system

Abstract

We provide the system. [Solution] The information processing device analyzes instantaneous power consumption data acquired from multiple terminal devices and identifies consumption patterns. A means of calculating the optimal electricity usage schedule based on the rate plan obtained from the electricity supplier, A means of monitoring the deterioration of connected electrical equipment using a preventative maintenance algorithm and notifying the user of the maintenance timing, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a conventional power management system, detailed understanding of power consumption and optimal power usage proposals considering changes in tariff plans have not been sufficiently carried out, and users have had to exert a great deal of effort to achieve efficient energy utilization. In addition, there is a lack of mechanisms to prevent a decrease in energy efficiency due to deterioration of electrical equipment in homes and offices and the risk of failure. As a result, there has been a problem that wasteful energy consumption and unnecessary costs are likely to occur.

Means for Solving the Problems

[0005] This invention provides a means for identifying a user's consumption patterns and calculating an optimal power usage schedule by analyzing instantaneous power consumption data acquired from multiple terminal devices using an information processing device. Furthermore, it supports efficient energy use by sending power consumption optimization suggestions to the user based on rate plans acquired from power supply companies. In addition, it includes a mechanism that monitors the deterioration of each electrical device and notifies the user of maintenance timing, thereby reducing the risk of decreased energy efficiency and malfunctions. This achieves optimization of power consumption, cost reduction, and prevention of electrical equipment deterioration.

[0006] An "information processing device" is a computer system that analyzes power consumption data and optimizes pricing plans.

[0007] A "terminal device" is a device that connects to each electrical device to monitor power consumption and transmit data to an information processing device.

[0008] "Instantaneous power consumption data" refers to data that shows the amount of power used at a specific moment in time, and is used to understand the operating status of electrical equipment in real time.

[0009] "Consumption patterns" refer to the trends in how users and electrical devices use electricity, and describe the movements and habits of electricity consumption over a period of time.

[0010] A "pricing plan" refers to the structure and conditions of electricity rates offered by a power supply company, and is related to the pricing of electricity usage by users.

[0011] A "preventive maintenance algorithm" is a calculation method that analyzes the usage of electrical equipment to detect signs of deterioration or failure.

[0012] "Maintenance timing" refers to the appropriate time to perform maintenance work necessary to prevent deterioration of electrical equipment and ensure its efficient operation.

[0013] An "alert" is a notification that informs the user of unusual circumstances, such as abnormal power consumption. [Brief explanation of the drawing]

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

Embodiments for Carrying Out the Invention

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

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

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

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by a processor.

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

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

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

[0022] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0035] The system of the present invention mainly comprises three elements: a server, a terminal, and a user. This system is designed to optimize power consumption, reduce costs, and prevent the deterioration of power equipment.

[0036] Server operation

[0037] The server receives instantaneous power consumption data transmitted from multiple terminals. This data is analyzed using a generating AI algorithm to identify user consumption patterns. This analysis is used not only to understand power usage trends but also to detect anomalies in consumption. The server also obtains the latest pricing plans provided by power suppliers and calculates the optimal power usage schedule for each user.

[0038] Furthermore, based on the analysis results, the server generates and notifies the user of power consumption optimization suggestions. This includes specific schedule suggestions to avoid peak hours and recommended actions to improve energy efficiency.

[0039] Terminal operation

[0040] The terminal monitors the power consumption of each connected electrical device in real time. The terminal sends the collected data to the server and immediately notifies the user of any abnormal power consumption.

[0041] The terminal further executes a preventative maintenance algorithm and monitors the condition of electrical equipment. When signs of deterioration are detected, it notifies the user of the appropriate maintenance time, preventing failures before they occur.

[0042] User interaction

[0043] Users receive reports sent from the server via their terminals. These reports include a detailed analysis of past power consumption and suggestions for improving energy efficiency. Users can use the system interface to view power consumption details and adjust settings as needed.

[0044] Furthermore, users can ask questions to the server through a chatbot powered by AI generation when they encounter any unclear points or problems. The chatbot responds immediately and provides appropriate support.

[0045] For example, if the air conditioner's power consumption is high during certain times of the summer, the server uses that data to notify the user to use the air conditioner during off-peak hours. The terminal also detects if the air conditioner's filter is clogged and sends a notification to the user prompting them to clean it. In this way, the entire system works together to enable efficient power consumption and equipment management.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] The terminal monitors the power consumption data of each connected electrical device in real time and periodically sends this data to the server.

[0049] Step 2:

[0050] The server analyzes the received instantaneous power consumption data using a generated AI algorithm to identify the user's power consumption patterns. For example, it can identify peak usage trends and signs of abnormal consumption.

[0051] Step 3:

[0052] The server retrieves the latest pricing plans from the power company and calculates the optimal power usage schedule by comparing it with the user's consumption patterns. This schedule is updated in real time to reflect changes in pricing plans.

[0053] Step 4:

[0054] Based on the calculation results, the server generates suggestions for improving energy efficiency and notifies the user. Specific examples include methods to reduce power consumption and time periods when usage should be reviewed.

[0055] Step 5:

[0056] The terminal runs a preventative maintenance algorithm and monitors the deterioration status of electrical equipment. When a warning sign is detected, the terminal notifies the user of the need for maintenance.

[0057] Step 6:

[0058] Users access reports sent from the server by operating a terminal or dedicated application. These reports include historical consumption data and optimization suggestions.

[0059] Step 7:

[0060] Users receive support when they have questions or problems through an AI-powered chatbot within the system. This bot responds to user inquiries and provides appropriate solutions.

[0061] (Example 1)

[0062] 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."

[0063] In modern electricity consumption, inefficient use can occur due to improper use of equipment and fluctuations in electricity rates. Furthermore, equipment failures due to deterioration are unpredictable, leading to sudden power outages and high repair costs. It is necessary to solve these problems and achieve efficient electricity use and extend the lifespan of equipment.

[0064] 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.

[0065] In this invention, the server includes means for an information processing device to analyze instantaneous power consumption information obtained from multiple terminal devices and identify consumption patterns, means for calculating an optimal power usage schedule based on a rate plan obtained from a power supplier, and means for monitoring the deterioration of connected power equipment using a predictive maintenance algorithm and notifying users of the repair timing. This enables efficient power use, prevention of equipment failures, and cost reduction.

[0066] An "information processing device" is a central device that collects and analyzes data acquired from multiple terminal devices and performs specific functions.

[0067] "Instantaneous power consumption information" is data that shows the amount of power a power appliance is consuming at a given point in time in real time.

[0068] "Consumption patterns" refer to patterns or trends that indicate how users or power appliances use electricity.

[0069] A "rate plan" is a detailed plan regarding electricity rates provided by electricity suppliers, and it serves as the basis for determining the optimal timing for electricity usage for electricity consumers.

[0070] "Optimal electricity usage plan" refers to an electricity usage schedule calculated based on electricity consumption data and pricing plans, with the aim of improving the efficiency of electricity consumption and reducing costs.

[0071] A "predictive maintenance algorithm" is a computational method that analyzes past usage data and operating patterns of equipment to predict the likelihood of future deterioration or failure.

[0072] "Power equipment" refers to all kinds of devices and equipment that operate using electricity.

[0073] The "repair period" refers to the time when deterioration of electrical equipment is expected, and by performing preventative maintenance at this time, malfunctions can be prevented before they occur.

[0074] "Users" refers to consumers and operators who use power systems and related equipment.

[0075] This invention is a system designed to optimize power consumption and prevent the deterioration of power equipment, and it operates primarily through the cooperation of servers, terminals, and users.

