, if the emotion engine detects that a user working in an office is experiencing stress, the server will suggest adjusting the air conditioning system settings to optimize environmental conditions. This adjustment aims to reduce user stress by setting the temperature and humidity within a comfortable range.

[0117] Furthermore, the emotion engine also plays a role in optimizing how suggestions are notified. For example, it provides gentler and more flexible suggestions to users in high-stress states, rather than direct notifications, thus increasing the receptiveness of suggestions based on the user's state.

[0118] In this way, the system of the present invention realizes intelligent energy management that takes into account the user's emotional state, providing a better user experience and improving the efficiency of energy management.

[0119] The following describes the processing flow.

[0120] Step 1:

[0121] The server collects various energy data in real time via sensors and forecasting systems. This includes direct energy consumption, equipment operating status, and external weather conditions.

[0122] Step 2:

[0123] The server preprocesses the collected raw data. Specifically, it corrects missing values, removes outliers, and standardizes the data as needed. The preprocessed data is then converted into a format suitable for analysis and saved.

[0124] Step 3:

[0125] The server uses an AI model to predict energy consumption based on pre-processed data. This involves identifying short-term and long-term demand trends by taking into account past usage history and weather data.

[0126] Step 4:

[0127] The server generates proposals for energy-saving measures and renewable energy utilization based on predictions obtained from the AI ​​model. These proposals include methods for peak shifting and cost reduction effects.

[0128] Step 5:

[0129] The server uses an emotion engine to recognize user emotions and perform real-time analysis of the user's emotional state. Specifically, it detects changes in the user's emotional state from their actions and facial expressions.

[0130] Step 6:

[0131] The server utilizes emotional data obtained by the emotion engine to customize the generated suggestions according to the user's current emotional state. For example, a user experiencing high stress will be presented with calm and gentle suggestions.

[0132] Step 7:

[0133] The server notifies the user's device of the optimized suggestions. The user receives the notification on their device and can review and understand the details of the suggestions.

[0134] Step 8:

[0135] Users can ask the system questions if they have doubts about the proposal or require additional information. The server will respond immediately to these inquiries and provide additional analysis and simulation results.

[0136] This series of steps allows users to effectively manage energy use while improving comfort and reducing mental stress.

[0137] (Example 2)

[0138] 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".

[0139] Conventional energy management systems focus on optimizing energy use but fail to consider the feelings and circumstances of users. Therefore, energy-saving measures are not always comfortable for users, which can result in decreased user satisfaction and undermine the effectiveness of the proposed solutions.

[0140] 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.

[0141] In this invention, the server includes means for collecting energy usage information in real time, means for recognizing the user's emotional state and generating adaptive suggestions using a generative AI model, and means for notifying the user of the generated plan and engaging in interactive communication with the user. This enables intelligent energy management that takes into account the user's emotional state, thereby improving user satisfaction and the efficiency of energy management.

[0142] "Real-time" refers to acquiring and processing information at the very moment an event occurs.

[0143] "Energy utilization information" refers to information that includes various data on energy consumption and supply status.

[0144] "Preprocessing" refers to initial data processing techniques used to convert collected information into a format suitable for analysis.

[0145] "Renewable energy" refers to energy sources that are constantly regenerated through natural processes, such as solar, wind, hydro, and geothermal energy.

[0146] A "generative AI model" refers to a form of artificial intelligence trained to analyze data and predict new suggestions or outcomes.

[0147] A "detector" refers to a device or system that observes specific environmental conditions or physical states and collects that data.

[0148] A "forecasting system" refers to a system that analyzes past and present data to predict future conditions and provides the results.

[0149] "Cost reduction effect" refers to the expected benefit of a particular measure in reducing economic costs.

[0150] "Environmental impact reduction effect" refers to the effectiveness of efforts to mitigate the negative impacts that human activities have on the natural environment.

[0151] Modes for carrying out the invention

[0152] This energy management system aims to achieve both efficient energy use and user comfort. A specific implementation is shown below.

[0153] The server first collects energy usage information in real time from detectors and forecasting mechanisms. This information includes data on power consumption, temperature, humidity, and weather. This data is automatically collected on the server via an API and stored in a database.

[0154] The server then preprocesses the collected information. The Python Pandas library is used for preprocessing, including data imputation and type conversion. The resulting dataset is then used for pattern analysis and prediction of energy utilization.

[0155] The device operates an emotion engine to recognize the user's emotional state. The emotion engine utilizes libraries such as TENSORFLOW® and OpenCV to analyze the user's facial expression data, inferring stress levels and comfort levels. This information is transmitted to the server in real time.

[0156] By using a generative AI model, the server generates energy-saving measures based on pre-processed information and the user's emotional state. It utilizes tools such as PyTorch and the Chatbot API to provide optimal suggestions tailored to the user's state. Specifically, this could include automatic temperature control for heating and cooling, and lighting adjustments. Furthermore, the generated suggestions include quantified cost reduction and environmental impact reduction effects.

[0157] The server notifies the user of the generated suggestions. These notifications are sent via smartphone apps or email, and a flexible format is used to enhance user acceptance. For example, on a sunny afternoon, a suggestion might be presented to change the timing of electricity usage to maximize the use of solar power.

[0158] An example of a prompt message for the generating AI model is: "Consider the energy consumption and stress levels of users working in an office, and generate comfortable and efficient energy-saving suggestions. If the user's stress level is high, be flexible in how you notify them."

[0159] This system enables intelligent energy management, allowing for both user comfort and energy savings.

[0160] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0161] Step 1:

[0162] The server collects energy usage information from detectors and forecasting mechanisms. During this process, data such as temperature, humidity, power consumption, and weather information are input in real time. The input data is sent to the server via an API and stored in a database. Specifically, this involves acquiring measurements from various sensors and collecting them on the server via the network. The output is formalized energy data stored in the database.

[0163] Step 2:

[0164] The server preprocesses the collected energy data. The raw data saved in step 1 is used as input. The Python Pandas library is used to impute missing values, convert data types, and denoise. Specifically, outlier detection and correction, and data smoothing are performed. As a result of this data processing, a clean dataset suitable for analysis is obtained. This output is the data for subsequent analysis and model input.

[0165] Step 3:

[0166] The device operates an emotion engine to recognize the user's emotional state. The input here consists of the user's facial expressions and voice data. Using TensorFlow and OpenCV, this data is analyzed in real time to estimate stress levels and emotional states. Specific operations include facial expression recognition using the camera and voice tone analysis using the microphone. The output is the analyzed user's emotional state, which is then sent to the server.

[0167] Step 4:

[0168] The server generates energy-saving measures using pre-processed energy data and the user's emotional state. The input here is the data output in steps 2 and 3. A generative AI model is used, and prompts are applied to create suggestions. The output generates recommendations for specific energy-saving measures, such as adjusting heating and cooling temperatures and automatically controlling lighting.

[0169] Step 5:

[0170] The server notifies the user of the generated energy-saving measures. The input is the proposal content from step 4. The proposal is sent to the user via a smartphone app or email. Specifically, the server generates and delivers the notification message. The output is the energy-saving proposal received by the user, which allows the user to adjust their environment according to the proposal.

[0171] (Application Example 2)

[0172] 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".

[0173] In energy management systems, uniform energy-saving suggestions that disregard the user's emotional state have problems with low feasibility and a poor user experience. Furthermore, because the suggestions ignore the user's feelings, there are challenges such as increased stress and decreased motivation for energy-saving behavior.

[0174] 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.

[0175] In this invention, the server includes means for collecting energy data in real time, means for recognizing emotions and reflecting them in energy-saving suggestions, and means for adjusting the suggestion content based on the user's emotional state. This enables suggestions that are adapted to the user's emotional state, resulting in more effective and user-friendly energy management.

[0176] A "device for collecting energy data in real time" is a device that continuously acquires information on energy usage from sensors and forecasting systems and immediately reflects it in the system.

[0177] A "device that preprocesses collected data and converts it into a format suitable for analysis" is a means of processing acquired energy data and shaping it into a format necessary for prediction and analysis.

[0178] A "device that predicts energy consumption using pre-processed data" is a system that predicts future energy use based on pre-processed data.

[0179] A "device that generates energy-saving measures and renewable energy utilization proposals" is a device that proposes effective energy-saving methods and renewable energy utilization methods to users based on predicted energy data.

[0180] A "device that recognizes emotions and reflects them in energy-saving proposals" is a device that detects the user's emotional state and takes it into consideration when deciding on energy-saving proposals.

[0181] A "device that adjusts suggested content based on the user's emotional state" is a system that appropriately changes the format of energy-saving suggestions and notifications according to the user's current emotions and stress level.

[0182] This invention is a system that optimizes energy management based on the user's emotional state. The server can collect energy data in real time through multiple sensors and forecasting systems. This data is transformed into a format suitable for analysis through initial preprocessing. Preprocessing includes data formatting and noise filtering.

[0183] Next, the server uses the pre-processed data to predict energy consumption. This prediction is performed using statistical modeling techniques to show future usage trends. Based on the prediction results, energy-saving suggestions and renewable energy utilization proposals are generated.

[0184] The emotion engine runs on the user's device and detects the user's emotional state using facial recognition and voice analysis. This emotional data is sent to a server and reflected in the generated energy-saving suggestions. For example, if the user is feeling stressed, suggestions will be made to adjust the temperature and humidity or change the brightness of the lighting.

[0185] Suggestions for users are provided through a notification system. The content of the suggestions is tailored based on the user's state detected by the emotion engine. For example, users in a relaxed state will receive detailed suggestions, while users experiencing high stress levels will receive simple and calming suggestions.

[0186] As a concrete example, a smartphone application monitors household energy management in real time and prompts the user to automatically adjust the air conditioner settings when they are feeling stressed in a hot environment. An example of a prompt message generated using a generative AI model is, "Your home's energy consumption is on the rise. Would you like to set the air conditioner temperature to 25 degrees Celsius as an optimal setting to minimize stress?"

[0187] In this way, the present invention can take into account the emotional state of the user and manage energy safely and efficiently, thereby providing an optimal energy-saving solution for each individual user.

[0188] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0189] Step 1:

[0190] The server collects energy data in real time from sensors and forecasting systems. Input is data from sensors, and output is raw data. This data is used to instantly understand energy consumption patterns.

[0191] Step 2:

[0192] The server preprocesses the collected raw data and converts it into a format suitable for analysis. The input is raw data, and through noise filtering and scaling, the output is transformed into an analyzable data format. This process eliminates data bias, enabling accurate analysis.

[0193] Step 3:

[0194] The server uses pre-processed data to predict energy consumption. The input is in an analyzable data format, and by performing calculations using a statistical model, the output is an estimate of future consumption. This prediction forms the basis for energy-saving proposals.

[0195] Step 4:

[0196] The device recognizes the user's emotions and sends that data to the server. The input consists of the user's facial expressions and voice, which are analyzed by an emotion engine to produce the user's emotional state. This emotional information influences energy-saving suggestions.

[0197] Step 5:

[0198] The server generates energy-saving suggestions based on predicted energy consumption and the user's emotional state. The input is the predicted energy consumption and emotional state, and the output is a tailored energy-saving suggestion. The priority and wording of the suggestions change depending on the emotional state.

