system

Abstract

We provide the system. [Solution] Information transmission means for collecting power usage information, A generative model means for analyzing the aforementioned power usage information and learning usage patterns, A plan generation means that generates an optimal power usage schedule based on the analyzed patterns, Control means for causing each consumer device to execute the aforementioned schedule, A means of collecting device data from across the city and providing residents with an optimal electricity usage schedule, 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, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, power consumption in homes and offices has been increasing, and efficient management thereof is required. However, it is not easy for ordinary users to optimize power usage, and in particular, cost management due to fluctuations in electricity bills is difficult. In addition, there are also risks due to malfunctions and failures of electrical appliances, and a method for effectively preventing these is necessary. Therefore, there is a demand for a system that can improve power consumption efficiency, reduce usage costs, and also perform risk management of electrical appliances.

Means for Solving the Problems

[0005] The present invention solves the above problems by providing a communication means for collecting power usage data, a generation model means for analyzing this data to learn power usage patterns, and a plan generation means for providing an optimal schedule. It also includes a control means for controlling electrical appliances to operate based on the proposed schedule, and a rate optimization means for collecting power rate information and optimizing according to rate fluctuations. Furthermore, it has a preventive maintenance function to reduce the risk of electrical appliance failure, thereby achieving comprehensive power consumption and risk management.

[0006] "Electricity usage data" refers to information about the amount of electricity consumed by each electrical appliance in a home or office.

[0007] "Communication means" refers to a technical device or method for collecting power usage data and transmitting it to a cloud server or other device.

[0008] "Generative model means" refers to an algorithm or AI technology for analyzing collected power usage data and learning usage patterns.

[0009] "Plan generation means" refers to a process or device for generating an optimal power usage schedule based on data analysis results.

[0010] "Control means" refers to a system that automatically controls the operation of electrical appliances based on a schedule generated by the plan generation means.

[0011] "Electricity rate information" refers to information about fluctuations in electricity rates, and the data used to optimize electricity usage schedules based on this information.

[0012] "Rate optimization measures" refer to processes or systems that use electricity rate information to formulate an optimal electricity usage schedule and reduce electricity costs.

[0013] "Preventive maintenance" refers to a method of reducing the risk of malfunctions by monitoring the condition of electrical appliances and taking measures before a failure occurs. [Brief explanation of the drawing]

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

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

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

[0018] In the following embodiments, a RAM (Random Access Memory) with a reference number 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 storage with a reference number 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, and the like.

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

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

[0022] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0035] This invention is comprised of a cloud server, a user terminal, and various IoT-enabled electrical appliances. The following describes how the system's program is implemented.

[0036] First, the device collects power usage data from each IoT-enabled electrical appliance operating in the home or office. This data includes the on / off status, power consumption, and operating time of each product. The device then transmits this data to the server at regular intervals.

[0037] Next, the server receives the transmitted data and records it in a database. Based on the data, the server uses AI to analyze power usage patterns. Based on the analysis results, and taking into account past usage trends and current electricity price information, the server creates an optimal power usage schedule using a planning and generation system. At this stage, a schedule is created with the aim of reducing energy costs.

[0038] The created schedule is sent to the terminal and used as instructions to control the operation of each electrical appliance. The terminal automatically turns the power of the appliances on and off according to the proposed schedule, preventing unnecessary power consumption. For example, measures such as reducing the operation of air conditioners during peak hours when energy rates are high are taken.

[0039] Furthermore, to mitigate the risk of failure, the server implements preventative maintenance functions. This involves monitoring the status of electrical appliances and sending alerts to users if signs of abnormality are detected. These alerts are generated based on programmatic status monitoring and data analysis.

[0040] Users can view suggested schedules and alerts via their device. They can manually change settings as needed or receive support using an AI chatbot.

[0041] In this way, the system of the present invention achieves optimization of power usage and effective energy management, contributing to cost reduction and improved convenience for users.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The device collects real-time power usage data from various IoT-enabled electrical appliances in the home or office. This data includes information on each product's power consumption, usage status, and usage time.

[0045] Step 2:

[0046] The device sends collected power usage data to a cloud server at regular intervals. Encryption is applied to ensure the security of the data during this process.

[0047] Step 3:

[0048] The server stores the received power usage data in a database and analyzes the data using a generating AI. This analysis identifies past usage patterns and abnormal usage trends to create user profiles.

[0049] Step 4:

[0050] The server uses analysis results, electricity rates, and market data to create an optimal electricity usage schedule using a planning and generation system. This system proposes efficient electricity plans, such as avoiding peak hours for electricity usage.

[0051] Step 5:

[0052] The server sends the generated optimal schedule to the terminal. Since the control instructions for each electrical appliance are also sent at this time, the terminal can perform automatic control.

[0053] Step 6:

[0054] The terminal controls the on / off status of electrical appliances based on the received schedule. For example, it may turn off unnecessary lights or devices at night.

[0055] Step 7:

[0056] Users can review the suggested schedule through their device and manually adjust it as needed. They can also receive assistance and troubleshooting through an AI chatbot.

[0057] Step 8:

[0058] The server monitors the status of electrical appliances using preventative maintenance functions. If an anomaly is detected, it sends an alert to the user and provides advice to reduce the risk of failure.

[0059] Through these steps, this system achieves sustainable energy management and efficient use of electricity.

[0060] (Example 1)

[0061] 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."

[0062] To optimize energy consumption and ensure efficient operation of electrical appliances, it is necessary to automate the generation and execution of schedules that respond to current electricity usage and fluctuations in electricity rates. Furthermore, it is essential to proactively detect the risk of appliance failure and take preventative measures.

[0063] 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.

[0064] In this invention, the server includes a transmission means for collecting information, a generative model mechanism for analyzing the information and understanding usage patterns, and a planning means for generating an optimal usage schedule based on the analyzed patterns. This enables efficient energy management and reduction of electricity costs.

[0065] A "means of transmission for collecting information" refers to a communication mechanism that has the function of acquiring data from various devices and sensors and transmitting it to other components within the system.

[0066] A "generative model mechanism" refers to algorithms and technologies that analyze data patterns and trends based on acquired data, and learn about the usage of equipment and optimized operating methods.

[0067] "Planning means" refers to a procedure for automatically creating daily operating schedules for equipment and devices based on analysis results obtained from a generative model mechanism, thereby promoting efficient energy use.

[0068] "Operational means" refers to a mechanism for applying the schedule created by the planning means to actual devices and equipment, and for controlling their operation.

[0069] A "storage mechanism" is a device that securely and efficiently records and stores data and information used within a system, making it available for later analysis and comparison.

[0070] A "monitoring mechanism" is a function that continuously checks the status of devices and systems, and issues warnings or notifications when abnormalities occur, thereby enabling early detection and countermeasures for problems.

[0071] This invention is a system for achieving efficient energy management and optimal operation of electrical appliances, in which a server, terminals, and users work together.

[0072] The terminal collects power usage information from various electrical appliances in homes and offices. This involves using IoT-enabled sensors and communication modules to acquire data such as the on / off status, power consumption, and operating time of each product. The collected data is transferred to a server at regular intervals via a secure protocol.

[0073] The server securely records received data in a high-performance database. Next, a generative AI model is used to analyze the accumulated data and understand power usage patterns. This generates an optimal power usage schedule based on past usage trends and current electricity price information. This schedule aims to reduce electricity costs while avoiding peak charges.

[0074] The created schedule is sent to the terminal and used as instructions to control each electrical appliance. The terminal prevents unnecessary power consumption by automatically turning the power of the appliances on and off according to the received schedule. For example, it can reduce the operation of air conditioners during peak hours when electricity rates are high.

[0075] Furthermore, the server monitors the status of electrical appliances and provides maintenance functions to prevent malfunctions. When an anomaly is detected, an alert is sent to the user, and inspection and repair procedures are recommended based on the content of the alert. Users can check the provided schedule and alerts via their terminal and, if necessary, manually adjust settings or receive further support using an AI chatbot.

[0076] As a concrete example, consider a system that manages the power usage of air conditioners, refrigerators, and washing machines in a home. During peak daytime hours when electricity rates are high, the server can limit the operation of the air conditioner and schedule laundry to run during cheaper nighttime hours, thereby enabling efficient power usage.

[0077] An example of a prompt message is: "Based on power usage data from IoT appliances in your home, generate an optimal usage schedule to reduce energy costs. Please provide specific examples of situations where daytime electricity rates are high."

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The device collects power usage data in real time from various electrical appliances in homes and offices. It receives data such as the on / off status, instantaneous power consumption, and operating time of each product as input. This data is temporarily stored in the device's memory and used for subsequent processing. Specific operations include acquiring data from sensors via Wi-Fi and Bluetooth.

[0081] Step 2:

[0082] The terminal encrypts the collected power usage data at regular intervals and sends it to the server. The input here is the power data collected in step 1, and the output is a secure data packet sent to the server. Specifically, this involves protecting the data using AES encryption and transmitting it via the HTTPS protocol.

[0083] Step 3:

[0084] The server receives data sent from terminals and stores it in a database. It receives encrypted data packets as input, decrypts them, and stores them. The output is organized and indexed database entries. Specific operations include data validation and filtering to remove invalid packets.

[0085] Step 4:

[0086] The server analyzes accumulated data using a generating AI model to extract power usage patterns. The input is power usage data from a database, and the output is a model of the analyzed usage patterns. This model uses machine learning techniques to learn trends from historical data and improve prediction accuracy. Specifically, it applies time series analysis algorithms to identify peak usage times.

[0087] Step 5:

[0088] The server generates an optimal power usage schedule using the analysis results. It accepts a power usage pattern model and current electricity price information as input. The output is the proposed power usage schedule, which can improve energy efficiency. Specific operations include executing a scheduling algorithm and optimization aimed at reducing costs.

[0089] Step 6:

[0090] The server sends the generated schedule to the terminal, which then controls the operation of each electrical appliance based on the received schedule. The input is the schedule information from the server, and the output is the actual operating status of the electrical appliance. Specifically, the terminal sends control signals to the product, adjusting its operation according to the specified on / off times.

[0091] Step 7:

[0092] The server continuously monitors the operating status of electrical appliances and sends alerts to users if any abnormalities are detected. Input is operational data from the terminal, and output is notifications to the user. Specific operations may include utilizing an anomaly detection algorithm to generate messages when certain thresholds are exceeded.

[0093] Step 8:

[0094] Users can view system suggestions and alerts through their devices, manually change settings as needed, and receive support from an AI chatbot. Based on the information provided by the user as input, the adjusted settings are applied as output. Specific actions include operations via the user interface and interactions with the support chatbot.

[0095] (Application Example 1)

[0096] 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."

[0097] In modern cities, electricity consumption is rapidly increasing, leading to rising electricity costs and a demand for more efficient power supply. Simultaneously, in environments with diverse IoT devices, managing individual devices is becoming more complex, making efficient and comprehensive power management a challenge. Furthermore, there is a need to enhance preventative maintenance functions to minimize inconvenience caused by electrical appliance failures.

[0098] 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.

[0099] In this invention, the server includes information transmission means for collecting power usage information, generation model means for analyzing the power usage information and learning usage patterns, plan generation means for generating an optimal power usage schedule based on the analyzed patterns, and means for collecting device data from across the city and providing residents with an optimal power usage schedule. This enables efficient power management and optimal operation of consuming devices throughout the city.

[0100] "Power usage information" refers to data related to the power consumption of consumer devices, their on / off status, and operating time.

[0101] "Information transmission means" refers to a communication function for collecting power usage information and transmitting it to other components such as servers.

[0102] The "generative model means" is an artificial intelligence model used to analyze and learn usage patterns using collected power usage information.

[0103] A "plan generation means" is a device or program for generating an optimal power usage schedule based on analyzed patterns.

[0104] "Control means" refers to a function that causes each consumer device to execute instructions based on the generated schedule.

[0105] "City-wide device data" refers to data related to all IoT devices and consumer equipment present within a specific city.

[0106] A "power usage schedule" is a plan for power consumption created based on analyzed usage patterns, with the aim of efficient power consumption.

[0107] The system that implements this application aims to efficiently manage electricity usage information and reduce energy costs across the entire city. Details of the invention are as follows:

[0108] The server collects power usage information from consumer devices. Using information transmission means, the server aggregates data such as power consumption, on / off status, and operating time for each device. This collected data is stored within the server and analyzed by a generative model. The generative model uses a generative AI model to learn past usage patterns and predict the optimal power usage schedule.

[0109] Next, the server uses a plan generation mechanism to generate an optimal power usage schedule based on the analyzed data. This schedule takes into account device data from across the city and is designed to promote efficient power use. The generated schedule is transmitted to the terminal and notified to each consuming device via the control mechanism.

[0110] The device automatically controls consumer devices according to the received schedule, preventing unnecessary power consumption. A specific example is adjusting the operation of air conditioners during peak hours when electricity rates are high.

[0111] Furthermore, the server monitors device data across the entire city and identifies failure risks. If an anomaly is detected, it issues a warning to the user, enabling preventative maintenance and extending the lifespan of consumer equipment.