[0076] Server Role

[0077] The server, acting as an information processing device, receives instantaneous power consumption information transmitted from terminals and analyzes it using a generation AI algorithm. Data analysis includes data processing using Python and identification of consumption patterns using machine learning with TENSORFLOW®. The server also acquires pricing plan data from power suppliers and calculates the optimal power usage plan based on this data. Furthermore, a predictive maintenance algorithm monitors the deterioration of connected power equipment and notifies the user of the necessary repairs.

[0078] Terminal role

[0079] The terminal is a device that monitors the power consumption of each power device in real time. The terminal uses a current sensor to measure instantaneous power and transmits the data to a server via Wi-Fi. When the terminal detects abnormal power consumption, it notifies the user with an alert. Equipped with a predictive maintenance algorithm, it learns the normal operating patterns of power devices, so it can react immediately when an anomaly occurs and notify the user.

[0080] User roles

[0081] Users receive optimization suggestions and anomaly notifications from the server via their devices, and adjust power consumption settings accordingly. Users can easily implement specific power-saving measures using a smartphone application. They can also obtain questions and support through a chatbot powered by a generative AI model.

[0082] For example, the server might suggest optimizing the use of air conditioning by shifting it from daytime to nighttime during the summer based on the analyzed data. A possible prompt might be, "What advice do you have for avoiding peak electricity rates?"

[0083] Through this configuration, the system aims to support efficient power consumption and prevent equipment degradation.

[0084] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0085] Step 1:

[0086] The terminal collects instantaneous power consumption information in real time from each connected power device. The input is raw data obtained through power sensors. This data is processed internally and converted into a timestamped dataset. The output is ready-to-send statistical information to the server. Specifically, the terminal is configured to send data to the server at regular intervals.

[0087] Step 2:

[0088] The server receives instantaneous power consumption information sent from the terminal. The input is timestamped power consumption data sent from the terminal. The server applies a generative AI model to analyze this data and identify consumption patterns. The output is a report based on each user's consumption patterns. Specifically, the server uses Python and a machine learning framework to analyze the data and understand consumption trends.

[0089] Step 3:

[0090] The server retrieves pricing plan data obtained from power suppliers. The input is the latest information regarding pricing plans. The server combines this information with consumption patterns reports to calculate the optimal power usage schedule. The output is an optimized usage schedule. The server generates the schedule and suggests specific strategies for the user to achieve maximum cost-effectiveness.

[0091] Step 4:

[0092] The server uses a predictive maintenance algorithm to monitor the degradation of connected power equipment. Inputs are power consumption data and equipment status information. The algorithm analyzes the data and detects signs of equipment degradation. Outputs are alerts indicating when repairs are needed. This information is communicated to the user, who is then given a specific maintenance schedule.

[0093] Step 5:

[0094] Users receive optimization suggestions and degradation alerts sent from the server via their devices. Input consists of reports and notifications from the server. Users use the received information to adjust power consumption settings. Output is optimized consumption behavior. Users can easily change settings using a smartphone app.

[0095] Step 6:

[0096] Users can use a chatbot powered by a generative AI model to ask questions and receive support regarding the system. The input consists of the user's questions and prompts. The generative AI model uses a predetermined algorithm to quickly respond to questions and provide appropriate advice. The output is real-time answers and support information. For example, a user can ask, "What are the cheapest times of day for electricity?" and receive an answer from the server.

[0097] (Application Example 1)

[0098] 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."

[0099] Modern cities require the efficient use of energy, and optimizing electricity consumption in homes and businesses is particularly important. However, there is a challenge in improving the efficiency of energy management due to a lack of optimization suggestions and warning notifications tailored to individual consumption patterns and equipment degradation. Therefore, a system is needed that provides consumers with suggestions and warnings based on real-time data to promote the efficient use of electricity.

[0100] 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.

[0101] In this invention, the server includes means for an information processing device to analyze instantaneous power consumption information acquired from multiple measuring devices and identify consumption behavior; means for calculating an optimal usage schedule based on a rate plan acquired from a power supplier; means for monitoring the deterioration of connected equipment using a preventive maintenance algorithm and notifying users of the timing of maintenance; and means for supplying a program that operates on a mobile communication terminal to acquire information in real time and provide efficient consumption suggestions based on the analysis results. This makes it possible to provide efficient power consumption suggestions to residents of urban areas in real time.

[0102] An "information processing device" is a device that analyzes power consumption information acquired from multiple measuring devices and identifies consumption behavior.

[0103] A "measuring device" is a device that acquires instantaneous power consumption information from each piece of equipment in real time.

[0104] A "power supplier" is an energy supply company that provides electricity and presents pricing plans.

[0105] A "rate plan" refers to the pricing structure for electricity usage presented by electricity suppliers, and is used to calculate the optimal schedule based on the user's consumption trends.

[0106] A "preventive maintenance algorithm" is an algorithm used to monitor equipment deterioration and identify the appropriate timing for maintenance.

[0107] "Deterioration" refers to the phenomenon where the performance of equipment declines with use, and it serves as an indicator for determining the appropriate timing for maintenance.

[0108] A "mobile communication terminal" is a portable communication device that can acquire information in real time and provide efficient consumption suggestions based on the analysis results.

[0109] "Means for supplying programs" refers to means for providing a method for executing efficient consumption suggestions based on analysis on a mobile communication terminal.

[0110] This invention is a system that collects power consumption information using multiple measuring devices, and an information processing device analyzes that data to identify consumption behavior and provide an efficient power usage schedule. The server calculates the optimal power usage schedule based on the analysis results and proposes it to the user.

[0111] Specifically, the server receives power consumption data acquired in real time from measuring devices and performs analysis using a generative AI model. Machine learning software frameworks such as TensorFlow and PyTorch are used for the analysis. This makes it possible to identify patterns in consumption behavior and detect anomalies.

[0112] Next, the device uses this analysis result to notify the user in real time of suggestions for optimizing consumption. Firebase Cloud Messaging is used for notifications, and since the device is a mobile communication device such as a smartphone or tablet, it offers high convenience.

[0113] This system can optimize power usage by suggesting efficient times to use air conditioners, for example, during peak summer usage periods when air conditioner use is concentrated. Specifically, a prompt message such as, "Generate optimization suggestions for efficiency based on real-time power consumption data and create alerts to prevent malfunctions," will be sent to the customer, suggesting a concrete usage schedule.

[0114] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0115] Step 1:

[0116] The server acquires real-time power consumption data from multiple measuring devices. This input data represents instantaneous power consumption information for each piece of equipment. The server aggregates this data and stores it in a database, processing it into a format that can be referenced.

[0117] Step 2:

[0118] The server inputs aggregated power consumption data into a generating AI model. Here, machine learning algorithms using TensorFlow and PyTorch are employed for analysis. The output includes each user's consumption pattern and indicators of potential anomalies. This analysis allows for the identification of specific consumption behaviors.

[0119] Step 3:

[0120] The server receives pricing plan information from power suppliers and calculates the optimal power usage schedule based on the analysis results. This calculation uses pricing plan and consumption pattern data as input, and the result is output as an efficient schedule.

[0121] Step 4:

[0122] The device receives schedules and anomaly detection information provided by the server. Based on this information, it performs actions to notify the user in real time. Specifically, push notifications are sent to smartphones via Firebase Cloud Messaging.

[0123] Step 5:

[0124] Users check notifications received on their devices and refer to the suggested power usage schedule. Furthermore, if they receive a warning about an anomaly, they can consider appropriate actions based on the content of the warning, thereby achieving optimal power usage.

