system

Abstract

We provide the system. [Solution] A means for acquiring energy consumption data from a data collection device, Means for preprocessing the acquired data, Means for analyzing the aforementioned preprocessed data to identify energy consumption patterns, A means for creating data to visualize the aforementioned analysis results, A means for generating energy-saving advice based on the aforementioned visualization data, Means for providing the aforementioned energy-saving advice and visualization data to user equipment, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] With the depletion of energy resources and the soaring prices, the increase in utility bills such as electricity, gas, and water is inevitable. In such a situation, there is a need for a method that allows users to appropriately manage energy consumption and achieve energy savings. However, conventional methods have the problem that they cannot promote effective energy-saving actions because they do not provide sufficient real-time information on energy usage and specific energy-saving advice.

Means for Solving the Problems

[0005] To solve this problem, the present invention provides a means for acquiring energy consumption data in real time from a data collection device, pre-processing and analyzing the data to identify energy consumption patterns. Furthermore, it incorporates a means for visualizing the analysis results and presenting them in a user-friendly format, and for generating energy-saving advice based on individual behavioral patterns. It also includes a means for calculating and presenting set discounts by providing the obtained information to the user's device. This realizes a system in which users can intuitively feel the effects of energy saving and actively take energy-saving actions.

[0006] A "data acquisition device" is a device used to measure energy usage and collect that data.

[0007] "Energy consumption data" refers to data showing the amount of energy resources used, such as electricity, gas, and water.

[0008] "Preprocessing" refers to processes such as missing value imputation and noise reduction performed to improve the quality of collected data.

[0009] "Data analysis" refers to computational methods performed to identify patterns and anomalies in energy consumption based on pre-processed data.

[0010] "Visualization" refers to the visual display of analysis results in a format that is easy for users to understand.

[0011] "Energy-saving advice" refers to information that provides specific action guidelines for reducing energy consumption.

[0012] "User equipment" refers to terminals or devices used to display energy consumption information and energy-saving advice.

[0013] A "package discount" refers to a price reduction applied when using multiple energy services in combination. [Brief explanation of the drawing]

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

Embodiments for Carrying Out the Invention

[0015] There seems to be a mistake in the provided text where the line with ID=25 has a question mark in the translation. It should be "It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment." as per the original text's pattern. 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 labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a labeled 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 labeled 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, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

[0035] This invention is a system aimed at managing energy resources and promoting energy conservation. It collects energy consumption data such as electricity, gas, and water from homes and businesses, analyzes and visualizes this data, and provides specific advice on energy conservation.

[0036] First, the server acquires energy consumption data in real time from data collection devices. Various sensors and smart meters are used for this purpose, and the amount of each type of energy used is recorded in detail.

[0037] The server then preprocesses the acquired data. Preprocessing involves imputing missing values ​​and removing noise to improve data quality. This increases the reliability of the analysis.

[0038] The pre-processed data is analyzed using AI algorithms. The server then detects energy consumption trends and anomalies, and identifies consumption patterns. Based on this, potential peak consumption reductions and abnormal consumption patterns are highlighted.

[0039] The information obtained as a result of the analysis is visualized. The server generates this as graphs and charts, converting it into a format that can be intuitively understood. For example, monthly energy usage trends and daily consumption patterns can be visualized.

[0040] Next, energy-saving advice is generated based on the analysis results. The server provides customized advice based on each consumer's past usage patterns and behavioral characteristics. This includes specific actions such as how to avoid peak usage and how to adjust temperature settings.

[0041] The generated visualization data and energy-saving advice are provided to the user via the device. Through the application, users can check their current energy consumption and energy-saving suggestions. For example, the app's dashboard might display a message such as, "Your electricity usage this month is 10% higher than normal. Try reducing your usage at night."

[0042] Furthermore, bundled discounts are offered. The server calculates the optimal discount plan based on energy usage and informs the user. This makes it easy for users to have options for reducing costs.

[0043] In summary, this system aims to raise consumers' awareness of energy conservation and achieve efficient energy management.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The server periodically acquires energy consumption data for electricity, gas, and water from data collection devices. Each data collection device transmits usage data to the server in real time using a recommended protocol. The server receives this data and stores it in a database.

[0047] Step 2:

[0048] The server preprocesses the collected data. Specifically, it fills in missing data points using surrounding data and removes outliers such as sensor noise. The preprocessed data is then passed on to the next analysis phase in a clean state.

[0049] Step 3:

[0050] The server processes pre-processed data using an AI algorithm to analyze energy consumption patterns. This analysis includes detecting seasonal variations in consumption and identifying peak usage times. As a result, abnormal patterns and consumption levels are predicted.

[0051] Step 4:

[0052] The server generates data that visualizes the analysis results. This generated data is then converted into a format easily understood by consumers, such as line graphs or pie charts. This allows users to see consumption trends over time.

[0053] Step 5:

[0054] The server generates energy-saving advice based on energy consumption patterns and user usage history. The generated advice is tailored to individual consumption situations and includes specific suggestions, such as "adjust heating and cooling to reduce daytime electricity usage."

[0055] Step 6:

[0056] The terminal displays visualization data and energy-saving advice provided by the server on its user interface. Through this application, users can check their energy consumption and take appropriate action based on the advice provided.

[0057] Step 7:

[0058] The server calculates a set discount based on the user's energy usage and notifies the user of the result. By presenting discount options, users have the opportunity to choose a plan that takes into account the reduction of their usage costs.

[0059] (Example 1)

[0060] 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."

[0061] Conventional energy management systems have limited energy consumption data collection and analysis capabilities, resulting in insufficient provision of specific energy-saving suggestions based on user consumption patterns and inadequate calculation of personalized pricing plans. Furthermore, the lack of adequate data visualization makes it difficult for users to intuitively understand their energy usage.

[0062] 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.

[0063] In this invention, the server includes means for acquiring energy consumption information from a data collection mechanism, means for preprocessing the acquired information, means for analyzing the preprocessed information to identify energy consumption trends, means for creating information to visualize the analysis results, means for generating energy-saving suggestions, means for notifying the user device, means for calculating and presenting a pricing plan based on energy usage, and means for using a generated AI model. This enables users to efficiently manage their energy consumption through specific and personalized energy-saving suggestions based on energy consumption pattern analysis and intuitive data visualization.

[0064] A "data collection mechanism" refers to a device or system used to collect energy consumption information from households and businesses in real time.

[0065] "Energy consumption information" refers to data on the amount of energy used, such as electricity, gas, and water.

[0066] "Preprocessing" is a process that improves data quality by removing noise and imputing missing values ​​from acquired raw data.

[0067] "Consumption trends" refer to energy usage patterns analyzed based on past energy usage history.

[0068] "Visualization" refers to making data easier to understand intuitively by displaying analysis results in concrete forms such as graphs and charts.

[0069] An "energy-saving proposal" is information that presents specific improvement measures tailored to individual usage patterns in order to improve the efficiency of energy use.

[0070] A "pricing plan" is an optimal pricing structure provided based on energy usage.

[0071] A "generative AI model" is an algorithm that uses machine learning and artificial intelligence to analyze data and predict results.

[0072] "Notifications" refer to the means of communicating generated visualization information and energy-saving suggestions to users.

[0073] A "user device" refers to a terminal or device used to display energy usage status and suggested advice.

[0074] This invention is an energy management system aimed at promoting energy conservation and optimizing consumer energy use. Specific embodiments for carrying out this invention are described below.

[0075] First, the server acquires energy consumption information in real time via data collection devices installed in homes and businesses. Hardware such as sensors and smart meters are used for this data collection. The data is stored in a database on the server, preparing it for subsequent processing.

[0076] Next, the server preprocesses the acquired information, performing noise reduction and missing value imputation. Preprocessing utilizes software tools to improve data accuracy. Through this process, the data quality is enhanced, preparing it for subsequent analysis.

[0077] The pre-processed data is analyzed by a server-generated AI model. This analysis utilizes machine learning algorithms, particularly for analyzing energy consumption patterns and detecting anomalies. This reveals past consumption trends and potential for reducing peak consumption.

[0078] The server then outputs the analysis results in a visually understandable format. Specifically, software libraries are used to visualize the data as graphs and charts. This makes it easy to see monthly consumption trends and daily usage patterns.

[0079] In addition, the server creates energy-saving suggestions tailored to each individual user. It proposes optimal energy-saving advice based on past usage patterns and behavioral characteristics. For example, it provides specific improvement measures such as, "Raising the air conditioner's temperature setting by 1 degree will reduce power consumption by 5%."

[0080] Finally, the generated visualization information and energy-saving suggestions are provided to the user via their device. Users can review these suggestions using the application. For example, the app's notification function might send a message such as, "Changing your current usage plan could save you 20,000 yen per year."

[0081] A concrete example of a prompt message would be something like, "I want to generate energy-saving suggestions based on household energy consumption data. Please tell me the best suggestions."

[0082] Through these functions, the system of the present invention enables efficient energy management and increased awareness for energy conservation.

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

[0084] Step 1:

[0085] The server acquires energy consumption information, such as electricity, gas, and water, in real time through a data collection mechanism.

[0086] Input: Raw data from sensors and smart meters in homes and businesses.

[0087] Specific operation: The server receives information from the sensor using a data transfer protocol and stores it in the database.

[0088] Output: The raw data with a timestamp is saved to the database.

[0089] Step 2:

[0090] The server performs preprocessing on the acquired raw data.

[0091] Input: Raw data stored on the server

[0092] Specific operation: Missing data values ​​are inferred and imputed from surrounding data, and a denoising algorithm is applied to remove outliers.

[0093] Output: High-quality, pre-processed dataset

[0094] Step 3:

[0095] The server analyzes the pre-processed data using an AI model.

[0096] Input: Improved quality dataset

[0097] Specific actions: Apply a generative AI model to analyze consumption trends. This uses machine learning algorithms to identify peak usage times and detect abnormal consumption activity.

[0098] Output: Results of detecting consumption patterns and anomalies

[0099] Step 4:

[0100] The server visualizes the analysis results by converting them into graphs and charts.

[0101] Input: Analysis results of consumption patterns and anomaly detection

[0102] Specific operation: Use the visualization library to generate graphs and charts to display the analysis results on a dashboard.

[0103] Output: Image data of visualized energy consumption trends

[0104] Step 5:

[0105] The server generates energy-saving suggestions based on the visualized data.

[0106] Input: Visualized consumer trend data

[0107] Specific operation: Referencing past consumption data, the system uses an AI model to generate specific advice on how to reduce consumption during peak hours.

[0108] Output: A series of customized energy-saving proposals

[0109] Step 6:

[0110] The terminal provides the user with generated visualization data and energy-saving suggestions.

[0111] Input: Visualization data and energy-saving suggestions sent from the server.

[0112] Specific actions: The application will display these suggestions on the dashboard and notify the user of the suggestions through notifications.

[0113] Output: Visualization graphs on the dashboard and energy-saving advice messages.

[0114] Step 7:

[0115] The server calculates the pricing plan based on energy consumption and notifies the user.

[0116] Input: Analysis results of consumption data and various pricing information.

[0117] Specific operation: Uses AI to calculate pricing plans and discount options and proposes the optimal plan for the user.

[0118] Output: Optimized pricing plan suggestion message

[0119] (Application Example 1)

[0120] 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."

[0121] In recent years, large-scale production facilities such as factories have been required to improve energy efficiency, with particular emphasis on optimizing energy use. However, there is a lack of concrete means to monitor the energy consumption of individual machines in real time and to implement efficient energy management. Furthermore, there is a need to customize energy consumption advice based on individual behavioral characteristics and improve usability. Therefore, these challenges need to be addressed effectively.

[0122] 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.

[0123] In this invention, the server includes means for acquiring energy consumption information from a data collection mechanism, means for preprocessing the acquired information, and means for analyzing the preprocessed information to identify energy utilization patterns. This enables real-time monitoring of the operating status of individual machines within a factory and optimization of energy consumption.

[0124] A "data collection mechanism" is a device designed to accurately and efficiently acquire energy consumption information.

[0125] "Energy consumption information" refers to data showing the usage status of energy resources such as electricity, gas, and water.

[0126] "Preprocessing" refers to processes such as data interpolation and noise reduction performed to improve the completeness and quality of acquired data.

[0127] "Energy utilization patterns" refer to trends and regularities that can be obtained by analyzing energy consumption trends and the characteristics of consumption associated with specific behaviors.

[0128] "Visualization" is the process of transforming analysis results into a form that can be intuitively understood by displaying them in the format of graphs and charts.

[0129] "Energy conservation advice" refers to specific action suggestions provided to improve the efficiency of energy consumption.

[0130] "User equipment" refers to electronic devices used to present users with visualized data and energy-saving advice.

[0131] "Operating status" refers to the current operating status and load conditions of machinery and equipment.

[0132] "Real-time monitoring" refers to the act of immediately checking the current situation and recording any changes as they occur.

[0133] "Optimizing energy consumption" is the process of making adjustments and improvements to achieve efficient use of energy resources.

[0134] As a concrete example of implementing this invention, an energy management system will be constructed in a factory. The server will acquire energy consumption information in real time using data collection mechanisms installed in robots and machinery within the factory. Various sensors and internet-connected measuring instruments will be used for this purpose.

[0135] Next, the server preprocesses the acquired energy consumption information. Preprocessing involves imputing missing values ​​and removing noise to improve the quality of the information. The preprocessed information is then analyzed using AI algorithms on the server. The analysis is performed in a Python environment, using machine learning libraries such as Scikit-learn to identify energy usage patterns.

[0136] The server visualizes the analysis results and utilizes visualization tools such as Tableau to create intuitively understandable graphs and charts. Based on these results, the server generates energy-saving advice and provides it to the user's equipment. The advice is customized to take into account the operating conditions and peak energy consumption of individual machines.

[0137] As a concrete example, if a factory experiences high energy consumption during certain operating times for robot A, the system will review those times and provide energy-saving advice to enable more efficient operation. Furthermore, the AI ​​model will be provided with prompts such as, "Analyze the collected robot energy data and create optimization suggestions. Specifically, describe methods for reducing energy consumption during peak hours."