[0199] Step 6:

[0200] The server communicates the generated energy-saving suggestions to the user using a notification system. The input is a pre-adjusted suggestion, and it is output in a way that suits the user's emotions. For example, calmer language is selected for highly stressed users.

[0201] Step 7:

[0202] Users adjust energy settings and provide feedback to the device based on the suggestions they receive. Input is energy-saving suggestions, and output is actual actions and setting changes. This feedback helps further refine the system.

[0203] 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.

[0204] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0205] 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.

[0206] [Second Embodiment]

[0207] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0208] 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.

[0209] 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).

[0210] 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.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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".

[0219] This invention provides a system for efficient energy management. This system effectively reduces energy consumption and supports the optimal use of renewable energy. The following describes in detail the configurations for implementing this system.

[0220] The entire system consists of four main functions: data collection, data analysis, proposal generation, and interactive response. First, the server collects data in real time from various sensors and existing energy management systems. This allows for monitoring of energy usage for each piece of equipment and facility.

[0221] Next, the server analyzes the collected data to identify energy waste. This analysis includes demand forecasting that takes into account past usage data and weather forecasts. Based on the analysis results, the system also proposes specific energy-saving measures that contribute to peak shifting and cost reduction.

[0222] As a concrete example, analysis revealed that a certain factory had extremely high daytime electricity consumption. Based on the system's suggestion, the user installed solar power panels and stored the generated electricity in batteries for use at night, resulting in a 10% reduction in overall electricity consumption.

[0223] Furthermore, users can interact with the system to resolve questions immediately. For example, if a user is curious about the effectiveness of introducing new energy-saving equipment, they can ask the system a question, and the server can present simulation results on the spot.

[0224] In this way, the system of the present invention provides an efficient and sustainable solution to the energy management challenges faced by companies and public institutions.

[0225] The following describes the processing flow.

[0226] Step 1:

[0227] The server acquires real-time energy data from sensors placed in each piece of equipment and facility, as well as from existing energy management systems. This data includes information such as power consumption, temperature, and equipment operating status. Furthermore, it also acquires weather data from weather forecasting systems.

[0228] Step 2:

[0229] The server preprocesses the acquired data. Specifically, it ensures data accuracy by correcting missing values ​​and removing outliers. To prepare the data for easier analysis, it organizes and smooths it as time-series data.

[0230] Step 3:

[0231] The server inputs pre-processed data into an AI model. Here, short-term and long-term forecasts of energy demand are made, taking into account historical data and weather conditions. The forecast results are used to identify energy consumption trends and peak demand.

[0232] Step 4:

[0233] Based on the prediction results, the server generates proposals regarding energy-saving measures and the feasibility of introducing renewable energy. These proposals include expected cost reductions and reductions in environmental impact. For example, it can create proposals for shifting the load to meet peak demand or introducing solar power generation.

[0234] Step 5:

[0235] The server notifies the user's device of the generated suggestions. The user can review the suggestions on the dashboard and evaluate the proposed actions. Important suggestions may be sent as alerts.

[0236] Step 6:

[0237] Users can ask the system questions about the proposals and receive interactive responses. Based on the user's inquiries, the server performs additional simulations and data analysis, providing detailed results immediately. This allows users to further understand the feasibility and effectiveness of the proposals.

[0238] (Example 1)

[0239] 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."

[0240] Modern energy management demands efficient energy consumption reduction and optimal utilization of renewable energy. However, conventional systems struggle with real-time data collection, and data analysis and proposal generation often involve manual operations, hindering rapid response. Furthermore, there is a need for technology that automatically generates effective proposals from the perspectives of environmental impact and cost, while ensuring the reliability of the data and analysis results that form the basis of the proposals.

[0241] 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.

[0242] In this invention, the server includes information gathering means for acquiring energy data in real time, processing means for processing the acquired data and converting it into a format suitable for analysis, analysis means for predicting energy demand using the processed data, and a proposal generation device for recommending energy-saving measures and the use of renewable energy based on the analysis results, which includes means for constructing proposals using prompt sentences based on a generated AI model, and bidirectional communication means for communicating the proposal content to the user and responding to responses from the user. This makes it possible to consistently perform everything from real-time data collection to automatic proposal generation and interaction with the user.

[0243] "Information gathering means for acquiring energy data in real time" refers to devices or methods that have the function of instantly acquiring various data related to energy use.

[0244] A "processing device that processes acquired data and converts it into a format suitable for analysis" is a component within a system that converts raw data into a format that can be easily analyzed.

[0245] An "analytical device for predicting energy demand using processed data" is a device that has the function of predicting future energy consumption based on converted data.

[0246] A "device that constructs proposals using prompt sentences based on a generative AI model" is a device that uses generative artificial intelligence technology to automatically create proposals related to energy management from specific inputs (prompts).

[0247] A "two-way communication device for conveying proposals to users and responding to user responses" is a device that has communication functions to inform users of proposals and to respond dynamically to user questions and requests.

[0248] This system is a comprehensive platform for achieving efficient energy management. The server collects data in real time to monitor energy consumption. Hardware-wise, it acquires data using various sensors and energy meters. For example, it aggregates electricity meter data from the entire building and temperature data from temperature sensors to the server via the network. The software utilizes an IoT platform for efficient data collection and management.

[0249] After data collection, the server analyzes the collected data using data analysis software such as Python or R. The collected data is preprocessed using libraries such as Pandas and NumPy to remove outliers and interpolate time series. Subsequently, energy demand is predicted using statistical models, taking into account past energy usage data and weather forecasts. Based on these analysis results, the server generates energy-saving suggestions using a generative AI model.

[0250] For example, if analysis reveals that a factory has extremely high daytime electricity consumption, the server will generate a prompt message such as, "Consider installing a solar power generation system to reduce daytime electricity peaks." The user can then use this suggestion to decide whether or not to invest in actual energy facilities.

[0251] Furthermore, users can obtain additional information from the system by making interactive inquiries. For example, if a user wants to know the energy reduction effect of introducing new equipment immediately, they can prompt the server with "Please simulate the effect of energy cost reduction from introducing new energy-saving equipment." In response to this prompt, the server immediately runs the simulation and provides the results to the user.

[0252] In this way, the server supports efficient and sustainable energy management, addressing the energy challenges faced by businesses and public institutions.

[0253] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0254] Step 1:

[0255] The server collects energy data in real time from various sensors and energy meters. Inputs include temperature sensor readings and power meter consumption data. The server aggregates this data via the network and stores it in a database. This allows for monitoring the energy usage of each piece of equipment.

[0256] Step 2:

[0257] The server processes the collected data and converts it into a format suitable for analysis. The input data includes raw sensor data. Specific data processing steps include conversion to a Pandas dataframe, removal of outliers, and imputation of time-series gaps. The output is a clean, analyzable dataset.

[0258] Step 3:

[0259] The server performs analysis to predict energy demand based on processed data. Inputs include pre-processed data and statistical models. Specifically, it performs regression analysis and time series analysis, and also considers weather forecast data. The output provides predicted future energy consumption patterns.

[0260] Step 4:

[0261] The server generates energy-saving suggestions using a generative AI model based on the prediction results. The input is the analysis results, and a prompt is provided. Specifically, the prompt "Please provide the optimal energy reduction suggestion under the following conditions" is passed to the generative AI model. The output is an energy-saving suggestion that the user can implement.

[0262] Step 5:

[0263] The user receives suggestions from the server and asks additional questions as needed. The input is the generated suggestions, and the output provides further analysis or simulation results. Specifically, interactive inquiries such as "Please simulate the effects of introducing new equipment" are possible, and the server responds dynamically accordingly.

[0264] (Application Example 1)

[0265] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0266] To optimize energy consumption in cities and efficiently utilize renewable energy, residents need to manage their own energy use and take energy-saving actions at the optimal time. However, existing technologies have limitations in providing residents with real-time, appropriate energy use suggestions and comprehensively optimizing energy consumption across the entire city.

[0267] 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.

[0268] In this invention, the server includes means for collecting energy data in real time, means for preprocessing the collected data and converting it into a format suitable for analysis, means for generating energy-saving measures and recommendations for renewable energy use based on the analysis results, means for providing suggestions for efficient energy use in real time in conjunction with weather conditions, and means for generating instructions for optimizing the energy consumption of members on a city scale. This makes it possible to effectively suppress energy consumption throughout the city and maximize the use of renewable energy.

[0269] A "device for accumulating energy data in real time" is a device that instantly grasps the status of energy use and continuously collects the latest data.

[0270] A "device for preprocessing and converting data into a format suitable for analysis" is a device that processes collected data and prepares it in a format that facilitates analysis.

[0271] A "device for predicting energy consumption" is a system that estimates future energy usage based on collected data.

[0272] A "device for generating energy-saving measures and recommendations for renewable energy use" is a device that creates proposals for reducing energy consumption and effectively utilizing renewable energy based on analysis results.

[0273] A "device that notifies members and responds to interactive inquiries from members" is a system that sends generated energy-saving measures information to users and provides real-time answers to user questions and concerns.

[0274] A "device that provides real-time suggestions for efficient energy use in conjunction with weather conditions" is a device that uses weather data to instantly show users the optimal way to use energy.

[0275] A "device that generates instructions to optimize the energy consumption of its members on a city-wide scale" is a device that integrates energy data from multiple members and creates commands aimed at optimizing energy consumption throughout the entire city.

[0276] This invention provides a system that collects and analyzes vast amounts of energy data in real time and efficiently manages energy consumption on a city scale.

[0277] The server collects energy data in real time by receiving data from sensing devices and information from forecasting systems. This data is first preprocessed and converted into a format suitable for analysis. This process utilizes Python programs and Apache Kafka. Next, the server analyzes the converted data and predicts future energy demand. This generates recommendations aimed at reducing costs and environmental impact.

[0278] The generated recommendations are notified to members' devices via the network. Members can review and implement the recommended energy-saving actions on devices such as smartphones and smart glasses. Real-time responses to interactive inquiries from members are also possible. AWS is a platform likely to be used for this purpose.

[0279] For example, if the weather forecast for a given day is sunny, the server will generate a suggestion such as "We recommend doing your laundry in the morning tomorrow" and display it on the terminal. This allows members to easily select and perform actions that optimize their energy consumption.

[0280] Examples of prompt texts include the following: "Please generate specific proposals for maximizing the utilization of renewable energy based on tomorrow's weather."

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

[0282] Step 1:

[0283] The server receives data from the sensing device and the forecasting system in real time. The input is the current consumption data and weather information from the energy sensor. This is collected on the server and stored in the record.

[0284] Step 2:

[0285] The server preprocesses the collected energy data and converts it into a format suitable for analysis. The input is the raw energy data, and the output is the cleansed data. In this process, missing values in the data are complemented, and outliers are removed.

[0286] Step 3:

[0287] The server predicts energy consumption using the preprocessed data. The input is the preprocessed data, and the output is the predicted value of future energy consumption. The server uses a generated AI model to make predictions from past data and saves the results.