[0125] 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.

[0126] The system of the present invention consists of a server with information processing capabilities, multiple terminals, and an emotion engine that recognizes the user's emotions. In addition to optimizing power consumption, this system aims to improve the user experience by providing interactions that take into account the user's emotional state.

[0127] Server operation

[0128] The server analyzes power consumption data transmitted from the terminal to identify the user's consumption patterns. Furthermore, it applies the latest pricing plans from the power supplier to calculate the optimal power usage schedule. An emotion engine acquires the user's emotional data, and based on the analysis results, the server adjusts the content and presentation of suggestions. For example, for a user experiencing stress, it provides suggestions in simpler and more considerate language.

[0129] Terminal operation

[0130] The terminal monitors the power consumption of each electrical device in real time and immediately notifies the user of any abnormalities. It also acquires emotional data based on the user's voice commands and camera input and provides it to the emotion engine. Based on the emotional state, it dynamically adjusts the timing of optimization suggestions and alerts to provide appropriate user support.

[0131] How the emotion engine works

[0132] The emotion engine evaluates the user's emotional state in real time based on their voice and facial expression data. Based on this evaluation, it supports suggestions and feedback to reduce the user's emotional burden. For example, if the emotion engine recognizes user frustration, the device will modify its suggestions and explain things in a gentler tone.

[0133] User interaction

[0134] Users can receive reports and notifications from the server on their devices, allowing them to view details and suggestions regarding power consumption. Furthermore, they can provide emotional data via voice input and touchscreen to assist the emotional engine. Receiving emotionally sensitive suggestions allows users to manage their power consumption in a more relaxed state.

[0135] For example, if a user is using a high-power device during a busy morning, the device reports this to the server. The server then gently suggests shifting usage to off-peak hours to reduce user stress. In this way, the system integrates seamlessly into the user's life, enabling more personalized optimization of power consumption.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] The terminal collects real-time power consumption data from each electrical device and periodically sends it to the server. This data includes instantaneous consumption and usage time.

[0139] Step 2:

[0140] The device acquires voice input and camera footage from the user and sends this data to the emotion engine. Here, data is collected to understand the user's emotional state.

[0141] Step 3:

[0142] The server analyzes power consumption data received from the terminal to identify the user's consumption patterns. This provides basic information for optimizing power usage.

[0143] Step 4:

[0144] The server references the latest pricing plans obtained from the power company and combines them with consumption patterns to calculate the optimal power usage schedule. This schedule is designed to maximize cost savings.

[0145] Step 5:

[0146] The emotion engine analyzes audio and video data provided by the device to evaluate the user's emotional state. Based on the emotional state, it instructs the server to adjust the suggestions.

[0147] Step 6:

[0148] The server receives feedback from the emotion engine and adjusts power usage optimization suggestions according to the user's emotions. For example, if the user is stressed, the suggestions will be made in a more considerate tone.

[0149] Step 7:

[0150] The device notifies the user with tailored suggestions and alerts. The content and timing of these notifications are optimized to take into account the user's emotional state.

[0151] Step 8:

[0152] Users review the suggestions through their devices and adjust their power usage schedules as needed. They also receive feedback based on the emotion engine's evaluation, which they use to improve their power management.

[0153] Step 9:

[0154] When a user changes their power usage pattern according to the suggestion, the device collects the resulting data again and sends it to the server to help optimize it for the next time.

[0155] Through this process, the system optimizes power consumption in real time and improves the user experience by incorporating emotionally sensitive elements.

[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 society, the efficient use of electricity is a crucial issue. However, simply proposing optimization of electricity usage fails to consider the emotional state of users, making it difficult to improve the quality of the user experience. Furthermore, unexpected problems due to insufficient monitoring of electrical equipment deterioration and malfunctions are also a major issue. Therefore, there is a need for a system that optimizes electricity consumption while considering user emotions and also taking equipment maintenance into account.

[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 means for analyzing instantaneous power consumption data acquired from terminal devices by an information processing device and identifying consumption patterns, means for calculating an optimal power usage schedule based on rate plans acquired from power supply organizations, means for evaluating the user's emotional state and adjusting the content and presentation method of suggestions accordingly, and means for monitoring the deterioration of connected electrical equipment using preventive maintenance techniques and notifying the user of maintenance timing. This makes it possible to optimize power consumption and detect deterioration early, taking into account the user's emotions.

[0161] An "information processing device" is a computer system that collects, analyzes, and processes data, primarily functioning as a server to provide users with necessary suggestions and information.

[0162] A "terminal device" is a device that communicates with a user and an information processing device, and is responsible for data collection and alert notifications.

[0163] "Instantaneous power consumption data" refers to data that shows the power usage status of each electrical device at any given moment, and is data that can be monitored in real time.

[0164] A "consumption pattern" refers to a characteristic usage trend that shows how users and devices utilize electricity within a specific period of time.

[0165] A "pricing plan" is a plan provided by an electricity supplier that outlines pricing based on electricity consumption and time of day.

[0166] A "power usage schedule" is a specific schedule of times and amounts of power usage planned to optimize power consumption and reduce costs.

[0167] "User emotional state" refers to the user's current psychological situation and emotions as judged from factors such as voice and facial expressions, and is information used to adjust the interface.

[0168] "Preventive maintenance" refers to a method of monitoring the condition of equipment and planning in advance the necessary maintenance and actions to prevent failures.

[0169] "Monitoring" refers to observation and recording conducted to monitor the status of target equipment or systems and to detect abnormalities or deterioration.

[0170] The system of the present invention consists of a server equipped with information processing functions, multiple terminal devices, and an emotion engine that recognizes the user's emotions.

[0171] The server receives real-time power consumption data transmitted from terminals and uses a database management system to analyze this data. Python libraries are used for analysis to identify consumption patterns. This allows for the calculation of power usage schedules optimized for the pricing plans provided by power suppliers. Furthermore, using user sentiment data obtained from the sentiment engine, the server adjusts suggestions to achieve more personalized interactions.

[0172] The device is equipped with smart meters and sensors to monitor the power consumption of each electrical appliance. If any anomalies are detected, the device immediately alerts the user. The device also provides voice commands and camera input data to an emotion engine. As a simple example, at night when the user is relaxed, the device can send emotion data to the server in real time, allowing the user to receive suggestions that are appropriate for a quiet time.

[0173] Users can view reports and notifications received from the server via their device, gaining a detailed understanding of their power consumption. They can also provide subjective emotional data through voice and touch input, supporting the system's adaptive responses. A specific prompt might read, "I would like to receive information on my current power consumption and emotionally sensitive optimization suggestions." In this way, it's possible to optimize user power consumption and provide emotionally responsive support.

[0174] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0175] Step 1:

[0176] The server receives power consumption data transmitted from terminals. The input is power consumption data monitored by each terminal, and the output is stored in a database. The server uses a data processing program to organize and store instantaneous power consumption in real time.

[0177] Step 2:

[0178] The server analyzes accumulated power consumption data. The input is historical power consumption data stored in a database, and the output identifies consumption patterns. The server uses a Python data analysis library to calculate consumption trends and peak times within a specific period.

[0179] Step 3:

[0180] The server calculates the optimal power usage schedule by applying rate plans from power suppliers. The input is analyzed consumption patterns and rate plan information, and the output is a recommended power usage schedule. The server uses mathematical optimization techniques to generate a cost-effective schedule.

[0181] Step 4:

[0182] The server retrieves user emotion data from the emotion engine and adjusts the suggestions based on it. The input is the user's emotional state provided by the emotion engine via the terminal, and the output is user-friendly suggestions. The server uses an emotion analysis model to create messages that take into account stress and relaxation levels.