[0112] In this way, servers, terminals, and users work together to optimize power management and help reduce energy costs across the entire city.

[0113] Examples of prompt messages are as follows:

[0114] "Based on past electricity usage data, please generate an optimized summer air conditioning usage schedule to reduce energy costs."

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] The server collects power usage information from each consumer device. Using an information transmission method, the server receives data such as power consumption, on / off status, and operating time from the consumer devices. The input is raw data from each consumer device, and the output is power usage information data stored within the server.

[0118] Step 2:

[0119] The server analyzes the collected power usage information using a generating AI model. This model learns from past usage patterns and analyzes usage patterns based on the input data. Here, data analysis and modeling calculations are performed, and the output is a prediction of usage patterns.

[0120] Step 3:

[0121] The server creates a power usage schedule using a planning generation mechanism. Based on the analysis results, the server generates a schedule aimed at efficient power use. The input here is the predicted usage pattern, and the output is the optimized power usage schedule.

[0122] Step 4:

[0123] The server sends the generated power usage schedule to the terminal. The terminal receives this schedule and uses it as instructions to automatically manage each consuming device via the control system. The input is the generated schedule, and the output is the control signal to the consuming device.

[0124] Step 5:

[0125] The server monitors device data across the entire city for preventative maintenance. If an anomaly is detected, the server sends an alert to the user. Inputs are real-time data from each device, and outputs are alert information for the user.

[0126] Step 6:

[0127] Users check the schedule and alert information received via their devices and make manual adjustments as needed. User input is manual setting changes, and output is the operation of the consumer devices reflecting those changes.

[0128] 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.

[0129] This invention relates to a system that includes a cloud server, a user terminal, IoT-enabled electrical appliances, and an emotion engine that recognizes the user's emotions. The following describes how this system is implemented.

[0130] The terminal collects power usage data from IoT-enabled electrical appliances placed in homes and offices. This data includes information such as power consumption, usage status, and operating time for each product. The terminal is responsible for transmitting the collected data in real time to a cloud server.

[0131] The server stores the received data and uses generated AI to analyze power usage patterns. This analysis creates an optimal power usage schedule to reduce costs. At the same time, the server considers power rates and market data to optimize in response to price fluctuations.

[0132] Furthermore, the system uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's facial expressions and voice through the camera and microphone, and determines their emotional state based on the results. This emotional information is used to adjust the power usage schedule. For example, if the user is feeling stressed, the system will adjust the lighting to help them relax and provide a comfortable environment.

[0133] The created schedule and emotion recognition results are reflected in the control of electrical appliances, adjusting the operation of each product based on the schedule. This simultaneously achieves efficient energy use and improved user comfort.

[0134] Furthermore, the system includes a preventative maintenance function that detects abnormalities in electrical appliances in advance and sends alerts to the user, thereby reducing the risk of failure. This extends the lifespan of electrical appliances and helps reduce costs in the long term.

[0135] As described above, the system of the present invention achieves both optimization of power usage and improvement of user comfort, providing sustainable energy management.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] The device collects power usage data in real time from various IoT-enabled electrical appliances in the home or office. This includes information such as power consumption, operating status, and usage time for each product.

[0139] Step 2:

[0140] The device sends collected power usage data to a cloud server at regular intervals. Data is encrypted during transfer to protect the information.

[0141] Step 3:

[0142] The server records the received data in a database and uses a generating AI to analyze power usage patterns. Based on the results of this analysis, it creates an optimal power usage schedule based on past trends.

[0143] Step 4:

[0144] The server collects electricity rate information and combines it with analysis patterns to optimize electricity usage schedules based on rate fluctuations. The goal is to minimize costs.

[0145] Step 5:

[0146] The device has a built-in emotion engine that uses the camera and microphone to analyze the user's facial expressions and voice. From this data, it recognizes the user's emotions and identifies factors that affect their comfort level.

[0147] Step 6:

[0148] Based on information from the emotion engine, the server determines the user's current emotional state and adjusts the power usage schedule as needed. For example, if it determines that the user is highly stressed, it may dim the lighting.

[0149] Step 7:

[0150] The device automatically controls the on / off status and settings of each electrical appliance according to a pre-configured power usage schedule. This includes adjusting the temperature of air conditioners and the brightness of lighting based on the schedule.

[0151] Step 8:

[0152] Users can review suggested schedules and emotion-based adjustments through their device's display or app, and manually modify them as needed.

[0153] Step 9:

[0154] The server uses a preventative maintenance function to monitor the operation of electrical appliances and sends an alert to the user when an anomaly is detected. This makes it possible to prevent malfunctions and failures of electrical appliances before they occur.

[0155] Through these steps, the system simultaneously achieves improved energy efficiency and enhanced user comfort, enabling sustainable energy management.

[0156] (Example 2)

[0157] 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".

[0158] In modern life, many information devices and equipment consume electricity, making it urgent to optimize their usage efficiency. Furthermore, providing a comfortable environment that responds to the user's emotional state is also important. However, achieving these simultaneously is difficult, and systems that manage power in accordance with emotional states are still insufficient. Therefore, the present invention aims to provide an efficient and comfortable power management system that combines power usage and emotional state.

[0159] 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.

[0160] In this invention, the server includes communication means for acquiring information on power usage, generation means for analyzing the information on power usage and learning usage patterns, and adjustment means for adjusting the power usage schedule based on the emotional state. This makes it possible to achieve optimal power usage that responds to the user's emotions, thereby improving comfort and efficiency.

[0161] "Communication means" refers to systems or devices used to collect and transmit information about power usage to a server.

[0162] "Generation means" refers to functions and processes for analyzing collected information on electricity usage and learning usage patterns.

[0163] A "planning mechanism" is a mechanism for formulating an efficient power usage schedule based on analyzed usage patterns.

[0164] "Recognition means" refers to a technology or process that uses devices such as cameras and microphones to determine the emotional state of a user.

[0165] "Adjustment measures" refer to methods or systems for appropriately adjusting the electricity usage schedule according to the user's emotional state.

[0166] A "control system" refers to a system or technology that controls each device based on the created power usage schedule.

[0167] "Rate optimization measures" refer to processes and functions for adjusting the optimal electricity usage schedule in response to fluctuations in electricity rates.

[0168] "Preventive maintenance" is a method of preventing problems before they occur by predicting the risk of equipment failure and taking countermeasures in advance.

[0169] This invention is a system aimed at optimizing power usage and improving user comfort. The system primarily consists of a server located in the cloud, terminals placed in homes and offices, and a device for recognizing the user's emotions.

[0170] The server is the core of this system and is equipped with communication means to collect information on power usage. Power usage data transmitted from terminals is stored on the cloud server. The server uses a generative AI model to analyze this data and learn usage patterns. In this process, it identifies peak power consumption times and times when efficient use is possible.

[0171] Based on the analyzed data, the server generates an optimal power usage schedule and uses a planning mechanism to construct control commands for each device to execute that schedule. Furthermore, a cost optimization mechanism takes into account fluctuations in electricity prices to propose the most economical usage method.

[0172] The user's emotional state is determined by recognition mechanisms built into the device. Using cameras and microphones, the user's facial expressions and voice are analyzed to evaluate their emotional state. This information is then reflected in the power usage schedule by adjustment mechanisms, automatically adjusting lighting and activating heating appliances according to the user's emotional state.

[0173] As a concrete example, if a user is relaxing in the living room, the system will determine the user's stress level from their facial expression and use a generative AI model to input a prompt to the server: "Create an optimal power usage schedule for the next 24 hours based on the user's emotional state, and maximize the relaxation effect." As a result, the system will automatically change the lighting to a warmer tone and adjust the air conditioning to the optimal level to provide a comfortable environment.

[0174] Furthermore, the system incorporates a preventative maintenance function that detects signs of equipment failure early. By analyzing abnormal data and sending alerts to the user when necessary, it helps extend the lifespan of the equipment. In this way, the present invention achieves efficient energy management and improves the quality of life for users.

[0175] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0176] Step 1:

[0177] The terminal collects power usage information from various information devices installed in homes and offices. It uses sensors and smart meters to acquire data on power consumption, usage frequency, and operating time. This data is transmitted in real time to a cloud server using communication methods. The input is the power usage status of each device, and the output is a data packet summarizing this data.

[0178] Step 2:

[0179] The server stores power usage information received from terminals in a database. The received data is analyzed by a generating AI model to learn usage patterns. Specific data processing includes statistical analysis of peak usage time and average consumption. The input is the collected power usage information, and the output is the result of the usage pattern analysis.

[0180] Step 3:

[0181] Based on the analysis results, the server uses a generative model to create an optimal power usage schedule. This process also considers electricity rates and market trends to propose economically efficient power usage. Data processing here includes simulations of rate fluctuations. The input consists of the analysis results of usage patterns and market data, while the output is the power usage schedule.

[0182] Step 4:

[0183] The emotion recognition system installed on the user's device analyzes the user's facial expressions and voice using a camera and microphone. It sends the emotional state to a server as a prompt message, which is then reflected in the schedule. Specifically, it generates an emotion label such as "relaxed state" and adjusts the lighting and music based on it. The input is the user's real-time emotion data, and the output is the adjusted power usage schedule.

[0184] Step 5:

[0185] The server controls the operation of each information device based on a coordinated schedule. It sends control signals to terminals, adjusting the on / off status and operating levels of the devices to improve energy efficiency and user comfort. The input is the coordinated schedule, and the output is the control signals for the devices.

[0186] Step 6:

[0187] The system includes a preventative maintenance function, and the server continuously monitors data to detect signs of failure. It analyzes vibration and abnormal temperature data and sends alerts to the user if a problem is predicted. The input is equipment operating status data, and the output is alert notifications.

[0188] (Application Example 2)

[0189] 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".

[0190] In modern smart cities, improving energy efficiency and enhancing user comfort are crucial challenges. However, conventional methods struggle to consider user emotions and environmental changes when optimizing electricity use, resulting in a failure to adequately meet individual needs.

[0191] 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.

[0192] In this invention, the server includes communication means for collecting power usage information, generation engine means for analyzing the power usage information and learning usage patterns, and emotion recognition means for recognizing the user's emotions and adjusting environmental settings based on those emotions. This makes it possible to achieve both efficient power use and improved user comfort.

[0193] "Electricity usage information" refers to data on the power consumption, usage status, and operating time of each electrical appliance.

[0194] "Communication methods" refer to technologies for collecting data from various devices located in homes and offices and transmitting it to cloud servers.

[0195] The "generation engine means" is a system for analyzing collected information and learning trends in power usage.

[0196] A "plan generation means" is a mechanism that creates an optimal power usage plan based on the analysis results.

[0197] "Control means" refers to the technology for adjusting and operating the operation of each electrification device according to the generated plan.

[0198] "Emotion recognition means" refers to a function that recognizes the user's emotional state through a camera or microphone and optimizes the environment settings accordingly.

[0199] This invention is a system that improves energy efficiency and user comfort within smart cities. As a specific embodiment of the invention, an application system using smartphones and smart glasses is designed.

[0200] The main components of the system are a user terminal, a cloud server, a group of IoT appliances, and an emotion engine for emotion recognition. The user terminal has the function of collecting power usage information from each IoT appliance in the home or office and sending it to the cloud server. The cloud server analyzes the received data and uses a generation engine to learn power usage patterns. This generates an optimal power usage plan and appropriately controls the operation of the appliances.

[0201] Furthermore, the cloud server uses data acquired from cameras and microphones to recognize the user's emotions and analyzes it using emotion recognition tools. For example, if the user is feeling stressed, the cloud server controls various electrical devices to adjust lighting and temperature to provide a comfortable environment.

[0202] For example, when a user desires relaxation, the system can adjust the room lighting to an appropriate brightness and play relaxation music. In this case, a generative AI model can be used to prompt messages such as, "Generate an ideal room environment based on the user's emotional state," or "Suggest possible adjustments to optimize current power usage."

[0203] In summary, this system provides a solution that enables both efficient use of electricity and user comfort simultaneously.

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] The terminal collects real-time power usage information from various IoT electrical appliances placed in homes and offices. This information includes data on the power consumption, usage status, and operating time of each electrical device. The collected data is sent to a cloud server.

[0207] Step 2:

[0208] The server uses a generation engine to analyze power usage patterns based on the received power usage information. During this analysis, data is input into a learning model to understand power usage trends. As a result, an optimal power usage plan is generated.

[0209] Step 3:

[0210] The server, using a plan generation mechanism, creates specific control commands to execute the optimal power usage plan for each electrification device based on the analysis results. Here, the plan is adjusted to ensure each device operates efficiently.

[0211] Step 4:

[0212] The server uses data acquired from the camera and microphone to analyze the user's emotions using emotion recognition technology. This process uses image recognition and voice analysis technologies to identify the user's emotional state from their facial expressions and voice, and outputs the result as an emotional situation.

[0213] Step 5:

[0214] The server optimizes the environmental settings of each electrical appliance based on the user's emotional state. In doing so, it uses a generative AI model to generate adjustment suggestions to provide the user with a comfortable environment, and outputs a prompt message such as "Generate an ideal indoor environment based on the user's emotional state."