[0288] Step 4:

[0289] The server generates proposals for energy-saving measures and the use of renewable energy based on the analysis results. The input is the prediction data, and the output is the content of the proposals regarding energy conservation. This also includes data on cost reduction effects and environmental load reduction effects.

[0290] Step 5:

[0291] The server notifies the member's terminal of the generated proposal and responds to interactive inquiries from the member. The input is the member's inquiry, and the output is the response message. The terminal displays the analysis results and proposal, and the member reviews the content.

[0292] Step 6:

[0293] The server works in conjunction with weather conditions to provide real-time suggestions for efficient energy use. The input is the latest weather forecast data, and the output is a suggestion for the optimal timing of energy use for each member. The server detects weather patterns and makes corresponding suggestions.

[0294] Step 7:

[0295] The terminal receives instructions to optimize the energy consumption of its members on a city-wide scale and prompts users to take action. Input is instruction data from the server, and output is specific action suggestions for the user. Users follow the terminal's guidance to implement energy-saving measures.

[0296] 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.

[0297] This invention aims to achieve more effective and user-friendly energy management by incorporating an emotion engine that recognizes the user's emotions into an energy management system. This system generates adaptive energy-saving suggestions based on the user's emotional state, thereby optimizing energy consumption.

[0298] The main components of this system are: "energy data collection," "data analysis and suggestion generation," "user emotion recognition using an emotion engine," and "notifications and interactive responses." First, the server acquires energy data in real time from sensors and existing forecasting systems. Based on this data, it analyzes energy usage trends and identifies peak demand.

[0299] Next, the server proposes energy-saving measures and renewable energy implementations based on the analysis. These proposals take into account the user's emotional state as recognized by the emotion engine. For example, if a user is in a high-stress state, the tone and priority of the proposals are adjusted, and energy-saving suggestions are provided to reduce stress.

[0300] For example, if the emotion engine detects that a user working in an office is experiencing stress, the server will suggest adjusting the air conditioning system settings to optimize environmental conditions. This adjustment aims to reduce user stress by setting the temperature and humidity within a comfortable range.

[0301] Furthermore, the emotion engine also plays a role in optimizing how suggestions are notified. For example, it provides gentler and more flexible suggestions to users in high-stress states, rather than direct notifications, thus increasing the receptiveness of suggestions based on the user's state.

[0302] In this way, the system of the present invention realizes intelligent energy management that takes into account the user's emotional state, providing a better user experience and improving the efficiency of energy management.

[0303] The following describes the processing flow.

[0304] Step 1:

[0305] The server collects various energy data in real time through sensors and a forecasting system. This includes direct energy consumption, equipment operating status, external weather conditions, etc.

[0306] Step 2:

[0307] The server preprocesses the collected raw data. Specifically, it corrects missing values, removes outliers, and standardizes the data as necessary. The preprocessed data is converted into a format suitable for analysis and saved.

[0308] Step 3:

[0309] Based on the preprocessed data, the server uses an AI model to predict energy consumption. Here, past usage history and weather data are taken into account to identify short-term and long-term demand trends.

[0310] Step 4:

[0311] Based on the prediction results obtained from the AI model, the server generates proposals for energy-saving measures and the utilization of renewable energy. This proposal includes means of peak shifting and cost reduction effects.

[0312] Step 5:

[0313] The server uses an emotion engine that recognizes the user's emotions to perform real-time analysis of the user's emotional state. Specifically, it detects changes in the emotional state from the user's actions and expressions.

[0314] Step 6:

[0315] The server utilizes the emotion data obtained by the emotion engine to customize the generated proposals according to the user's current emotional state. For example, for a user in a high-stress state, calm and gentle proposals are selected and notified.

[0316] Step 7:

[0317] The server notifies the user's device of the optimized suggestions. The user receives the notification on their device and can review and understand the details of the suggestions.

[0318] Step 8:

[0319] Users can ask the system questions if they have doubts about the proposal or require additional information. The server will respond immediately to these inquiries and provide additional analysis and simulation results.

[0320] This series of steps allows users to effectively manage energy use while improving comfort and reducing mental stress.

[0321] (Example 2)

[0322] 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".

[0323] Conventional energy management systems focus on optimizing energy use but fail to consider the feelings and circumstances of users. Therefore, energy-saving measures are not always comfortable for users, which can result in decreased user satisfaction and undermine the effectiveness of the proposed solutions.

[0324] 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.

[0325] In this invention, the server includes means for collecting energy usage information in real time, means for recognizing the user's emotional state and generating adaptive suggestions using a generative AI model, and means for notifying the user of the generated plan and engaging in interactive communication with the user. This enables intelligent energy management that takes into account the user's emotional state, thereby improving user satisfaction and the efficiency of energy management.

[0326] "Real-time" refers to acquiring and processing information at the very moment an event occurs.

[0327] "Energy utilization information" refers to information that includes various data on energy consumption and supply status.

[0328] "Preprocessing" refers to initial data processing techniques used to convert collected information into a format suitable for analysis.

[0329] "Renewable energy" refers to energy sources that are constantly regenerated through natural processes, such as solar, wind, hydro, and geothermal energy.

[0330] A "generative AI model" refers to a form of artificial intelligence trained to analyze data and predict new suggestions or outcomes.

[0331] A "detector" refers to a device or system that observes specific environmental conditions or physical states and collects that data.

[0332] A "forecasting system" refers to a system that analyzes past and present data to predict future conditions and provides the results.

[0333] "Cost reduction effect" refers to the expected benefit of a particular measure in reducing economic costs.

[0334] "Environmental impact reduction effect" refers to the effectiveness of efforts to mitigate the negative impacts that human activities have on the natural environment.

[0335] Modes for carrying out the invention

[0336] This energy management system aims to achieve both efficient energy use and user comfort. A specific implementation is shown below.

[0337] The server first collects energy usage information in real time from detectors and forecasting mechanisms. This information includes data on power consumption, temperature, humidity, and weather. This data is automatically collected on the server via an API and stored in a database.

[0338] The server then preprocesses the collected information. The Python Pandas library is used for preprocessing, including data imputation and type conversion. The resulting dataset is then used for pattern analysis and prediction of energy utilization.

[0339] The device runs an emotion engine to recognize the user's emotional state. The emotion engine uses libraries such as TensorFlow and OpenCV to analyze the user's facial expressions and estimate their stress level and comfort level. This information is sent to the server in real time.

[0340] By using a generative AI model, the server generates energy-saving measures based on pre-processed information and the user's emotional state. It utilizes tools such as PyTorch and the Chatbot API to provide optimal suggestions tailored to the user's state. Specifically, this could include automatic temperature control for heating and cooling, and lighting adjustments. Furthermore, the generated suggestions include quantified cost reduction and environmental impact reduction effects.

[0341] The server notifies the user of the generated suggestions. These notifications are sent via smartphone apps or email, and a flexible format is used to enhance user acceptance. For example, on a sunny afternoon, a suggestion might be presented to change the timing of electricity usage to maximize the use of solar power.

[0342] An example of a prompt message for the generating AI model is: "Consider the energy consumption and stress levels of users working in an office, and generate comfortable and efficient energy-saving suggestions. If the user's stress level is high, be flexible in how you notify them."

[0343] This system enables intelligent energy management, allowing for both user comfort and energy savings.

[0344] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0345] Step 1:

[0346] The server collects energy usage information from detectors and forecasting mechanisms. During this process, data such as temperature, humidity, power consumption, and weather information are input in real time. The input data is sent to the server via an API and stored in a database. Specifically, this involves acquiring measurements from various sensors and collecting them on the server via the network. The output is formalized energy data stored in the database.

[0347] Step 2:

[0348] The server preprocesses the collected energy data. The raw data saved in step 1 is used as input. The Python Pandas library is used to impute missing values, convert data types, and denoise. Specifically, outlier detection and correction, and data smoothing are performed. As a result of this data processing, a clean dataset suitable for analysis is obtained. This output is the data for subsequent analysis and model input.

[0349] Step 3:

[0350] The device operates an emotion engine to recognize the user's emotional state. The input here consists of the user's facial expressions and voice data. Using TensorFlow and OpenCV, this data is analyzed in real time to estimate stress levels and emotional states. Specific operations include facial expression recognition using the camera and voice tone analysis using the microphone. The output is the analyzed user's emotional state, which is then sent to the server.

[0351] Step 4:

[0352] The server generates energy-saving measures using pre-processed energy data and the user's emotional state. The input here is the data output in steps 2 and 3. A generative AI model is used, and prompts are applied to create suggestions. The output generates recommendations for specific energy-saving measures, such as adjusting heating and cooling temperatures and automatically controlling lighting.

[0353] Step 5:

[0354] The server notifies the user of the generated energy-saving measures. The input is the proposal content from step 4. The proposal is sent to the user via a smartphone app or email. Specifically, the server generates and delivers the notification message. The output is the energy-saving proposal received by the user, which allows the user to adjust their environment according to the proposal.

[0355] (Application Example 2)

[0356] 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".

[0357] In energy management systems, uniform energy-saving suggestions that disregard the user's emotional state have problems with low feasibility and a poor user experience. Furthermore, because the suggestions ignore the user's feelings, there are challenges such as increased stress and decreased motivation for energy-saving behavior.

[0358] 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.

[0359] In this invention, the server includes means for collecting energy data in real time, means for recognizing emotions and reflecting them in energy-saving suggestions, and means for adjusting the suggestion content based on the user's emotional state. This enables suggestions that are adapted to the user's emotional state, resulting in more effective and user-friendly energy management.

[0360] A "device for collecting energy data in real time" is a device that continuously acquires information on energy usage from sensors and forecasting systems and immediately reflects it in the system.

[0361] A "device that preprocesses collected data and converts it into a format suitable for analysis" is a means of processing acquired energy data and shaping it into a format necessary for prediction and analysis.

[0362] A "device that predicts energy consumption using pre-processed data" is a system that predicts future energy use based on pre-processed data.

[0363] A "device that generates energy-saving measures and renewable energy utilization proposals" is a device that proposes effective energy-saving methods and renewable energy utilization methods to users based on predicted energy data.

[0364] A "device that recognizes emotions and reflects them in energy-saving proposals" is a device that detects the user's emotional state and takes it into consideration when deciding on energy-saving proposals.

[0365] A "device that adjusts suggested content based on the user's emotional state" is a system that appropriately changes the format of energy-saving suggestions and notifications according to the user's current emotions and stress level.

[0366] This invention is a system that optimizes energy management based on the user's emotional state. The server can collect energy data in real time through multiple sensors and forecasting systems. This data is transformed into a format suitable for analysis through initial preprocessing. Preprocessing includes data formatting and noise filtering.

[0367] Next, the server uses the pre-processed data to predict energy consumption. This prediction is performed using statistical modeling techniques to show future usage trends. Based on the prediction results, energy-saving suggestions and renewable energy utilization proposals are generated.

[0368] The emotion engine runs on the user's device and detects the user's emotional state using facial recognition and voice analysis. This emotional data is sent to a server and reflected in the generated energy-saving suggestions. For example, if the user is feeling stressed, suggestions will be made to adjust the temperature and humidity or change the brightness of the lighting.