[0183] Step 5:

[0184] The terminal monitors the power consumption of each electrical device in real time and detects anomalies. Input is real-time data from smart meters and sensors, and output is an alert generated when an anomaly is detected. The terminal processes the data using a microcontroller and immediately notifies the user.

[0185] Step 6:

[0186] The device transmits the user's voice data and camera footage to the emotion engine. The input is the voice and video data recorded by the device, and the output is the storage of this information in the emotion engine's database. The device uses voice recognition software to format the data and transfers it to the emotion engine via the network.

[0187] Step 7:

[0188] The user receives optimization suggestions from the server via their device and modifies their behavior accordingly. Inputs are optimization suggestions and schedules provided by the server, and output is optimized power consumption behavior for the user. The user responds using voice and touch controls, leveraging the device's interface.

[0189] In this way, a series of processing steps work in an interconnected manner, resulting in user-friendly power consumption and emotional management.

[0190] (Application Example 2)

[0191] 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".

[0192] The challenge lies in simultaneously achieving efficient electricity consumption in cities and optimal use of public facilities and transportation based on individual emotional states. Conventional systems focused on optimizing electricity consumption but failed to consider users' emotional states, limiting their ability to reduce user stress and improve comfort. Furthermore, there was a problem with the difficulty in providing timely and appropriate information regarding the use of public facilities and transportation within cities.

[0193] 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.

[0194] In this invention, the server includes an information processing device that analyzes instantaneous power consumption data acquired from multiple computer devices and identifies consumption characteristics, a means for calculating an optimal power usage plan based on rate plans acquired from power supply organizations, a means for monitoring the deterioration of connected electrical equipment using a preventive maintenance algorithm and notifying users of maintenance timing, and an emotion recognition engine that analyzes the user's voice and facial expression data and evaluates their emotional state, and a means for suggesting optimal routes and methods for using public facilities and transportation based on the emotional state. This enables efficient power consumption in cities and lifestyle improvements that take into account individual emotions.

[0195] An "information processing device" is an electronic device that analyzes data acquired from multiple computer devices and performs specified tasks as needed.

[0196] A "computer device" is an electronic device that has the function of collecting, processing, and communicating data.

[0197] "Instantaneous power consumption data" refers to information that shows the amount of electricity used at a specific point in time.

[0198] "Consumption characteristics" refer to features that indicate patterns and trends in electricity usage.

[0199] A "power supply organization" is an organization or company that generates electricity and supplies it to consumers.

[0200] A "pricing plan" is a pricing structure for electricity usage presented by an electricity supplier.

[0201] A "power usage plan" is a schedule or guideline created to optimize power consumption.

[0202] A "preventive maintenance algorithm" is a computational method that monitors the status of connected equipment and prevents failures from occurring before they happen.

[0203] "Maintenance time" refers to the time when connected electrical equipment requires repair or adjustment.

[0204] An "emotion recognition engine" is a system that evaluates a user's emotional state from voice and facial expression data.

[0205] "Emotional state" refers to information that indicates the user's psychological and emotional state.

[0206] "Public facilities" are buildings and equipment installed for use by the general public.

[0207] "Transportation" refers to means of transport and systems that support the movement of users.

[0208] The "optimal route or method" refers to a path or technique that efficiently achieves a goal while minimizing stress and burden.

[0209] The system implementing this invention comprises a server, multiple terminals, and an emotion recognition engine. The server primarily functions as an information processing device, acquiring instantaneous power consumption data from multiple computer devices. Using this data, it identifies consumption characteristics and calculates an optimal power usage plan based on rate plans obtained from power supply organizations. Furthermore, it monitors the status of connected electrical equipment using a preventative maintenance algorithm and notifies users of maintenance schedules.

[0210] On the other hand, the terminal is a device operated by the user themselves, and it is a system that monitors the power consumption of each electrical device in real time and notifies the user if an abnormal condition is detected. This terminal has a camera and microphone built in, and uses these to transmit the user's voice and facial expression data to the emotion recognition engine. The emotion recognition engine analyzes the received data and evaluates the user's emotional state.

[0211] Based on the assessment of emotional state, the server proposes the optimal way to use public facilities and transportation. This proposal includes an approach aimed at reducing user stress, selecting the best route and method. For example, if a user is using crowded transportation and experiencing stress, the server will suggest an alternative route and notify the user of this information via their device. This allows users to move through urban spaces comfortably and efficiently.

[0212] An example of a prompt might be, "If the user is experiencing mild frustration, suggest an alternative route that allows them to relax without using regular public transport." This prompt facilitates the data analysis and decision-making process in the generative AI model.

[0213] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0214] Step 1:

[0215] The server collects instantaneous power consumption data from each computer. Using this data, it applies specific algorithms to identify consumption characteristics. The input is power consumption data from each electrical device, and the output is an analysis of consumption patterns. This analysis includes, for example, using statistical methods to extract peak times and consumption trends.

[0216] Step 2:

[0217] The server calculates the optimal power usage plan by matching consumption characteristics with the latest rate plans obtained from the power supplier. The input includes consumption characteristics and rate plans, and the output generates a power usage schedule proposed to the user. This schedule applies optimization techniques to minimize consumption costs.

[0218] Step 3:

[0219] The terminal uses internal sensors to monitor the status of connected electrical equipment in real time and notifies the user of any abnormalities. Data input includes measured power usage, and abnormalities are detected by analyzing this data. The output is an alert message presented to the user.

[0220] Step 4:

[0221] The device's camera and microphone are used to collect user voice and facial expression data, which is then sent to an emotion recognition engine. The input includes real-time user data, and the output is the emotion recognition engine's evaluation of the user's emotional state. This process utilizes a machine learning model to identify the user's emotions.

[0222] Step 5:

[0223] Based on the evaluation results of the emotion recognition engine, the server proposes optimal routes and methods for using public facilities and transportation, taking into account the user's emotional state. Inputs include evaluated emotion data and urban traffic information, and output is specific travel suggestions for the user. These suggestions include a decision-making process aimed at reducing user stress and improving comfort.

[0224] Step 6:

[0225] The user receives suggestions from the server via their terminal and adjusts power management and actions accordingly. The input is the suggestion information from the server, and the output is the action options selected by the user. A specific example of this action would be when the user chooses to travel via an alternative route suggested.

[0226] Through this series of processes, servers, terminals, and users work together to create a system that improves efficiency and comfort in urban environments.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] [Second Embodiment]

[0231] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0232] 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.

[0233] 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).

[0234] 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.

[0235] 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.

[0236] 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).

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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".

[0243] The system of the present invention mainly comprises three elements: a server, a terminal, and a user. This system is designed to optimize power consumption, reduce costs, and prevent the deterioration of power equipment.

[0244] Server operation

[0245] The server receives instantaneous power consumption data transmitted from multiple terminals. This data is analyzed using a generating AI algorithm to identify user consumption patterns. This analysis is used not only to understand power usage trends but also to detect anomalies in consumption. The server also obtains the latest pricing plans provided by power suppliers and calculates the optimal power usage schedule for each user.

[0246] Furthermore, based on the analysis results, the server generates and notifies the user of power consumption optimization suggestions. This includes specific schedule suggestions to avoid peak hours and recommended actions to improve energy efficiency.

[0247] Terminal operation

[0248] The terminal monitors the power consumption of each connected electrical device in real time. The terminal sends the collected data to the server and immediately notifies the user of any abnormal power consumption.