[0138] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0139] Step 1:

[0140] The server acquires energy consumption information in real time from data collection mechanisms installed in each robot and piece of machinery within the factory. This information includes the moment-by-moment power consumption and operating time of each robot. Based on this input data, it is stored in a database for recording.

[0141] Step 2:

[0142] The server preprocesses the acquired energy consumption information. Specifically, it uses a Python environment to impute missing values ​​and perform data cleaning to remove noise. It generates reliable, clean data from the input data and prepares it for subsequent analysis processes. This output data is then prepared as data for analysis.

[0143] Step 3:

[0144] The server starts analysis using AI algorithms with pre-processed information. It uses machine learning libraries such as Scikit-learn to identify energy consumption trends and anomalies. This process identifies energy usage patterns using clean data as input, and the output includes consumption trend data and anomaly detection results.

[0145] Step 4:

[0146] The server generates energy-saving advice based on the analysis results. It then formulates concrete action plans and proposes optimized equipment operation schedules based on the analysis results to provide actionable advice for the user. The generated advice is used as input data for a visualization program, yielding output data that encompasses energy-saving measures.

[0147] Step 5:

[0148] The server visualizes the analysis results in graphs and charts using visualization tools such as Tableau. It analyzes the visualized data based on a generation AI model and prepares it in a format that can be easily presented to the user. Based on this process, it outputs visualization results that include the prompt message "Analyze the collected robot energy data and create suggestions for optimization. Specifically show how to reduce energy consumption during peak times."

[0149] Step 6:

[0150] The terminal provides the user with generated visualization data and energy-saving advice. In this final process, the user can check energy consumption in real time through the application and take concrete actions to implement efficient energy management. A responsive interface is presented to the user during this output process.

[0151] 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.

[0152] This invention is a system that recognizes the user's emotional state and manages their energy based on that state. It primarily involves the coordinated functioning of a data collection device, an emotion engine, a server, and a terminal.

[0153] First, the server acquires energy consumption data in real time from data collection devices installed in homes and businesses. This uses smart meters and IoT sensors to record detailed usage of electricity, gas, and water.

[0154] Next, the server uses an emotion engine to acquire the user's emotional data. This emotional data is obtained in real time through wearable devices, cameras, voice analysis, and other means. The emotion engine determines the user's emotions by analyzing factors such as the frequency of their smiles, stress levels, and tone of voice.

[0155] Based on the acquired energy consumption and emotional data, the server preprocesses them to generate a clean dataset. The preprocessed data is then subjected to an AI algorithm to analyze trends related to energy consumption patterns and emotions. This analysis observes changes in the user's emotions during energy use and develops energy-saving advice that is tailored to their emotional state.

[0156] The generated advice is customized based on each user's individual energy consumption habits and emotional patterns. For example, during times of high stress, it provides suggestions for relaxing lighting settings and positive messages encouraging improvements to energy consumption patterns. It also utilizes an emotional engine to recommend adopting new energy-saving behaviors during times of emotional stability.

[0157] The resulting visualization data and energy-saving advice are provided to the user via the device. The application on the device intuitively displays consumption status and advice to the user and encourages energy-saving behavior by providing emotionally resonant feedback.

[0158] Thus, this invention aims to enhance energy conservation awareness and make users' lives more comfortable and efficient by linking an emotion engine with energy consumption data.

[0159] The following describes the processing flow.

[0160] Step 1:

[0161] The server acquires real-time energy consumption data for electricity, gas, and water from data collection devices. This includes receiving data from smart meters and IoT sensors and storing it in a database.

[0162] Step 2:

[0163] The server collects user emotional data through an emotion engine. It uses wearable devices, cameras, and voice analysis to evaluate the user's emotional state based on facial expressions, voice tone, heart rate, etc., and stores this data.

[0164] Step 3:

[0165] The server preprocesses the acquired energy consumption and sentiment data. This includes imputing missing values ​​and removing noise. It also standardizes the sentiment data and formats it into a format that is easy to analyze.

[0166] Step 4:

[0167] The server feeds pre-processed data into an AI algorithm to simultaneously analyze energy consumption patterns and emotional trends. This process detects how energy consumption influences emotions, and vice versa.

[0168] Step 5:

[0169] The server generates energy-saving advice based on the analysis results. It creates specific advice that improves energy consumption patterns according to emotional state. For example, it might suggest, "You seem tired. Try using warm lighting in the evening to relax."

[0170] Step 6:

[0171] The terminal displays visualization data and energy-saving advice provided by the server in its user interface. The user interface is designed to visually represent daily consumption trends and the associated emotional impact, making it easy for users to understand the information.

[0172] Step 7:

[0173] Based on the information provided through the application, users adjust their daily energy consumption behaviors. Furthermore, they actively create a more relaxing environment based on feedback from the emotional engine.

[0174] In this way, this system provides a next-generation energy use support tool that integrates energy management with emotional considerations.

[0175] (Example 2)

[0176] 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".

[0177] Energy consumption is increasing in modern homes and businesses, making efficient energy-saving measures increasingly necessary. However, energy-saving proposals that take into account the emotional state of users are often lacking. Conventional systems focus solely on energy consumption, ignoring the impact of emotional states, which can lead to them being unacceptable to users. It is necessary to address these challenges and propose more effective and user-friendly energy-saving measures.

[0178] 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.

[0179] In this invention, the server includes means for acquiring information related to energy consumption from a data collection mechanism, means for acquiring emotional information via an emotional engine for detecting the user's emotional state, and means for preprocessing the acquired information and integrating it with the emotional information. This enables the provision of customized energy-saving advice based on emotions.

[0180] A "data collection mechanism" refers to any device that measures energy consumption and provides that data to a server.

[0181] "Information related to energy consumption" refers to data on the amount and status of use of various types of energy, such as electricity, gas, and water.

[0182] An "emotion engine" refers to a collection of software and hardware that analyzes a user's emotional state and generates data based on that analysis.

[0183] "Emotional information" refers to data that quantifies a user's emotional state, and is usually based on facial expressions, voice tone, heart rate, etc.

[0184] "Preprocessing" refers to a series of processes that prepare raw data for analysis, including data cleaning and imputation of missing values.

[0185] "Consumption patterns" refer to behavioral patterns that show certain trends or tendencies in energy consumption.

[0186] "Energy-saving advice" refers to information that includes suggestions and recommended actions to promote energy conservation, and is customized for each user.

[0187] "User device" refers to a terminal or application that the user directly operates, and is the medium on which analysis results and advice are displayed.

[0188] This invention relates to a system that provides energy-saving advice that takes into account the user's emotional state. It primarily involves the coordinated functioning of a data collection mechanism, an emotion engine, a server, and a terminal.

[0189] The server acquires energy consumption-related information in real time from data collection devices installed in homes and businesses. For example, it records electricity, gas, and water usage obtained from smart meters and IoT sensors. This information is stored as a dataset that forms the basis for analysis.

[0190] Next, the server uses an emotion engine to acquire the user's emotional information. This emotional information is collected through wearable devices, surveillance cameras, and voice analysis software. The emotion engine analyzes data such as the user's facial expressions, voice tone, and heart rate to determine their emotional state.

[0191] The obtained energy consumption and emotional data are preprocessed on the server and prepared into an analyzable dataset. This preprocessing includes data cleaning and formatting standardization.

[0192] Next, the pre-processed data is analyzed by a generative AI model. In particular, it analyzes the relationship between energy consumption patterns and emotional information, and generates energy-saving advice based on the user's emotional state. This generative AI model can identify periods when emotions are stable and periods when stress levels are high, and propose optimal consumption behaviors accordingly.

[0193] The generated advice and its analysis results are provided to the user via the terminal. The interface on the terminal intuitively visualizes consumption status and energy-saving advice, outputting information in a format that is easy for the user to understand. For example, it may recommend playing relaxing music during the evening hours when stress levels tend to be higher.

[0194] Another example of an appropriate prompt is, "Analyze the user's energy consumption patterns during times when their stress levels are high, and suggest ways to help them relax."

[0195] In this way, this system can promote energy-saving behavior that takes into account the emotional state of users, thereby improving energy consumption.

[0196] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0197] Step 1:

[0198] The server acquires information related to energy consumption from the data collection mechanism. This input data is periodically transmitted through smart meters and IoT sensors, and the usage of electricity, gas, and water is recorded as numerical values. The server stores this raw data in a temporary database. The output of this step is raw energy consumption data.

[0199] Step 2:

[0200] The server uses an emotion engine to acquire data on the user's emotional state. Inputs include heart rate data collected by wearable devices, facial expression analysis from cameras, and voice tone analysis from voice analysis. This information is transmitted to the server in real time and converted into numerical values ​​and indicators defined as emotional states. The output of this step is the user's current emotional state data.

[0201] Step 3:

[0202] The server preprocesses the acquired energy consumption and sentiment data. The input dataset undergoes a cleaning process, imputing missing values ​​and removing noise. It also unifies different data formats and formats them into an analyzable form. This process allows the server to output a clean dataset.

[0203] Step 4:

[0204] The server uses pre-processed data to perform analysis using a generative AI model. This analysis examines the correlation between energy consumption patterns and emotional states based on the input data. Using prompts, it outputs suggestions for energy-saving actions under various scenarios. The output of this step is energy-saving advice based on predicted consumption patterns and emotions.

[0205] Step 5:

[0206] The terminal displays energy-saving advice and analysis results sent from the server to the user. The input includes analysis results and advice data, which the application on the terminal displays graphically. This is a means of clearly communicating suggestions based on consumption patterns and emotions to the user. The output of this step is visualized information through the user interface.

[0207] Step 6:

[0208] Based on the information displayed on the device, users take specific energy-saving actions. The input of operations and actions is fed back to the server through the data collection mechanism. This allows the server to incorporate the latest information into subsequent data analysis and advice. This feedback loop enables even more sophisticated energy-saving suggestions.

[0209] (Application Example 2)

[0210] 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".

[0211] Optimizing energy consumption and improving working conditions are critical challenges in many industries. In particular, in workplaces like factories, considering workers' emotional states while optimizing energy consumption is essential for simultaneously improving work efficiency and employee satisfaction. However, an integrated system for managing these simultaneously does not yet exist. Therefore, there is a need for the development of a user-centered energy management system that utilizes emotional data.

[0212] 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.

[0213] In this invention, the server includes means for acquiring energy usage data from a data acquisition device, means for acquiring user emotional data using an emotional analysis device, and means for performing preprocessing of the acquired data. This makes it possible to optimize energy usage based on the user's emotional state.

[0214] A "data acquisition device" is a device used to collect information related to energy use, and includes smart meters and IoT sensors.

[0215] "Energy usage data" refers to information showing the consumption status of electricity, gas, water, etc., and is acquired in real time.

[0216] "Preprocessing" is the stage of converting acquired data into a format that is easy to analyze, and includes noise reduction and data formatting.

[0217] An "emotion analysis device" is a device that analyzes a user's emotional state from their facial expressions and voice, and utilizes a camera and voice recognition system.

[0218] "Visualization data" refers to information used to visually display analyzed energy utilization patterns and emotional states, and is expressed in the form of graphs and charts.

[0219] "Energy-saving advice" refers to suggestions for making users' energy use more efficient, and is generated based on individual behaviors and emotional patterns.

[0220] A "user device" is a terminal used to provide users with visualized data and energy-saving advice, and includes smartphones and tablets.

[0221] A "means for generating commands" is a mechanism that generates instructions for controlling systems and devices based on emotional data.

[0222] The system for carrying out this invention includes a data acquisition device, an emotion analysis device, a server, and a user device. The server acquires energy usage data from the data acquisition device and performs preprocessing. Subsequently, it integrates the user's emotion data acquired by the emotion analysis device and analyzes this data using an AI algorithm. The analyzed data links energy usage patterns with the user's emotional state and is used to adjust the operating environment and generate energy-saving advice as needed.

[0223] This system uses general-purpose computers and data analysis software, and specific hardware platforms such as NVIDIA Jetson and Raspberry Pi can be utilized. For sentiment analysis, software such as Microsoft® Azure® Face API and Google® Cloud Speech-to-Text API are effective. The user device is responsible for receiving the generated energy-saving advice and visualization data and displaying it to the user.

[0224] As a concrete example, in the work environment of factory worker A, the system acquires emotional data in real time and, based on the analysis, determines that A is feeling fatigued. In this case, it can immediately suggest adjusting the lighting brightness to create a more relaxing work environment.

[0225] An example of a prompt message could be: "Optimize the energy settings in the factory based on the stress levels of the workers, and explain how to do so." This makes it possible to optimize energy consumption while providing a more comfortable working environment for the workers.

[0226] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0227] Step 1:

[0228] The server acquires energy usage data in real time from data acquisition devices. The input is raw energy usage data transmitted from smart meters and IoT sensors. This data is temporarily stored on the server side in storage. The output is energy usage data stored in a standardized format.

[0229] Step 2:

[0230] The server collects user emotional data using an emotion analysis device. Inputs include raw data from wearable devices and visual and auditory information captured through cameras and microphones. This data is analyzed using an emotion analysis API to extract the user's emotional state as digital data. The output is numerical data related to the user's emotions.

[0231] Step 3:

[0232] The server preprocesses energy utilization data and sentiment data to generate a consistent dataset. The input is the raw data obtained in steps 1 and 2. Data quality is improved by removing noise and imputing missing values. The output is a clean, analyzable dataset.

[0233] Step 4:

[0234] The server feeds the preprocessed dataset into an AI algorithm for analysis. The input is the clean dataset obtained in step 3. Analysis using a machine learning model is performed to identify trends related to energy consumption patterns and emotions. The output is the analyzed energy-emotion correspondence data.

[0235] Step 5:

[0236] The server generates energy-saving advice based on the analysis results. The input is the analyzed data obtained from step 4. Prompts are used to instruct the generating AI, which outputs helpful energy-saving advice. The output is personalized energy-saving advice based on the user's energy usage and emotional state.

[0237] Step 6:

[0238] The terminal displays energy-saving advice and visualization data sent from the server to the user. The input consists of the energy-saving advice and data processed for visualization obtained in step 5. This is visually presented through the terminal's interface and provided in a format that is easy for the user to understand. The output is information to encourage specific energy-saving actions for the user and guidance on what actions the user should take.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] [Second Embodiment]

[0243] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0244] 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.