[0215] Step 6:

[0216] The user experiences a customized environment based on the output from the server. For example, if the user is feeling stressed, the lighting is set to a calming color and relaxation music is played. This sequence of actions simultaneously achieves efficient power use and user comfort.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] 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.

[0223] 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).

[0224] 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.

[0225] 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.

[0226] 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).

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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".

[0233] This invention is comprised of a cloud server, a user terminal, and various IoT-enabled electrical appliances. The following describes how the system's program is implemented.

[0234] First, the device collects power usage data from each IoT-enabled electrical appliance operating in the home or office. This data includes the on / off status, power consumption, and operating time of each product. The device then transmits this data to the server at regular intervals.

[0235] Next, the server receives the transmitted data and records it in a database. Based on the data, the server uses AI to analyze power usage patterns. Based on the analysis results, and taking into account past usage trends and current electricity price information, the server creates an optimal power usage schedule using a planning and generation system. At this stage, a schedule is created with the aim of reducing energy costs.

[0236] The created schedule is sent to the terminal and used as instructions to control the operation of each electrical appliance. The terminal automatically turns the power of the appliances on and off according to the proposed schedule, preventing unnecessary power consumption. For example, measures such as reducing the operation of air conditioners during peak hours when energy rates are high are taken.

[0237] Furthermore, to mitigate the risk of failure, the server implements preventative maintenance functions. This involves monitoring the status of electrical appliances and sending alerts to users if signs of abnormality are detected. These alerts are generated based on programmatic status monitoring and data analysis.

[0238] Users can view suggested schedules and alerts via their device. They can manually change settings as needed or receive support using an AI chatbot.

[0239] In this way, the system of the present invention achieves optimization of power usage and effective energy management, contributing to cost reduction and improved convenience for users.

[0240] The following describes the processing flow.

[0241] Step 1:

[0242] The device collects real-time power usage data from various IoT-enabled electrical appliances in the home or office. This data includes information on each product's power consumption, usage status, and usage time.

[0243] Step 2:

[0244] The device sends collected power usage data to a cloud server at regular intervals. Encryption is applied to ensure the security of the data during this process.

[0245] Step 3:

[0246] The server stores the received power usage data in a database and analyzes the data using a generating AI. This analysis identifies past usage patterns and abnormal usage trends to create user profiles.

[0247] Step 4:

[0248] The server uses analysis results, electricity rates, and market data to create an optimal electricity usage schedule using a planning and generation system. This system proposes efficient electricity plans, such as avoiding peak hours for electricity usage.

[0249] Step 5:

[0250] The server sends the generated optimal schedule to the terminal. Since the control instructions for each electrical appliance are also sent at this time, the terminal can perform automatic control.

[0251] Step 6:

[0252] The terminal controls the on / off status of electrical appliances based on the received schedule. For example, it may turn off unnecessary lights or devices at night.

[0253] Step 7:

[0254] Users can review the suggested schedule through their device and manually adjust it as needed. They can also receive assistance and troubleshooting through an AI chatbot.

[0255] Step 8:

[0256] The server monitors the status of electrical appliances using preventative maintenance functions. If an anomaly is detected, it sends an alert to the user and provides advice to reduce the risk of failure.

[0257] Through these steps, this system achieves sustainable energy management and efficient use of electricity.

[0258] (Example 1)

[0259] 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."

[0260] To optimize energy consumption and ensure efficient operation of electrical appliances, it is necessary to automate the generation and execution of schedules that respond to current electricity usage and fluctuations in electricity rates. Furthermore, it is essential to proactively detect the risk of appliance failure and take preventative measures.

[0261] 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.

[0262] In this invention, the server includes a transmission means for collecting information, a generative model mechanism for analyzing the information and understanding usage patterns, and a planning means for generating an optimal usage schedule based on the analyzed patterns. This enables efficient energy management and reduction of electricity costs.

[0263] "Means of transmission for collecting information" refers to a communication mechanism that has the function of acquiring data from various devices and sensors and transmitting it to other components within the system.

[0264] A "generative model mechanism" refers to algorithms and technologies that analyze data patterns and trends based on acquired data, and learn about the usage of equipment and optimized operating methods.

[0265] "Planning means" refers to a procedure for automatically creating daily operating schedules for equipment and devices based on analysis results obtained from a generative model mechanism, thereby promoting efficient energy use.

[0266] "Operational means" refers to a mechanism for applying the schedule created by the planning means to actual devices and equipment, and for controlling their operation.

[0267] A "storage mechanism" is a device that securely and efficiently records and stores data and information used within a system, making it available for later analysis and comparison.

[0268] A "monitoring mechanism" is a function that continuously checks the status of devices and systems, and issues warnings or notifications when abnormalities occur, thereby enabling early detection and countermeasures for problems.

[0269] This invention is a system for achieving efficient energy management and optimal operation of electrical appliances, in which a server, terminals, and users work together.

[0270] The terminal collects power usage information from various electrical appliances in homes and offices. This involves using IoT-enabled sensors and communication modules to acquire data such as the on / off status, power consumption, and operating time of each product. The collected data is transferred to a server at regular intervals via a secure protocol.

[0271] The server securely records received data in a high-performance database. Next, a generative AI model is used to analyze the accumulated data and understand power usage patterns. This generates an optimal power usage schedule based on past usage trends and current electricity price information. This schedule aims to reduce electricity costs while avoiding peak charges.

[0272] The created schedule is sent to the terminal and used as instructions to control each electrical appliance. The terminal prevents unnecessary power consumption by automatically turning the power of the appliances on and off according to the received schedule. For example, it can reduce the operation of air conditioners during peak hours when electricity rates are high.

[0273] Furthermore, the server monitors the status of electrical appliances and provides maintenance functions to prevent malfunctions. When an anomaly is detected, an alert is sent to the user, and inspection and repair procedures are recommended based on the content of the alert. Users can check the provided schedule and alerts via their terminal and, if necessary, manually adjust settings or receive further support using an AI chatbot.

[0274] As a concrete example, consider a system that manages the power usage of air conditioners, refrigerators, and washing machines in a home. During peak daytime hours when electricity rates are high, the server can limit the operation of the air conditioner and schedule laundry to run during cheaper nighttime hours, thereby enabling efficient power usage.

[0275] An example of a prompt message is: "Based on power usage data from IoT appliances in your home, generate an optimal usage schedule to reduce energy costs. Please provide specific examples of situations where daytime electricity rates are high."

[0276] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0277] Step 1:

[0278] The device collects power usage data in real time from various electrical appliances in homes and offices. It receives data such as the on / off status, instantaneous power consumption, and operating time of each product as input. This data is temporarily stored in the device's memory and used for subsequent processing. Specific operations include acquiring data from sensors via Wi-Fi and Bluetooth.

[0279] Step 2:

[0280] The terminal encrypts the collected power usage data at regular time intervals and sends it to the server. The input here is the power data collected in Step 1, and the output is the secure data packet sent to the server. Specific operations include protecting the data using AES encryption and sending it via the HTTPS protocol.

[0281] Step 3:

[0282] The server receives the data sent from the terminal and stores it in the database. As input, it receives the encrypted data packet, decrypts it, and saves it. The output is an organized and indexed database entry. Specific operations include data verification and filtering to exclude invalid packets.

[0283] Step 4:

[0284] The server analyzes the stored data using a generative AI model to extract power usage patterns. The input is the power usage data from the database, and the output is a model of the analyzed usage patterns. This model uses machine learning techniques to learn trends from past data and improve prediction accuracy. Specific operations include applying a time series analysis algorithm to identify peak usage times.

[0285] Step 5:

[0286] The server generates an optimal power usage schedule using the analysis results. As input, it receives the power usage pattern model and current electricity tariff information. The output is the proposed power usage schedule, which can improve energy efficiency. Specific operations include executing a scheduling algorithm and optimization aimed at reducing costs.

[0287] Step 6:

[0288] The server sends the generated schedule to the terminal, which then controls the operation of each electrical appliance based on the received schedule. The input is the schedule information from the server, and the output is the actual operating status of the electrical appliance. Specifically, the terminal sends control signals to the product, adjusting its operation according to the specified on / off times.

[0289] Step 7:

[0290] The server continuously monitors the operating status of electrical appliances and sends alerts to users if any abnormalities are detected. Input is operational data from the terminal, and output is notifications to the user. Specific operations may include utilizing an anomaly detection algorithm to generate messages when certain thresholds are exceeded.

[0291] Step 8:

[0292] Users can view system suggestions and alerts through their devices, manually change settings as needed, and receive support from an AI chatbot. Based on the information provided by the user as input, the adjusted settings are applied as output. Specific actions include operations via the user interface and interactions with the support chatbot.

[0293] (Application Example 1)

[0294] 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."

[0295] In modern cities, electricity consumption is rapidly increasing, leading to rising electricity costs and a demand for more efficient power supply. Simultaneously, in environments with diverse IoT devices, managing individual devices is becoming more complex, making efficient and comprehensive power management a challenge. Furthermore, there is a need to enhance preventative maintenance functions to minimize inconvenience caused by electrical appliance failures.

[0296] 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.

[0297] In this invention, the server includes information transmission means for collecting power usage information, generation model means for analyzing the power usage information and learning usage patterns, plan generation means for generating an optimal power usage schedule based on the analyzed patterns, and means for collecting device data from across the city and providing residents with an optimal power usage schedule. This enables efficient power management and optimal operation of consuming devices throughout the city.

[0298] "Power usage information" refers to data related to the power consumption of consumer devices, their on / off status, and operating time.

[0299] "Information transmission means" refers to a communication function for collecting power usage information and transmitting it to other components such as servers.

[0300] The "generative model means" is an artificial intelligence model used to analyze and learn usage patterns using collected power usage information.

[0301] A "plan generation means" is a device or program for generating an optimal power usage schedule based on analyzed patterns.

[0302] "Control means" refers to a function that causes each consumer device to execute instructions based on the generated schedule.

[0303] "City-wide device data" refers to data related to all IoT devices and consumer equipment present within a specific city.

[0304] A "power usage schedule" is a plan for power consumption created based on analyzed usage patterns, with the aim of efficient power consumption.

[0305] The system that realizes this application example aims to efficiently manage power usage information and reduce the energy cost across the city. The details of the invention are as follows.

[0306] The server collects power usage information from consumer devices. By using information transmission means, the server aggregates data such as the power consumption, on / off state, and operating time of each device. This collected data is stored in the server and analyzed by the generation model means. The generation model means uses a generative AI model to learn past usage patterns and predict an optimal power usage schedule.

[0307] Next, the server uses the plan generation means to generate an optimal power usage schedule based on the analyzed data. This schedule takes into account the device data across the city and is for promoting efficient power usage. The generated schedule is transmitted to the terminal and notified to each consumer device via the control means.

[0308] The terminal automatically controls the consumer devices according to the received schedule to prevent wasteful power consumption. Specific examples include adjusting the operation of the air conditioner during high electricity rate hours.

[0309] Also, the server monitors the device data across the city and identifies the risk of failure. When an abnormality is detected, a warning is sent to the user, and by enabling preventive maintenance, it is possible to extend the lifespan of the consumer devices.

[0310] In this way, the server, terminal, and user cooperate to achieve the optimization of power management and support the reduction of the energy cost across the city.

[0311] Examples of prompt texts are as follows.

[0312] "Please generate an air conditioner usage schedule for summer optimized for energy cost reduction based on past power usage data."

[0313] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0314] Step 1:

[0315] The server collects power usage information from each consumer device. Using an information transmission method, the server receives data such as power consumption, on / off status, and operating time from the consumer devices. The input is raw data from each consumer device, and the output is power usage information data stored within the server.

[0316] Step 2:

[0317] The server analyzes the collected power usage information using a generating AI model. This model learns from past usage patterns and analyzes usage patterns based on the input data. Here, data analysis and modeling calculations are performed, and the output is a prediction of usage patterns.

[0318] Step 3:

[0319] The server creates a power usage schedule using a planning generation mechanism. Based on the analysis results, the server generates a schedule aimed at efficient power use. The input here is the predicted usage pattern, and the output is the optimized power usage schedule.

[0320] Step 4:

[0321] The server sends the generated power usage schedule to the terminal. The terminal receives this schedule and uses it as instructions to automatically manage each consuming device via the control system. The input is the generated schedule, and the output is the control signal to the consuming device.

[0322] Step 5:

[0323] The server monitors device data across the entire city for preventative maintenance. If an anomaly is detected, the server sends an alert to the user. Inputs are real-time data from each device, and outputs are alert information for the user.

[0324] Step 6:

[0325] Users check the schedule and alert information received via their devices and make manual adjustments as needed. User input is manual setting changes, and output is the operation of the consumer devices reflecting those changes.

[0326] 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.

[0327] This invention relates to a system that includes a cloud server, a user terminal, IoT-enabled electrical appliances, and an emotion engine that recognizes the user's emotions. The following describes how this system is implemented.