[0369] Suggestions for users are provided through a notification system. The content of the suggestions is tailored based on the user's state detected by the emotion engine. For example, users in a relaxed state will receive detailed suggestions, while users experiencing high stress levels will receive simple and calming suggestions.

[0370] As a concrete example, a smartphone application monitors household energy management in real time and prompts the user to automatically adjust the air conditioner settings when they are feeling stressed in a hot environment. An example of a prompt message generated using a generative AI model is, "Your home's energy consumption is on the rise. Would you like to set the air conditioner temperature to 25 degrees Celsius as an optimal setting to minimize stress?"

[0371] In this way, the present invention can take into account the emotional state of the user and manage energy safely and efficiently, thereby providing an optimal energy-saving solution for each individual user.

[0372] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0373] Step 1:

[0374] The server collects energy data in real time from sensors and forecasting systems. Input is data from sensors, and output is raw data. This data is used to instantly understand energy consumption patterns.

[0375] Step 2:

[0376] The server preprocesses the collected raw data and converts it into a format suitable for analysis. The input is raw data, and through noise filtering and scaling, the output is transformed into an analyzable data format. This process eliminates data bias, enabling accurate analysis.

[0377] Step 3:

[0378] The server uses pre-processed data to predict energy consumption. The input is in an analyzable data format, and by performing calculations using a statistical model, the output is an estimate of future consumption. This prediction forms the basis for energy-saving proposals.

[0379] Step 4:

[0380] The device recognizes the user's emotions and sends that data to the server. The input consists of the user's facial expressions and voice, which are analyzed by an emotion engine to produce the user's emotional state. This emotional information influences energy-saving suggestions.

[0381] Step 5:

[0382] The server generates energy-saving suggestions based on predicted energy consumption and the user's emotional state. The input is the predicted energy consumption and emotional state, and the output is a tailored energy-saving suggestion. The priority and wording of the suggestions change depending on the emotional state.

[0383] Step 6:

[0384] The server communicates the generated energy-saving suggestions to the user using a notification system. The input is a pre-adjusted suggestion, and it is output in a way that suits the user's emotions. For example, calmer language is selected for highly stressed users.

[0385] Step 7:

[0386] Users adjust energy settings and provide feedback to the device based on the suggestions they receive. Input is energy-saving suggestions, and output is actual actions and setting changes. This feedback helps further refine the system.

[0387] 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.

[0388] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0389] 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.

[0390] [Third Embodiment]

[0391] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0392] 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.

[0393] 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).

[0394] 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.

[0395] 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.

[0396] 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).

[0397] 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.

[0398] 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.

[0399] 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.

[0400] 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.

[0401] 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.

[0402] 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".

[0403] This invention provides a system for efficient energy management. This system effectively reduces energy consumption and supports the optimal use of renewable energy. The following describes in detail the configurations for implementing this system.

[0404] The entire system consists of four main functions: data collection, data analysis, proposal generation, and interactive response. First, the server collects data in real time from various sensors and existing energy management systems. This allows for monitoring of energy usage for each piece of equipment and facility.

[0405] Next, the server analyzes the collected data to identify energy waste. This analysis includes demand forecasting that takes into account past usage data and weather forecasts. Based on the analysis results, the system also proposes specific energy-saving measures that contribute to peak shifting and cost reduction.

[0406] As a concrete example, analysis revealed that a certain factory had extremely high daytime electricity consumption. Based on the system's suggestion, the user installed solar power panels and stored the generated electricity in batteries for use at night, resulting in a 10% reduction in overall electricity consumption.

[0407] Furthermore, users can interact with the system to resolve questions immediately. For example, if a user is curious about the effectiveness of introducing new energy-saving equipment, they can ask the system a question, and the server can present simulation results on the spot.

[0408] In this way, the system of the present invention provides an efficient and sustainable solution to the energy management challenges faced by companies and public institutions.

[0409] The following describes the processing flow.

[0410] Step 1:

[0411] The server acquires real-time energy data from sensors placed in each piece of equipment and facility, as well as from existing energy management systems. This data includes information such as power consumption, temperature, and equipment operating status. Furthermore, it also acquires weather data from weather forecasting systems.

[0412] Step 2:

[0413] The server preprocesses the acquired data. Specifically, it ensures data accuracy by correcting missing values ​​and removing outliers. To prepare the data for easier analysis, it organizes and smooths it as time-series data.

[0414] Step 3:

[0415] The server inputs pre-processed data into an AI model. Here, short-term and long-term forecasts of energy demand are made, taking into account historical data and weather conditions. The forecast results are used to identify energy consumption trends and peak demand.

[0416] Step 4:

[0417] Based on the prediction results, the server generates proposals regarding energy-saving measures and the feasibility of introducing renewable energy. These proposals include expected cost reductions and reductions in environmental impact. For example, it can create proposals for shifting the load to meet peak demand or introducing solar power generation.

[0418] Step 5:

[0419] The server notifies the user's device of the generated suggestions. The user can review the suggestions on the dashboard and evaluate the proposed actions. Important suggestions may be sent as alerts.

[0420] Step 6:

[0421] Users can ask the system questions about the proposals and receive interactive responses. Based on the user's inquiries, the server performs additional simulations and data analysis, providing detailed results immediately. This allows users to further understand the feasibility and effectiveness of the proposals.

[0422] (Example 1)

[0423] 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."

[0424] Modern energy management demands efficient energy consumption reduction and optimal utilization of renewable energy. However, conventional systems struggle with real-time data collection, and data analysis and proposal generation often involve manual operations, hindering rapid response. Furthermore, there is a need for technology that automatically generates effective proposals from the perspectives of environmental impact and cost, while ensuring the reliability of the data and analysis results that form the basis of the proposals.

[0425] 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.

[0426] In this invention, the server includes information gathering means for acquiring energy data in real time, processing means for processing the acquired data and converting it into a format suitable for analysis, analysis means for predicting energy demand using the processed data, and a proposal generation device for recommending energy-saving measures and the use of renewable energy based on the analysis results, which includes means for constructing proposals using prompt sentences based on a generated AI model, and bidirectional communication means for communicating the proposal content to the user and responding to responses from the user. This makes it possible to consistently perform everything from real-time data collection to automatic proposal generation and interaction with the user.

[0427] "Information gathering means for acquiring energy data in real time" refers to devices or methods that have the function of instantly acquiring various data related to energy use.

[0428] A "processing device that processes acquired data and converts it into a format suitable for analysis" is a component within a system that converts raw data into a format that can be easily analyzed.

[0429] An "analytical device for predicting energy demand using processed data" is a device that has the function of predicting future energy consumption based on converted data.

[0430] A "device that constructs proposals using prompt sentences based on a generative AI model" is a device that uses generative artificial intelligence technology to automatically create proposals related to energy management from specific inputs (prompts).

[0431] A "two-way communication device for conveying proposals to users and responding to user responses" is a device that has communication functions to inform users of proposals and to respond dynamically to user questions and requests.

[0432] This system is a comprehensive platform for achieving efficient energy management. The server collects data in real time to monitor energy consumption. Hardware-wise, it acquires data using various sensors and energy meters. For example, it aggregates electricity meter data from the entire building and temperature data from temperature sensors to the server via the network. The software utilizes an IoT platform for efficient data collection and management.

[0433] After data collection, the server analyzes the collected data using data analysis software such as Python or R. The collected data is preprocessed using libraries such as Pandas and NumPy to remove outliers and interpolate time series. Subsequently, energy demand is predicted using statistical models, taking into account past energy usage data and weather forecasts. Based on these analysis results, the server generates energy-saving suggestions using a generative AI model.

[0434] For example, if analysis reveals that a factory has extremely high daytime electricity consumption, the server will generate a prompt message such as, "Consider installing a solar power generation system to reduce daytime electricity peaks." The user can then use this suggestion to decide whether or not to invest in actual energy facilities.

[0435] Furthermore, users can obtain additional information from the system by making interactive inquiries. For example, if a user wants to know the energy reduction effect of introducing new equipment immediately, they can prompt the server with "Please simulate the effect of energy cost reduction from introducing new energy-saving equipment." In response to this prompt, the server immediately runs the simulation and provides the results to the user.

[0436] In this way, the server supports efficient and sustainable energy management, addressing the energy challenges faced by businesses and public institutions.

[0437] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0438] Step 1:

[0439] The server collects energy data in real time from various sensors and energy meters. Inputs include temperature sensor readings and power meter consumption data. The server aggregates this data via the network and stores it in a database. This allows for monitoring the energy usage of each piece of equipment.

[0440] Step 2:

[0441] The server processes the collected data and converts it into a format suitable for analysis. The input data includes raw sensor data. Specific data processing steps include conversion to a Pandas dataframe, removal of outliers, and imputation of time-series gaps. The output is a clean, analyzable dataset.

[0442] Step 3:

[0443] The server performs analysis to predict energy demand based on processed data. Inputs include pre-processed data and statistical models. Specifically, it performs regression analysis and time series analysis, and also considers weather forecast data. The output provides predicted future energy consumption patterns.

[0444] Step 4:

[0445] The server generates energy-saving suggestions using a generative AI model based on the prediction results. The input is the analysis results, and a prompt is provided. Specifically, the prompt "Please provide the optimal energy reduction suggestion under the following conditions" is passed to the generative AI model. The output is an energy-saving suggestion that the user can implement.

[0446] Step 5:

[0447] The user receives suggestions from the server and asks additional questions as needed. The input is the generated suggestions, and the output provides further analysis or simulation results. Specifically, interactive inquiries such as "Please simulate the effects of introducing new equipment" are possible, and the server responds dynamically accordingly.

[0448] (Application Example 1)

[0449] 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."

[0450] To optimize energy consumption in cities and efficiently utilize renewable energy, residents need to manage their own energy use and take energy-saving actions at the optimal time. However, existing technologies have limitations in providing residents with real-time, appropriate energy use suggestions and comprehensively optimizing energy consumption across the entire city.

[0451] 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.

[0452] In this invention, the server includes means for collecting energy data in real time, means for preprocessing the collected data and converting it into a format suitable for analysis, means for generating energy-saving measures and recommendations for renewable energy use based on the analysis results, means for providing suggestions for efficient energy use in real time in conjunction with weather conditions, and means for generating instructions for optimizing the energy consumption of members on a city scale. This makes it possible to effectively suppress energy consumption throughout the city and maximize the use of renewable energy.

[0453] A "device for accumulating energy data in real time" is a device that instantly grasps the status of energy use and continuously collects the latest data.

[0454] A "device for preprocessing and converting data into a format suitable for analysis" is a device that processes collected data and prepares it in a format that facilitates analysis.

[0455] A "device for predicting energy consumption" is a system that estimates future energy usage based on collected data.

[0456] A "device for generating energy-saving measures and recommendations for renewable energy use" is a device that creates proposals for reducing energy consumption and effectively utilizing renewable energy based on analysis results.

[0457] A "device that notifies members and responds to interactive inquiries from members" is a system that sends generated energy-saving measures information to users and provides real-time answers to user questions and concerns.