[0245] 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).

[0246] 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.

[0247] 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.

[0248] 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).

[0249] 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.

[0250] 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.

[0251] 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.

[0252] 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.

[0253] 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.

[0254] 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".

[0255] This invention is a system aimed at managing energy resources and promoting energy conservation. It collects energy consumption data such as electricity, gas, and water from homes and businesses, analyzes and visualizes this data, and provides specific advice on energy conservation.

[0256] First, the server acquires energy consumption data in real time from data collection devices. Various sensors and smart meters are used for this purpose, and the amount of each type of energy used is recorded in detail.

[0257] The server then preprocesses the acquired data. Preprocessing involves imputing missing values ​​and removing noise to improve data quality. This increases the reliability of the analysis.

[0258] The pre-processed data is analyzed using AI algorithms. The server then detects energy consumption trends and anomalies, and identifies consumption patterns. Based on this, potential peak consumption reductions and abnormal consumption patterns are highlighted.

[0259] The information obtained as a result of the analysis is visualized. The server generates this as graphs and charts, converting it into a format that can be intuitively understood. For example, monthly energy usage trends and daily consumption patterns can be visualized.

[0260] Next, energy-saving advice is generated based on the analysis results. The server provides customized advice based on each consumer's past usage patterns and behavioral characteristics. This includes specific actions such as how to avoid peak usage and how to adjust temperature settings.

[0261] The generated visualization data and energy-saving advice are provided to the user via the device. Through the application, users can check their current energy consumption and energy-saving suggestions. For example, the app's dashboard might display a message such as, "Your electricity usage this month is 10% higher than normal. Try reducing your usage at night."

[0262] Furthermore, bundled discounts are offered. The server calculates the optimal discount plan based on energy usage and informs the user. This makes it easy for users to have options for reducing costs.

[0263] In summary, this system aims to raise consumers' awareness of energy conservation and achieve efficient energy management.

[0264] The following describes the processing flow.

[0265] Step 1:

[0266] The server periodically acquires energy consumption data for electricity, gas, and water from data collection devices. Each data collection device transmits usage data to the server in real time using a recommended protocol. The server receives this data and stores it in a database.

[0267] Step 2:

[0268] The server preprocesses the collected data. Specifically, it fills in missing data points using surrounding data and removes outliers such as sensor noise. The preprocessed data is then passed on to the next analysis phase in a clean state.

[0269] Step 3:

[0270] The server processes pre-processed data using an AI algorithm to analyze energy consumption patterns. This analysis includes detecting seasonal variations in consumption and identifying peak usage times. As a result, abnormal patterns and consumption levels are predicted.

[0271] Step 4:

[0272] The server generates data that visualizes the analysis results. This generated data is then converted into a format easily understood by consumers, such as line graphs or pie charts. This allows users to see consumption trends over time.

[0273] Step 5:

[0274] The server generates energy-saving advice based on energy consumption patterns and user usage history. The generated advice is tailored to individual consumption situations and includes specific suggestions, such as "adjust heating and cooling to reduce daytime electricity usage."

[0275] Step 6:

[0276] The terminal displays visualization data and energy-saving advice provided by the server on its user interface. Through this application, users can check their energy consumption and take appropriate action based on the advice provided.

[0277] Step 7:

[0278] The server calculates a set discount based on the user's energy usage and notifies the user of the result. By presenting discount options, users have the opportunity to choose a plan that takes into account the reduction of their usage costs.

[0279] (Example 1)

[0280] 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."

[0281] Conventional energy management systems have limited energy consumption data collection and analysis capabilities, resulting in insufficient provision of specific energy-saving suggestions based on user consumption patterns and inadequate calculation of personalized pricing plans. Furthermore, the lack of adequate data visualization makes it difficult for users to intuitively understand their energy usage.

[0282] 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.

[0283] In this invention, the server includes means for obtaining energy consumption information from a data collection mechanism, means for preprocessing the obtained information, means for analyzing the preprocessed information to identify energy consumption trends, means for creating information for visualizing the analysis results, means for generating energy-saving proposals, means for notifying a user device, means for calculating and presenting a tariff plan based on the energy usage situation, and means for using a generated AI model. As a result, it becomes possible for users to efficiently manage energy through specific and individualized energy-saving proposals based on pattern analysis of energy consumption and intuitive data visualization.

[0284] The "data collection mechanism" refers to devices or systems for collecting real-time energy consumption information of households and enterprises.

[0285] The "energy consumption information" is data related to the usage amounts of energy such as electricity, gas, and water.

[0286] "Preprocessing" is a process of improving the quality of data by removing noise from the obtained raw data and complementing missing values.

[0287] The "consumption trend" is an energy usage pattern analyzed based on past energy usage history.

[0288] "Visualization" is to make data easier to intuitively understand by specifically displaying the analysis results in the form of graphs, charts, etc.

[0289] The "energy-saving proposal" is information presenting specific improvement measures according to individual usage situations in order to improve the efficiency of energy usage.

[0290] The "tariff plan" is an optimal tariff system provided based on the energy usage situation.

[0291] The "generated AI model" is an algorithm for analyzing data using machine learning and artificial intelligence and predicting results.

[0292] "Notifications" refer to the means of communicating generated visualization information and energy-saving suggestions to users.

[0293] A "user device" refers to a terminal or device used to display energy usage status and suggested advice.

[0294] This invention is an energy management system aimed at promoting energy conservation and optimizing consumer energy use. Specific embodiments for carrying out this invention are described below.

[0295] First, the server acquires energy consumption information in real time via data collection devices installed in homes and businesses. Hardware such as sensors and smart meters are used for this data collection. The data is stored in a database on the server, preparing it for subsequent processing.

[0296] Next, the server preprocesses the acquired information, performing noise reduction and missing value imputation. Preprocessing utilizes software tools to improve data accuracy. Through this process, the data quality is enhanced, preparing it for subsequent analysis.

[0297] The pre-processed data is analyzed by a server-generated AI model. This analysis utilizes machine learning algorithms, particularly for analyzing energy consumption patterns and detecting anomalies. This reveals past consumption trends and potential for reducing peak consumption.

[0298] The server then outputs the analysis results in a visually understandable format. Specifically, software libraries are used to visualize the data as graphs and charts. This makes it easy to see monthly consumption trends and daily usage patterns.

[0299] In addition, the server creates energy-saving proposals tailored to individual users. It proposes optimal energy-saving advice considering past usage patterns and behavioral characteristics. For example, specific improvement measures are provided in the form of "Raising the set temperature of the air conditioner by 1 degree can reduce power consumption by 5%".

[0300] Finally, the generated visualization information and energy-saving proposals are provided to the user through the terminal. The user can view these proposals using the application. For example, messages such as "Changing the current usage plan can result in a cost reduction of 20,000 yen annually" are sent using the notification function of the app.

[0301] Examples of specific prompt texts include those in the form of "I want to generate energy-saving proposals based on my home's energy consumption data. Please provide the optimal proposals".

[0302] Through these functions, the system of the present invention enables efficient energy management and increased awareness for energy conservation.

[0303] The flow of specific processing in Example 1 will be described using FIG. 11.

[0304] Step 1:

[0305] The server obtains energy consumption information such as electricity, gas, and water in real time through a data collection mechanism.

[0306] Input: Raw data from sensors and smart meters of each household and enterprise

[0307] Specific operation: The server receives information from the sensors using a data transfer protocol and stores it in the database.

[0308] Output: Raw data with timestamps is saved in the database.

[0309] Step 2:

[0310] The server performs preprocessing on the acquired raw data.

[0311] Input: Raw data stored on the server

[0312] Specific operation: Missing data values ​​are inferred and imputed from surrounding data, and a denoising algorithm is applied to remove outliers.

[0313] Output: High-quality, pre-processed dataset

[0314] Step 3:

[0315] The server analyzes the pre-processed data using an AI model.

[0316] Input: Improved quality dataset

[0317] Specific actions: Apply a generative AI model to analyze consumption trends. This uses machine learning algorithms to identify peak usage times and detect abnormal consumption activity.

[0318] Output: Results of detecting consumption patterns and anomalies

[0319] Step 4:

[0320] The server visualizes the analysis results by converting them into graphs and charts.

[0321] Input: Analysis results of consumption patterns and anomaly detection

[0322] Specific operation: Use the visualization library to generate graphs and charts to display the analysis results on a dashboard.

[0323] Output: Image data of visualized energy consumption trends

[0324] Step 5:

[0325] The server generates energy-saving suggestions based on the visualized data.

[0326] Input: Visualized consumer trend data

[0327] Specific operation: Referencing past consumption data, the system uses an AI model to generate specific advice on how to reduce consumption during peak hours.

[0328] Output: A series of customized energy-saving proposals

[0329] Step 6:

[0330] The terminal provides the user with generated visualization data and energy-saving suggestions.

[0331] Input: Visualization data and energy-saving suggestions sent from the server.

[0332] Specific actions: The application will display these suggestions on the dashboard and notify the user of the suggestions through notifications.

[0333] Output: Visualization graphs on the dashboard and energy-saving advice messages.

[0334] Step 7:

[0335] The server calculates the pricing plan based on energy consumption and notifies the user.

[0336] Input: Analysis results of consumption data and various pricing information.

[0337] Specific operation: Uses AI to calculate pricing plans and discount options and proposes the optimal plan for the user.

[0338] Output: Optimized pricing plan suggestion message

[0339] (Application Example 1)

[0340] 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."

[0341] In recent years, large-scale production facilities such as factories have been required to improve energy efficiency, with particular emphasis on optimizing energy use. However, there is a lack of concrete means to monitor the energy consumption of individual machines in real time and to implement efficient energy management. Furthermore, there is a need to customize energy consumption advice based on individual behavioral characteristics and improve usability. Therefore, these challenges need to be addressed effectively.

[0342] 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.

[0343] In this invention, the server includes means for acquiring energy consumption information from a data collection mechanism, means for preprocessing the acquired information, and means for analyzing the preprocessed information to identify energy utilization patterns. This enables real-time monitoring of the operating status of individual machines within a factory and optimization of energy consumption.

[0344] A "data collection mechanism" is a device designed to accurately and efficiently acquire energy consumption information.

[0345] "Energy consumption information" refers to data showing the usage status of energy resources such as electricity, gas, and water.

[0346] "Preprocessing" refers to processes such as data interpolation and noise reduction performed to improve the completeness and quality of acquired data.

[0347] "Energy utilization patterns" refer to trends and regularities that can be obtained by analyzing energy consumption trends and the characteristics of consumption associated with specific behaviors.

[0348] "Visualization" is the process of transforming analysis results into a form that can be intuitively understood by displaying them in the format of graphs and charts.

[0349] "Energy conservation advice" refers to specific action suggestions provided to improve the efficiency of energy consumption.

[0350] "User equipment" refers to electronic devices used to present users with visualized data and energy-saving advice.

[0351] "Operating status" refers to the current operating status and load conditions of machinery and equipment.

[0352] "Real-time monitoring" refers to the act of immediately checking the current situation and recording any changes as they occur.

[0353] "Optimizing energy consumption" is the process of making adjustments and improvements to achieve efficient use of energy resources.

[0354] As a concrete example of implementing this invention, an energy management system will be constructed in a factory. The server will acquire energy consumption information in real time using data collection mechanisms installed in robots and machinery within the factory. Various sensors and internet-connected measuring instruments will be used for this purpose.

[0355] Next, the server preprocesses the acquired energy consumption information. Preprocessing involves imputing missing values ​​and removing noise to improve the quality of the information. The preprocessed information is then analyzed using AI algorithms on the server. The analysis is performed in a Python environment, using machine learning libraries such as Scikit-learn to identify energy usage patterns.

[0356] The server visualizes the analysis results and utilizes visualization tools such as Tableau to create intuitively understandable graphs and charts. Based on these results, the server generates energy-saving advice and provides it to the user's equipment. The advice is customized to take into account the operating conditions and peak energy consumption of individual machines.

[0357] As a concrete example, if a factory experiences high energy consumption during certain operating times for robot A, the system will review those times and provide energy-saving advice to enable more efficient operation. Furthermore, the AI ​​model will be provided with prompts such as, "Analyze the collected robot energy data and create optimization suggestions. Specifically, describe methods for reducing energy consumption during peak hours."

[0358] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0359] Step 1:

[0360] The server acquires energy consumption information in real time from data collection mechanisms installed in each robot and piece of machinery within the factory. This information includes the moment-by-moment power consumption and operating time of each robot. Based on this input data, it is stored in a database for recording.

[0361] Step 2:

[0362] The server preprocesses the acquired energy consumption information. Specifically, it uses a Python environment to impute missing values ​​and perform data cleaning to remove noise. It generates reliable, clean data from the input data and prepares it for subsequent analysis processes. This output data is then prepared as data for analysis.

[0363] Step 3:

[0364] The server starts analysis using AI algorithms with pre-processed information. It uses machine learning libraries such as Scikit-learn to identify energy consumption trends and anomalies. This process identifies energy usage patterns using clean data as input, and the output includes consumption trend data and anomaly detection results.

[0365] Step 4:

[0366] The server generates energy-saving advice based on the analysis results. It then formulates concrete action plans and proposes optimized equipment operation schedules based on the analysis results to provide actionable advice for the user. The generated advice is used as input data for a visualization program, yielding output data that encompasses energy-saving measures.

[0367] Step 5:

[0368] The server visualizes the analysis results in graphs and charts using visualization tools such as Tableau. It analyzes the visualized data based on a generation AI model and prepares it in a format that can be easily presented to the user. Based on this process, it outputs visualization results that include the prompt message "Analyze the collected robot energy data and create suggestions for optimization. Specifically show how to reduce energy consumption during peak times."

[0369] Step 6:

[0370] The terminal provides the user with generated visualization data and energy-saving advice. In this final process, the user can check energy consumption in real time through the application and take concrete actions to implement efficient energy management. A responsive interface is presented to the user during this output process.

[0371] 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.

[0372] This invention is a system that recognizes the user's emotional state and manages their energy based on that state. It primarily involves the coordinated functioning of a data collection device, an emotion engine, a server, and a terminal.