[0328] The terminal collects power usage data from IoT-enabled electrical appliances placed in homes and offices. This data includes information such as power consumption, usage status, and operating time for each product. The terminal is responsible for transmitting the collected data in real time to a cloud server.

[0329] The server stores the received data and uses generated AI to analyze power usage patterns. This analysis creates an optimal power usage schedule to reduce costs. At the same time, the server considers power rates and market data to optimize in response to price fluctuations.

[0330] Furthermore, the system uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's facial expressions and voice through the camera and microphone, and determines their emotional state based on the results. This emotional information is used to adjust the power usage schedule. For example, if the user is feeling stressed, the system adjusts the lighting to help them relax and provides a comfortable environment.

[0331] The created schedule and emotion recognition results are reflected in the control of electrical appliances, adjusting the operation of each product based on the schedule. This simultaneously achieves efficient energy use and improved user comfort.

[0332] Furthermore, the system includes a preventative maintenance function that detects abnormalities in electrical appliances in advance and sends alerts to the user, thereby reducing the risk of failure. This extends the lifespan of electrical appliances and helps reduce costs in the long term.

[0333] As described above, the system of the present invention achieves both optimization of power usage and improvement of user comfort, providing sustainable energy management.

[0334] The following describes the processing flow.

[0335] Step 1:

[0336] The device collects power usage data in real time from various IoT-enabled electrical appliances in the home or office. This includes information such as power consumption, operating status, and usage time for each product.

[0337] Step 2:

[0338] The device sends collected power usage data to a cloud server at regular intervals. Data is encrypted during transfer to protect the information.

[0339] Step 3:

[0340] The server records the received data in a database and uses a generating AI to analyze power usage patterns. Based on this analysis, it creates an optimal power usage schedule based on past trends.

[0341] Step 4:

[0342] The server collects electricity rate information and combines it with analysis patterns to optimize electricity usage schedules based on rate fluctuations. This aims to minimize costs.

[0343] Step 5:

[0344] The device has a built-in emotion engine that uses the camera and microphone to analyze the user's facial expressions and voice. From this data, it recognizes the user's emotions and identifies factors that affect their comfort level.

[0345] Step 6:

[0346] Based on information from the emotion engine, the server determines the user's current emotional state and adjusts the power usage schedule as needed. For example, if it determines that the user is highly stressed, it may dim the lighting.

[0347] Step 7:

[0348] The device automatically controls the on / off status and settings of each electrical appliance according to a pre-configured power usage schedule. This includes adjusting the temperature of air conditioners and the brightness of lighting based on the schedule.

[0349] Step 8:

[0350] Users can review suggested schedules and adjustments based on sentiment recognition through their device's display or app, and manually modify them as needed.

[0351] Step 9:

[0352] The server uses a preventative maintenance function to monitor the operation of electrical appliances and sends an alert to the user when an anomaly is detected. This makes it possible to prevent malfunctions and failures of electrical appliances before they occur.

[0353] Through these steps, the system simultaneously achieves improved energy efficiency and enhanced user comfort, enabling sustainable energy management.

[0354] (Example 2)

[0355] 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".

[0356] In modern life, many information devices and equipment consume electricity, making it urgent to optimize their usage efficiency. Furthermore, providing a comfortable environment that responds to the user's emotional state is also important. However, achieving these simultaneously is difficult, and systems that manage power in accordance with emotional states are still insufficient. Therefore, the present invention aims to provide an efficient and comfortable power management system that combines power usage and emotional state.

[0357] 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.

[0358] In this invention, the server includes communication means for acquiring information on power usage, generation means for analyzing the information on power usage and learning usage patterns, and adjustment means for adjusting the power usage schedule based on the emotional state. This makes it possible to achieve optimal power usage that responds to the user's emotions, thereby improving comfort and efficiency.

[0359] "Communication means" refers to systems or devices used to collect and transmit information about power usage to a server.

[0360] "Generation means" refers to functions and processes for analyzing collected information on electricity usage and learning usage patterns.

[0361] A "planning mechanism" is a mechanism for formulating an efficient power usage schedule based on analyzed usage patterns.

[0362] "Recognition means" refers to a technology or process that uses devices such as cameras and microphones to determine the emotional state of a user.

[0363] "Adjustment measures" refer to methods or systems for appropriately adjusting the electricity usage schedule according to the user's emotional state.

[0364] A "control system" refers to a system or technology that controls each device based on the created power usage schedule.

[0365] "Rate optimization measures" refer to processes and functions for adjusting the optimal electricity usage schedule in response to fluctuations in electricity rates.

[0366] "Preventive maintenance" is a method of preventing problems before they occur by predicting the risk of equipment failure and taking countermeasures in advance.

[0367] This invention is a system aimed at optimizing power usage and improving user comfort. The system primarily consists of a server located in the cloud, terminals placed in homes and offices, and a device for recognizing the user's emotions.

[0368] The server is the core of this system and is equipped with communication means to collect information on power usage. Power usage data transmitted from terminals is stored on the cloud server. The server uses a generative AI model to analyze this data and learn usage patterns. In this process, it identifies peak power consumption times and times when efficient use is possible.

[0369] Based on the analyzed data, the server generates an optimal power usage schedule and uses a planning mechanism to construct control commands for each device to execute that schedule. Furthermore, a cost optimization mechanism takes into account fluctuations in electricity prices to propose the most economical usage method.

[0370] The user's emotional state is determined by recognition mechanisms built into the device. Using cameras and microphones, the user's facial expressions and voice are analyzed to evaluate their emotional state. This information is then reflected in the power usage schedule by adjustment mechanisms, automatically adjusting lighting and activating heating appliances according to the user's emotional state.

[0371] As a concrete example, if a user is relaxing in the living room, the system will determine the user's stress level from their facial expression and use a generative AI model to input a prompt to the server: "Create an optimal power usage schedule for the next 24 hours based on the user's emotional state, and maximize the relaxation effect." As a result, the system will automatically change the lighting to a warmer tone and adjust the air conditioning to the optimal level to provide a comfortable environment.

[0372] Furthermore, the system incorporates a preventative maintenance function that detects signs of equipment failure early. By analyzing abnormal data and sending alerts to the user when necessary, it helps extend the lifespan of the equipment. In this way, the present invention achieves efficient energy management and improves the quality of life for users.

[0373] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0374] Step 1:

[0375] The terminal collects power usage information from various information devices installed in homes and offices. It uses sensors and smart meters to acquire data on power consumption, usage frequency, and operating time. This data is transmitted in real time to a cloud server using communication methods. The input is the power usage status of each device, and the output is a data packet summarizing this data.

[0376] Step 2:

[0377] The server stores power usage information received from terminals in a database. The received data is analyzed by a generating AI model to learn usage patterns. Specific data processing includes statistical analysis of peak usage time and average consumption. The input is the collected power usage information, and the output is the result of the usage pattern analysis.

[0378] Step 3:

[0379] Based on the analysis results, the server uses a generative model to create an optimal power usage schedule. This process also considers electricity rates and market trends to propose economically efficient power usage. Data processing here includes simulations of rate fluctuations. The input consists of the analysis results of usage patterns and market data, while the output is the power usage schedule.

[0380] Step 4:

[0381] The emotion recognition system installed on the user's device analyzes the user's facial expressions and voice using a camera and microphone. It sends the emotional state to a server as a prompt message, which is then reflected in the schedule. Specifically, it generates an emotion label such as "relaxed state" and adjusts the lighting and music based on it. The input is the user's real-time emotion data, and the output is the adjusted power usage schedule.

[0382] Step 5:

[0383] The server controls the operation of each information device based on a coordinated schedule. It sends control signals to terminals, adjusting the on / off status and operating levels of the devices to improve energy efficiency and user comfort. The input is the coordinated schedule, and the output is the control signals for the devices.

[0384] Step 6:

[0385] The system includes a preventative maintenance function, and the server continuously monitors data to detect signs of failure. It analyzes vibration and abnormal temperature data and sends alerts to the user if a problem is predicted. The input is equipment operating status data, and the output is alert notifications.

[0386] (Application Example 2)

[0387] 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."

[0388] In modern smart cities, improving energy efficiency and enhancing user comfort are crucial challenges. However, conventional methods struggle to consider user emotions and environmental changes when optimizing electricity use, resulting in a failure to adequately meet individual needs.

[0389] 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.

[0390] In this invention, the server includes communication means for collecting power usage information, generation engine means for analyzing the power usage information and learning usage patterns, and emotion recognition means for recognizing the user's emotions and adjusting environmental settings based on those emotions. This makes it possible to achieve both efficient power use and improved user comfort.

[0391] "Electricity usage information" refers to data on the power consumption, usage status, and operating time of each electrical appliance.

[0392] "Communication methods" refer to technologies for collecting data from various devices located in homes and offices and transmitting it to cloud servers.

[0393] The "generation engine means" is a system for analyzing collected information and learning trends in power usage.

[0394] A "plan generation means" is a mechanism that creates an optimal power usage plan based on the analysis results.

[0395] "Control means" refers to the technology for adjusting and operating the operation of each electrification device according to the generated plan.

[0396] "Emotion recognition means" refers to a function that recognizes the user's emotional state through a camera or microphone and optimizes the environment settings accordingly.

[0397] This invention is a system that improves energy efficiency and user comfort within smart cities. As a specific embodiment of the invention, an application system using smartphones and smart glasses is designed.

[0398] The main components of the system are a user terminal, a cloud server, a group of IoT appliances, and an emotion engine for emotion recognition. The user terminal has the function of collecting power usage information from each IoT appliance in the home or office and sending it to the cloud server. The cloud server analyzes the received data and uses a generation engine to learn power usage patterns. This generates an optimal power usage plan and appropriately controls the operation of the appliances.

[0399] Furthermore, the cloud server uses data acquired from cameras and microphones to recognize the user's emotions and analyzes it using emotion recognition tools. For example, if the user is feeling stressed, the cloud server controls various electrical devices to adjust lighting and temperature to provide a comfortable environment.

[0400] For example, when a user desires relaxation, the system can adjust the room lighting to an appropriate brightness and play relaxation music. In this case, a generative AI model can be used to prompt messages such as, "Generate an ideal room environment based on the user's emotional state," or "Suggest possible adjustments to optimize current power usage."

[0401] In summary, this system provides a solution that enables both efficient use of electricity and user comfort simultaneously.

[0402] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0403] Step 1:

[0404] The terminal collects real-time power usage information from various IoT electrical appliances placed in homes and offices. This information includes data on the power consumption, usage status, and operating time of each electrical device. The collected data is sent to a cloud server.

[0405] Step 2:

[0406] The server uses a generation engine to analyze power usage patterns based on the received power usage information. During this analysis, the data is input into a learning model to understand power usage trends. As a result, an optimal power usage plan is generated.

[0407] Step 3:

[0408] The server, using a plan generation mechanism, creates specific control commands to execute the optimal power usage plan for each electrification device based on the analysis results. Here, the plan is adjusted so that each device operates efficiently.

[0409] Step 4:

[0410] The server uses data acquired from the camera and microphone to analyze the user's emotions using emotion recognition technology. This process uses image recognition and voice analysis technologies to identify the user's emotional state from their facial expressions and voice, and outputs the result as an emotional situation.

[0411] Step 5:

[0412] The server optimizes the environmental settings of each electrical appliance based on the user's emotional state. In doing so, it uses a generative AI model to generate adjustment suggestions to provide the user with a comfortable environment, and outputs a prompt message such as "Generate an ideal indoor environment based on the user's emotional state."

[0413] Step 6:

[0414] The user experiences a customized environment based on the output from the server. For example, if the user is feeling stressed, the lighting is set to a calming color and relaxation music is played. This sequence of actions simultaneously achieves efficient power use and user comfort.

[0415] 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.

[0416] 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.

[0417] 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.

[0418] [Third Embodiment]

[0419] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0420] 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.

[0421] 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).

[0422] 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.

[0423] 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.

[0424] 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).

[0425] 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.

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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".

[0431] This invention is comprised of a cloud server, a user terminal, and various IoT-enabled electrical appliances. The following describes how the system's program is implemented.

[0432] First, the device collects power usage data from each IoT-enabled electrical appliance operating in the home or office. This data includes the on / off status, power consumption, and operating time of each product. The device then transmits this data to the server at regular intervals.

[0433] Next, the server receives the transmitted data and records it in a database. Based on the data, the server uses AI to analyze power usage patterns. Based on the analysis results, and taking into account past usage trends and current electricity price information, the server creates an optimal power usage schedule using a planning and generation system. At this stage, a schedule is created with the aim of reducing energy costs.

[0434] The created schedule is sent to the terminal and used as instructions to control the operation of each electrical appliance. The terminal automatically turns the power of the appliances on and off according to the proposed schedule, preventing unnecessary power consumption. For example, measures such as reducing the operation of air conditioners during peak hours when energy rates are high are taken.

[0435] Furthermore, to mitigate the risk of failure, the server implements preventative maintenance functions. This involves monitoring the status of electrical appliances and sending alerts to users if signs of abnormality are detected. These alerts are generated based on programmatic status monitoring and data analysis.

[0436] Users can view suggested schedules and alerts via their device. They can manually change settings as needed or receive support using an AI chatbot.