[0458] A "device that provides real-time suggestions for efficient energy use in conjunction with weather conditions" is a device that uses weather data to instantly show users the optimal way to use energy.

[0459] A "device that generates instructions to optimize the energy consumption of its members on a city-wide scale" is a device that integrates energy data from multiple members and creates commands aimed at optimizing energy consumption throughout the entire city.

[0460] This invention provides a system that collects and analyzes vast amounts of energy data in real time and efficiently manages energy consumption on a city scale.

[0461] The server collects energy data in real time by receiving data from sensing devices and information from forecasting systems. This data is first preprocessed and converted into a format suitable for analysis. This process utilizes Python programs and Apache Kafka. Next, the server analyzes the converted data and predicts future energy demand. This generates recommendations aimed at reducing costs and environmental impact.

[0462] The generated recommendations are notified to members' devices via the network. Members can review and implement the recommended energy-saving actions on devices such as smartphones and smart glasses. Real-time responses to interactive inquiries from members are also possible. AWS is a platform likely to be used for this purpose.

[0463] For example, if the weather forecast for a given day is sunny, the server will generate a suggestion such as "We recommend doing your laundry in the morning tomorrow" and display it on the terminal. This allows members to easily select and perform actions that optimize their energy consumption.

[0464] An example of a prompt message is: "Generate specific suggestions for maximizing the use of renewable energy based on tomorrow's weather forecast."

[0465] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0466] Step 1:

[0467] The server receives data in real time from sensing devices and forecasting systems. Inputs include current energy consumption data from energy sensors and weather information. This data is collected and stored on the server.

[0468] Step 2:

[0469] The server preprocesses the collected energy data and converts it into a format suitable for analysis. The input is raw energy data, and the output is cleaned data. This process imputes missing values ​​and removes outliers.

[0470] Step 3:

[0471] The server uses pre-processed data to predict energy consumption. The input is pre-processed data, and the output is a prediction of future energy consumption. The server uses a generative AI model to make predictions from historical data and saves the results.

[0472] Step 4:

[0473] The server generates energy-saving measures and renewable energy utilization proposals based on the analysis results. The input is predictive data, and the output is energy-saving proposals. This includes data on cost reduction effects and environmental impact reduction effects.

[0474] Step 5:

[0475] The server notifies the member's terminal of the generated proposal and responds to interactive inquiries from the member. The input is the member's inquiry, and the output is the response message. The terminal displays the analysis results and proposal, and the member reviews the content.

[0476] Step 6:

[0477] The server works in conjunction with weather conditions to provide real-time suggestions for efficient energy use. The input is the latest weather forecast data, and the output is a suggestion for the optimal timing of energy use for each member. The server detects weather patterns and makes corresponding suggestions.

[0478] Step 7:

[0479] The terminal receives instructions to optimize the energy consumption of its members on a city-wide scale and prompts users to take action. Input is instruction data from the server, and output is specific action suggestions for the user. Users follow the terminal's guidance to implement energy-saving measures.

[0480] 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.

[0481] This invention aims to achieve more effective and user-friendly energy management by incorporating an emotion engine that recognizes the user's emotions into an energy management system. This system generates adaptive energy-saving suggestions based on the user's emotional state, thereby optimizing energy consumption.

[0482] The main components of this system are: "energy data collection," "data analysis and suggestion generation," "user emotion recognition using an emotion engine," and "notifications and interactive responses." First, the server acquires energy data in real time from sensors and existing forecasting systems. Based on this data, it analyzes energy usage trends and identifies peak demand.

[0483] Next, the server proposes energy-saving measures and renewable energy implementations based on the analysis. These proposals take into account the user's emotional state as recognized by the emotion engine. For example, if a user is in a high-stress state, the tone and priority of the proposals are adjusted, and energy-saving suggestions are provided to reduce stress.

[0484] For example, if the emotion engine detects that a user working in an office is experiencing stress, the server will suggest adjusting the air conditioning system settings to optimize environmental conditions. This adjustment aims to reduce user stress by setting the temperature and humidity within a comfortable range.

[0485] Furthermore, the emotion engine also plays a role in optimizing how suggestions are notified. For example, it provides gentler and more flexible suggestions to users in high-stress states, rather than direct notifications, thus increasing the receptiveness of suggestions based on the user's state.

[0486] In this way, the system of the present invention realizes intelligent energy management that takes into account the user's emotional state, providing a better user experience and improving the efficiency of energy management.

[0487] The following describes the processing flow.

[0488] Step 1:

[0489] The server collects various energy data in real time via sensors and forecasting systems. This includes direct energy consumption, equipment operating status, and external weather conditions.

[0490] Step 2:

[0491] The server preprocesses the collected raw data. Specifically, it corrects missing values, removes outliers, and standardizes the data as needed. The preprocessed data is then converted into a format suitable for analysis and saved.

[0492] Step 3:

[0493] The server uses an AI model to predict energy consumption based on pre-processed data. This involves identifying short-term and long-term demand trends by taking into account past usage history and weather data.

[0494] Step 4:

[0495] The server generates proposals for energy-saving measures and renewable energy utilization based on predictions obtained from the AI ​​model. These proposals include methods for peak shifting and cost reduction effects.

[0496] Step 5:

[0497] The server uses an emotion engine to recognize user emotions and perform real-time analysis of the user's emotional state. Specifically, it detects changes in the user's emotional state from their actions and facial expressions.

[0498] Step 6:

[0499] The server utilizes emotional data obtained by the emotion engine to customize the generated suggestions according to the user's current emotional state. For example, a user experiencing high stress will be presented with calm and gentle suggestions.

[0500] Step 7:

[0501] The server notifies the user's device of the optimized suggestions. The user receives the notification on their device and can review and understand the details of the suggestions.

[0502] Step 8:

[0503] Users can ask the system questions if they have doubts about the proposal or require additional information. The server will respond immediately to these inquiries and provide additional analysis and simulation results.

[0504] This series of steps allows users to effectively manage energy use while improving comfort and reducing mental stress.

[0505] (Example 2)

[0506] 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."

[0507] Conventional energy management systems focus on optimizing energy use but fail to consider the feelings and circumstances of users. Therefore, energy-saving measures are not always comfortable for users, which can result in decreased user satisfaction and undermine the effectiveness of the proposed solutions.

[0508] 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.

[0509] In this invention, the server includes means for collecting energy usage information in real time, means for recognizing the user's emotional state and generating adaptive suggestions using a generative AI model, and means for notifying the user of the generated plan and engaging in interactive communication with the user. This enables intelligent energy management that takes into account the user's emotional state, thereby improving user satisfaction and the efficiency of energy management.

[0510] "Real-time" refers to acquiring and processing information at the very moment an event occurs.

[0511] "Energy utilization information" refers to information that includes various data on energy consumption and supply status.

[0512] "Preprocessing" refers to initial data processing techniques used to convert collected information into a format suitable for analysis.

[0513] "Renewable energy" refers to energy sources that are constantly regenerated through natural processes, such as solar, wind, hydro, and geothermal energy.

[0514] A "generative AI model" refers to a form of artificial intelligence trained to analyze data and predict new suggestions or outcomes.

[0515] A "detector" refers to a device or system that observes specific environmental conditions or physical states and collects that data.

[0516] A "forecasting system" refers to a system that analyzes past and present data to predict future conditions and provides the results.

[0517] "Cost reduction effect" refers to the expected benefit of a particular measure in reducing economic costs.

[0518] "Environmental impact reduction effect" refers to the effectiveness of efforts to mitigate the negative impacts that human activities have on the natural environment.

[0519] Modes for carrying out the invention

[0520] This energy management system aims to achieve both efficient energy use and user comfort. A specific implementation is shown below.

[0521] The server first collects energy usage information in real time from detectors and forecasting mechanisms. This information includes data on power consumption, temperature, humidity, and weather. This data is automatically collected on the server via an API and stored in a database.

[0522] The server then preprocesses the collected information. The Python Pandas library is used for preprocessing, including data imputation and type conversion. The resulting dataset is then used for pattern analysis and prediction of energy utilization.

[0523] The device runs an emotion engine to recognize the user's emotional state. The emotion engine uses libraries such as TensorFlow and OpenCV to analyze the user's facial expressions and estimate their stress level and comfort level. This information is sent to the server in real time.

[0524] By using a generative AI model, the server generates energy-saving measures based on pre-processed information and the user's emotional state. It utilizes tools such as PyTorch and the Chatbot API to provide optimal suggestions tailored to the user's state. Specifically, this could include automatic temperature control for heating and cooling, and lighting adjustments. Furthermore, the generated suggestions include quantified cost reduction and environmental impact reduction effects.

[0525] The server notifies the user of the generated suggestions. These notifications are sent via smartphone apps or email, and a flexible format is used to enhance user acceptance. For example, on a sunny afternoon, a suggestion might be presented to change the timing of electricity usage to maximize the use of solar power.

[0526] An example of a prompt message for the generating AI model is: "Consider the energy consumption and stress levels of users working in an office, and generate comfortable and efficient energy-saving suggestions. If the user's stress level is high, be flexible in how you notify them."

[0527] This system enables intelligent energy management, allowing for both user comfort and energy savings.

[0528] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0529] Step 1:

[0530] The server collects energy usage information from detectors and forecasting mechanisms. During this process, data such as temperature, humidity, power consumption, and weather information are input in real time. The input data is sent to the server via an API and stored in a database. Specifically, this involves acquiring measurements from various sensors and collecting them on the server via the network. The output is formalized energy data stored in the database.

[0531] Step 2:

[0532] The server preprocesses the collected energy data. The raw data saved in step 1 is used as input. The Python Pandas library is used to impute missing values, convert data types, and denoise. Specifically, outlier detection and correction, and data smoothing are performed. As a result of this data processing, a clean dataset suitable for analysis is obtained. This output is the data for subsequent analysis and model input.

[0533] Step 3:

[0534] The device operates an emotion engine to recognize the user's emotional state. The input here consists of the user's facial expressions and voice data. Using TensorFlow and OpenCV, this data is analyzed in real time to estimate stress levels and emotional states. Specific operations include facial expression recognition using the camera and voice tone analysis using the microphone. The output is the analyzed user's emotional state, which is then sent to the server.

[0535] Step 4:

[0536] The server generates energy-saving measures using pre-processed energy data and the user's emotional state. The input here is the data output in steps 2 and 3. A generative AI model is used, and prompts are applied to create suggestions. The output generates recommendations for specific energy-saving measures, such as adjusting heating and cooling temperatures and automatically controlling lighting.

[0537] Step 5:

[0538] The server notifies the user of the generated energy-saving measures. The input is the proposal content from step 4. The proposal is sent to the user via a smartphone app or email. Specifically, the server generates and delivers the notification message. The output is the energy-saving proposal received by the user, which allows the user to adjust their environment according to the proposal.

[0539] (Application Example 2)

[0540] 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."

[0541] In energy management systems, uniform energy-saving suggestions that disregard the user's emotional state have problems with low feasibility and a poor user experience. Furthermore, because the suggestions ignore the user's feelings, there are challenges such as increased stress and decreased motivation for energy-saving behavior.