[0373] First, the server acquires energy consumption data in real time from data collection devices installed in homes and businesses. This uses smart meters and IoT sensors to record detailed usage of electricity, gas, and water.

[0374] Next, the server uses an emotion engine to acquire the user's emotional data. This emotional data is obtained in real time through wearable devices, cameras, voice analysis, and other means. The emotion engine determines the user's emotions by analyzing factors such as the frequency of their smiles, stress levels, and tone of voice.

[0375] Based on the acquired energy consumption and emotional data, the server preprocesses them to generate a clean dataset. The preprocessed data is then subjected to an AI algorithm to analyze trends related to energy consumption patterns and emotions. This analysis observes changes in the user's emotions during energy use and develops energy-saving advice that is tailored to their emotional state.

[0376] The generated advice is customized based on each user's individual energy consumption habits and emotional patterns. For example, during times of high stress, it provides suggestions for relaxing lighting settings and positive messages encouraging improvements to energy consumption patterns. It also utilizes an emotional engine to recommend adopting new energy-saving behaviors during times of emotional stability.

[0377] The resulting visualization data and energy-saving advice are provided to the user via the device. The application on the device intuitively displays consumption status and advice to the user and encourages energy-saving behavior by providing emotionally resonant feedback.

[0378] Thus, this invention aims to enhance energy conservation awareness and make users' lives more comfortable and efficient by linking an emotion engine with energy consumption data.

[0379] The following describes the processing flow.

[0380] Step 1:

[0381] The server acquires real-time energy consumption data for electricity, gas, and water from data collection devices. This includes receiving data from smart meters and IoT sensors and storing it in a database.

[0382] Step 2:

[0383] The server collects user emotional data through an emotion engine. It uses wearable devices, cameras, and voice analysis to evaluate the user's emotional state based on facial expressions, voice tone, heart rate, etc., and stores this data.

[0384] Step 3:

[0385] The server preprocesses the acquired energy consumption and sentiment data. This includes imputing missing values ​​and removing noise. It also standardizes the sentiment data and formats it into a format that is easy to analyze.

[0386] Step 4:

[0387] The server feeds pre-processed data into an AI algorithm to simultaneously analyze energy consumption patterns and emotional trends. This process detects how energy consumption influences emotions, and vice versa.

[0388] Step 5:

[0389] The server generates energy-saving advice based on the analysis results. It creates specific advice that improves energy consumption patterns according to emotional state. For example, it might suggest, "You seem tired. Try using warm lighting in the evening to relax."

[0390] Step 6:

[0391] The terminal displays visualization data and energy-saving advice provided by the server in its user interface. The user interface is designed to visually represent daily consumption trends and the associated emotional impact, making it easy for users to understand the information.

[0392] Step 7:

[0393] Based on the information provided through the application, users adjust their daily energy consumption behaviors. Furthermore, they actively create a more relaxing environment based on feedback from the emotional engine.

[0394] In this way, this system provides a next-generation energy use support tool that integrates energy management with emotional considerations.

[0395] (Example 2)

[0396] 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".

[0397] Energy consumption is increasing in modern homes and businesses, making efficient energy-saving measures increasingly necessary. However, energy-saving proposals that take into account the emotional state of users are often lacking. Conventional systems focus solely on energy consumption, ignoring the impact of emotional states, which can lead to them being unacceptable to users. It is necessary to address these challenges and propose more effective and user-friendly energy-saving measures.

[0398] 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.

[0399] In this invention, the server includes means for acquiring information related to energy consumption from a data collection mechanism, means for acquiring emotional information via an emotional engine for detecting the user's emotional state, and means for preprocessing the acquired information and integrating it with the emotional information. This enables the provision of customized energy-saving advice based on emotions.

[0400] A "data collection mechanism" refers to any device that measures energy consumption and provides that data to a server.

[0401] "Information related to energy consumption" refers to data on the amount and status of use of various types of energy, such as electricity, gas, and water.

[0402] An "emotion engine" refers to a collection of software and hardware that analyzes a user's emotional state and generates data based on that analysis.

[0403] "Emotional information" refers to data that quantifies a user's emotional state, and is usually based on facial expressions, voice tone, heart rate, etc.

[0404] "Preprocessing" refers to a series of processes that prepare raw data for analysis, including data cleaning and imputation of missing values.

[0405] "Consumption patterns" refer to behavioral patterns that show certain trends or tendencies in energy consumption.

[0406] "Energy-saving advice" refers to information that includes suggestions and recommended actions to promote energy conservation, and is customized for each user.

[0407] "User device" refers to a terminal or application that the user directly operates, and is the medium on which analysis results and advice are displayed.

[0408] This invention relates to a system that provides energy-saving advice that takes into account the user's emotional state. It primarily involves the coordinated functioning of a data collection mechanism, an emotion engine, a server, and a terminal.

[0409] The server acquires energy consumption-related information in real time from data collection devices installed in homes and businesses. For example, it records electricity, gas, and water usage obtained from smart meters and IoT sensors. This information is stored as a dataset that forms the basis for analysis.

[0410] Next, the server uses an emotion engine to acquire the user's emotional information. This emotional information is collected through wearable devices, surveillance cameras, and voice analysis software. The emotion engine analyzes data such as the user's facial expressions, voice tone, and heart rate to determine their emotional state.

[0411] The obtained energy consumption and emotional data are preprocessed on the server and prepared into an analyzable dataset. This preprocessing includes data cleaning and formatting standardization.

[0412] Next, the pre-processed data is analyzed by a generative AI model. In particular, it analyzes the relationship between energy consumption patterns and emotional information, and generates energy-saving advice based on the user's emotional state. This generative AI model can identify periods when emotions are stable and periods when stress levels are high, and propose optimal consumption behaviors accordingly.

[0413] The generated advice and its analysis results are provided to the user via the terminal. The interface on the terminal intuitively visualizes consumption status and energy-saving advice, outputting information in a format that is easy for the user to understand. For example, it may recommend playing relaxing music during the evening hours when stress levels tend to be higher.

[0414] Another example of an appropriate prompt is, "Analyze the user's energy consumption patterns during times when their stress levels are high, and suggest ways to help them relax."

[0415] In this way, this system can promote energy-saving behavior that takes into account the emotional state of users, thereby improving energy consumption.

[0416] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0417] Step 1:

[0418] The server acquires information related to energy consumption from the data collection mechanism. This input data is periodically transmitted through smart meters and IoT sensors, and the usage of electricity, gas, and water is recorded as numerical values. The server stores this raw data in a temporary database. The output of this step is raw energy consumption data.

[0419] Step 2:

[0420] The server uses an emotion engine to acquire data on the user's emotional state. Inputs include heart rate data collected by wearable devices, facial expression analysis from cameras, and voice tone analysis from voice analysis. This information is transmitted to the server in real time and converted into numerical values ​​and indicators defined as emotional states. The output of this step is the user's current emotional state data.

[0421] Step 3:

[0422] The server preprocesses the acquired energy consumption and sentiment data. The input dataset undergoes a cleaning process, imputing missing values ​​and removing noise. It also unifies different data formats and formats them into an analyzable form. This process allows the server to output a clean dataset.

[0423] Step 4:

[0424] The server uses pre-processed data to perform analysis using a generative AI model. This analysis examines the correlation between energy consumption patterns and emotional states based on the input data. Using prompts, it outputs suggestions for energy-saving actions under various scenarios. The output of this step is energy-saving advice based on predicted consumption patterns and emotions.

[0425] Step 5:

[0426] The terminal displays energy-saving advice and analysis results sent from the server to the user. The input includes analysis results and advice data, which the application on the terminal displays graphically. This is a means of clearly communicating suggestions based on consumption patterns and emotions to the user. The output of this step is visualized information through the user interface.

[0427] Step 6:

[0428] Based on the information displayed on the device, users take specific energy-saving actions. The input of operations and actions is fed back to the server through the data collection mechanism. This allows the server to incorporate the latest information into subsequent data analysis and advice. This feedback loop enables even more sophisticated energy-saving suggestions.

[0429] (Application Example 2)

[0430] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0431] Optimizing energy consumption and improving working conditions are critical challenges in many industries. In particular, in workplaces like factories, considering workers' emotional states while optimizing energy consumption is essential for simultaneously improving work efficiency and employee satisfaction. However, an integrated system for managing these simultaneously does not yet exist. Therefore, there is a need for the development of a user-centered energy management system that utilizes emotional data.

[0432] 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.

[0433] In this invention, the server includes means for acquiring energy usage data from a data acquisition device, means for acquiring user emotional data using an emotional analysis device, and means for performing preprocessing of the acquired data. This makes it possible to optimize energy usage based on the user's emotional state.

[0434] A "data acquisition device" is a device used to collect information related to energy use, and includes smart meters and IoT sensors.

[0435] "Energy usage data" refers to information showing the consumption status of electricity, gas, water, etc., and is acquired in real time.

[0436] "Preprocessing" is the stage of converting acquired data into a format that is easy to analyze, and includes noise reduction and data formatting.

[0437] An "emotion analysis device" is a device that analyzes a user's emotional state from their facial expressions and voice, and utilizes a camera and voice recognition system.

[0438] "Visualization data" refers to information used to visually display analyzed energy utilization patterns and emotional states, and is expressed in the form of graphs and charts.

[0439] "Energy-saving advice" refers to suggestions for making users' energy use more efficient, and is generated based on individual behaviors and emotional patterns.

[0440] A "user device" is a terminal used to provide users with visualized data and energy-saving advice, and includes smartphones and tablets.

[0441] A "means for generating commands" is a mechanism that generates instructions for controlling systems and devices based on emotional data.

[0442] The system for carrying out this invention includes a data acquisition device, an emotion analysis device, a server, and a user device. The server acquires energy usage data from the data acquisition device and performs preprocessing. Subsequently, it integrates the user's emotion data acquired by the emotion analysis device and analyzes this data using an AI algorithm. The analyzed data links energy usage patterns with the user's emotional state and is used to adjust the operating environment and generate energy-saving advice as needed.

[0443] This system uses general-purpose computers and data analysis software, and specific hardware platforms such as NVIDIA Jetson and Raspberry Pi can be utilized. For sentiment analysis, software such as Microsoft Azure's Face API and Google Cloud Speech-to-Text API are effective. The user device is responsible for receiving the generated energy-saving advice and visualization data and displaying it to the user.

[0444] As a concrete example, in the work environment of factory worker A, the system acquires emotional data in real time and, based on the analysis, determines that A is feeling fatigued. In this case, it can immediately suggest adjusting the lighting brightness to create a more relaxing work environment.

[0445] An example of a prompt message could be: "Optimize the energy settings in the factory based on the stress levels of the workers, and explain how to do so." This makes it possible to optimize energy consumption while providing a more comfortable working environment for the workers.

[0446] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0447] Step 1:

[0448] The server acquires energy usage data in real time from data acquisition devices. The input is raw energy usage data transmitted from smart meters and IoT sensors. This data is temporarily stored on the server side in storage. The output is energy usage data stored in a standardized format.

[0449] Step 2:

[0450] The server collects user emotional data using an emotion analysis device. Inputs include raw data from wearable devices and visual and auditory information captured through cameras and microphones. This data is analyzed using an emotion analysis API to extract the user's emotional state as digital data. The output is numerical data related to the user's emotions.

[0451] Step 3:

[0452] The server preprocesses energy utilization data and sentiment data to generate a consistent dataset. The input is the raw data obtained in steps 1 and 2. Data quality is improved by removing noise and imputing missing values. The output is a clean, analyzable dataset.

[0453] Step 4:

[0454] The server feeds the preprocessed dataset into an AI algorithm for analysis. The input is the clean dataset obtained in step 3. Analysis using a machine learning model is performed to identify trends related to energy consumption patterns and emotions. The output is the analyzed energy-emotion correspondence data.

[0455] Step 5:

[0456] The server generates energy-saving advice based on the analysis results. The input is the analyzed data obtained from step 4. Prompts are used to instruct the generating AI, which outputs helpful energy-saving advice. The output is personalized energy-saving advice based on the user's energy usage and emotional state.

[0457] Step 6:

[0458] The terminal displays energy-saving advice and visualization data sent from the server to the user. The input consists of the energy-saving advice and data processed for visualization obtained in step 5. This is visually presented through the terminal's interface and provided in a format that is easy for the user to understand. The output is information to encourage specific energy-saving actions for the user and guidance on what actions the user should take.

[0459] 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.

[0460] 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.

[0461] 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.

[0462] [Third Embodiment]

[0463] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0464] 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.

[0465] 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).

[0466] 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.

[0467] 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.

[0468] 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).

[0469] 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.

[0470] 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.

[0471] 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.

[0472] 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.

[0473] 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.

[0474] 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".

[0475] This invention is a system aimed at managing energy resources and promoting energy conservation. It collects energy consumption data such as electricity, gas, and water from homes and businesses, analyzes and visualizes this data, and provides specific advice on energy conservation.

[0476] First, the server acquires energy consumption data in real time from data collection devices. Various sensors and smart meters are used for this purpose, and the amount of each type of energy used is recorded in detail.

[0477] The server then preprocesses the acquired data. Preprocessing involves imputing missing values ​​and removing noise to improve data quality. This increases the reliability of the analysis.

[0478] The pre-processed data is analyzed using AI algorithms. The server then detects energy consumption trends and anomalies, and identifies consumption patterns. Based on this, potential peak consumption reductions and abnormal consumption patterns are highlighted.

[0479] The information obtained as a result of the analysis is visualized. The server generates this as graphs and charts, converting it into a format that can be intuitively understood. For example, monthly energy usage trends and daily consumption patterns can be visualized.

[0480] Next, energy-saving advice is generated based on the analysis results. The server provides customized advice based on each consumer's past usage patterns and behavioral characteristics. This includes specific actions such as how to avoid peak usage and how to adjust temperature settings.

[0481] The generated visualization data and energy-saving advice are provided to the user via the device. Through the application, users can check their current energy consumption and energy-saving suggestions. For example, the app's dashboard might display a message such as, "Your electricity usage this month is 10% higher than normal. Try reducing your usage at night."

[0482] Furthermore, bundled discounts are offered. The server calculates the optimal discount plan based on energy usage and informs the user. This makes it easy for users to have options for reducing costs.