[0249] The terminal further executes a preventative maintenance algorithm and monitors the condition of electrical equipment. When signs of deterioration are detected, it notifies the user of the appropriate maintenance time, preventing failures before they occur.

[0250] User interaction

[0251] Users receive reports sent from the server via their terminals. These reports include a detailed analysis of past power consumption and suggestions for improving energy efficiency. Users can use the system interface to view power consumption details and adjust settings as needed.

[0252] Furthermore, users can ask questions to the server through a chatbot powered by AI generation when they encounter any unclear points or problems. The chatbot responds immediately and provides appropriate support.

[0253] For example, if the air conditioner's power consumption is high during certain times of the summer, the server uses that data to notify the user to use the air conditioner during off-peak hours. The terminal also detects if the air conditioner's filter is clogged and sends a notification to the user prompting them to clean it. In this way, the entire system works together to enable efficient power consumption and equipment management.

[0254] The following describes the processing flow.

[0255] Step 1:

[0256] The terminal monitors the power consumption data of each connected electrical device in real time and periodically sends this data to the server.

[0257] Step 2:

[0258] The server analyzes the received instantaneous power consumption data using a generated AI algorithm to identify the user's power consumption patterns. For example, it can identify peak usage trends and signs of abnormal consumption.

[0259] Step 3:

[0260] The server retrieves the latest pricing plans from the power company and calculates the optimal power usage schedule by comparing it with the user's consumption patterns. This schedule is updated in real time to reflect changes in pricing plans.

[0261] Step 4:

[0262] Based on the calculation results, the server generates suggestions for improving energy efficiency and notifies the user. Specific examples include methods to reduce power consumption and time periods when usage should be reviewed.

[0263] Step 5:

[0264] The terminal runs a preventative maintenance algorithm and monitors the deterioration status of electrical equipment. When a warning sign is detected, the terminal notifies the user of the need for maintenance.

[0265] Step 6:

[0266] Users access reports sent from the server by operating a terminal or dedicated application. These reports include historical consumption data and optimization suggestions.

[0267] Step 7:

[0268] Users receive support when they have questions or problems through an AI-powered chatbot within the system. This bot responds to user inquiries and provides appropriate solutions.

[0269] (Example 1)

[0270] 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."

[0271] In modern electricity consumption, inefficient use can occur due to improper use of equipment and fluctuations in electricity rates. Furthermore, equipment failures due to deterioration are unpredictable, leading to sudden power outages and high repair costs. It is necessary to solve these problems and achieve efficient electricity use and extend the lifespan of equipment.

[0272] 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.

[0273] In this invention, the server includes means for an information processing device to analyze instantaneous power consumption information obtained from multiple terminal devices and identify consumption patterns, means for calculating an optimal power usage schedule based on a rate plan obtained from a power supplier, and means for monitoring the deterioration of connected power equipment using a predictive maintenance algorithm and notifying users of the repair timing. This enables efficient power use, prevention of equipment failures, and cost reduction.

[0274] An "information processing device" is a central device that collects and analyzes data obtained from multiple terminal devices and executes specific functions.

[0275] "Instantaneous power consumption information" is data that shows in real-time the amount of electric power consumed by an electrical device at a certain point in time.

[0276] "Consumption pattern" refers to a pattern or trend indicating how a user or an electrical device uses electric power.

[0277] "Tariff plan" is a detailed plan regarding electricity tariffs provided by an electricity supplier and serves as a basis for determining the optimal usage timing for electricity consumers.

[0278] "Optimal electricity usage schedule" is a schedule for using electricity calculated based on electricity consumption data and a tariff plan for the purpose of improving the efficiency of electricity consumption and reducing costs.

[0279] "Predictive maintenance algorithm" is a computational means for analyzing past usage data and operation patterns of a device to predict the possibility of future deterioration and failure.

[0280] "Electrical device" refers to any type of device or apparatus that operates using electricity.

[0281] "Repair time" refers to the time when deterioration of an electrical device is expected, and preventive maintenance performed at this time can prevent failures.

[0282] "User" refers to consumers or operators who use an electric power system and its related equipment.

[0283] The present invention is a system designed to optimize electricity consumption and prevent deterioration of electrical devices, and mainly operates through the cooperation of a server, terminals, and users.

[0284] Role of the server

[0285] As an information processing device, the server receives the instantaneous power consumption information transmitted from the terminal and analyzes it using a generated AI algorithm. Data analysis includes data processing using Python and identification of consumption patterns through machine learning using TensorFlow. The server further obtains tariff plan data from the power supplier and calculates an optimal power usage plan based on this. Also, using a predictive maintenance algorithm, it monitors the deterioration of the connected power equipment and notifies the user of the repair time.

[0286] Role of the Terminal

[0287] The terminal is a device that monitors the power consumption of each power equipment in real time. The terminal measures the instantaneous power using a current sensor and transmits the data to the server via Wi-Fi. When the terminal detects abnormal consumption, it notifies the user with an alert. It is equipped with a predictive maintenance algorithm and learns the normal operation pattern of the power equipment, so that it can react immediately when an abnormality occurs and notify the user.

[0288] Role of the User

[0289] The user receives optimization proposals and abnormality notifications from the server via the terminal and adjusts the power consumption settings based on this. The user can easily execute specific power-saving methods using the application on the smartphone. Also, it is possible to obtain questions and support using a chatbot using the generated AI model.

[0290] As a specific example, based on the analyzed data, the server may make an optimization proposal to shift the air conditioner to nighttime during the day in summer. As an example of a prompt sentence, it is conceivable that the user inputs "What advice is there to avoid high electricity bill times?"

[0291] Through such a configuration, the system aims to support efficient power consumption and prevention of equipment deterioration.

[0292] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0293] Step 1:

[0294] The terminal collects instantaneous power consumption information in real time from each connected power device. The input is raw data obtained through power sensors. This data is processed internally and converted into a timestamped dataset. The output is ready-to-send statistical information to the server. Specifically, the terminal is configured to send data to the server at regular intervals.

[0295] Step 2:

[0296] The server receives instantaneous power consumption information sent from the terminal. The input is timestamped power consumption data sent from the terminal. The server applies a generative AI model to analyze this data and identify consumption patterns. The output is a report based on each user's consumption patterns. Specifically, the server uses Python and a machine learning framework to analyze the data and understand consumption trends.

[0297] Step 3:

[0298] The server retrieves pricing plan data obtained from power suppliers. The input is the latest information regarding pricing plans. The server combines this information with consumption patterns reports to calculate the optimal power usage schedule. The output is an optimized usage schedule. The server generates the schedule and suggests specific strategies for the user to achieve maximum cost-effectiveness.

[0299] Step 4:

[0300] The server uses a predictive maintenance algorithm to monitor the degradation of connected power equipment. Inputs are power consumption data and equipment status information. The algorithm analyzes the data and detects signs of equipment degradation. Outputs are alerts indicating when repairs are needed. This information is communicated to the user, who is then given a specific maintenance schedule.

[0301] Step 5:

[0302] Users receive optimization suggestions and degradation alerts sent from the server via their devices. Input consists of reports and notifications from the server. Users use the received information to adjust power consumption settings. Output is optimized consumption behavior. Users can easily change settings using a smartphone app.

[0303] Step 6:

[0304] Users can use a chatbot powered by a generative AI model to ask questions and receive support regarding the system. The input consists of the user's questions and prompts. The generative AI model uses a predetermined algorithm to quickly respond to questions and provide appropriate advice. The output is real-time answers and support information. For example, a user can ask, "What are the cheapest times of day for electricity?" and receive an answer from the server.