[0437] In this way, the system of the present invention achieves optimization of power usage and effective energy management, contributing to cost reduction and improved convenience for users.

[0438] The following describes the processing flow.

[0439] Step 1:

[0440] The device collects real-time power usage data from various IoT-enabled electrical appliances in the home or office. This data includes information on each product's power consumption, usage status, and usage time.

[0441] Step 2:

[0442] The device sends collected power usage data to a cloud server at regular intervals. Encryption is applied to ensure the security of the data during this process.

[0443] Step 3:

[0444] The server stores the received power usage data in a database and analyzes the data using a generating AI. This analysis identifies past usage patterns and abnormal usage trends to create user profiles.

[0445] Step 4:

[0446] The server uses analysis results, electricity rates, and market data to create an optimal electricity usage schedule using a planning and generation system. This system proposes efficient electricity plans, such as avoiding peak hours for electricity usage.

[0447] Step 5:

[0448] The server sends the generated optimal schedule to the terminal. Since the control instructions for each electrical appliance are also sent at this time, the terminal can perform automatic control.

[0449] Step 6:

[0450] The terminal controls the on / off status of electrical appliances based on the received schedule. For example, it may turn off unnecessary lights or devices at night.

[0451] Step 7:

[0452] Users can review the suggested schedule through their device and manually adjust it as needed. They can also receive assistance and troubleshooting through an AI chatbot.

[0453] Step 8:

[0454] The server monitors the status of electrical appliances using preventative maintenance functions. If an anomaly is detected, it sends an alert to the user and provides advice to reduce the risk of failure.

[0455] Through these steps, this system achieves sustainable energy management and efficient use of electricity.

[0456] (Example 1)

[0457] 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."

[0458] To optimize energy consumption and ensure efficient operation of electrical appliances, it is necessary to automate the generation and execution of schedules that respond to current electricity usage and fluctuations in electricity rates. Furthermore, it is essential to proactively detect the risk of appliance failure and take preventative measures.

[0459] 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.

[0460] In this invention, the server includes a transmission means for collecting information, a generative model mechanism for analyzing the information and understanding usage patterns, and a planning means for generating an optimal usage schedule based on the analyzed patterns. This enables efficient energy management and reduction of electricity costs.

[0461] "Means of transmission for collecting information" refers to a communication mechanism that has the function of acquiring data from various devices and sensors and transmitting it to other components within the system.

[0462] A "generative model mechanism" refers to algorithms and technologies that analyze data patterns and trends based on acquired data, and learn about the usage of equipment and optimized operating methods.

[0463] "Planning means" refers to a procedure for automatically creating daily operating schedules for equipment and devices based on analysis results obtained from a generative model mechanism, thereby promoting efficient energy use.

[0464] "Operational means" refers to a mechanism for applying the schedule created by the planning means to actual devices and equipment, and for controlling their operation.

[0465] A "storage mechanism" is a device that securely and efficiently records and stores data and information used within a system, making it available for later analysis and comparison.

[0466] A "monitoring mechanism" is a function that continuously checks the status of devices and systems, and issues warnings or notifications when abnormalities occur, thereby enabling early detection and countermeasures for problems.

[0467] This invention is a system for achieving efficient energy management and optimal operation of electrical appliances, in which a server, terminals, and users work together.

[0468] The terminal collects power usage information from various electrical appliances in homes and offices. This involves using IoT-enabled sensors and communication modules to acquire data such as the on / off status, power consumption, and operating time of each product. The collected data is transferred to a server at regular intervals via a secure protocol.

[0469] The server securely records received data in a high-performance database. Next, a generative AI model is used to analyze the accumulated data and understand power usage patterns. This generates an optimal power usage schedule based on past usage trends and current electricity price information. This schedule aims to reduce electricity costs while avoiding peak charges.

[0470] The created schedule is sent to the terminal and used as instructions to control each electrical appliance. The terminal prevents unnecessary power consumption by automatically turning the power of the appliances on and off according to the received schedule. For example, it can reduce the operation of air conditioners during peak hours when electricity rates are high.

[0471] Furthermore, the server monitors the status of electrical appliances and provides maintenance functions to prevent malfunctions. When an anomaly is detected, an alert is sent to the user, and inspection and repair procedures are recommended based on the content of the alert. Users can check the provided schedule and alerts via their terminal and, if necessary, manually adjust settings or receive further support using an AI chatbot.

[0472] As a concrete example, consider a system that manages the power usage of air conditioners, refrigerators, and washing machines in a home. During peak daytime hours when electricity rates are high, the server can limit the operation of the air conditioner and schedule laundry to run during cheaper nighttime hours, thereby enabling efficient power usage.

[0473] An example of a prompt message is: "Based on power usage data from IoT appliances in your home, generate an optimal usage schedule to reduce energy costs. Please provide specific examples of situations where daytime electricity rates are high."

[0474] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0475] Step 1:

[0476] The device collects power usage data in real time from various electrical appliances in homes and offices. It receives data such as the on / off status, instantaneous power consumption, and operating time of each product as input. This data is temporarily stored in the device's memory and used for subsequent processing. Specific operations include acquiring data from sensors via Wi-Fi and Bluetooth.

[0477] Step 2:

[0478] The terminal encrypts the collected power usage data at regular intervals and sends it to the server. The input here is the power data collected in step 1, and the output is a secure data packet sent to the server. Specifically, this involves protecting the data using AES encryption and transmitting it via the HTTPS protocol.

[0479] Step 3:

[0480] The server receives data sent from terminals and stores it in a database. It receives encrypted data packets as input, decrypts them, and stores them. The output is organized and indexed database entries. Specific operations include data validation and filtering to remove invalid packets.

[0481] Step 4:

[0482] The server analyzes accumulated data using a generating AI model to extract power usage patterns. The input is power usage data from a database, and the output is a model of the analyzed usage patterns. This model uses machine learning techniques to learn trends from historical data and improve prediction accuracy. Specifically, it applies time series analysis algorithms to identify peak usage times.

[0483] Step 5:

[0484] The server generates an optimal power usage schedule using the analysis results. It accepts a power usage pattern model and current electricity price information as input. The output is the proposed power usage schedule, which can improve energy efficiency. Specific operations include executing a scheduling algorithm and optimization aimed at reducing costs.

[0485] Step 6:

[0486] The server sends the generated schedule to the terminal, which then controls the operation of each electrical appliance based on the received schedule. The input is the schedule information from the server, and the output is the actual operating status of the electrical appliance. Specifically, the terminal sends control signals to the product, adjusting its operation according to the specified on / off times.

[0487] Step 7:

[0488] The server continuously monitors the operating status of electrical appliances and sends alerts to users if any abnormalities are detected. Input is operational data from the terminal, and output is notifications to the user. Specific operations may include utilizing an anomaly detection algorithm to generate messages when certain thresholds are exceeded.

[0489] Step 8:

[0490] Users can view system suggestions and alerts through their devices, manually change settings as needed, and receive support from an AI chatbot. Based on the information provided by the user as input, the adjusted settings are applied as output. Specific actions include operations via the user interface and interactions with the support chatbot.

[0491] (Application Example 1)

[0492] 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."

[0493] In modern cities, electricity consumption is rapidly increasing, leading to rising electricity costs and a demand for more efficient power supply. Simultaneously, in environments with diverse IoT devices, managing individual devices is becoming more complex, making efficient and comprehensive power management a challenge. Furthermore, there is a need to enhance preventative maintenance functions to minimize inconvenience caused by electrical appliance failures.

[0494] 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.

[0495] In this invention, the server includes information transmission means for collecting power usage information, generation model means for analyzing the power usage information and learning usage patterns, plan generation means for generating an optimal power usage schedule based on the analyzed patterns, and means for collecting device data from across the city and providing residents with an optimal power usage schedule. This enables efficient power management and optimal operation of consuming devices throughout the city.

[0496] "Power usage information" refers to data related to the power consumption of consumer devices, their on / off status, and operating time.

[0497] "Information transmission means" refers to a communication function for collecting power usage information and transmitting it to other components such as servers.

[0498] The "generative model means" is an artificial intelligence model used to analyze and learn usage patterns using collected power usage information.

[0499] A "plan generation means" is a device or program for generating an optimal power usage schedule based on analyzed patterns.

[0500] "Control means" refers to a function that causes each consumer device to execute instructions based on the generated schedule.

[0501] "City-wide device data" refers to data related to all IoT devices and consumer equipment present within a specific city.

[0502] A "power usage schedule" is a plan for power consumption created based on analyzed usage patterns, with the aim of efficient power consumption.

[0503] The system that implements this application aims to efficiently manage electricity usage information and reduce energy costs across the entire city. Details of the invention are as follows:

[0504] The server collects power usage information from consumer devices. Using information transmission means, the server aggregates data such as power consumption, on / off status, and operating time for each device. This collected data is stored within the server and analyzed by a generative model. The generative model uses a generative AI model to learn past usage patterns and predict the optimal power usage schedule.

[0505] Next, the server uses a plan generation mechanism to generate an optimal power usage schedule based on the analyzed data. This schedule takes into account device data from across the city and is designed to promote efficient power use. The generated schedule is transmitted to the terminal and notified to each consuming device via the control mechanism.

[0506] The device automatically controls consumer devices according to the received schedule, preventing unnecessary power consumption. A specific example is adjusting the operation of air conditioners during peak hours when electricity rates are high.

[0507] Furthermore, the server monitors device data across the entire city and identifies failure risks. If an anomaly is detected, it issues a warning to the user, enabling preventative maintenance and extending the lifespan of consumer equipment.

[0508] In this way, servers, terminals, and users work together to optimize power management and help reduce energy costs across the entire city.

[0509] Examples of prompt messages are as follows:

[0510] "Based on past electricity usage data, please generate an optimized summer air conditioning usage schedule to reduce energy costs."

[0511] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0512] Step 1:

[0513] The server collects power usage information from each consumer device. Using an information transmission method, the server receives data such as power consumption, on / off status, and operating time from the consumer devices. The input is raw data from each consumer device, and the output is power usage information data stored within the server.

[0514] Step 2:

[0515] The server analyzes the collected power usage information using a generating AI model. This model learns from past usage patterns and analyzes usage patterns based on the input data. Here, data analysis and modeling calculations are performed, and the output is a prediction of usage patterns.

[0516] Step 3:

[0517] The server creates a power usage schedule using a planning generation mechanism. Based on the analysis results, the server generates a schedule aimed at efficient power use. The input here is the predicted usage pattern, and the output is the optimized power usage schedule.

[0518] Step 4:

[0519] The server sends the generated power usage schedule to the terminal. The terminal receives this schedule and uses it as instructions to automatically manage each consuming device via the control system. The input is the generated schedule, and the output is the control signal to the consuming device.

[0520] Step 5:

[0521] The server monitors device data across the entire city for preventative maintenance. If an anomaly is detected, the server sends an alert to the user. Inputs are real-time data from each device, and outputs are alert information for the user.

[0522] Step 6:

[0523] Users check the schedule and alert information received via their devices and make manual adjustments as needed. User input is manual setting changes, and output is the operation of the consumer devices reflecting those changes.

[0524] 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.

[0525] This invention relates to a system that includes a cloud server, a user terminal, IoT-enabled electrical appliances, and an emotion engine that recognizes the user's emotions. The following describes how this system is implemented.

[0526] The terminal collects power usage data from IoT-enabled electrical appliances placed in homes and offices. This data includes information such as power consumption, usage status, and operating time for each product. The terminal is responsible for transmitting the collected data in real time to a cloud server.

[0527] The server stores the received data and uses generated AI to analyze power usage patterns. This analysis creates an optimal power usage schedule to reduce costs. At the same time, the server considers power rates and market data to optimize in response to price fluctuations.

[0528] Furthermore, the system uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's facial expressions and voice through the camera and microphone, and determines their emotional state based on the results. This emotional information is used to adjust the power usage schedule. For example, if the user is feeling stressed, the system adjusts the lighting to help them relax and provides a comfortable environment.

[0529] The created schedule and emotion recognition results are reflected in the control of electrical appliances, adjusting the operation of each product based on the schedule. This simultaneously achieves efficient energy use and improved user comfort.

[0530] Furthermore, the system includes a preventative maintenance function that detects abnormalities in electrical appliances in advance and sends alerts to the user, thereby reducing the risk of failure. This extends the lifespan of electrical appliances and helps reduce costs in the long term.

[0531] As described above, the system of the present invention achieves both optimization of power usage and improvement of user comfort, providing sustainable energy management.

[0532] The following describes the processing flow.

[0533] Step 1:

[0534] The device collects power usage data in real time from various IoT-enabled electrical appliances in the home or office. This includes information such as power consumption, operating status, and usage time for each product.

[0535] Step 2:

[0536] The device sends collected power usage data to a cloud server at regular intervals. Data is encrypted during transfer to protect the information.

[0537] Step 3:

[0538] The server records the received data in a database and uses a generating AI to analyze power usage patterns. Based on this analysis, it creates an optimal power usage schedule based on past trends.