[0483] In summary, this system aims to raise consumers' awareness of energy conservation and achieve efficient energy management.

[0484] The following describes the processing flow.

[0485] Step 1:

[0486] The server periodically acquires energy consumption data for electricity, gas, and water from data collection devices. Each data collection device transmits usage data to the server in real time using a recommended protocol. The server receives this data and stores it in a database.

[0487] Step 2:

[0488] The server preprocesses the collected data. Specifically, it fills in missing data points using surrounding data and removes outliers such as sensor noise. The preprocessed data is then passed on to the next analysis phase in a clean state.

[0489] Step 3:

[0490] The server processes pre-processed data using an AI algorithm to analyze energy consumption patterns. This analysis includes detecting seasonal variations in consumption and identifying peak usage times. As a result, abnormal patterns and consumption levels are predicted.

[0491] Step 4:

[0492] The server generates data that visualizes the analysis results. This generated data is then converted into a format easily understood by consumers, such as line graphs or pie charts. This allows users to see consumption trends over time.

[0493] Step 5:

[0494] The server generates energy-saving advice based on energy consumption patterns and user usage history. The generated advice is tailored to individual consumption situations and includes specific suggestions, such as "adjust heating and cooling to reduce daytime electricity usage."

[0495] Step 6:

[0496] The terminal displays visualization data and energy-saving advice provided by the server on its user interface. Through this application, users can check their energy consumption and take appropriate action based on the advice provided.

[0497] Step 7:

[0498] The server calculates a set discount based on the user's energy usage and notifies the user of the result. By presenting discount options, users have the opportunity to choose a plan that takes into account the reduction of their usage costs.

[0499] (Example 1)

[0500] 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."

[0501] Conventional energy management systems have limited energy consumption data collection and analysis capabilities, resulting in insufficient provision of specific energy-saving suggestions based on user consumption patterns and inadequate calculation of personalized pricing plans. Furthermore, the lack of adequate data visualization makes it difficult for users to intuitively understand their energy usage.

[0502] 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.

[0503] In this invention, the server includes means for acquiring energy consumption information from a data collection mechanism, means for preprocessing the acquired information, means for analyzing the preprocessed information to identify energy consumption trends, means for creating information to visualize the analysis results, means for generating energy-saving suggestions, means for notifying the user device, means for calculating and presenting a pricing plan based on energy usage, and means for using a generated AI model. This enables users to efficiently manage their energy consumption through specific and personalized energy-saving suggestions based on energy consumption pattern analysis and intuitive data visualization.

[0504] A "data collection mechanism" refers to a device or system used to collect energy consumption information from households and businesses in real time.

[0505] "Energy consumption information" refers to data on the amount of energy used, such as electricity, gas, and water.

[0506] "Preprocessing" is a process that improves data quality by removing noise and imputing missing values ​​from acquired raw data.

[0507] "Consumption trends" refer to energy usage patterns analyzed based on past energy usage history.

[0508] "Visualization" refers to making data easier to understand intuitively by displaying analysis results in concrete forms such as graphs and charts.

[0509] An "energy-saving proposal" is information that presents specific improvement measures tailored to individual usage patterns in order to improve the efficiency of energy use.

[0510] A "pricing plan" is an optimal pricing structure provided based on energy usage.

[0511] A "generative AI model" is an algorithm that uses machine learning and artificial intelligence to analyze data and predict results.

[0512] "Notifications" refer to the means of communicating generated visualization information and energy-saving suggestions to users.

[0513] A "user device" refers to a terminal or device used to display energy usage status and suggested advice.

[0514] This invention is an energy management system aimed at promoting energy conservation and optimizing consumer energy use. Specific embodiments for carrying out this invention are described below.

[0515] First, the server acquires energy consumption information in real time via data collection devices installed in homes and businesses. Hardware such as sensors and smart meters are used for this data collection. The data is stored in a database on the server, preparing it for subsequent processing.

[0516] Next, the server preprocesses the acquired information, performing noise reduction and missing value imputation. Preprocessing utilizes software tools to improve data accuracy. Through this process, the data quality is enhanced, preparing it for subsequent analysis.

[0517] The pre-processed data is analyzed by a server-generated AI model. This analysis utilizes machine learning algorithms, particularly for analyzing energy consumption patterns and detecting anomalies. This reveals past consumption trends and potential for reducing peak consumption.

[0518] The server then outputs the analysis results in a visually understandable format. Specifically, software libraries are used to visualize the data as graphs and charts. This makes it easy to see monthly consumption trends and daily usage patterns.

[0519] In addition, the server creates energy-saving suggestions tailored to each individual user. It proposes optimal energy-saving advice based on past usage patterns and behavioral characteristics. For example, it provides specific improvement measures such as, "Raising the air conditioner's temperature setting by 1 degree will reduce power consumption by 5%."

[0520] Finally, the generated visualization information and energy-saving suggestions are provided to the user via their device. Users can review these suggestions using the application. For example, the app's notification function might send a message such as, "Changing your current usage plan could save you 20,000 yen per year."

[0521] A concrete example of a prompt message would be something like, "I want to generate energy-saving suggestions based on household energy consumption data. Please tell me the best suggestions."

[0522] Through these functions, the system of the present invention enables efficient energy management and increased awareness for energy conservation.

[0523] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0524] Step 1:

[0525] The server acquires energy consumption information, such as electricity, gas, and water, in real time through a data collection mechanism.

[0526] Input: Raw data from sensors and smart meters in homes and businesses.

[0527] Specific operation: The server receives information from the sensor using a data transfer protocol and stores it in the database.

[0528] Output: The raw data with a timestamp is saved to the database.

[0529] Step 2:

[0530] The server performs preprocessing on the acquired raw data.

[0531] Input: Raw data stored on the server

[0532] Specific operation: Missing data values ​​are inferred and imputed from surrounding data, and a denoising algorithm is applied to remove outliers.

[0533] Output: High-quality, pre-processed dataset

[0534] Step 3:

[0535] The server analyzes the pre-processed data using an AI model.

[0536] Input: Improved quality dataset

[0537] Specific actions: Apply a generative AI model to analyze consumption trends. This uses machine learning algorithms to identify peak usage times and detect abnormal consumption activity.

[0538] Output: Results of detecting consumption patterns and anomalies

[0539] Step 4:

[0540] The server visualizes the analysis results by converting them into graphs and charts.

[0541] Input: Analysis results of consumption patterns and anomaly detection

[0542] Specific operation: Use the visualization library to generate graphs and charts to display the analysis results on a dashboard.

[0543] Output: Image data of visualized energy consumption trends

[0544] Step 5:

[0545] The server generates energy-saving suggestions based on the visualized data.

[0546] Input: Visualized consumer trend data

[0547] Specific operation: Referencing past consumption data, the system uses an AI model to generate specific advice on how to reduce consumption during peak hours.

[0548] Output: A series of customized energy-saving proposals

[0549] Step 6:

[0550] The terminal provides the user with generated visualization data and energy-saving suggestions.

[0551] Input: Visualization data and energy-saving suggestions sent from the server.

[0552] Specific actions: The application will display these suggestions on the dashboard and notify the user of the suggestions through notifications.

[0553] Output: Visualization graphs on the dashboard and energy-saving advice messages.

[0554] Step 7:

[0555] The server calculates the pricing plan based on energy consumption and notifies the user.

[0556] Input: Analysis results of consumption data and various pricing information.

[0557] Specific operation: Uses AI to calculate pricing plans and discount options and proposes the optimal plan for the user.

[0558] Output: Optimized pricing plan suggestion message

[0559] (Application Example 1)

[0560] 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."

[0561] In recent years, large-scale production facilities such as factories have been required to improve energy efficiency, with particular emphasis on optimizing energy use. However, there is a lack of concrete means to monitor the energy consumption of individual machines in real time and to implement efficient energy management. Furthermore, there is a need to customize energy consumption advice based on individual behavioral characteristics and improve usability. Therefore, these challenges need to be addressed effectively.

[0562] 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.

[0563] In this invention, the server includes means for acquiring energy consumption information from a data collection mechanism, means for preprocessing the acquired information, and means for analyzing the preprocessed information to identify energy utilization patterns. This enables real-time monitoring of the operating status of individual machines within a factory and optimization of energy consumption.

[0564] A "data collection mechanism" is a device designed to accurately and efficiently acquire energy consumption information.

[0565] "Energy consumption information" refers to data showing the usage status of energy resources such as electricity, gas, and water.

[0566] "Preprocessing" refers to processes such as data interpolation and noise reduction performed to improve the completeness and quality of acquired data.

[0567] "Energy utilization patterns" refer to trends and regularities that can be obtained by analyzing energy consumption trends and the characteristics of consumption associated with specific behaviors.

[0568] "Visualization" is the process of transforming analysis results into a form that can be intuitively understood by displaying them in the format of graphs and charts.

[0569] "Energy conservation advice" refers to specific action suggestions provided to improve the efficiency of energy consumption.

[0570] "User equipment" refers to electronic devices used to present users with visualized data and energy-saving advice.

[0571] "Operating status" refers to the current operating status and load conditions of machinery and equipment.

[0572] "Real-time monitoring" refers to the act of immediately checking the current situation and recording any changes as they occur.

[0573] "Optimizing energy consumption" is the process of making adjustments and improvements to achieve efficient use of energy resources.

[0574] As a concrete example of implementing this invention, an energy management system will be constructed in a factory. The server will acquire energy consumption information in real time using data collection mechanisms installed in robots and machinery within the factory. Various sensors and internet-connected measuring instruments will be used for this purpose.

[0575] Next, the server preprocesses the acquired energy consumption information. Preprocessing involves imputing missing values ​​and removing noise to improve the quality of the information. The preprocessed information is then analyzed using AI algorithms on the server. The analysis is performed in a Python environment, using machine learning libraries such as Scikit-learn to identify energy usage patterns.

[0576] The server visualizes the analysis results and utilizes visualization tools such as Tableau to create intuitively understandable graphs and charts. Based on these results, the server generates energy-saving advice and provides it to the user's equipment. The advice is customized to take into account the operating conditions and peak energy consumption of individual machines.

[0577] As a concrete example, if a factory experiences high energy consumption during certain operating times for robot A, the system will review those times and provide energy-saving advice to enable more efficient operation. Furthermore, the AI ​​model will be provided with prompts such as, "Analyze the collected robot energy data and create optimization suggestions. Specifically, describe methods for reducing energy consumption during peak hours."

[0578] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0579] Step 1:

[0580] The server acquires energy consumption information in real time from data collection mechanisms installed in each robot and piece of machinery within the factory. This information includes the moment-by-moment power consumption and operating time of each robot. Based on this input data, it is stored in a database for recording.

[0581] Step 2:

[0582] The server preprocesses the acquired energy consumption information. Specifically, it uses a Python environment to impute missing values ​​and perform data cleaning to remove noise. It generates reliable, clean data from the input data and prepares it for subsequent analysis processes. This output data is then prepared as data for analysis.

[0583] Step 3:

[0584] The server starts analysis using AI algorithms with pre-processed information. It uses machine learning libraries such as Scikit-learn to identify energy consumption trends and anomalies. This process identifies energy usage patterns using clean data as input, and the output includes consumption trend data and anomaly detection results.

[0585] Step 4:

[0586] The server generates energy-saving advice based on the analysis results. It then formulates concrete action plans and proposes optimized equipment operation schedules based on the analysis results to provide actionable advice for the user. The generated advice is used as input data for a visualization program, yielding output data that encompasses energy-saving measures.

[0587] Step 5:

[0588] The server visualizes the analysis results in graphs and charts using visualization tools such as Tableau. It analyzes the visualized data based on a generation AI model and prepares it in a format that can be easily presented to the user. Based on this process, it outputs visualization results that include the prompt message "Analyze the collected robot energy data and create suggestions for optimization. Specifically show how to reduce energy consumption during peak times."

[0589] Step 6:

[0590] The terminal provides the user with generated visualization data and energy-saving advice. In this final process, the user can check energy consumption in real time through the application and take concrete actions to implement efficient energy management. A responsive interface is presented to the user during this output process.

[0591] 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.

[0592] This invention is a system that recognizes the user's emotional state and manages their energy based on that state. It primarily involves the coordinated functioning of a data collection device, an emotion engine, a server, and a terminal.

[0593] First, the server acquires energy consumption data in real time from data collection devices installed in homes and businesses. This uses smart meters and IoT sensors to record detailed usage of electricity, gas, and water.

[0594] Next, the server uses an emotion engine to acquire the user's emotional data. This emotional data is obtained in real time through wearable devices, cameras, voice analysis, and other means. The emotion engine determines the user's emotions by analyzing factors such as the frequency of their smiles, stress levels, and tone of voice.

[0595] Based on the acquired energy consumption and emotional data, the server preprocesses them to generate a clean dataset. The preprocessed data is then subjected to an AI algorithm to analyze trends related to energy consumption patterns and emotions. This analysis observes changes in the user's emotions during energy use and develops energy-saving advice that is tailored to their emotional state.

[0596] The generated advice is customized based on each user's individual energy consumption habits and emotional patterns. For example, during times of high stress, it provides suggestions for relaxing lighting settings and positive messages encouraging improvements to energy consumption patterns. It also utilizes an emotional engine to recommend adopting new energy-saving behaviors during times of emotional stability.

[0597] The resulting visualization data and energy-saving advice are provided to the user via the device. The application on the device intuitively displays consumption status and advice to the user and encourages energy-saving behavior by providing emotionally resonant feedback.

[0598] Thus, this invention aims to enhance energy conservation awareness and make users' lives more comfortable and efficient by linking an emotion engine with energy consumption data.

[0599] The following describes the processing flow.

[0600] Step 1:

[0601] The server acquires real-time energy consumption data for electricity, gas, and water from data collection devices. This includes receiving data from smart meters and IoT sensors and storing it in a database.

[0602] Step 2:

[0603] The server collects user emotional data through an emotion engine. It uses wearable devices, cameras, and voice analysis to evaluate the user's emotional state based on facial expressions, voice tone, heart rate, etc., and stores this data.