[0305] (Application Example 1)

[0306] 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."

[0307] Modern cities require efficient use of energy, and in particular, it is important to optimize the electricity consumption of households and businesses. However, there is a problem that the efficiency of energy management cannot be improved because there is a lack of optimization proposals and warning notifications tailored to individual consumption behaviors and equipment degradation. Therefore, there is a need for a system that provides proposals and warnings based on real-time data to consumers and promotes efficient use of electricity.

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

[0309] In this invention, the server includes means for an information processing device to analyze instantaneous power consumption information acquired from a plurality of measurement devices and identify consumption behaviors, means for calculating an optimal usage schedule based on a tariff plan acquired from a power supply company, means for monitoring the degradation of connected equipment using a preventive maintenance algorithm and notifying the user of the maintenance time, and means for supplying a program that operates on a mobile communication terminal to acquire information in real time and provide an efficient consumption proposal based on the analysis result. Thereby, it becomes possible to provide an efficient electricity consumption proposal to residents in urban areas in real time.

[0310] The "information processing device" is a device that analyzes power consumption information acquired from a plurality of measurement devices and identifies consumption behaviors.

[0311] The "measurement device" is a device that acquires instantaneous power consumption information from each piece of equipment in real time.

[0312] The "power supply company" is an energy supply company that provides electricity and presents a tariff plan.

[0313] The "tariff plan" is a tariff system regarding electricity use presented by the power supply company and is used to calculate an optimal schedule based on the consumption trend of the user.

[0314] A "preventive maintenance algorithm" is an algorithm used to monitor equipment deterioration and identify the appropriate timing for maintenance.

[0315] "Deterioration" refers to the phenomenon where the performance of equipment declines with use, and it serves as an indicator for determining the appropriate timing for maintenance.

[0316] A "mobile communication terminal" is a portable communication device that can acquire information in real time and provide efficient consumption suggestions based on the analysis results.

[0317] "Means for supplying programs" refers to means for providing a method for executing efficient consumption suggestions based on analysis on a mobile communication terminal.

[0318] This invention is a system that collects power consumption information using multiple measuring devices, and an information processing device analyzes that data to identify consumption behavior and provide an efficient power usage schedule. The server calculates the optimal power usage schedule based on the analysis results and proposes it to the user.

[0319] Specifically, the server receives power consumption data acquired in real time from measuring devices and performs analysis using a generative AI model. Machine learning software frameworks such as TensorFlow and PyTorch are used for the analysis. This makes it possible to identify patterns in consumption behavior and detect anomalies.

[0320] Next, the device uses this analysis result to notify the user in real time of suggestions for optimizing consumption. Firebase Cloud Messaging is used for notifications, and since the device is a mobile communication device such as a smartphone or tablet, it offers high convenience.

[0321] This system can optimize power usage by suggesting efficient times to use air conditioners, for example, during peak summer usage periods when air conditioner use is concentrated. Specifically, a prompt message such as, "Generate optimization suggestions for efficiency based on real-time power consumption data and create alerts to prevent malfunctions," will be sent to the customer, suggesting a concrete usage schedule.

[0322] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0323] Step 1:

[0324] The server acquires real-time power consumption data from multiple measuring devices. This input data represents instantaneous power consumption information for each piece of equipment. The server aggregates this data and stores it in a database, processing it into a format that can be referenced.

[0325] Step 2:

[0326] The server inputs aggregated power consumption data into a generating AI model. Here, machine learning algorithms using TensorFlow and PyTorch are employed for analysis. The output includes each user's consumption pattern and indicators of potential anomalies. This analysis allows for the identification of specific consumption behaviors.

[0327] Step 3:

[0328] The server receives pricing plan information from power suppliers and calculates the optimal power usage schedule based on the analysis results. This calculation uses pricing plan and consumption pattern data as input, and the result is output as an efficient schedule.

[0329] Step 4:

[0330] The device receives schedules and anomaly detection information provided by the server. Based on this information, it performs actions to notify the user in real time. Specifically, push notifications are sent to smartphones via Firebase Cloud Messaging.

[0331] Step 5:

[0332] Users check notifications received on their devices and refer to the suggested power usage schedule. Furthermore, if they receive a warning about an anomaly, they can consider appropriate actions based on the content of the warning, thereby achieving optimal power usage.

[0333] 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.

[0334] The system of the present invention consists of a server with information processing capabilities, multiple terminals, and an emotion engine that recognizes the user's emotions. In addition to optimizing power consumption, this system aims to improve the user experience by providing interactions that take into account the user's emotional state.

[0335] Server operation

[0336] The server analyzes power consumption data transmitted from the terminal to identify the user's consumption patterns. Furthermore, it applies the latest pricing plans from the power supplier to calculate the optimal power usage schedule. An emotion engine acquires the user's emotional data, and based on the analysis results, the server adjusts the content and presentation of suggestions. For example, for a user experiencing stress, it provides suggestions in simpler and more considerate language.

[0337] Terminal operation

[0338] The terminal monitors the power consumption of each electrical device in real time and immediately notifies the user of any abnormalities. It also acquires emotional data based on the user's voice commands and camera input and provides it to the emotion engine. Based on the emotional state, it dynamically adjusts the timing of optimization suggestions and alerts to provide appropriate user support.

[0339] How the emotion engine works

[0340] The emotion engine evaluates the user's emotional state in real time based on their voice and facial expression data. Based on this evaluation, it supports suggestions and feedback to reduce the user's emotional burden. For example, if the emotion engine recognizes user frustration, the device will modify its suggestions and explain things in a gentler tone.

[0341] User interaction

[0342] Users can receive reports and notifications from the server on their devices, allowing them to view details and suggestions regarding power consumption. Furthermore, they can provide emotional data via voice input and touchscreen to assist the emotional engine. Receiving emotionally sensitive suggestions allows users to manage their power consumption in a more relaxed state.

[0343] For example, if a user is using a high-power device during a busy morning, the device reports this to the server. The server then gently suggests shifting usage to off-peak hours to reduce user stress. In this way, the system integrates seamlessly into the user's life, enabling more personalized optimization of power consumption.

[0344] The following describes the processing flow.

[0345] Step 1:

[0346] The terminal collects real-time power consumption data from each electrical device and periodically sends it to the server. This data includes instantaneous consumption and usage time.

[0347] Step 2:

[0348] The device acquires voice input and camera footage from the user and sends this data to the emotion engine. Here, data is collected to understand the user's emotional state.

[0349] Step 3:

[0350] The server analyzes power consumption data received from the terminal to identify the user's consumption patterns. This provides basic information for optimizing power usage.

[0351] Step 4:

[0352] The server references the latest pricing plans obtained from the power company and combines them with consumption patterns to calculate the optimal power usage schedule. This schedule is designed to maximize cost savings.

[0353] Step 5:

[0354] The emotion engine analyzes audio and video data provided by the device to evaluate the user's emotional state. Based on the emotional state, it instructs the server to adjust the suggestions.

[0355] Step 6:

[0356] The server receives feedback from the emotion engine and adjusts power usage optimization suggestions according to the user's emotions. For example, if the user is stressed, the suggestions will be made in a more considerate tone.

[0357] Step 7:

[0358] The device notifies the user with tailored suggestions and alerts. The content and timing of these notifications are optimized to take into account the user's emotional state.

[0359] Step 8:

[0360] Users review the suggestions through their devices and adjust their power usage schedules as needed. They also receive feedback based on the emotion engine's evaluation, which they use to improve their power management.

[0361] Step 9:

[0362] When a user changes their power usage pattern according to the suggestion, the device collects the resulting data again and sends it to the server to help optimize it for the next time.