[0539] Step 4:

[0540] The server collects electricity rate information and combines it with analysis patterns to optimize electricity usage schedules based on rate fluctuations. This aims to minimize costs.

[0541] Step 5:

[0542] The device has a built-in emotion engine that uses the camera and microphone to analyze the user's facial expressions and voice. From this data, it recognizes the user's emotions and identifies factors that affect their comfort level.

[0543] Step 6:

[0544] Based on information from the emotion engine, the server determines the user's current emotional state and adjusts the power usage schedule as needed. For example, if it determines that the user is highly stressed, it may dim the lighting.

[0545] Step 7:

[0546] The device automatically controls the on / off status and settings of each electrical appliance according to a pre-configured power usage schedule. This includes adjusting the temperature of air conditioners and the brightness of lighting based on the schedule.

[0547] Step 8:

[0548] Users can review suggested schedules and adjustments based on sentiment recognition through their device's display or app, and manually modify them as needed.

[0549] Step 9:

[0550] The server uses a preventative maintenance function to monitor the operation of electrical appliances and sends an alert to the user when an anomaly is detected. This makes it possible to prevent malfunctions and failures of electrical appliances before they occur.

[0551] Through these steps, the system simultaneously achieves improved energy efficiency and enhanced user comfort, enabling sustainable energy management.

[0552] (Example 2)

[0553] 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."

[0554] In modern life, many information devices and equipment consume electricity, making it urgent to optimize their usage efficiency. Furthermore, providing a comfortable environment that responds to the user's emotional state is also important. However, achieving these simultaneously is difficult, and systems that manage power in accordance with emotional states are still insufficient. Therefore, the present invention aims to provide an efficient and comfortable power management system that combines power usage and emotional state.

[0555] 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.

[0556] In this invention, the server includes communication means for acquiring information on power usage, generation means for analyzing the information on power usage and learning usage patterns, and adjustment means for adjusting the power usage schedule based on the emotional state. This makes it possible to achieve optimal power usage that responds to the user's emotions, thereby improving comfort and efficiency.

[0557] "Communication means" refers to systems or devices used to collect and transmit information about power usage to a server.

[0558] "Generation means" refers to functions and processes for analyzing collected information on electricity usage and learning usage patterns.

[0559] A "planning mechanism" is a mechanism for formulating an efficient power usage schedule based on analyzed usage patterns.

[0560] "Recognition means" refers to a technology or process that uses devices such as cameras and microphones to determine the emotional state of a user.

[0561] "Adjustment measures" refer to methods or systems for appropriately adjusting the electricity usage schedule according to the user's emotional state.

[0562] A "control system" refers to a system or technology that controls each device based on the created power usage schedule.

[0563] "Rate optimization measures" refer to processes and functions for adjusting the optimal electricity usage schedule in response to fluctuations in electricity rates.

[0564] "Preventive maintenance" is a method of preventing problems before they occur by predicting the risk of equipment failure and taking countermeasures in advance.

[0565] This invention is a system aimed at optimizing power usage and improving user comfort. The system primarily consists of a server located in the cloud, terminals placed in homes and offices, and a device for recognizing the user's emotions.

[0566] The server is the core of this system and is equipped with communication means to collect information on power usage. Power usage data transmitted from terminals is stored on the cloud server. The server uses a generative AI model to analyze this data and learn usage patterns. In this process, it identifies peak power consumption times and times when efficient use is possible.

[0567] Based on the analyzed data, the server generates an optimal power usage schedule and uses a planning mechanism to construct control commands for each device to execute that schedule. Furthermore, a cost optimization mechanism takes into account fluctuations in electricity prices to propose the most economical usage method.

[0568] The user's emotional state is determined by recognition mechanisms built into the device. Using cameras and microphones, the user's facial expressions and voice are analyzed to evaluate their emotional state. This information is then reflected in the power usage schedule by adjustment mechanisms, automatically adjusting lighting and activating heating appliances according to the user's emotional state.

[0569] As a concrete example, if a user is relaxing in the living room, the system will determine the user's stress level from their facial expression and use a generative AI model to input a prompt to the server: "Create an optimal power usage schedule for the next 24 hours based on the user's emotional state, and maximize the relaxation effect." As a result, the system will automatically change the lighting to a warmer tone and adjust the air conditioning to the optimal level to provide a comfortable environment.

[0570] Furthermore, the system incorporates a preventative maintenance function that detects signs of equipment failure early. By analyzing abnormal data and sending alerts to the user when necessary, it helps extend the lifespan of the equipment. In this way, the present invention achieves efficient energy management and improves the quality of life for users.

[0571] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0572] Step 1:

[0573] The terminal collects power usage information from various information devices installed in homes and offices. It uses sensors and smart meters to acquire data on power consumption, usage frequency, and operating time. This data is transmitted in real time to a cloud server using communication methods. The input is the power usage status of each device, and the output is a data packet summarizing this data.

[0574] Step 2:

[0575] The server stores power usage information received from terminals in a database. The received data is analyzed by a generating AI model to learn usage patterns. Specific data processing includes statistical analysis of peak usage time and average consumption. The input is the collected power usage information, and the output is the result of the usage pattern analysis.

[0576] Step 3:

[0577] Based on the analysis results, the server uses a generative model to create an optimal power usage schedule. This process also considers electricity rates and market trends to propose economically efficient power usage. Data processing here includes simulations of rate fluctuations. The input consists of the analysis results of usage patterns and market data, while the output is the power usage schedule.

[0578] Step 4:

[0579] The emotion recognition system installed on the user's device analyzes the user's facial expressions and voice using a camera and microphone. It sends the emotional state to a server as a prompt message, which is then reflected in the schedule. Specifically, it generates an emotion label such as "relaxed state" and adjusts the lighting and music based on it. The input is the user's real-time emotion data, and the output is the adjusted power usage schedule.

[0580] Step 5:

[0581] The server controls the operation of each information device based on a coordinated schedule. It sends control signals to terminals, adjusting the on / off status and operating levels of the devices to improve energy efficiency and user comfort. The input is the coordinated schedule, and the output is the control signals for the devices.

[0582] Step 6:

[0583] The system includes a preventative maintenance function, and the server continuously monitors data to detect signs of failure. It analyzes vibration and abnormal temperature data and sends alerts to the user if a problem is predicted. The input is equipment operating status data, and the output is alert notifications.

[0584] (Application Example 2)

[0585] 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."

[0586] In modern smart cities, improving energy efficiency and enhancing user comfort are crucial challenges. However, conventional methods struggle to consider user emotions and environmental changes when optimizing electricity use, resulting in a failure to adequately meet individual needs.

[0587] 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.

[0588] In this invention, the server includes communication means for collecting power usage information, generation engine means for analyzing the power usage information and learning usage patterns, and emotion recognition means for recognizing the user's emotions and adjusting environmental settings based on those emotions. This makes it possible to achieve both efficient power use and improved user comfort.

[0589] "Electricity usage information" refers to data on the power consumption, usage status, and operating time of each electrical appliance.

[0590] "Communication methods" refer to technologies for collecting data from various devices located in homes and offices and transmitting it to cloud servers.

[0591] The "generation engine means" is a system for analyzing collected information and learning trends in power usage.

[0592] A "plan generation means" is a mechanism that creates an optimal power usage plan based on the analysis results.

[0593] "Control means" refers to the technology for adjusting and operating the operation of each electrification device according to the generated plan.

[0594] "Emotion recognition means" refers to a function that recognizes the user's emotional state through a camera or microphone and optimizes the environment settings accordingly.

[0595] This invention is a system that improves energy efficiency and user comfort within smart cities. As a specific embodiment of the invention, an application system using smartphones and smart glasses is designed.

[0596] The main components of the system are a user terminal, a cloud server, a group of IoT appliances, and an emotion engine for emotion recognition. The user terminal has the function of collecting power usage information from each IoT appliance in the home or office and sending it to the cloud server. The cloud server analyzes the received data and uses a generation engine to learn power usage patterns. This generates an optimal power usage plan and appropriately controls the operation of the appliances.

[0597] Furthermore, the cloud server uses data acquired from cameras and microphones to recognize the user's emotions and analyzes it using emotion recognition tools. For example, if the user is feeling stressed, the cloud server controls various electrical devices to adjust lighting and temperature to provide a comfortable environment.

[0598] For example, when a user desires relaxation, the system can adjust the room lighting to an appropriate brightness and play relaxation music. In this case, a generative AI model can be used to prompt messages such as, "Generate an ideal room environment based on the user's emotional state," or "Suggest possible adjustments to optimize current power usage."

[0599] In summary, this system provides a solution that enables both efficient use of electricity and user comfort simultaneously.

[0600] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0601] Step 1:

[0602] The terminal collects real-time power usage information from various IoT electrical appliances placed in homes and offices. This information includes data on the power consumption, usage status, and operating time of each electrical device. The collected data is sent to a cloud server.

[0603] Step 2:

[0604] The server uses a generation engine to analyze power usage patterns based on the received power usage information. During this analysis, the data is input into a learning model to understand power usage trends. As a result, an optimal power usage plan is generated.

[0605] Step 3:

[0606] The server, using a plan generation mechanism, creates specific control commands to execute the optimal power usage plan for each electrification device based on the analysis results. Here, the plan is adjusted so that each device operates efficiently.

[0607] Step 4:

[0608] The server uses data acquired from the camera and microphone to analyze the user's emotions using emotion recognition technology. This process uses image recognition and voice analysis technologies to identify the user's emotional state from their facial expressions and voice, and outputs the result as an emotional situation.

[0609] Step 5:

[0610] The server optimizes the environmental settings of each electrical appliance based on the user's emotional state. In doing so, it uses a generative AI model to generate adjustment suggestions to provide the user with a comfortable environment, and outputs a prompt message such as "Generate an ideal indoor environment based on the user's emotional state."

[0611] Step 6:

[0612] The user experiences a customized environment based on the output from the server. For example, if the user is feeling stressed, the lighting is set to a calming color and relaxation music is played. This sequence of actions simultaneously achieves efficient power use and user comfort.

[0613] 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.

[0614] 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.

[0615] 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.

[0616] [Fourth Embodiment]

[0617] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0618] 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.

[0619] 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).

[0620] 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.

[0621] 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.

[0622] 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).

[0623] 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.

[0624] 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.

[0625] 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.

[0626] 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.

[0627] 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.

[0628] 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.

[0629] 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".

[0630] This invention is comprised of a cloud server, a user terminal, and various IoT-enabled electrical appliances. The following describes how the system's program is implemented.

[0631] First, the device collects power usage data from each IoT-enabled electrical appliance operating in the home or office. This data includes the on / off status, power consumption, and operating time of each product. The device then transmits this data to the server at regular intervals.

[0632] Next, the server receives the transmitted data and records it in a database. Based on the data, the server uses AI to analyze power usage patterns. Based on the analysis results, and taking into account past usage trends and current electricity price information, the server creates an optimal power usage schedule using a planning and generation system. At this stage, a schedule is created with the aim of reducing energy costs.

[0633] The created schedule is sent to the terminal and used as instructions to control the operation of each electrical appliance. The terminal automatically turns the power of the appliances on and off according to the proposed schedule, preventing unnecessary power consumption. For example, measures such as reducing the operation of air conditioners during peak hours when energy rates are high are taken.

[0634] Furthermore, to mitigate the risk of failure, the server implements preventative maintenance functions. This involves monitoring the status of electrical appliances and sending alerts to users if signs of abnormality are detected. These alerts are generated based on programmatic status monitoring and data analysis.

[0635] Users can view suggested schedules and alerts via their device. They can manually change settings as needed or receive support using an AI chatbot.

[0636] In this way, the system of the present invention achieves optimization of power usage and effective energy management, contributing to cost reduction and improved convenience for users.

[0637] The following describes the processing flow.

[0638] Step 1:

[0639] The device collects real-time power usage data from various IoT-enabled electrical appliances in the home or office. This data includes information on each product's power consumption, usage status, and usage time.

[0640] Step 2:

[0641] The device sends collected power usage data to a cloud server at regular intervals. Encryption is applied to ensure the security of the data during this process.

[0642] Step 3:

[0643] The server stores the received power usage data in a database and analyzes the data using a generating AI. This analysis identifies past usage patterns and abnormal usage trends to create user profiles.

[0644] Step 4:

[0645] The server uses analysis results, electricity rates, and market data to create an optimal electricity usage schedule using a planning and generation system. This system proposes efficient electricity plans, such as avoiding peak hours for electricity usage.

[0646] Step 5:

[0647] The server sends the generated optimal schedule to the terminal. Since the control instructions for each electrical appliance are also sent at this time, the terminal can perform automatic control.

[0648] Step 6:

[0649] The terminal controls the on / off status of electrical appliances based on the received schedule. For example, it may turn off unnecessary lights or devices at night.

[0650] Step 7:

[0651] Users can review the suggested schedule through their device and manually adjust it as needed. They can also receive assistance and troubleshooting through an AI chatbot.

[0652] Step 8:

[0653] The server monitors the status of electrical appliances using preventative maintenance functions. If an anomaly is detected, it sends an alert to the user and provides advice to reduce the risk of failure.