[0604] Step 3:

[0605] The server preprocesses the acquired energy consumption and sentiment data. This includes imputing missing values ​​and removing noise. It also standardizes the sentiment data and formats it into a format that is easy to analyze.

[0606] Step 4:

[0607] The server feeds pre-processed data into an AI algorithm to simultaneously analyze energy consumption patterns and emotional trends. This process detects how energy consumption influences emotions, and vice versa.

[0608] Step 5:

[0609] The server generates energy-saving advice based on the analysis results. It creates specific advice that improves energy consumption patterns according to emotional state. For example, it might suggest, "You seem tired. Try using warm lighting in the evening to relax."

[0610] Step 6:

[0611] The terminal displays visualization data and energy-saving advice provided by the server in its user interface. The user interface is designed to visually represent daily consumption trends and the associated emotional impact, making it easy for users to understand the information.

[0612] Step 7:

[0613] Based on the information provided through the application, users adjust their daily energy consumption behaviors. Furthermore, they actively create a more relaxing environment based on feedback from the emotional engine.

[0614] In this way, this system provides a next-generation energy use support tool that integrates energy management with emotional considerations.

[0615] (Example 2)

[0616] 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."

[0617] Energy consumption is increasing in modern homes and businesses, making efficient energy-saving measures increasingly necessary. However, energy-saving proposals that take into account the emotional state of users are often lacking. Conventional systems focus solely on energy consumption, ignoring the impact of emotional states, which can lead to them being unacceptable to users. It is necessary to address these challenges and propose more effective and user-friendly energy-saving measures.

[0618] 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.

[0619] In this invention, the server includes means for acquiring information related to energy consumption from a data collection mechanism, means for acquiring emotional information via an emotional engine for detecting the user's emotional state, and means for preprocessing the acquired information and integrating it with the emotional information. This enables the provision of customized energy-saving advice based on emotions.

[0620] A "data collection mechanism" refers to any device that measures energy consumption and provides that data to a server.

[0621] "Information related to energy consumption" refers to data on the amount and status of use of various types of energy, such as electricity, gas, and water.

[0622] An "emotion engine" refers to a collection of software and hardware that analyzes a user's emotional state and generates data based on that analysis.

[0623] "Emotional information" refers to data that quantifies a user's emotional state, and is usually based on facial expressions, voice tone, heart rate, etc.

[0624] "Preprocessing" refers to a series of processes that prepare raw data for analysis, including data cleaning and imputation of missing values.

[0625] "Consumption patterns" refer to behavioral patterns that show certain trends or tendencies in energy consumption.

[0626] "Energy-saving advice" refers to information that includes suggestions and recommended actions to promote energy conservation, and is customized for each user.

[0627] "User device" refers to a terminal or application that the user directly operates, and is the medium on which analysis results and advice are displayed.

[0628] This invention relates to a system that provides energy-saving advice that takes into account the user's emotional state. It primarily involves the coordinated functioning of a data collection mechanism, an emotion engine, a server, and a terminal.

[0629] The server acquires energy consumption-related information in real time from data collection devices installed in homes and businesses. For example, it records electricity, gas, and water usage obtained from smart meters and IoT sensors. This information is stored as a dataset that forms the basis for analysis.

[0630] Next, the server uses an emotion engine to acquire the user's emotional information. This emotional information is collected through wearable devices, surveillance cameras, and voice analysis software. The emotion engine analyzes data such as the user's facial expressions, voice tone, and heart rate to determine their emotional state.

[0631] The obtained energy consumption and emotional data are preprocessed on the server and prepared into an analyzable dataset. This preprocessing includes data cleaning and formatting standardization.

[0632] Next, the pre-processed data is analyzed by a generative AI model. In particular, it analyzes the relationship between energy consumption patterns and emotional information, and generates energy-saving advice based on the user's emotional state. This generative AI model can identify periods when emotions are stable and periods when stress levels are high, and propose optimal consumption behaviors accordingly.

[0633] The generated advice and its analysis results are provided to the user via the terminal. The interface on the terminal intuitively visualizes consumption status and energy-saving advice, outputting information in a format that is easy for the user to understand. For example, it may recommend playing relaxing music during the evening hours when stress levels tend to be higher.

[0634] Another example of an appropriate prompt is, "Analyze the user's energy consumption patterns during times when their stress levels are high, and suggest ways to help them relax."

[0635] In this way, this system can promote energy-saving behavior that takes into account the emotional state of users, thereby improving energy consumption.

[0636] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0637] Step 1:

[0638] The server acquires information related to energy consumption from the data collection mechanism. This input data is periodically transmitted through smart meters and IoT sensors, and the usage of electricity, gas, and water is recorded as numerical values. The server stores this raw data in a temporary database. The output of this step is raw energy consumption data.

[0639] Step 2:

[0640] The server uses an emotion engine to acquire data on the user's emotional state. Inputs include heart rate data collected by wearable devices, facial expression analysis from cameras, and voice tone analysis from voice analysis. This information is transmitted to the server in real time and converted into numerical values ​​and indicators defined as emotional states. The output of this step is the user's current emotional state data.

[0641] Step 3:

[0642] The server preprocesses the acquired energy consumption and sentiment data. The input dataset undergoes a cleaning process, imputing missing values ​​and removing noise. It also unifies different data formats and formats them into an analyzable form. This process allows the server to output a clean dataset.

[0643] Step 4:

[0644] The server uses pre-processed data to perform analysis using a generative AI model. This analysis examines the correlation between energy consumption patterns and emotional states based on the input data. Using prompts, it outputs suggestions for energy-saving actions under various scenarios. The output of this step is energy-saving advice based on predicted consumption patterns and emotions.

[0645] Step 5:

[0646] The terminal displays energy-saving advice and analysis results sent from the server to the user. The input includes analysis results and advice data, which the application on the terminal displays graphically. This is a means of clearly communicating suggestions based on consumption patterns and emotions to the user. The output of this step is visualized information through the user interface.

[0647] Step 6:

[0648] Based on the information displayed on the device, users take specific energy-saving actions. The input of operations and actions is fed back to the server through the data collection mechanism. This allows the server to incorporate the latest information into subsequent data analysis and advice. This feedback loop enables even more sophisticated energy-saving suggestions.

[0649] (Application Example 2)

[0650] 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."

[0651] Optimizing energy consumption and improving working conditions are critical challenges in many industries. In particular, in workplaces like factories, considering workers' emotional states while optimizing energy consumption is essential for simultaneously improving work efficiency and employee satisfaction. However, an integrated system for managing these simultaneously does not yet exist. Therefore, there is a need for the development of a user-centered energy management system that utilizes emotional data.

[0652] 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.

[0653] In this invention, the server includes means for acquiring energy usage data from a data acquisition device, means for acquiring user emotional data using an emotional analysis device, and means for performing preprocessing of the acquired data. This makes it possible to optimize energy usage based on the user's emotional state.

[0654] A "data acquisition device" is a device used to collect information related to energy use, and includes smart meters and IoT sensors.

[0655] "Energy usage data" refers to information showing the consumption status of electricity, gas, water, etc., and is acquired in real time.

[0656] "Preprocessing" is the stage of converting acquired data into a format that is easy to analyze, and includes noise reduction and data formatting.

[0657] An "emotion analysis device" is a device that analyzes a user's emotional state from their facial expressions and voice, and utilizes a camera and voice recognition system.

[0658] "Visualization data" refers to information used to visually display analyzed energy utilization patterns and emotional states, and is expressed in the form of graphs and charts.

[0659] "Energy-saving advice" refers to suggestions for making users' energy use more efficient, and is generated based on individual behaviors and emotional patterns.

[0660] A "user device" is a terminal used to provide users with visualized data and energy-saving advice, and includes smartphones and tablets.

[0661] A "means for generating commands" is a mechanism that generates instructions for controlling systems and devices based on emotional data.

[0662] The system for carrying out this invention includes a data acquisition device, an emotion analysis device, a server, and a user device. The server acquires energy usage data from the data acquisition device and performs preprocessing. Subsequently, it integrates the user's emotion data acquired by the emotion analysis device and analyzes this data using an AI algorithm. The analyzed data links energy usage patterns with the user's emotional state and is used to adjust the operating environment and generate energy-saving advice as needed.

[0663] This system uses general-purpose computers and data analysis software, and specific hardware platforms such as NVIDIA Jetson and Raspberry Pi can be utilized. For sentiment analysis, software such as Microsoft Azure's Face API and Google Cloud Speech-to-Text API are effective. The user device is responsible for receiving the generated energy-saving advice and visualization data and displaying it to the user.

[0664] As a concrete example, in the work environment of factory worker A, the system acquires emotional data in real time and, based on the analysis, determines that A is feeling fatigued. In this case, it can immediately suggest adjusting the lighting brightness to create a more relaxing work environment.

[0665] An example of a prompt message could be: "Optimize the energy settings in the factory based on the stress levels of the workers, and explain how to do so." This makes it possible to optimize energy consumption while providing a more comfortable working environment for the workers.

[0666] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0667] Step 1:

[0668] The server acquires energy usage data in real time from data acquisition devices. The input is raw energy usage data transmitted from smart meters and IoT sensors. This data is temporarily stored on the server side in storage. The output is energy usage data stored in a standardized format.

[0669] Step 2:

[0670] The server collects user emotional data using an emotion analysis device. Inputs include raw data from wearable devices and visual and auditory information captured through cameras and microphones. This data is analyzed using an emotion analysis API to extract the user's emotional state as digital data. The output is numerical data related to the user's emotions.

[0671] Step 3:

[0672] The server preprocesses energy utilization data and sentiment data to generate a consistent dataset. The input is the raw data obtained in steps 1 and 2. Data quality is improved by removing noise and imputing missing values. The output is a clean, analyzable dataset.

[0673] Step 4:

[0674] The server feeds the preprocessed dataset into an AI algorithm for analysis. The input is the clean dataset obtained in step 3. Analysis using a machine learning model is performed to identify trends related to energy consumption patterns and emotions. The output is the analyzed energy-emotion correspondence data.

[0675] Step 5:

[0676] The server generates energy-saving advice based on the analysis results. The input is the analyzed data obtained from step 4. Prompts are used to instruct the generating AI, which outputs advice that helps save energy. The output is personalized energy-saving advice based on the user's energy usage and emotional state.

[0677] Step 6:

[0678] The terminal displays energy-saving advice and visualization data sent from the server to the user. The input consists of the energy-saving advice and data processed for visualization obtained in step 5. This is visually presented through the terminal's interface and provided in a format that is easy for the user to understand. The output is information to encourage specific energy-saving actions for the user and guidance on what actions the user should take.

[0679] 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.

[0680] 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.

[0681] 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.

[0682] [Fourth Embodiment]

[0683] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0684] 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.

[0685] 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).

[0686] 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.

[0687] 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.

[0688] 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).

[0689] 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.

[0690] 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.

[0691] 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.

[0692] 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.

[0693] 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.

[0694] 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.

[0695] 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".

[0696] This invention is a system aimed at managing energy resources and promoting energy conservation. It collects energy consumption data such as electricity, gas, and water from homes and businesses, analyzes and visualizes this data, and provides specific advice on energy conservation.

[0697] First, the server acquires energy consumption data in real time from data collection devices. Various sensors and smart meters are used for this purpose, and the amount of each type of energy used is recorded in detail.

[0698] The server then preprocesses the acquired data. Preprocessing involves imputing missing values ​​and removing noise to improve data quality. This increases the reliability of the analysis.

[0699] The pre-processed data is analyzed using AI algorithms. The server then detects energy consumption trends and anomalies, and identifies consumption patterns. Based on this, potential peak consumption reductions and abnormal consumption patterns are highlighted.

[0700] The information obtained as a result of the analysis is visualized. The server generates this as graphs and charts, converting it into a format that can be intuitively understood. For example, monthly energy usage trends and daily consumption patterns can be visualized.

[0701] Next, energy-saving advice is generated based on the analysis results. The server provides customized advice based on each consumer's past usage patterns and behavioral characteristics. This includes specific actions such as how to avoid peak usage and how to adjust temperature settings.

[0702] The generated visualization data and energy-saving advice are provided to the user via the device. Through the application, users can check their current energy consumption and energy-saving suggestions. For example, the app's dashboard might display a message such as, "Your electricity usage this month is 10% higher than normal. Try reducing your usage at night."

[0703] Furthermore, bundled discounts are offered. The server calculates the optimal discount plan based on energy usage and informs the user. This makes it easy for users to have options for reducing costs.

[0704] In summary, this system aims to raise consumers' awareness of energy conservation and achieve efficient energy management.

[0705] The following describes the processing flow.

[0706] Step 1:

[0707] The server periodically acquires energy consumption data for electricity, gas, and water from data collection devices. Each data collection device transmits usage data to the server in real time using a recommended protocol. The server receives this data and stores it in a database.

[0708] Step 2:

[0709] The server preprocesses the collected data. Specifically, it fills in missing data points using surrounding data and removes outliers such as sensor noise. The preprocessed data is then passed on to the next analysis phase in a clean state.

[0710] Step 3:

[0711] The server processes pre-processed data using an AI algorithm to analyze energy consumption patterns. This analysis includes detecting seasonal variations in consumption and identifying peak usage times. As a result, abnormal patterns and consumption levels are predicted.

[0712] Step 4:

[0713] The server generates data that visualizes the analysis results. This generated data is then converted into a format easily understood by consumers, such as line graphs or pie charts. This allows users to see consumption trends over time.

[0714] Step 5:

[0715] The server generates energy-saving advice based on energy consumption patterns and user usage history. The generated advice is tailored to individual consumption situations and includes specific suggestions, such as "adjust heating and cooling to reduce daytime electricity usage."

[0716] Step 6:

[0717] The terminal displays visualization data and energy-saving advice provided by the server on its user interface. Through this application, users can check their energy consumption and take appropriate action based on the advice provided.

[0718] Step 7:

[0719] The server calculates a set discount based on the user's energy usage and notifies the user of the result. By presenting discount options, users have the opportunity to choose a plan that takes into account the reduction of their usage costs.

[0720] (Example 1)

[0721] 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".

[0722] Conventional energy management systems have limited energy consumption data collection and analysis capabilities, resulting in insufficient provision of specific energy-saving suggestions based on user consumption patterns and inadequate calculation of personalized pricing plans. Furthermore, the lack of adequate data visualization makes it difficult for users to intuitively understand their energy usage.