[0363] Through this process, the system optimizes power consumption in real time and improves the user experience by incorporating emotionally sensitive elements.

[0364] (Example 2)

[0365] 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".

[0366] In modern society, the efficient use of electricity is a crucial issue. However, simply proposing optimization of electricity usage fails to consider the emotional state of users, making it difficult to improve the quality of the user experience. Furthermore, unexpected problems due to insufficient monitoring of electrical equipment deterioration and malfunctions are also a major issue. Therefore, there is a need for a system that optimizes electricity consumption while considering user emotions and also taking equipment maintenance into account.

[0367] 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.

[0368] In this invention, the server includes means for analyzing instantaneous power consumption data acquired from terminal devices by an information processing device and identifying consumption patterns, means for calculating an optimal power usage schedule based on rate plans acquired from power supply organizations, means for evaluating the user's emotional state and adjusting the content and presentation method of suggestions accordingly, and means for monitoring the deterioration of connected electrical equipment using preventive maintenance techniques and notifying the user of maintenance timing. This makes it possible to optimize power consumption and detect deterioration early, taking into account the user's emotions.

[0369] An "information processing device" is a computer system that collects, analyzes, and processes data, primarily functioning as a server to provide users with necessary suggestions and information.

[0370] A "terminal device" is a device that communicates with a user and an information processing device, and is responsible for data collection and alert notifications.

[0371] "Instantaneous power consumption data" refers to data that shows the power usage status of each electrical device at any given moment, and is data that can be monitored in real time.

[0372] A "consumption pattern" refers to a characteristic usage trend that shows how users and devices utilize electricity within a specific period of time.

[0373] A "pricing plan" is a plan provided by an electricity supplier that outlines pricing based on electricity consumption and time of day.

[0374] A "power usage schedule" is a specific schedule of times and amounts of power usage planned to optimize power consumption and reduce costs.

[0375] "User emotional state" refers to the user's current psychological situation and emotions as judged from factors such as voice and facial expressions, and is information used to adjust the interface.

[0376] "Preventive maintenance" refers to a method of monitoring the condition of equipment and planning in advance the necessary maintenance and actions to prevent failures.

[0377] "Monitoring" refers to observation and recording conducted to monitor the status of target equipment or systems and to detect abnormalities or deterioration.

[0378] The system of the present invention consists of a server equipped with information processing functions, multiple terminal devices, and an emotion engine that recognizes the user's emotions.

[0379] The server receives real-time power consumption data transmitted from terminals and uses a database management system to analyze this data. Python libraries are used for analysis to identify consumption patterns. This allows for the calculation of power usage schedules optimized for the pricing plans provided by power suppliers. Furthermore, using user sentiment data obtained from the sentiment engine, the server adjusts suggestions to achieve more personalized interactions.

[0380] The device is equipped with smart meters and sensors to monitor the power consumption of each electrical appliance. If any anomalies are detected, the device immediately alerts the user. The device also provides voice commands and camera input data to an emotion engine. As a simple example, at night when the user is relaxed, the device can send emotion data to the server in real time, allowing the user to receive suggestions that are appropriate for a quiet time.

[0381] Users can view reports and notifications received from the server via their device, gaining a detailed understanding of their power consumption. They can also provide subjective emotional data through voice and touch input, supporting the system's adaptive responses. A specific prompt might read, "I would like to receive information on my current power consumption and emotionally sensitive optimization suggestions." In this way, it's possible to optimize user power consumption and provide emotionally responsive support.

[0382] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0383] Step 1:

[0384] The server receives power consumption data transmitted from terminals. The input is power consumption data monitored by each terminal, and the output is stored in a database. The server uses a data processing program to organize and store instantaneous power consumption in real time.

[0385] Step 2:

[0386] The server analyzes accumulated power consumption data. The input is historical power consumption data stored in a database, and the output identifies consumption patterns. The server uses a Python data analysis library to calculate consumption trends and peak times within a specific period.

[0387] Step 3:

[0388] The server calculates the optimal power usage schedule by applying rate plans from power suppliers. The input is analyzed consumption patterns and rate plan information, and the output is a recommended power usage schedule. The server uses mathematical optimization techniques to generate a cost-effective schedule.

[0389] Step 4:

[0390] The server retrieves user emotion data from the emotion engine and adjusts the suggestions based on it. The input is the user's emotional state provided by the emotion engine via the terminal, and the output is user-friendly suggestions. The server uses an emotion analysis model to create messages that take into account stress and relaxation levels.

[0391] Step 5:

[0392] The terminal monitors the power consumption of each electrical device in real time and detects anomalies. Input is real-time data from smart meters and sensors, and output is an alert generated when an anomaly is detected. The terminal processes the data using a microcontroller and immediately notifies the user.

[0393] Step 6:

[0394] The device transmits the user's voice data and camera footage to the emotion engine. The input is the voice and video data recorded by the device, and the output is the storage of this information in the emotion engine's database. The device uses voice recognition software to format the data and transfers it to the emotion engine via the network.

[0395] Step 7:

[0396] The user receives optimization suggestions from the server via their device and modifies their behavior accordingly. Inputs are optimization suggestions and schedules provided by the server, and output is optimized power consumption behavior for the user. The user responds using voice and touch controls, leveraging the device's interface.

[0397] In this way, a series of processing steps work in an interconnected manner, resulting in user-friendly power consumption and emotional management.

[0398] (Application Example 2)

[0399] 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."

[0400] The challenge lies in simultaneously achieving efficient electricity consumption in cities and optimal use of public facilities and transportation based on individual emotional states. Conventional systems focused on optimizing electricity consumption but failed to consider users' emotional states, limiting their ability to reduce user stress and improve comfort. Furthermore, there was a problem with the difficulty in providing timely and appropriate information regarding the use of public facilities and transportation within cities.

[0401] 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.

[0402] In this invention, the server includes an information processing device that analyzes instantaneous power consumption data acquired from multiple computer devices and identifies consumption characteristics, a means for calculating an optimal power usage plan based on rate plans acquired from power supply organizations, a means for monitoring the deterioration of connected electrical equipment using a preventive maintenance algorithm and notifying users of maintenance timing, and an emotion recognition engine that analyzes the user's voice and facial expression data and evaluates their emotional state, and a means for suggesting optimal routes and methods for using public facilities and transportation based on the emotional state. This enables efficient power consumption in cities and lifestyle improvements that take into account individual emotions.

[0403] An "information processing device" is an electronic device that analyzes data acquired from multiple computer devices and performs specified tasks as needed.

[0404] A "computer device" is an electronic device that has the function of collecting, processing, and communicating data.

[0405] "Instantaneous power consumption data" refers to information that shows the amount of electricity used at a specific point in time.

[0406] "Consumption characteristics" refer to features that indicate patterns and trends in electricity usage.

[0407] A "power supply organization" is an organization or company that generates electricity and supplies it to consumers.

[0408] A "pricing plan" is a pricing structure for electricity usage presented by an electricity supplier.

[0409] A "power usage plan" is a schedule or guideline created to optimize power consumption.

[0410] A "preventive maintenance algorithm" is a computational method that monitors the status of connected equipment and prevents failures from occurring before they happen.

[0411] "Maintenance time" refers to the time when connected electrical equipment requires repair or adjustment.

[0412] An "emotion recognition engine" is a system that evaluates a user's emotional state from voice and facial expression data.

[0413] "Emotional state" refers to information that indicates the user's psychological and emotional state.

[0414] "Public facilities" are buildings and equipment installed for use by the general public.