[0542] 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.

[0543] In this invention, the server includes means for collecting energy data in real time, means for recognizing emotions and reflecting them in energy-saving suggestions, and means for adjusting the suggestion content based on the user's emotional state. This enables suggestions that are adapted to the user's emotional state, resulting in more effective and user-friendly energy management.

[0544] A "device for collecting energy data in real time" is a device that continuously acquires information on energy usage from sensors and forecasting systems and immediately reflects it in the system.

[0545] A "device that preprocesses collected data and converts it into a format suitable for analysis" is a means of processing acquired energy data and shaping it into a format necessary for prediction and analysis.

[0546] A "device that predicts energy consumption using pre-processed data" is a system that predicts future energy use based on pre-processed data.

[0547] A "device that generates energy-saving measures and renewable energy utilization proposals" is a device that proposes effective energy-saving methods and renewable energy utilization methods to users based on predicted energy data.

[0548] A "device that recognizes emotions and reflects them in energy-saving proposals" is a device that detects the user's emotional state and takes it into consideration when deciding on energy-saving proposals.

[0549] A "device that adjusts suggested content based on the user's emotional state" is a system that appropriately changes the format of energy-saving suggestions and notifications according to the user's current emotions and stress level.

[0550] This invention is a system that optimizes energy management based on the user's emotional state. The server can collect energy data in real time through multiple sensors and forecasting systems. This data is transformed into a format suitable for analysis through initial preprocessing. Preprocessing includes data formatting and noise filtering.

[0551] Next, the server uses the pre-processed data to predict energy consumption. This prediction is performed using statistical modeling techniques to show future usage trends. Based on the prediction results, energy-saving suggestions and renewable energy utilization proposals are generated.

[0552] The emotion engine runs on the user's device and detects the user's emotional state using facial recognition and voice analysis. This emotional data is sent to a server and reflected in the generated energy-saving suggestions. For example, if the user is feeling stressed, suggestions will be made to adjust the temperature and humidity or change the brightness of the lighting.

[0553] Suggestions for users are provided through a notification system. The content of the suggestions is tailored based on the user's state detected by the emotion engine. For example, users in a relaxed state will receive detailed suggestions, while users experiencing high stress levels will receive simple and calming suggestions.

[0554] As a concrete example, a smartphone application monitors household energy management in real time and prompts the user to automatically adjust the air conditioner settings when they are feeling stressed in a hot environment. An example of a prompt message generated using a generative AI model is, "Your home's energy consumption is on the rise. Would you like to set the air conditioner temperature to 25 degrees Celsius as an optimal setting to minimize stress?"

[0555] In this way, the present invention can take into account the emotional state of the user and manage energy safely and efficiently, thereby providing an optimal energy-saving solution for each individual user.

[0556] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0557] Step 1:

[0558] The server collects energy data in real time from sensors and forecasting systems. Input is data from sensors, and output is raw data. This data is used to instantly understand energy consumption patterns.

[0559] Step 2:

[0560] The server preprocesses the collected raw data and converts it into a format suitable for analysis. The input is raw data, and through noise filtering and scaling, the output is transformed into an analyzable data format. This process eliminates data bias, enabling accurate analysis.

[0561] Step 3:

[0562] The server uses pre-processed data to predict energy consumption. The input is in an analyzable data format, and by performing calculations using a statistical model, the output is an estimate of future consumption. This prediction forms the basis for energy-saving proposals.

[0563] Step 4:

[0564] The device recognizes the user's emotions and sends that data to the server. The input consists of the user's facial expressions and voice, which are analyzed by an emotion engine to produce the user's emotional state. This emotional information influences energy-saving suggestions.

[0565] Step 5:

[0566] The server generates energy-saving suggestions based on predicted energy consumption and the user's emotional state. The input is the predicted energy consumption and emotional state, and the output is a tailored energy-saving suggestion. The priority and wording of the suggestions change depending on the emotional state.

[0567] Step 6:

[0568] The server communicates the generated energy-saving suggestions to the user using a notification system. The input is a pre-adjusted suggestion, and it is output in a way that suits the user's emotions. For example, calmer language is selected for highly stressed users.

[0569] Step 7:

[0570] Users adjust energy settings and provide feedback to the device based on the suggestions they receive. Input is energy-saving suggestions, and output is actual actions and setting changes. This feedback helps further refine the system.

[0571] 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.

[0572] 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.

[0573] 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.

[0574] [Fourth Embodiment]

[0575] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0576] 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.

[0577] 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).

[0578] 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.

[0579] 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.

[0580] 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).

[0581] 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.

[0582] 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.

[0583] 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.

[0584] 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.

[0585] 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.

[0586] 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.

[0587] 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".

[0588] This invention provides a system for efficient energy management. This system effectively reduces energy consumption and supports the optimal use of renewable energy. The following describes in detail the configurations for implementing this system.

[0589] The entire system consists of four main functions: data collection, data analysis, proposal generation, and interactive response. First, the server collects data in real time from various sensors and existing energy management systems. This allows for monitoring of energy usage for each piece of equipment and facility.

[0590] Next, the server analyzes the collected data to identify energy waste. This analysis includes demand forecasting that takes into account past usage data and weather forecasts. Based on the analysis results, the system also proposes specific energy-saving measures that contribute to peak shifting and cost reduction.

[0591] As a concrete example, analysis revealed that a certain factory had extremely high daytime electricity consumption. Based on the system's suggestion, the user installed solar power panels and stored the generated electricity in batteries for use at night, resulting in a 10% reduction in overall electricity consumption.

[0592] Furthermore, users can interact with the system to resolve questions immediately. For example, if a user is curious about the effectiveness of introducing new energy-saving equipment, they can ask the system a question, and the server can present simulation results on the spot.

[0593] In this way, the system of the present invention provides an efficient and sustainable solution to the energy management challenges faced by companies and public institutions.

[0594] The following describes the processing flow.

[0595] Step 1:

[0596] The server acquires real-time energy data from sensors placed in each piece of equipment and facility, as well as from existing energy management systems. This data includes information such as power consumption, temperature, and equipment operating status. Furthermore, it also acquires weather data from weather forecasting systems.

[0597] Step 2:

[0598] The server preprocesses the acquired data. Specifically, it ensures data accuracy by correcting missing values ​​and removing outliers. To prepare the data for easier analysis, it organizes and smooths it as time-series data.

[0599] Step 3:

[0600] The server inputs pre-processed data into an AI model. Here, short-term and long-term forecasts of energy demand are made, taking into account historical data and weather conditions. The forecast results are used to identify energy consumption trends and peak demand.

[0601] Step 4:

[0602] Based on the prediction results, the server generates proposals regarding energy-saving measures and the feasibility of introducing renewable energy. These proposals include expected cost reductions and reductions in environmental impact. For example, it can create proposals for shifting the load to meet peak demand or introducing solar power generation.

[0603] Step 5:

[0604] The server notifies the user's device of the generated suggestions. The user can review the suggestions on the dashboard and evaluate the proposed actions. Important suggestions may be sent as alerts.

[0605] Step 6:

[0606] Users can ask the system questions about the proposals and receive interactive responses. Based on the user's inquiries, the server performs additional simulations and data analysis, providing detailed results immediately. This allows users to further understand the feasibility and effectiveness of the proposals.

[0607] (Example 1)

[0608] 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".

[0609] Modern energy management demands efficient energy consumption reduction and optimal utilization of renewable energy. However, conventional systems struggle with real-time data collection, and data analysis and proposal generation often involve manual operations, hindering rapid response. Furthermore, there is a need for technology that automatically generates effective proposals from the perspectives of environmental impact and cost, while ensuring the reliability of the data and analysis results that form the basis of the proposals.

[0610] 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.

[0611] In this invention, the server includes information gathering means for acquiring energy data in real time, processing means for processing the acquired data and converting it into a format suitable for analysis, analysis means for predicting energy demand using the processed data, and a proposal generation device for recommending energy-saving measures and the use of renewable energy based on the analysis results, which includes means for constructing proposals using prompt sentences based on a generated AI model, and bidirectional communication means for communicating the proposal content to the user and responding to responses from the user. This makes it possible to consistently perform everything from real-time data collection to automatic proposal generation and interaction with the user.

[0612] "Information gathering means for acquiring energy data in real time" refers to devices or methods that have the function of instantly acquiring various data related to energy use.

[0613] A "processing device that processes acquired data and converts it into a format suitable for analysis" is a component within a system that converts raw data into a format that can be easily analyzed.

[0614] An "analytical device for predicting energy demand using processed data" is a device that has the function of predicting future energy consumption based on converted data.

[0615] A "device that constructs proposals using prompt sentences based on a generative AI model" is a device that uses generative artificial intelligence technology to automatically create proposals related to energy management from specific inputs (prompts).

[0616] A "two-way communication device for conveying proposals to users and responding to user responses" is a device that has communication functions to inform users of proposals and to respond dynamically to user questions and requests.

[0617] This system is a comprehensive platform for achieving efficient energy management. The server collects data in real time to monitor energy consumption. Hardware-wise, it acquires data using various sensors and energy meters. For example, it aggregates electricity meter data from the entire building and temperature data from temperature sensors to the server via the network. The software utilizes an IoT platform for efficient data collection and management.

[0618] After data collection, the server analyzes the collected data using data analysis software such as Python or R. The collected data is preprocessed using libraries such as Pandas and NumPy to remove outliers and interpolate time series. Subsequently, energy demand is predicted using statistical models, taking into account past energy usage data and weather forecasts. Based on these analysis results, the server generates energy-saving suggestions using a generative AI model.

[0619] For example, if analysis reveals that a factory has extremely high daytime electricity consumption, the server will generate a prompt message such as, "Consider installing a solar power generation system to reduce daytime electricity peaks." The user can then use this suggestion to decide whether or not to invest in actual energy facilities.

[0620] Furthermore, users can obtain additional information from the system by making interactive inquiries. For example, if a user wants to know the energy reduction effect of introducing new equipment immediately, they can prompt the server with "Please simulate the effect of energy cost reduction from introducing new energy-saving equipment." In response to this prompt, the server immediately runs the simulation and provides the results to the user.

[0621] In this way, the server supports efficient and sustainable energy management, addressing the energy challenges faced by businesses and public institutions.

[0622] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0623] Step 1:

[0624] The server collects energy data in real time from various sensors and energy meters. Inputs include temperature sensor readings and power meter consumption data. The server aggregates this data via the network and stores it in a database. This allows for monitoring the energy usage of each piece of equipment.

[0625] Step 2:

[0626] The server processes the collected data and converts it into a format suitable for analysis. The input data includes raw sensor data. Specific data processing steps include conversion to a Pandas dataframe, removal of outliers, and imputation of time-series gaps. The output is a clean, analyzable dataset.

[0627] Step 3:

[0628] The server performs analysis to predict energy demand based on processed data. Inputs include pre-processed data and statistical models. Specifically, it performs regression analysis and time series analysis, and also considers weather forecast data. The output provides predicted future energy consumption patterns.