[0723] 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.

[0724] In this invention, the server includes means for acquiring energy consumption information from a data collection mechanism, means for preprocessing the acquired information, means for analyzing the preprocessed information to identify energy consumption trends, means for creating information to visualize the analysis results, means for generating energy-saving suggestions, means for notifying the user device, means for calculating and presenting a pricing plan based on energy usage, and means for using a generated AI model. This enables users to efficiently manage their energy consumption through specific and personalized energy-saving suggestions based on energy consumption pattern analysis and intuitive data visualization.

[0725] A "data collection mechanism" refers to a device or system used to collect energy consumption information from households and businesses in real time.

[0726] "Energy consumption information" refers to data on the amount of energy used, such as electricity, gas, and water.

[0727] "Preprocessing" is a process that improves data quality by removing noise and imputing missing values ​​from acquired raw data.

[0728] "Consumption trends" refer to energy usage patterns analyzed based on past energy usage history.

[0729] "Visualization" refers to making data easier to understand intuitively by displaying analysis results in concrete forms such as graphs and charts.

[0730] An "energy-saving proposal" is information that presents specific improvement measures tailored to individual usage patterns in order to improve the efficiency of energy use.

[0731] A "pricing plan" is an optimal pricing structure provided based on energy usage.

[0732] A "generative AI model" is an algorithm that uses machine learning and artificial intelligence to analyze data and predict results.

[0733] "Notifications" refer to the means of communicating generated visualization information and energy-saving suggestions to users.

[0734] A "user device" refers to a terminal or device used to display energy usage status and suggested advice.

[0735] This invention is an energy management system aimed at promoting energy conservation and optimizing consumer energy use. Specific embodiments for carrying out this invention are described below.

[0736] First, the server acquires energy consumption information in real time via data collection devices installed in homes and businesses. Hardware such as sensors and smart meters are used for this data collection. The data is stored in a database on the server, preparing it for subsequent processing.

[0737] Next, the server preprocesses the acquired information, performing noise reduction and missing value imputation. Preprocessing utilizes software tools to improve data accuracy. Through this process, the data quality is enhanced, preparing it for subsequent analysis.

[0738] The pre-processed data is analyzed by a server-generated AI model. This analysis utilizes machine learning algorithms, particularly for analyzing energy consumption patterns and detecting anomalies. This reveals past consumption trends and potential for reducing peak consumption.

[0739] The server then outputs the analysis results in a visually understandable format. Specifically, software libraries are used to visualize the data as graphs and charts. This makes it easy to see monthly consumption trends and daily usage patterns.

[0740] In addition, the server creates energy-saving suggestions tailored to each individual user. It proposes optimal energy-saving advice based on past usage patterns and behavioral characteristics. For example, it provides specific improvement measures such as, "Raising the air conditioner's temperature setting by 1 degree will reduce power consumption by 5%."

[0741] Finally, the generated visualization information and energy-saving suggestions are provided to the user via their device. Users can review these suggestions using the application. For example, the app's notification function might send a message such as, "Changing your current usage plan could save you 20,000 yen per year."

[0742] A concrete example of a prompt message would be something like, "I want to generate energy-saving suggestions based on household energy consumption data. Please tell me the best suggestions."

[0743] Through these functions, the system of the present invention enables efficient energy management and increased awareness for energy conservation.

[0744] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0745] Step 1:

[0746] The server acquires energy consumption information, such as electricity, gas, and water, in real time through a data collection mechanism.

[0747] Input: Raw data from sensors and smart meters in homes and businesses.

[0748] Specific operation: The server receives information from the sensor using a data transfer protocol and stores it in the database.

[0749] Output: The raw data with a timestamp is saved to the database.

[0750] Step 2:

[0751] The server performs preprocessing on the acquired raw data.

[0752] Input: Raw data stored on the server

[0753] Specific operation: Missing data values ​​are inferred and imputed from surrounding data, and a denoising algorithm is applied to remove outliers.

[0754] Output: High-quality, pre-processed dataset

[0755] Step 3:

[0756] The server analyzes the pre-processed data using an AI model.

[0757] Input: Improved quality dataset

[0758] Specific actions: Apply a generative AI model to analyze consumption trends. This uses machine learning algorithms to identify peak usage times and detect abnormal consumption activity.

[0759] Output: Results of detecting consumption patterns and anomalies

[0760] Step 4:

[0761] The server visualizes the analysis results by converting them into graphs and charts.

[0762] Input: Analysis results of consumption patterns and anomaly detection

[0763] Specific operation: Use the visualization library to generate graphs and charts to display the analysis results on a dashboard.

[0764] Output: Image data of visualized energy consumption trends

[0765] Step 5:

[0766] The server generates energy-saving suggestions based on the visualized data.

[0767] Input: Visualized consumer trend data

[0768] Specific operation: Referencing past consumption data, the system uses an AI model to generate specific advice on how to reduce consumption during peak hours.

[0769] Output: A series of customized energy-saving proposals

[0770] Step 6:

[0771] The terminal provides the user with generated visualization data and energy-saving suggestions.

[0772] Input: Visualization data and energy-saving suggestions sent from the server.

[0773] Specific actions: The application will display these suggestions on the dashboard and notify the user of the suggestions through notifications.

[0774] Output: Visualization graphs on the dashboard and energy-saving advice messages.

[0775] Step 7:

[0776] The server calculates the pricing plan based on energy consumption and notifies the user.

[0777] Input: Analysis results of consumption data and various pricing information.

[0778] Specific operation: Uses AI to calculate pricing plans and discount options and proposes the optimal plan for the user.

[0779] Output: Optimized pricing plan suggestion message

[0780] (Application Example 1)

[0781] 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".

[0782] In recent years, large-scale production facilities such as factories have been required to improve energy efficiency, with particular emphasis on optimizing energy use. However, there is a lack of concrete means to monitor the energy consumption of individual machines in real time and to implement efficient energy management. Furthermore, there is a need to customize energy consumption advice based on individual behavioral characteristics and improve usability. Therefore, these challenges need to be addressed effectively.

[0783] 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.

[0784] In this invention, the server includes means for acquiring energy consumption information from a data collection mechanism, means for preprocessing the acquired information, and means for analyzing the preprocessed information to identify energy utilization patterns. This enables real-time monitoring of the operating status of individual machines within a factory and optimization of energy consumption.

[0785] A "data collection mechanism" is a device designed to accurately and efficiently acquire energy consumption information.

[0786] "Energy consumption information" refers to data showing the usage status of energy resources such as electricity, gas, and water.

[0787] "Preprocessing" refers to processes such as data interpolation and noise reduction performed to improve the completeness and quality of acquired data.

[0788] "Energy utilization patterns" refer to trends and regularities that can be obtained by analyzing energy consumption trends and the characteristics of consumption associated with specific behaviors.

[0789] "Visualization" is the process of transforming analysis results into a form that can be intuitively understood by displaying them in the format of graphs and charts.

[0790] "Energy conservation advice" refers to specific action suggestions provided to improve the efficiency of energy consumption.

[0791] "User equipment" refers to electronic devices used to present users with visualized data and energy-saving advice.

[0792] "Operating status" refers to the current operating status and load conditions of machinery and equipment.

[0793] "Real-time monitoring" refers to the act of immediately checking the current situation and recording any changes as they occur.

[0794] "Optimizing energy consumption" is the process of making adjustments and improvements to achieve efficient use of energy resources.

[0795] As a concrete example of implementing this invention, an energy management system will be constructed in a factory. The server will acquire energy consumption information in real time using data collection mechanisms installed in robots and machinery within the factory. Various sensors and internet-connected measuring instruments will be used for this purpose.

[0796] Next, the server preprocesses the acquired energy consumption information. Preprocessing involves imputing missing values ​​and removing noise to improve the quality of the information. The preprocessed information is then analyzed using AI algorithms on the server. The analysis is performed in a Python environment, using machine learning libraries such as Scikit-learn to identify energy usage patterns.

[0797] The server visualizes the analysis results and utilizes visualization tools such as Tableau to create intuitively understandable graphs and charts. Based on these results, the server generates energy-saving advice and provides it to the user's equipment. The advice is customized to take into account the operating conditions and peak energy consumption of individual machines.

[0798] As a concrete example, if a factory experiences high energy consumption during certain operating times for robot A, the system will review those times and provide energy-saving advice to enable more efficient operation. Furthermore, the AI ​​model will be provided with prompts such as, "Analyze the collected robot energy data and create optimization suggestions. Specifically, describe methods for reducing energy consumption during peak hours."

[0799] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0800] Step 1:

[0801] The server acquires energy consumption information in real time from data collection mechanisms installed in each robot and piece of machinery within the factory. This information includes the moment-by-moment power consumption and operating time of each robot. Based on this input data, it is stored in a database for recording.

[0802] Step 2:

[0803] The server preprocesses the acquired energy consumption information. Specifically, it uses a Python environment to impute missing values ​​and perform data cleaning to remove noise. It generates reliable, clean data from the input data and prepares it for subsequent analysis processes. This output data is then prepared as data for analysis.

[0804] Step 3:

[0805] The server starts analysis using AI algorithms with pre-processed information. It uses machine learning libraries such as Scikit-learn to identify energy consumption trends and anomalies. This process identifies energy usage patterns using clean data as input, and the output includes consumption trend data and anomaly detection results.

[0806] Step 4:

[0807] The server generates energy-saving advice based on the analysis results. It then formulates concrete action plans and proposes optimized equipment operation schedules based on the analysis results to provide actionable advice for the user. The generated advice is used as input data for a visualization program, yielding output data that encompasses energy-saving measures.

[0808] Step 5:

[0809] The server visualizes the analysis results in graphs and charts using visualization tools such as Tableau. It analyzes the visualized data based on a generation AI model and prepares it in a format that can be easily presented to the user. Based on this process, it outputs visualization results that include the prompt message "Analyze the collected robot energy data and create suggestions for optimization. Specifically show how to reduce energy consumption during peak times."

[0810] Step 6:

[0811] The terminal provides the user with generated visualization data and energy-saving advice. In this final process, the user can check energy consumption in real time through the application and take concrete actions to implement efficient energy management. A responsive interface is presented to the user during this output process.

[0812] 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.

[0813] This invention is a system that recognizes the user's emotional state and manages their energy based on that state. It primarily involves the coordinated functioning of a data collection device, an emotion engine, a server, and a terminal.

[0814] First, the server acquires energy consumption data in real time from data collection devices installed in homes and businesses. This uses smart meters and IoT sensors to record detailed usage of electricity, gas, and water.

[0815] Next, the server uses an emotion engine to acquire the user's emotional data. This emotional data is obtained in real time through wearable devices, cameras, voice analysis, and other means. The emotion engine determines the user's emotions by analyzing factors such as the frequency of their smiles, stress levels, and tone of voice.

[0816] Based on the acquired energy consumption and emotional data, the server preprocesses them to generate a clean dataset. The preprocessed data is then subjected to an AI algorithm to analyze trends related to energy consumption patterns and emotions. This analysis observes changes in the user's emotions during energy use and develops energy-saving advice that is tailored to their emotional state.

[0817] The generated advice is customized based on each user's individual energy consumption habits and emotional patterns. For example, during times of high stress, it provides suggestions for relaxing lighting settings and positive messages encouraging improvements to energy consumption patterns. It also utilizes an emotional engine to recommend adopting new energy-saving behaviors during times of emotional stability.

[0818] The resulting visualization data and energy-saving advice are provided to the user via the device. The application on the device intuitively displays consumption status and advice to the user and encourages energy-saving behavior by providing emotionally resonant feedback.

[0819] Thus, this invention aims to enhance energy conservation awareness and make users' lives more comfortable and efficient by linking an emotion engine with energy consumption data.

[0820] The following describes the processing flow.

[0821] Step 1:

[0822] The server acquires real-time energy consumption data for electricity, gas, and water from data collection devices. This includes receiving data from smart meters and IoT sensors and storing it in a database.

[0823] Step 2:

[0824] The server collects user emotional data through an emotion engine. It uses wearable devices, cameras, and voice analysis to evaluate the user's emotional state based on facial expressions, voice tone, heart rate, etc., and stores this data.

[0825] Step 3:

[0826] The server preprocesses the acquired energy consumption and sentiment data. This includes imputing missing values ​​and removing noise. It also standardizes the sentiment data and formats it into a format that is easy to analyze.

[0827] Step 4:

[0828] The server feeds pre-processed data into an AI algorithm to simultaneously analyze energy consumption patterns and emotional trends. This process detects how energy consumption influences emotions, and vice versa.

[0829] Step 5:

[0830] The server generates energy-saving advice based on the analysis results. It creates specific advice that improves energy consumption patterns according to emotional state. For example, it might suggest, "You seem tired. Try using warm lighting in the evening to relax."

[0831] Step 6:

[0832] The terminal displays visualization data and energy-saving advice provided by the server in its user interface. The user interface is designed to visually represent daily consumption trends and the associated emotional impact, making it easy for users to understand the information.

[0833] Step 7:

[0834] Based on the information provided through the application, users adjust their daily energy consumption behaviors. Furthermore, they actively create a more relaxing environment based on feedback from the emotional engine.

[0835] In this way, this system provides a next-generation energy use support tool that integrates energy management with emotional considerations.

[0836] (Example 2)

[0837] 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".

[0838] Energy consumption is increasing in modern homes and businesses, making efficient energy-saving measures increasingly necessary. However, energy-saving proposals that take into account the emotional state of users are often lacking. Conventional systems focus solely on energy consumption, ignoring the impact of emotional states, which can lead to them being unacceptable to users. It is necessary to address these challenges and propose more effective and user-friendly energy-saving measures.

[0839] 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.

[0840] In this invention, the server includes means for acquiring information related to energy consumption from a data collection mechanism, means for acquiring emotional information via an emotional engine for detecting the user's emotional state, and means for preprocessing the acquired information and integrating it with the emotional information. This enables the provision of customized energy-saving advice based on emotions.

[0841] A "data collection mechanism" refers to any device that measures energy consumption and provides that data to a server.