[0654] Through these steps, this system achieves sustainable energy management and efficient use of electricity.

[0655] (Example 1)

[0656] 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".

[0657] To optimize energy consumption and ensure efficient operation of electrical appliances, it is necessary to automate the generation and execution of schedules that respond to current electricity usage and fluctuations in electricity rates. Furthermore, it is essential to proactively detect the risk of appliance failure and take preventative measures.

[0658] 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.

[0659] In this invention, the server includes a transmission means for collecting information, a generative model mechanism for analyzing the information and understanding usage patterns, and a planning means for generating an optimal usage schedule based on the analyzed patterns. This enables efficient energy management and reduction of electricity costs.

[0660] "Means of transmission for collecting information" refers to a communication mechanism that has the function of acquiring data from various devices and sensors and transmitting it to other components within the system.

[0661] A "generative model mechanism" refers to algorithms and technologies that analyze data patterns and trends based on acquired data, and learn about the usage of equipment and optimized operating methods.

[0662] "Planning means" refers to a procedure for automatically creating daily operating schedules for equipment and devices based on analysis results obtained from a generative model mechanism, thereby promoting efficient energy use.

[0663] "Operational means" refers to a mechanism for applying the schedule created by the planning means to actual devices and equipment, and for controlling their operation.

[0664] A "storage mechanism" is a device that securely and efficiently records and stores data and information used within a system, making it available for later analysis and comparison.

[0665] A "monitoring mechanism" is a function that continuously checks the status of devices and systems, and issues warnings or notifications when abnormalities occur, thereby enabling early detection and countermeasures for problems.

[0666] This invention is a system for achieving efficient energy management and optimal operation of electrical appliances, in which a server, terminals, and users work together.

[0667] The terminal collects power usage information from various electrical appliances in homes and offices. This involves using IoT-enabled sensors and communication modules to acquire data such as the on / off status, power consumption, and operating time of each product. The collected data is transferred to a server at regular intervals via a secure protocol.

[0668] The server securely records received data in a high-performance database. Next, a generative AI model is used to analyze the accumulated data and understand power usage patterns. This generates an optimal power usage schedule based on past usage trends and current electricity price information. This schedule aims to reduce electricity costs while avoiding peak charges.

[0669] The created schedule is sent to the terminal and used as instructions to control each electrical appliance. The terminal prevents unnecessary power consumption by automatically turning the power of the appliances on and off according to the received schedule. For example, it can reduce the operation of air conditioners during peak hours when electricity rates are high.

[0670] Furthermore, the server monitors the status of electrical appliances and provides maintenance functions to prevent malfunctions. When an anomaly is detected, an alert is sent to the user, and inspection and repair procedures are recommended based on the content of the alert. Users can check the provided schedule and alerts via their terminal and, if necessary, manually adjust settings or receive further support using an AI chatbot.

[0671] As a concrete example, consider a system that manages the power usage of air conditioners, refrigerators, and washing machines in a home. During peak daytime hours when electricity rates are high, the server can limit the operation of the air conditioner and schedule laundry to run during cheaper nighttime hours, thereby enabling efficient power usage.

[0672] An example of a prompt message is: "Based on power usage data from IoT appliances in your home, generate an optimal usage schedule to reduce energy costs. Please provide specific examples of situations where daytime electricity rates are high."

[0673] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0674] Step 1:

[0675] The device collects power usage data in real time from various electrical appliances in homes and offices. It receives data such as the on / off status, instantaneous power consumption, and operating time of each product as input. This data is temporarily stored in the device's memory and used for subsequent processing. Specific operations include acquiring data from sensors via Wi-Fi and Bluetooth.

[0676] Step 2:

[0677] The terminal encrypts the collected power usage data at regular intervals and sends it to the server. The input here is the power data collected in step 1, and the output is a secure data packet sent to the server. Specifically, this involves protecting the data using AES encryption and transmitting it via the HTTPS protocol.

[0678] Step 3:

[0679] The server receives data sent from terminals and stores it in a database. It receives encrypted data packets as input, decrypts them, and stores them. The output is organized and indexed database entries. Specific operations include data validation and filtering to remove invalid packets.

[0680] Step 4:

[0681] The server analyzes accumulated data using a generating AI model to extract power usage patterns. The input is power usage data from a database, and the output is a model of the analyzed usage patterns. This model uses machine learning techniques to learn trends from historical data and improve prediction accuracy. Specifically, it applies time series analysis algorithms to identify peak usage times.

[0682] Step 5:

[0683] The server generates an optimal power usage schedule using the analysis results. It accepts a power usage pattern model and current electricity price information as input. The output is the proposed power usage schedule, which can improve energy efficiency. Specific operations include executing a scheduling algorithm and optimization aimed at reducing costs.

[0684] Step 6:

[0685] The server sends the generated schedule to the terminal, which then controls the operation of each electrical appliance based on the received schedule. The input is the schedule information from the server, and the output is the actual operating status of the electrical appliance. Specifically, the terminal sends control signals to the product, adjusting its operation according to the specified on / off times.

[0686] Step 7:

[0687] The server continuously monitors the operating status of electrical appliances and sends alerts to users if any abnormalities are detected. Input is operational data from the terminal, and output is notifications to the user. Specific operations may include utilizing an anomaly detection algorithm to generate messages when certain thresholds are exceeded.

[0688] Step 8:

[0689] Users can view system suggestions and alerts through their devices, manually change settings as needed, and receive support from an AI chatbot. Based on the information provided by the user as input, the adjusted settings are applied as output. Specific actions include operations via the user interface and interactions with the support chatbot.

[0690] (Application Example 1)

[0691] 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".

[0692] In modern cities, electricity consumption is rapidly increasing, leading to rising electricity costs and a demand for more efficient power supply. Simultaneously, in environments with diverse IoT devices, managing individual devices is becoming more complex, making efficient and comprehensive power management a challenge. Furthermore, there is a need to enhance preventative maintenance functions to minimize inconvenience caused by electrical appliance failures.

[0693] 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.

[0694] In this invention, the server includes information transmission means for collecting power usage information, generation model means for analyzing the power usage information and learning usage patterns, plan generation means for generating an optimal power usage schedule based on the analyzed patterns, and means for collecting device data from across the city and providing residents with an optimal power usage schedule. This enables efficient power management and optimal operation of consuming devices throughout the city.

[0695] "Power usage information" refers to data related to the power consumption of consumer devices, their on / off status, and operating time.

[0696] "Information transmission means" refers to a communication function for collecting power usage information and transmitting it to other components such as servers.

[0697] The "generative model means" is an artificial intelligence model used to analyze and learn usage patterns using collected power usage information.

[0698] A "plan generation means" is a device or program for generating an optimal power usage schedule based on analyzed patterns.

[0699] "Control means" refers to a function that causes each consumer device to execute instructions based on the generated schedule.

[0700] "City-wide device data" refers to data related to all IoT devices and consumer equipment present within a specific city.

[0701] A "power usage schedule" is a plan for power consumption created based on analyzed usage patterns, with the aim of efficient power consumption.

[0702] The system that implements this application aims to efficiently manage electricity usage information and reduce energy costs across the entire city. Details of the invention are as follows:

[0703] The server collects power usage information from consumer devices. Using information transmission means, the server aggregates data such as power consumption, on / off status, and operating time for each device. This collected data is stored within the server and analyzed by a generative model. The generative model uses a generative AI model to learn past usage patterns and predict the optimal power usage schedule.

[0704] Next, the server uses a plan generation mechanism to generate an optimal power usage schedule based on the analyzed data. This schedule takes into account device data from across the city and is designed to promote efficient power use. The generated schedule is transmitted to the terminal and notified to each consuming device via the control mechanism.

[0705] The device automatically controls consumer devices according to the received schedule, preventing unnecessary power consumption. A specific example is adjusting the operation of air conditioners during peak hours when electricity rates are high.

[0706] Furthermore, the server monitors device data across the entire city and identifies failure risks. If an anomaly is detected, it issues a warning to the user, enabling preventative maintenance and extending the lifespan of consumer equipment.

[0707] In this way, servers, terminals, and users work together to optimize power management and help reduce energy costs across the entire city.

[0708] Examples of prompt messages are as follows:

[0709] "Based on past electricity usage data, please generate an optimized summer air conditioning usage schedule to reduce energy costs."

[0710] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0711] Step 1:

[0712] The server collects power usage information from each consumer device. Using an information transmission method, the server receives data such as power consumption, on / off status, and operating time from the consumer devices. The input is raw data from each consumer device, and the output is power usage information data stored within the server.

[0713] Step 2:

[0714] The server analyzes the collected power usage information using a generating AI model. This model learns from past usage patterns and analyzes usage patterns based on the input data. Here, data analysis and modeling calculations are performed, and the output is a prediction of usage patterns.

[0715] Step 3:

[0716] The server creates a power usage schedule using a planning generation mechanism. Based on the analysis results, the server generates a schedule aimed at efficient power use. The input here is the predicted usage pattern, and the output is the optimized power usage schedule.

[0717] Step 4:

[0718] The server sends the generated power usage schedule to the terminal. The terminal receives this schedule and uses it as instructions to automatically manage each consuming device via the control system. The input is the generated schedule, and the output is the control signal to the consuming device.

[0719] Step 5:

[0720] The server monitors device data across the entire city for preventative maintenance. If an anomaly is detected, the server sends an alert to the user. Inputs are real-time data from each device, and outputs are alert information for the user.

[0721] Step 6:

[0722] Users check the schedule and alert information received via their devices and make manual adjustments as needed. User input is manual setting changes, and output is the operation of the consumer devices reflecting those changes.

[0723] 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.

[0724] This invention relates to a system that includes a cloud server, a user terminal, IoT-enabled electrical appliances, and an emotion engine that recognizes the user's emotions. The following describes how this system is implemented.

[0725] The terminal collects power usage data from IoT-enabled electrical appliances placed in homes and offices. This data includes information such as power consumption, usage status, and operating time for each product. The terminal is responsible for transmitting the collected data in real time to a cloud server.

[0726] The server stores the received data and uses generated AI to analyze power usage patterns. This analysis creates an optimal power usage schedule to reduce costs. At the same time, the server considers power rates and market data to optimize in response to price fluctuations.

[0727] Furthermore, the system uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's facial expressions and voice through the camera and microphone, and determines their emotional state based on the results. This emotional information is used to adjust the power usage schedule. For example, if the user is feeling stressed, the system adjusts the lighting to help them relax and provides a comfortable environment.

[0728] The created schedule and emotion recognition results are reflected in the control of electrical appliances, adjusting the operation of each product based on the schedule. This simultaneously achieves efficient energy use and improved user comfort.

[0729] Furthermore, the system includes a preventative maintenance function that detects abnormalities in electrical appliances in advance and sends alerts to the user, thereby reducing the risk of failure. This extends the lifespan of electrical appliances and helps reduce costs in the long term.

[0730] As described above, the system of the present invention achieves both optimization of power usage and improvement of user comfort, providing sustainable energy management.

[0731] The following describes the processing flow.

[0732] Step 1:

[0733] The device collects power usage data in real time from various IoT-enabled electrical appliances in the home or office. This includes information such as power consumption, operating status, and usage time for each product.

[0734] Step 2:

[0735] The device sends collected power usage data to a cloud server at regular intervals. Data is encrypted during transfer to protect the information.

[0736] Step 3:

[0737] The server records the received data in a database and uses a generating AI to analyze power usage patterns. Based on this analysis, it creates an optimal power usage schedule based on past trends.

[0738] Step 4:

[0739] The server collects electricity rate information and combines it with analysis patterns to optimize electricity usage schedules based on rate fluctuations. This aims to minimize costs.

[0740] Step 5:

[0741] The device has a built-in emotion engine that uses the camera and microphone to analyze the user's facial expressions and voice. From this data, it recognizes the user's emotions and identifies factors that affect their comfort level.

[0742] Step 6:

[0743] Based on information from the emotion engine, the server determines the user's current emotional state and adjusts the power usage schedule as needed. For example, if it determines that the user is highly stressed, it may dim the lighting.

[0744] Step 7:

[0745] The device automatically controls the on / off status and settings of each electrical appliance according to a pre-configured power usage schedule. This includes adjusting the temperature of air conditioners and the brightness of lighting based on the schedule.

[0746] Step 8:

[0747] Users can review suggested schedules and adjustments based on sentiment recognition through their device's display or app, and manually modify them as needed.

[0748] Step 9:

[0749] The server uses a preventative maintenance function to monitor the operation of electrical appliances and sends an alert to the user when an anomaly is detected. This makes it possible to prevent malfunctions and failures of electrical appliances before they occur.

[0750] Through these steps, the system simultaneously achieves improved energy efficiency and enhanced user comfort, enabling sustainable energy management.

[0751] (Example 2)

[0752] 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".

[0753] In modern life, many information devices and equipment consume electricity, making it urgent to optimize their usage efficiency. Furthermore, providing a comfortable environment that responds to the user's emotional state is also important. However, achieving these simultaneously is difficult, and systems that manage power in accordance with emotional states are still insufficient. Therefore, the present invention aims to provide an efficient and comfortable power management system that combines power usage and emotional state.