[0629] Step 4:

[0630] The server generates energy-saving suggestions using a generative AI model based on the prediction results. The input is the analysis results, and a prompt is provided. Specifically, the prompt "Please provide the optimal energy reduction suggestion under the following conditions" is passed to the generative AI model. The output is an energy-saving suggestion that the user can implement.

[0631] Step 5:

[0632] The user receives suggestions from the server and asks additional questions as needed. The input is the generated suggestions, and the output provides further analysis or simulation results. Specifically, interactive inquiries such as "Please simulate the effects of introducing new equipment" are possible, and the server responds dynamically accordingly.

[0633] (Application Example 1)

[0634] 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".

[0635] To optimize energy consumption in cities and efficiently utilize renewable energy, residents need to manage their own energy use and take energy-saving actions at the optimal time. However, existing technologies have limitations in providing residents with real-time, appropriate energy use suggestions and comprehensively optimizing energy consumption across the entire city.

[0636] 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.

[0637] In this invention, the server includes means for collecting energy data in real time, means for preprocessing the collected data and converting it into a format suitable for analysis, means for generating energy-saving measures and recommendations for renewable energy use based on the analysis results, means for providing suggestions for efficient energy use in real time in conjunction with weather conditions, and means for generating instructions for optimizing the energy consumption of members on a city scale. This makes it possible to effectively suppress energy consumption throughout the city and maximize the use of renewable energy.

[0638] A "device for accumulating energy data in real time" is a device that instantly grasps the status of energy use and continuously collects the latest data.

[0639] A "device for preprocessing and converting data into a format suitable for analysis" is a device that processes collected data and prepares it in a format that facilitates analysis.

[0640] A "device for predicting energy consumption" is a system that estimates future energy usage based on collected data.

[0641] A "device for generating energy-saving measures and recommendations for renewable energy use" is a device that creates proposals for reducing energy consumption and effectively utilizing renewable energy based on analysis results.

[0642] A "device that notifies members and responds to interactive inquiries from members" is a system that sends generated energy-saving measures information to users and provides real-time answers to user questions and concerns.

[0643] A "device that provides real-time suggestions for efficient energy use in conjunction with weather conditions" is a device that uses weather data to instantly show users the optimal way to use energy.

[0644] A "device that generates instructions to optimize the energy consumption of its members on a city-wide scale" is a device that integrates energy data from multiple members and creates commands aimed at optimizing energy consumption throughout the entire city.

[0645] This invention provides a system that collects and analyzes vast amounts of energy data in real time and efficiently manages energy consumption on a city scale.

[0646] The server collects energy data in real time by receiving data from sensing devices and information from forecasting systems. This data is first preprocessed and converted into a format suitable for analysis. This process utilizes Python programs and Apache Kafka. Next, the server analyzes the converted data and predicts future energy demand. This generates recommendations aimed at reducing costs and environmental impact.

[0647] The generated recommendations are notified to members' devices via the network. Members can review and implement the recommended energy-saving actions on devices such as smartphones and smart glasses. Real-time responses to interactive inquiries from members are also possible. AWS is a platform likely to be used for this purpose.

[0648] For example, if the weather forecast for a given day is sunny, the server will generate a suggestion such as "We recommend doing your laundry in the morning tomorrow" and display it on the terminal. This allows members to easily select and perform actions that optimize their energy consumption.

[0649] An example of a prompt message is: "Generate specific suggestions for maximizing the use of renewable energy based on tomorrow's weather forecast."

[0650] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0651] Step 1:

[0652] The server receives data in real time from sensing devices and forecasting systems. Inputs include current energy consumption data from energy sensors and weather information. This data is collected and stored on the server.

[0653] Step 2:

[0654] The server preprocesses the collected energy data and converts it into a format suitable for analysis. The input is raw energy data, and the output is cleaned data. This process imputes missing values ​​and removes outliers.

[0655] Step 3:

[0656] The server uses pre-processed data to predict energy consumption. The input is pre-processed data, and the output is a prediction of future energy consumption. The server uses a generative AI model to make predictions from historical data and saves the results.

[0657] Step 4:

[0658] The server generates energy-saving measures and renewable energy utilization proposals based on the analysis results. The input is predictive data, and the output is energy-saving proposals. This includes data on cost reduction effects and environmental impact reduction effects.

[0659] Step 5:

[0660] The server notifies the member's terminal of the generated proposal and responds to interactive inquiries from the member. The input is the member's inquiry, and the output is the response message. The terminal displays the analysis results and proposal, and the member reviews the content.

[0661] Step 6:

[0662] The server works in conjunction with weather conditions to provide real-time suggestions for efficient energy use. The input is the latest weather forecast data, and the output is a suggestion for the optimal timing of energy use for each member. The server detects weather patterns and makes corresponding suggestions.

[0663] Step 7:

[0664] The terminal receives instructions to optimize the energy consumption of its members on a city-wide scale and prompts users to take action. Input is instruction data from the server, and output is specific action suggestions for the user. Users follow the terminal's guidance to implement energy-saving measures.

[0665] 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.

[0666] This invention aims to achieve more effective and user-friendly energy management by incorporating an emotion engine that recognizes the user's emotions into an energy management system. This system generates adaptive energy-saving suggestions based on the user's emotional state, thereby optimizing energy consumption.

[0667] The main components of this system are: "energy data collection," "data analysis and suggestion generation," "user emotion recognition using an emotion engine," and "notifications and interactive responses." First, the server acquires energy data in real time from sensors and existing forecasting systems. Based on this data, it analyzes energy usage trends and identifies peak demand.

[0668] Next, the server proposes energy-saving measures and renewable energy implementations based on the analysis. These proposals take into account the user's emotional state as recognized by the emotion engine. For example, if a user is in a high-stress state, the tone and priority of the proposals are adjusted, and energy-saving suggestions are provided to reduce stress.

[0669] For example, if the emotion engine detects that a user working in an office is experiencing stress, the server will suggest adjusting the air conditioning system settings to optimize environmental conditions. This adjustment aims to reduce user stress by setting the temperature and humidity within a comfortable range.

[0670] Furthermore, the emotion engine also plays a role in optimizing how suggestions are notified. For example, it provides gentler and more flexible suggestions to users in high-stress states, rather than direct notifications, thus increasing the receptiveness of suggestions based on the user's state.

[0671] In this way, the system of the present invention realizes intelligent energy management that takes into account the user's emotional state, providing a better user experience and improving the efficiency of energy management.

[0672] The following describes the processing flow.

[0673] Step 1:

[0674] The server collects various energy data in real time via sensors and forecasting systems. This includes direct energy consumption, equipment operating status, and external weather conditions.

[0675] Step 2:

[0676] The server preprocesses the collected raw data. Specifically, it corrects missing values, removes outliers, and standardizes the data as needed. The preprocessed data is then converted into a format suitable for analysis and saved.

[0677] Step 3:

[0678] The server uses an AI model to predict energy consumption based on pre-processed data. This involves identifying short-term and long-term demand trends by taking into account past usage history and weather data.

[0679] Step 4:

[0680] The server generates proposals for energy-saving measures and renewable energy utilization based on predictions obtained from the AI ​​model. These proposals include methods for peak shifting and cost reduction effects.

[0681] Step 5:

[0682] The server uses an emotion engine to recognize user emotions and perform real-time analysis of the user's emotional state. Specifically, it detects changes in the user's emotional state from their actions and facial expressions.

[0683] Step 6:

[0684] The server utilizes emotional data obtained by the emotion engine to customize the generated suggestions according to the user's current emotional state. For example, a user experiencing high stress will be presented with calm and gentle suggestions.

[0685] Step 7:

[0686] The server notifies the user's device of the optimized suggestions. The user receives the notification on their device and can review and understand the details of the suggestions.

[0687] Step 8:

[0688] Users can ask the system questions if they have doubts about the proposal or require additional information. The server will respond immediately to these inquiries and provide additional analysis and simulation results.

[0689] This series of steps allows users to effectively manage energy use while improving comfort and reducing mental stress.

[0690] (Example 2)

[0691] 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".

[0692] Conventional energy management systems focus on optimizing energy use but fail to consider the feelings and circumstances of users. Therefore, energy-saving measures are not always comfortable for users, which can result in decreased user satisfaction and undermine the effectiveness of the proposed solutions.

[0693] 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.

[0694] In this invention, the server includes means for collecting energy usage information in real time, means for recognizing the user's emotional state and generating adaptive suggestions using a generative AI model, and means for notifying the user of the generated plan and engaging in interactive communication with the user. This enables intelligent energy management that takes into account the user's emotional state, thereby improving user satisfaction and the efficiency of energy management.

[0695] "Real-time" refers to acquiring and processing information at the very moment an event occurs.

[0696] "Energy utilization information" refers to information that includes various data on energy consumption and supply status.

[0697] "Preprocessing" refers to initial data processing techniques used to convert collected information into a format suitable for analysis.

[0698] "Renewable energy" refers to energy sources that are constantly regenerated through natural processes, such as solar, wind, hydro, and geothermal energy.

[0699] A "generative AI model" refers to a form of artificial intelligence trained to analyze data and predict new suggestions or outcomes.

[0700] A "detector" refers to a device or system that observes specific environmental conditions or physical states and collects that data.

[0701] A "forecasting system" refers to a system that analyzes past and present data to predict future conditions and provides the results.

[0702] "Cost reduction effect" refers to the expected benefit of a particular measure in reducing economic costs.

[0703] "Environmental impact reduction effect" refers to the effectiveness of efforts to mitigate the negative impacts that human activities have on the natural environment.

[0704] Modes for carrying out the invention

[0705] This energy management system aims to achieve both efficient energy use and user comfort. A specific implementation is shown below.

[0706] The server first collects energy usage information in real time from detectors and forecasting mechanisms. This information includes data on power consumption, temperature, humidity, and weather. This data is automatically collected on the server via an API and stored in a database.

[0707] The server then preprocesses the collected information. The Python Pandas library is used for preprocessing, including data imputation and type conversion. The resulting dataset is then used for pattern analysis and prediction of energy utilization.

[0708] The device runs an emotion engine to recognize the user's emotional state. The emotion engine uses libraries such as TensorFlow and OpenCV to analyze the user's facial expressions and estimate their stress level and comfort level. This information is sent to the server in real time.

[0709] By using a generative AI model, the server generates energy-saving measures based on pre-processed information and the user's emotional state. It utilizes tools such as PyTorch and the Chatbot API to provide optimal suggestions tailored to the user's state. Specifically, this could include automatic temperature control for heating and cooling, and lighting adjustments. Furthermore, the generated suggestions include quantified cost reduction and environmental impact reduction effects.

[0710] The server notifies the user of the generated suggestions. These notifications are sent via smartphone apps or email, and a flexible format is used to enhance user acceptance. For example, on a sunny afternoon, a suggestion might be presented to change the timing of electricity usage to maximize the use of solar power.

[0711] An example of a prompt message for the generating AI model is: "Consider the energy consumption and stress levels of users working in an office, and generate comfortable and efficient energy-saving suggestions. If the user's stress level is high, be flexible in how you notify them."

[0712] This system enables intelligent energy management, allowing for both user comfort and energy savings.