[0842] "Information related to energy consumption" refers to data on the amount and status of use of various types of energy, such as electricity, gas, and water.

[0843] An "emotion engine" refers to a collection of software and hardware that analyzes a user's emotional state and generates data based on that analysis.

[0844] "Emotional information" refers to data that quantifies a user's emotional state, and is usually based on facial expressions, voice tone, heart rate, etc.

[0845] "Preprocessing" refers to a series of processes that prepare raw data for analysis, including data cleaning and imputation of missing values.

[0846] "Consumption patterns" refer to behavioral patterns that show certain trends or tendencies in energy consumption.

[0847] "Energy-saving advice" refers to information that includes suggestions and recommended actions to promote energy conservation, and is customized for each user.

[0848] "User device" refers to a terminal or application that the user directly operates, and is the medium on which analysis results and advice are displayed.

[0849] This invention relates to a system that provides energy-saving advice that takes into account the user's emotional state. It primarily involves the coordinated functioning of a data collection mechanism, an emotion engine, a server, and a terminal.

[0850] The server acquires energy consumption-related information in real time from data collection devices installed in homes and businesses. For example, it records electricity, gas, and water usage obtained from smart meters and IoT sensors. This information is stored as a dataset that forms the basis for analysis.

[0851] Next, the server uses an emotion engine to acquire the user's emotional information. This emotional information is collected through wearable devices, surveillance cameras, and voice analysis software. The emotion engine analyzes data such as the user's facial expressions, voice tone, and heart rate to determine their emotional state.

[0852] The obtained energy consumption and emotional data are preprocessed on the server and prepared into an analyzable dataset. This preprocessing includes data cleaning and formatting standardization.

[0853] Next, the pre-processed data is analyzed by a generative AI model. In particular, it analyzes the relationship between energy consumption patterns and emotional information, and generates energy-saving advice based on the user's emotional state. This generative AI model can identify periods when emotions are stable and periods when stress levels are high, and propose optimal consumption behaviors accordingly.

[0854] The generated advice and its analysis results are provided to the user via the terminal. The interface on the terminal intuitively visualizes consumption status and energy-saving advice, outputting information in a format that is easy for the user to understand. For example, it may recommend playing relaxing music during the evening hours when stress levels tend to be higher.

[0855] Another example of an appropriate prompt is, "Analyze the user's energy consumption patterns during times when their stress levels are high, and suggest ways to help them relax."

[0856] In this way, this system can promote energy-saving behavior that takes into account the emotional state of users, thereby improving energy consumption.

[0857] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0858] Step 1:

[0859] The server acquires information related to energy consumption from the data collection mechanism. This input data is periodically transmitted through smart meters and IoT sensors, and the usage of electricity, gas, and water is recorded as numerical values. The server stores this raw data in a temporary database. The output of this step is raw energy consumption data.

[0860] Step 2:

[0861] The server uses an emotion engine to acquire data on the user's emotional state. Inputs include heart rate data collected by wearable devices, facial expression analysis from cameras, and voice tone analysis from voice analysis. This information is transmitted to the server in real time and converted into numerical values ​​and indicators defined as emotional states. The output of this step is the user's current emotional state data.

[0862] Step 3:

[0863] The server preprocesses the acquired energy consumption and sentiment data. The input dataset undergoes a cleaning process, imputing missing values ​​and removing noise. It also unifies different data formats and formats them into an analyzable form. This process allows the server to output a clean dataset.

[0864] Step 4:

[0865] The server uses pre-processed data to perform analysis using a generative AI model. This analysis examines the correlation between energy consumption patterns and emotional states based on the input data. Using prompts, it outputs suggestions for energy-saving actions under various scenarios. The output of this step is energy-saving advice based on predicted consumption patterns and emotions.

[0866] Step 5:

[0867] The terminal displays energy-saving advice and analysis results sent from the server to the user. The input includes analysis results and advice data, which the application on the terminal displays graphically. This is a means of clearly communicating suggestions based on consumption patterns and emotions to the user. The output of this step is visualized information through the user interface.

[0868] Step 6:

[0869] Based on the information displayed on the device, users take specific energy-saving actions. The input of operations and actions is fed back to the server through the data collection mechanism. This allows the server to incorporate the latest information into subsequent data analysis and advice. This feedback loop enables even more sophisticated energy-saving suggestions.

[0870] (Application Example 2)

[0871] 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".

[0872] Optimizing energy consumption and improving working conditions are critical challenges in many industries. In particular, in workplaces like factories, considering workers' emotional states while optimizing energy consumption is essential for simultaneously improving work efficiency and employee satisfaction. However, an integrated system for managing these simultaneously does not yet exist. Therefore, there is a need for the development of a user-centered energy management system that utilizes emotional data.

[0873] 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.

[0874] In this invention, the server includes means for acquiring energy usage data from a data acquisition device, means for acquiring user emotional data using an emotional analysis device, and means for performing preprocessing of the acquired data. This makes it possible to optimize energy usage based on the user's emotional state.

[0875] A "data acquisition device" is a device used to collect information related to energy use, and includes smart meters and IoT sensors.

[0876] "Energy usage data" refers to information showing the consumption status of electricity, gas, water, etc., and is acquired in real time.

[0877] "Preprocessing" is the stage of converting acquired data into a format that is easy to analyze, and includes noise reduction and data formatting.

[0878] An "emotion analysis device" is a device that analyzes a user's emotional state from their facial expressions and voice, and utilizes a camera and voice recognition system.

[0879] "Visualization data" refers to information used to visually display analyzed energy utilization patterns and emotional states, and is expressed in the form of graphs and charts.

[0880] "Energy-saving advice" refers to suggestions for making users' energy use more efficient, and is generated based on individual behaviors and emotional patterns.

[0881] A "user device" is a terminal used to provide users with visualized data and energy-saving advice, and includes smartphones and tablets.

[0882] A "means for generating commands" is a mechanism that generates instructions for controlling systems and devices based on emotional data.

[0883] The system for carrying out this invention includes a data acquisition device, an emotion analysis device, a server, and a user device. The server acquires energy usage data from the data acquisition device and performs preprocessing. Subsequently, it integrates the user's emotion data acquired by the emotion analysis device and analyzes this data using an AI algorithm. The analyzed data links energy usage patterns with the user's emotional state and is used to adjust the operating environment and generate energy-saving advice as needed.

[0884] This system uses general-purpose computers and data analysis software, and specific hardware platforms such as NVIDIA Jetson and Raspberry Pi can be utilized. For sentiment analysis, software such as Microsoft Azure's Face API and Google Cloud Speech-to-Text API are effective. The user device is responsible for receiving the generated energy-saving advice and visualization data and displaying it to the user.

[0885] As a concrete example, in the work environment of factory worker A, the system acquires emotional data in real time and, based on the analysis, determines that A is feeling fatigued. In this case, it can immediately suggest adjusting the lighting brightness to create a more relaxing work environment.

[0886] An example of a prompt message could be: "Optimize the energy settings in the factory based on the stress levels of the workers, and explain how to do so." This makes it possible to optimize energy consumption while providing a more comfortable working environment for the workers.

[0887] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0888] Step 1:

[0889] The server acquires energy usage data in real time from data acquisition devices. The input is raw energy usage data transmitted from smart meters and IoT sensors. This data is temporarily stored on the server side in storage. The output is energy usage data stored in a standardized format.

[0890] Step 2:

[0891] The server collects user emotional data using an emotion analysis device. Inputs include raw data from wearable devices and visual and auditory information captured through cameras and microphones. This data is analyzed using an emotion analysis API to extract the user's emotional state as digital data. The output is numerical data related to the user's emotions.

[0892] Step 3:

[0893] The server preprocesses energy utilization data and sentiment data to generate a consistent dataset. The input is the raw data obtained in steps 1 and 2. Data quality is improved by removing noise and imputing missing values. The output is a clean, analyzable dataset.

[0894] Step 4:

[0895] The server feeds the preprocessed dataset into an AI algorithm for analysis. The input is the clean dataset obtained in step 3. Analysis using a machine learning model is performed to identify trends related to energy consumption patterns and emotions. The output is the analyzed energy-emotion correspondence data.

[0896] Step 5:

[0897] The server generates energy-saving advice based on the analysis results. The input is the analyzed data obtained from step 4. Prompts are used to instruct the generating AI, which outputs helpful energy-saving advice. The output is personalized energy-saving advice based on the user's energy usage and emotional state.

[0898] Step 6:

[0899] The terminal displays energy-saving advice and visualization data sent from the server to the user. The input consists of the energy-saving advice and data processed for visualization obtained in step 5. This is visually presented through the terminal's interface and provided in a format that is easy for the user to understand. The output is information to encourage specific energy-saving actions for the user and guidance on what actions the user should take.

[0900] 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.

[0901] 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.

[0902] 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.

[0903] 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.

[0904] 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.

[0905] 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.

[0906] 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.

[0907] 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.

[0908] 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."

[0909] 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.

[0910] 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.

[0911] 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.

[0912] 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.

[0913] 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.

[0914] 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.

[0915] 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.

[0916] 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.

[0917] 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.

[0918] 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.

[0919] 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.

[0920] 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 as being incorporated by reference.

[0921] The following is further disclosed regarding the embodiments described above.

[0922] (Claim 1)

[0923] A means for acquiring energy consumption data from a data collection device,

[0924] Means for preprocessing the acquired data,

[0925] Means for analyzing the aforementioned preprocessed data to identify energy consumption patterns,

[0926] A means for creating data to visualize the aforementioned analysis results,

[0927] A means for generating energy-saving advice based on the aforementioned visualization data,

[0928] Means for providing the aforementioned energy-saving advice and visualization data to user equipment,

[0929] A system that includes this.

[0930] (Claim 2)

[0931] The system according to claim 1, further comprising means for customizing the energy-saving advice based on the behavioral patterns of individual users.

[0932] (Claim 3)

[0933] The system according to claim 1, further comprising means for calculating and presenting a set discount based on the aforementioned energy usage status.

[0934] "Example 1"

[0935] (Claim 1)

[0936] A means of obtaining energy consumption information from a data collection mechanism,

[0937] Means for preprocessing the acquired information,

[0938] A means for analyzing the pre-processed information to identify energy consumption trends,

[0939] A means for creating information to visualize the aforementioned analysis results,

[0940] Means for generating energy-saving proposals based on the aforementioned visualization information,

[0941] Means for providing the aforementioned energy-saving proposals and visualization information to the user device,

[0942] Means for notifying the user device,

[0943] A means for calculating and presenting a pricing plan based on the aforementioned energy usage,

[0944] A system that includes this.

[0945] (Claim 2)

[0946] The system according to claim 1, further comprising means for individualizing the aforementioned energy-saving proposals based on the behavioral tendencies of each user.

[0947] (Claim 3)

[0948] The system according to claim 1, further comprising means for using a generative AI model in the aforementioned analysis.

[0949] "Application Example 1"

[0950] (Claim 1)

[0951] A means of obtaining energy consumption information from a data collection mechanism,

[0952] Means for preprocessing the acquired information,

[0953] A means for analyzing the pre-processed information to identify energy utilization patterns,

[0954] A means for creating data to visualize the aforementioned analysis results,

[0955] Means for generating energy-saving advice based on the aforementioned visualization data,

[0956] Means for providing the aforementioned energy-saving advice and visualization data to user devices,

[0957] A means of monitoring the operating status of each machine in real time and optimizing energy consumption,

[0958] A system that includes this.

[0959] (Claim 2)

[0960] The system according to claim 1, further comprising means for adjusting the energy-saving advice based on the behavioral characteristics of individual users.

[0961] (Claim 3)

[0962] The system according to claim 1, further comprising means for calculating and displaying a collective discount based on the aforementioned energy usage status.

[0963] "Example 2 of combining an emotion engine"

[0964] (Claim 1)

[0965] A means of obtaining information related to energy consumption from a data collection mechanism,

[0966] A means for acquiring emotional information via an emotion engine for detecting the emotional state of the user,

[0967] A means for preprocessing the acquired information and integrating it with emotional information,

[0968] A means for analyzing the aforementioned pre-processed information and identifying trends based on consumption patterns and emotions,

[0969] A means of outputting advice to promote energy conservation based on the generated trends,

[0970] Means for providing the aforementioned advice and analysis results to the user device,

[0971] A system that includes this.

[0972] (Claim 2)

[0973] The system according to claim 1, further comprising means for customizing the energy-saving advice based on the emotional state and behavioral tendencies of individual users.

[0974] (Claim 3)

[0975] The system according to claim 1, further comprising means for encouraging and suggesting new energy-saving behaviors based on the aforementioned real-time updated emotional information.

[0976] "Application example 2 when combining with an emotional engine"

[0977] (Claim 1)

[0978] A means for acquiring energy utilization data from a data acquisition device,

[0979] Means for performing preprocessing of the acquired data,

[0980] Means for analyzing the pre-processed data to identify energy utilization patterns,

[0981] A means for creating data to visualize the aforementioned analysis results,

[0982] Means for generating energy-saving advice based on the aforementioned visualization data,

[0983] Means for providing the aforementioned energy-saving advice and visualization data to the user device,

[0984] A means of acquiring user emotional data using an emotion analysis device,

[0985] means for generating commands to control the system based on the aforementioned emotional data,

[0986] A system that includes this.

[0987] (Claim 2)

[0988] The system according to claim 1, further comprising means for adjusting the energy-saving advice based on the behavior and emotional patterns of individual users.

[0989] (Claim 3)

[0990] The system according to claim 1, comprising means for calculating and presenting a set discount based on the aforementioned energy consumption status and sentiment analysis. [Explanation of Symbols]

[0991] 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 for acquiring energy consumption data from a data collection device, Means for preprocessing the acquired data, Means for analyzing the aforementioned preprocessed data to identify energy consumption patterns, A means for creating data to visualize the aforementioned analysis results, A means for generating energy-saving advice based on the aforementioned visualization data, Means for providing the aforementioned energy-saving advice and visualization data to user equipment, A system that includes this.

2. The system according to claim 1, further comprising means for customizing the energy-saving advice based on the behavioral patterns of individual users.

3. The system according to claim 1, further comprising means for calculating and presenting a set discount based on the aforementioned energy usage status.

Images