[0754] 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.

[0755] In this invention, the server includes communication means for acquiring information on power usage, generation means for analyzing the information on power usage and learning usage patterns, and adjustment means for adjusting the power usage schedule based on the emotional state. This makes it possible to achieve optimal power usage that responds to the user's emotions, thereby improving comfort and efficiency.

[0756] "Communication means" refers to systems or devices used to collect and transmit information about power usage to a server.

[0757] "Generation means" refers to functions and processes for analyzing collected information on electricity usage and learning usage patterns.

[0758] A "planning mechanism" is a mechanism for formulating an efficient power usage schedule based on analyzed usage patterns.

[0759] "Recognition means" refers to a technology or process that uses devices such as cameras and microphones to determine the emotional state of a user.

[0760] "Adjustment measures" refer to methods or systems for appropriately adjusting the electricity usage schedule according to the user's emotional state.

[0761] A "control system" refers to a system or technology that controls each device based on the created power usage schedule.

[0762] "Rate optimization measures" refer to processes and functions for adjusting the optimal electricity usage schedule in response to fluctuations in electricity rates.

[0763] "Preventive maintenance" is a method of preventing problems before they occur by predicting the risk of equipment failure and taking countermeasures in advance.

[0764] This invention is a system aimed at optimizing power usage and improving user comfort. The system primarily consists of a server located in the cloud, terminals placed in homes and offices, and a device for recognizing the user's emotions.

[0765] The server is the core of this system and is equipped with communication means to collect information on power usage. Power usage data transmitted from terminals is stored on the cloud server. The server uses a generative AI model to analyze this data and learn usage patterns. In this process, it identifies peak power consumption times and times when efficient use is possible.

[0766] Based on the analyzed data, the server generates an optimal power usage schedule and uses a planning mechanism to construct control commands for each device to execute that schedule. Furthermore, a cost optimization mechanism takes into account fluctuations in electricity prices to propose the most economical usage method.

[0767] The user's emotional state is determined by recognition mechanisms built into the device. Using cameras and microphones, the user's facial expressions and voice are analyzed to evaluate their emotional state. This information is then reflected in the power usage schedule by adjustment mechanisms, automatically adjusting lighting and activating heating appliances according to the user's emotional state.

[0768] As a concrete example, if a user is relaxing in the living room, the system will determine the user's stress level from their facial expression and use a generative AI model to input a prompt to the server: "Create an optimal power usage schedule for the next 24 hours based on the user's emotional state, and maximize the relaxation effect." As a result, the system will automatically change the lighting to a warmer tone and adjust the air conditioning to the optimal level to provide a comfortable environment.

[0769] Furthermore, the system incorporates a preventative maintenance function that detects signs of equipment failure early. By analyzing abnormal data and sending alerts to the user when necessary, it helps extend the lifespan of the equipment. In this way, the present invention achieves efficient energy management and improves the quality of life for users.

[0770] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0771] Step 1:

[0772] The terminal collects power usage information from various information devices installed in homes and offices. It uses sensors and smart meters to acquire data on power consumption, usage frequency, and operating time. This data is transmitted in real time to a cloud server using communication methods. The input is the power usage status of each device, and the output is a data packet summarizing this data.

[0773] Step 2:

[0774] The server stores power usage information received from terminals in a database. The received data is analyzed by a generating AI model to learn usage patterns. Specific data processing includes statistical analysis of peak usage time and average consumption. The input is the collected power usage information, and the output is the result of the usage pattern analysis.

[0775] Step 3:

[0776] Based on the analysis results, the server uses a generative model to create an optimal power usage schedule. This process also considers electricity rates and market trends to propose economically efficient power usage. Data processing here includes simulations of rate fluctuations. The input consists of the analysis results of usage patterns and market data, while the output is the power usage schedule.

[0777] Step 4:

[0778] The emotion recognition system installed on the user's device analyzes the user's facial expressions and voice using a camera and microphone. It sends the emotional state to a server as a prompt message, which is then reflected in the schedule. Specifically, it generates an emotion label such as "relaxed state" and adjusts the lighting and music based on it. The input is the user's real-time emotion data, and the output is the adjusted power usage schedule.

[0779] Step 5:

[0780] The server controls the operation of each information device based on a coordinated schedule. It sends control signals to terminals, adjusting the on / off status and operating levels of the devices to improve energy efficiency and user comfort. The input is the coordinated schedule, and the output is the control signals for the devices.

[0781] Step 6:

[0782] The system includes a preventative maintenance function, and the server continuously monitors data to detect signs of failure. It analyzes vibration and abnormal temperature data and sends alerts to the user if a problem is predicted. The input is equipment operating status data, and the output is alert notifications.

[0783] (Application Example 2)

[0784] 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".

[0785] In modern smart cities, improving energy efficiency and enhancing user comfort are crucial challenges. However, conventional methods struggle to consider user emotions and environmental changes when optimizing electricity use, resulting in a failure to adequately meet individual needs.

[0786] 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.

[0787] In this invention, the server includes communication means for collecting power usage information, generation engine means for analyzing the power usage information and learning usage patterns, and emotion recognition means for recognizing the user's emotions and adjusting environmental settings based on those emotions. This makes it possible to achieve both efficient power use and improved user comfort.

[0788] "Electricity usage information" refers to data on the power consumption, usage status, and operating time of each electrical appliance.

[0789] "Communication methods" refer to technologies for collecting data from various devices located in homes and offices and transmitting it to cloud servers.

[0790] The "generation engine means" is a system for analyzing collected information and learning trends in power usage.

[0791] A "plan generation means" is a mechanism that creates an optimal power usage plan based on the analysis results.

[0792] "Control means" refers to the technology for adjusting and operating the operation of each electrification device according to the generated plan.

[0793] "Emotion recognition means" refers to a function that recognizes the user's emotional state through a camera or microphone and optimizes the environment settings accordingly.

[0794] This invention is a system that improves energy efficiency and user comfort within smart cities. As a specific embodiment of the invention, an application system using smartphones and smart glasses is designed.

[0795] The main components of the system are a user terminal, a cloud server, a group of IoT appliances, and an emotion engine for emotion recognition. The user terminal has the function of collecting power usage information from each IoT appliance in the home or office and sending it to the cloud server. The cloud server analyzes the received data and uses a generation engine to learn power usage patterns. This generates an optimal power usage plan and appropriately controls the operation of the appliances.

[0796] Furthermore, the cloud server uses data acquired from cameras and microphones to recognize the user's emotions and analyzes it using emotion recognition tools. For example, if the user is feeling stressed, the cloud server controls various electrical devices to adjust lighting and temperature to provide a comfortable environment.

[0797] For example, when a user desires relaxation, the system can adjust the room lighting to an appropriate brightness and play relaxation music. In this case, a generative AI model can be used to prompt messages such as, "Generate an ideal room environment based on the user's emotional state," or "Suggest possible adjustments to optimize current power usage."

[0798] In summary, this system provides a solution that enables both efficient use of electricity and user comfort simultaneously.

[0799] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0800] Step 1:

[0801] The terminal collects real-time power usage information from various IoT electrical appliances placed in homes and offices. This information includes data on the power consumption, usage status, and operating time of each electrical device. The collected data is sent to a cloud server.

[0802] Step 2:

[0803] The server uses a generation engine to analyze power usage patterns based on the received power usage information. During this analysis, the data is input into a learning model to understand power usage trends. As a result, an optimal power usage plan is generated.

[0804] Step 3:

[0805] The server, using a plan generation mechanism, creates specific control commands to execute the optimal power usage plan for each electrification device based on the analysis results. Here, the plan is adjusted so that each device operates efficiently.

[0806] Step 4:

[0807] The server uses data acquired from the camera and microphone to analyze the user's emotions using emotion recognition technology. This process uses image recognition and voice analysis technologies to identify the user's emotional state from their facial expressions and voice, and outputs the result as an emotional situation.

[0808] Step 5:

[0809] The server optimizes the environmental settings of each electrical appliance based on the user's emotional state. In doing so, it uses a generative AI model to generate adjustment suggestions to provide the user with a comfortable environment, and outputs a prompt message such as "Generate an ideal indoor environment based on the user's emotional state."

[0810] Step 6:

[0811] The user experiences a customized environment based on the output from the server. For example, if the user is feeling stressed, the lighting is set to a calming color and relaxation music is played. This sequence of actions simultaneously achieves efficient power use and user comfort.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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."

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] The following is further disclosed regarding the embodiments described above.

[0834] (Claim 1)

[0835] Communication means for collecting power usage data,

[0836] A generative model means for analyzing the aforementioned power usage data and learning usage patterns,

[0837] A plan generation means that generates an optimal power usage schedule based on the analyzed pattern,

[0838] Control means for causing each electrical appliance to execute the aforementioned schedule,

[0839] A system that includes this.

[0840] (Claim 2)

[0841] The system according to claim 1, further comprising electricity rate information collection and a rate optimization means for adjusting the optimal electricity usage schedule based on fluctuations in electricity rates.

[0842] (Claim 3)

[0843] The system according to claim 1, wherein the control means is equipped with a function for performing preventive maintenance on electrical appliances in order to reduce the risk of failure.

[0844] "Example 1"

[0845] (Claim 1)

[0846] A means of communication for collecting information,

[0847] A generative model mechanism that analyzes the aforementioned information to understand the form of use,

[0848] A planning means for generating an optimal usage schedule based on the analyzed morphology,

[0849] An operating means for causing each device to execute the aforementioned schedule,

[0850] A means of storing information,

[0851] A monitoring system that detects anomalies and issues warnings,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, further comprising a fee optimization mechanism that collects fee information and adjusts the optimal usage schedule based on fee fluctuations.

[0855] (Claim 3)

[0856] The system according to claim 1, wherein the operating means is equipped with a function for performing maintenance to prevent the occurrence of failures.

[0857] "Application Example 1"

[0858] (Claim 1)

[0859] Information transmission means for collecting power usage information,

[0860] A generative model means for analyzing the aforementioned power usage information and learning usage patterns,

[0861] A plan generation means that generates an optimal power usage schedule based on the analyzed patterns,

[0862] Control means for causing each consumer device to execute the aforementioned schedule,

[0863] A means of collecting device data from across the city and providing residents with an optimal electricity usage schedule,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The system according to claim 1, comprising electricity rate information collection and a rate optimization means for adjusting the optimal electricity usage schedule based on fluctuations in electricity rates.

[0867] (Claim 3)

[0868] The system according to claim 1, wherein the control means is equipped with a function to perform preventive maintenance on consumer equipment in order to reduce the risk of failure.

[0869] "Example 2 of combining an emotion engine"

[0870] (Claim 1)

[0871] A means of communication for obtaining information on electricity usage,

[0872] A generation means for analyzing the information regarding power usage and learning usage patterns,

[0873] A planning means for generating an optimal power usage schedule based on analyzed patterns,

[0874] A means of recognition for determining the emotional state of the user,

[0875] An adjustment means for adjusting the power usage schedule based on the aforementioned emotional state,

[0876] Control means for causing each device to execute the aforementioned schedule,

[0877] A system that includes this.

[0878] (Claim 2)

[0879] The system according to claim 1, further comprising a charge optimization means for collecting information on electricity charges and adjusting the optimal electricity usage schedule based on fluctuations in electricity charges.

[0880] (Claim 3)

[0881] The system according to claim 1, wherein the control means is equipped with a function for performing preventive maintenance on the equipment in order to reduce the risk of failure.

[0882] "Application example 2 when combining with an emotional engine"

[0883] (Claim 1)

[0884] A communication means for collecting electricity usage information,

[0885] A generation engine means that analyzes the aforementioned power usage information and learns usage patterns,

[0886] A plan generation means that generates an optimal power usage plan based on the analyzed pattern,

[0887] Control means for causing each electrification device to execute the aforementioned plan,

[0888] An emotion recognition means that recognizes the user's emotions and adjusts the environmental settings based on those emotions,

[0889] A system that includes this.

[0890] (Claim 2)

[0891] The system according to claim 1, further comprising electricity rate information collection and rate optimization means for adjusting the optimal electricity usage plan based on fluctuations in electricity rates.

[0892] (Claim 3)

[0893] The system according to claim 1, wherein the control means is equipped with a function for performing preventive maintenance on electrical equipment in order to reduce safety risks. [Explanation of Symbols]

[0894] 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. Information transmission means for collecting power usage information, A generative model means for analyzing the aforementioned power usage information and learning usage patterns, A plan generation means that generates an optimal power usage schedule based on the analyzed patterns, Control means for causing each consumer device to execute the aforementioned schedule, A means of collecting device data from across the city and providing residents with an optimal electricity usage schedule, A system that includes this.

2. The system according to claim 1, further comprising electricity rate information collection and a rate optimization means for adjusting the optimal electricity usage schedule based on fluctuations in electricity rates.

3. The system according to claim 1, wherein the control means is equipped with a function to perform preventive maintenance on consumer equipment in order to reduce the risk of failure.

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