[0713] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0714] Step 1:

[0715] The server collects energy usage information from detectors and forecasting mechanisms. During this process, data such as temperature, humidity, power consumption, and weather information are input in real time. The input data is sent to the server via an API and stored in a database. Specifically, this involves acquiring measurements from various sensors and collecting them on the server via the network. The output is formalized energy data stored in the database.

[0716] Step 2:

[0717] The server preprocesses the collected energy data. The raw data saved in step 1 is used as input. The Python Pandas library is used to impute missing values, convert data types, and denoise. Specifically, outlier detection and correction, and data smoothing are performed. As a result of this data processing, a clean dataset suitable for analysis is obtained. This output is the data for subsequent analysis and model input.

[0718] Step 3:

[0719] The device operates an emotion engine to recognize the user's emotional state. The input here consists of the user's facial expressions and voice data. Using TensorFlow and OpenCV, this data is analyzed in real time to estimate stress levels and emotional states. Specific operations include facial expression recognition using the camera and voice tone analysis using the microphone. The output is the analyzed user's emotional state, which is then sent to the server.

[0720] Step 4:

[0721] The server generates energy-saving measures using pre-processed energy data and the user's emotional state. The input here is the data output in steps 2 and 3. A generative AI model is used, and prompts are applied to create suggestions. The output generates recommendations for specific energy-saving measures, such as adjusting heating and cooling temperatures and automatically controlling lighting.

[0722] Step 5:

[0723] The server notifies the user of the generated energy-saving measures. The input is the proposal content from step 4. The proposal is sent to the user via a smartphone app or email. Specifically, the server generates and delivers the notification message. The output is the energy-saving proposal received by the user, which allows the user to adjust their environment according to the proposal.

[0724] (Application Example 2)

[0725] 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".

[0726] In energy management systems, uniform energy-saving suggestions that disregard the user's emotional state have problems with low feasibility and a poor user experience. Furthermore, because the suggestions ignore the user's feelings, there are challenges such as increased stress and decreased motivation for energy-saving behavior.

[0727] 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.

[0728] In this invention, the server includes means for collecting energy data in real time, means for recognizing emotions and reflecting them in energy-saving suggestions, and means for adjusting the suggestion content based on the user's emotional state. This enables suggestions that are adapted to the user's emotional state, resulting in more effective and user-friendly energy management.

[0729] A "device for collecting energy data in real time" is a device that continuously acquires information on energy usage from sensors and forecasting systems and immediately reflects it in the system.

[0730] A "device that preprocesses collected data and converts it into a format suitable for analysis" is a means of processing acquired energy data and shaping it into a format necessary for prediction and analysis.

[0731] A "device that predicts energy consumption using pre-processed data" is a system that predicts future energy use based on pre-processed data.

[0732] A "device that generates energy-saving measures and renewable energy utilization proposals" is a device that proposes effective energy-saving methods and renewable energy utilization methods to users based on predicted energy data.

[0733] A "device that recognizes emotions and reflects them in energy-saving proposals" is a device that detects the user's emotional state and takes it into consideration when deciding on energy-saving proposals.

[0734] A "device that adjusts suggested content based on the user's emotional state" is a system that appropriately changes the format of energy-saving suggestions and notifications according to the user's current emotions and stress level.

[0735] This invention is a system that optimizes energy management based on the user's emotional state. The server can collect energy data in real time through multiple sensors and forecasting systems. This data is transformed into a format suitable for analysis through initial preprocessing. Preprocessing includes data formatting and noise filtering.

[0736] Next, the server uses the pre-processed data to predict energy consumption. This prediction is performed using statistical modeling techniques to show future usage trends. Based on the prediction results, energy-saving suggestions and renewable energy utilization proposals are generated.

[0737] The emotion engine runs on the user's device and detects the user's emotional state using facial recognition and voice analysis. This emotional data is sent to a server and reflected in the generated energy-saving suggestions. For example, if the user is feeling stressed, suggestions will be made to adjust the temperature and humidity or change the brightness of the lighting.

[0738] Suggestions for users are provided through a notification system. The content of the suggestions is tailored based on the user's state detected by the emotion engine. For example, users in a relaxed state will receive detailed suggestions, while users experiencing high stress levels will receive simple and calming suggestions.

[0739] As a concrete example, a smartphone application monitors household energy management in real time and prompts the user to automatically adjust the air conditioner settings when they are feeling stressed in a hot environment. An example of a prompt message generated using a generative AI model is, "Your home's energy consumption is on the rise. Would you like to set the air conditioner temperature to 25 degrees Celsius as an optimal setting to minimize stress?"

[0740] In this way, the present invention can take into account the emotional state of the user and manage energy safely and efficiently, thereby providing an optimal energy-saving solution for each individual user.

[0741] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0742] Step 1:

[0743] The server collects energy data in real time from sensors and forecasting systems. Input is data from sensors, and output is raw data. This data is used to instantly understand energy consumption patterns.

[0744] Step 2:

[0745] The server preprocesses the collected raw data and converts it into a format suitable for analysis. The input is raw data, and through noise filtering and scaling, the output is transformed into an analyzable data format. This process eliminates data bias, enabling accurate analysis.

[0746] Step 3:

[0747] The server uses pre-processed data to predict energy consumption. The input is in an analyzable data format, and by performing calculations using a statistical model, the output is an estimate of future consumption. This prediction forms the basis for energy-saving proposals.

[0748] Step 4:

[0749] The device recognizes the user's emotions and sends that data to the server. The input consists of the user's facial expressions and voice, which are analyzed by an emotion engine to produce the user's emotional state. This emotional information influences energy-saving suggestions.

[0750] Step 5:

[0751] The server generates energy-saving suggestions based on predicted energy consumption and the user's emotional state. The input is the predicted energy consumption and emotional state, and the output is a tailored energy-saving suggestion. The priority and wording of the suggestions change depending on the emotional state.

[0752] Step 6:

[0753] The server communicates the generated energy-saving suggestions to the user using a notification system. The input is a pre-adjusted suggestion, and it is output in a way that suits the user's emotions. For example, calmer language is selected for highly stressed users.

[0754] Step 7:

[0755] Users adjust energy settings and provide feedback to the device based on the suggestions they receive. Input is energy-saving suggestions, and output is actual actions and setting changes. This feedback helps further refine the system.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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."

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] The following is further disclosed regarding the embodiments described above.

[0778] (Claim 1)

[0779] A means of collecting energy data in real time,

[0780] A means for preprocessing the collected data and converting it into a format suitable for analysis,

[0781] A means for predicting energy consumption using preprocessed data,

[0782] A means for generating energy-saving measures and proposals for the use of renewable energy based on analysis results,

[0783] A means of notifying users of the proposed content and responding to interactive inquiries from users,

[0784] A system that includes this.

[0785] (Claim 2)

[0786] The system according to claim 1, wherein the collected energy data is obtained from sensors and a forecasting system.

[0787] (Claim 3)

[0788] The system according to claim 1, which quantifies and provides cost reduction effects and environmental impact reduction effects when generating proposals.

[0789] "Example 1"

[0790] (Claim 1)

[0791] Information gathering methods for acquiring energy data in real time,

[0792] A processing means for processing the acquired data and converting it into a format suitable for analysis,

[0793] An analytical method for predicting energy demand using processed data,

[0794] This is a proposal generation device for recommending energy-saving measures and the use of renewable energy based on analysis results, and includes means for constructing proposals using prompt sentences based on a generated AI model.

[0795] A two-way communication means for conveying the proposed content to users and responding to user feedback,

[0796] A system that includes this.

[0797] (Claim 2)

[0798] The system according to claim 1, wherein the collected energy data is obtained from a measuring device and a prediction system.

[0799] (Claim 3)

[0800] The system according to claim 1, which quantifies and provides cost reduction effects and environmental burden reduction effects when generating proposals.

[0801] "Application Example 1"

[0802] (Claim 1)

[0803] A means of collecting energy data in real time,

[0804] A means for preprocessing the accumulated data and converting it into a format suitable for analysis,

[0805] A means for predicting energy consumption using preprocessed data,

[0806] A means for generating energy conservation measures and recommendations for renewable energy use based on analysis results,

[0807] A means of notifying members of the recommendations and responding to interactive inquiries from members,

[0808] A means of providing real-time suggestions for efficient energy use in conjunction with weather conditions,

[0809] A means for generating instructions to optimize the energy consumption of members on a city scale,

[0810] A system that includes this.

[0811] (Claim 2)

[0812] The system according to claim 1, wherein the accumulated energy data is obtained from a sensing device and a forecasting system.

[0813] (Claim 3)

[0814] The system according to claim 1, which quantifies and provides cost reduction effects and environmental load reduction effects when generating recommendations.

[0815] "Example 2 of combining an emotion engine"

[0816] (Claim 1)

[0817] A means of collecting energy usage information in real time,

[0818] A means for preprocessing collected information and converting it into a format suitable for analysis,

[0819] A means for predicting energy use using pre-processed information,

[0820] A means for generating energy conservation measures and renewable energy utilization plans based on analysis results,

[0821] A means for notifying the user of the contents of the generated plan and for engaging in interactive communication with the user,

[0822] A means for recognizing the user's emotional state and generating adaptive suggestions using a generative AI model,

[0823] A system that includes this.

[0824] (Claim 2)

[0825] The system according to claim 1, wherein the collected energy utilization information is obtained from a detector and a forecasting mechanism.

[0826] (Claim 3)

[0827] The system according to claim 1, which quantifies and provides cost reduction effects and environmental load reduction effects in the generated plan.

[0828] "Application example 2 when combining with an emotional engine"

[0829] (Claim 1)

[0830] A means of collecting energy data in real time,

[0831] A means for preprocessing the collected data and converting it into a format suitable for analysis,

[0832] A means for predicting energy consumption using preprocessed data,

[0833] A means for generating energy-saving measures and proposals for the use of renewable energy based on analysis results,

[0834] A means of recognizing emotions and reflecting them in energy-saving proposals,

[0835] A means of notifying the user of the proposed content and adjusting the proposal based on the user's emotional state,

[0836] A system that includes this.

[0837] (Claim 2)

[0838] The system according to claim 1, wherein the collected energy data is obtained from a detector and a forecasting system.

[0839] (Claim 3)

[0840] The system according to claim 1, which quantifies and provides cost reduction effects and environmental impact reduction effects when generating proposals, and also generates programs that correspond to emotional states. [Explanation of Symbols]

[0841] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting energy data in real time, A means for preprocessing the accumulated data and converting it into a format suitable for analysis, A means for predicting energy consumption using preprocessed data, A means for generating energy conservation measures and recommendations for renewable energy use based on analysis results, A means of notifying members of the recommendations and responding to interactive inquiries from members, A means of providing real-time suggestions for efficient energy use in conjunction with weather conditions, A means for generating instructions to optimize the energy consumption of members on a city scale, A system that includes this.

2. The system according to claim 1, wherein the accumulated energy data is obtained from a sensing device and a forecasting system.

3. The system according to claim 1, which quantifies and provides cost reduction effects and environmental load reduction effects when generating recommendations.

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