[0415] "Transportation" refers to means of transport and systems that support the movement of users.

[0416] The "optimal route or method" refers to a path or technique that efficiently achieves a goal while minimizing stress and burden.

[0417] The system implementing this invention comprises a server, multiple terminals, and an emotion recognition engine. The server primarily functions as an information processing device, acquiring instantaneous power consumption data from multiple computer devices. Using this data, it identifies consumption characteristics and calculates an optimal power usage plan based on rate plans obtained from power supply organizations. Furthermore, it monitors the status of connected electrical equipment using a preventative maintenance algorithm and notifies users of maintenance schedules.

[0418] On the other hand, the terminal is a device operated by the user themselves, and it is a system that monitors the power consumption of each electrical device in real time and notifies the user if an abnormal condition is detected. This terminal has a camera and microphone built in, and uses these to transmit the user's voice and facial expression data to the emotion recognition engine. The emotion recognition engine analyzes the received data and evaluates the user's emotional state.

[0419] Based on the assessment of emotional state, the server proposes the optimal way to use public facilities and transportation. This proposal includes an approach aimed at reducing user stress, selecting the best route and method. For example, if a user is using crowded transportation and experiencing stress, the server will suggest an alternative route and notify the user of this information via their device. This allows users to move through urban spaces comfortably and efficiently.

[0420] An example of a prompt might be, "If the user is experiencing mild frustration, suggest an alternative route that allows them to relax without using regular public transport." This prompt facilitates the data analysis and decision-making process in the generative AI model.

[0421] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0422] Step 1:

[0423] The server collects instantaneous power consumption data from each computer. Using this data, it applies specific algorithms to identify consumption characteristics. The input is power consumption data from each electrical device, and the output is an analysis of consumption patterns. This analysis includes, for example, using statistical methods to extract peak times and consumption trends.

[0424] Step 2:

[0425] The server calculates the optimal power usage plan by matching consumption characteristics with the latest rate plans obtained from the power supplier. The input includes consumption characteristics and rate plans, and the output generates a power usage schedule proposed to the user. This schedule applies optimization techniques to minimize consumption costs.

[0426] Step 3:

[0427] The terminal uses internal sensors to monitor the status of connected electrical equipment in real time and notifies the user of any abnormalities. Data input includes measured power usage, and abnormalities are detected by analyzing this data. The output is an alert message presented to the user.

[0428] Step 4:

[0429] The device's camera and microphone are used to collect user voice and facial expression data, which is then sent to an emotion recognition engine. The input includes real-time user data, and the output is the emotion recognition engine's evaluation of the user's emotional state. This process utilizes a machine learning model to identify the user's emotions.

[0430] Step 5:

[0431] Based on the evaluation results of the emotion recognition engine, the server proposes optimal routes and methods for using public facilities and transportation, taking into account the user's emotional state. Inputs include evaluated emotion data and urban traffic information, and output is specific travel suggestions for the user. These suggestions include a decision-making process aimed at reducing user stress and improving comfort.

[0432] Step 6:

[0433] The user receives suggestions from the server via their terminal and adjusts power management and actions accordingly. The input is the suggestion information from the server, and the output is the action options selected by the user. A specific example of this action would be when the user chooses to travel via an alternative route suggested.

[0434] Through this series of processes, servers, terminals, and users work together to create a system that improves efficiency and comfort in urban environments.

[0435] 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.

[0436] 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.

[0437] 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.

[0438] [Third Embodiment]

[0439] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0440] 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.

[0441] 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).

[0442] 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.

[0443] 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.

[0444] 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).

[0445] 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.

[0446] 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.

[0447] 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.

[0448] 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.

[0449] 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.

[0450] 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".

[0451] The system of the present invention mainly comprises three elements: a server, a terminal, and a user. This system is designed to optimize power consumption, reduce costs, and prevent the deterioration of power equipment.

[0452] Server operation

[0453] The server receives instantaneous power consumption data transmitted from multiple terminals. This data is analyzed using a generating AI algorithm to identify user consumption patterns. This analysis is used not only to understand power usage trends but also to detect anomalies in consumption. The server also obtains the latest pricing plans provided by power suppliers and calculates the optimal power usage schedule for each user.

[0454] Furthermore, based on the analysis results, the server generates and notifies the user of power consumption optimization suggestions. This includes specific schedule suggestions to avoid peak hours and recommended actions to improve energy efficiency.

[0455] Terminal operation

[0456] The terminal monitors the power consumption of each connected electrical device in real time. The terminal sends the collected data to the server and immediately notifies the user of any abnormal power consumption.

[0457] The terminal further executes a preventative maintenance algorithm and monitors the condition of electrical equipment. When signs of deterioration are detected, it notifies the user of the appropriate maintenance time, preventing failures before they occur.

[0458] User interaction

[0459] Users receive reports sent from the server via their terminals. These reports include a detailed analysis of past power consumption and suggestions for improving energy efficiency. Users can use the system interface to view power consumption details and adjust settings as needed.

[0460] Furthermore, users can ask questions to the server through a chatbot powered by AI generation when they encounter any unclear points or problems. The chatbot responds immediately and provides appropriate support.

[0461] For example, if the air conditioner's power consumption is high during certain times of the summer, the server uses that data to notify the user to use the air conditioner during off-peak hours. The terminal also detects if the air conditioner's filter is clogged and sends a notification to the user prompting them to clean it. In this way, the entire system works together to enable efficient power consumption and equipment management.

[0462] The following describes the processing flow.

[0463] Step 1:

[0464] The terminal monitors the power consumption data of each connected electrical device in real time and periodically sends this data to the server.

[0465] Step 2:

[0466] The server analyzes the received instantaneous power consumption data using a generated AI algorithm to identify the user's power consumption patterns. For example, it can identify peak usage trends and signs of abnormal consumption.

[0467] Step 3:

[0468] The server retrieves the latest pricing plans from the power company and calculates the optimal power usage schedule by comparing it with the user's consumption patterns. This schedule is updated in real time to reflect changes in pricing plans.

[0469] Step 4:

[0470] Based on the calculation results, the server generates suggestions for improving energy efficiency and notifies the user. Specific examples include methods to reduce power consumption and time periods when usage should be reviewed.

[0471] Step 5:

[0472] The terminal runs a preventative maintenance algorithm and monitors the deterioration status of electrical equipment. When a warning sign is detected, the terminal notifies the user of the need for maintenance.

[0473] Step 6:

[0474] Users access reports sent from the server by operating a terminal or dedicated application. These reports include historical consumption data and optimization suggestions.

[0475] Step 7:

[0476] Users receive support when they have questions or problems through an AI-powered chatbot within the system. This bot responds to user inquiries and provides appropriate solutions.

[0477] (Example 1)

[0478] 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."

[0479] In modern electricity consumption, inefficient use can occur due to improper use of equipment and fluctuations in electricity rates. Furthermore, equipment failures due to deterioration are unpredictable, leading to sudden power outages and high repair costs. It is necessary to solve these problems and achieve efficient electricity use and extend the lifespan of equipment.

[0480] 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.

[0481] In this invention, the server includes means for an information processing device to analyze instantaneous power consumption information obtained from multiple terminal devices and identify consumption patterns, means for calculating an optimal power usage schedule based on a rate plan obtained from a power supplier, and means for monitoring the deterioration of connected power equipment using a predictive maintenance algorithm and notifying users of the repair timing. This enables efficient power use, prevention of equipment failures, and cost reduction.

[0482] An "information processing device" is a central device that collects and analyzes data acquired from multiple terminal devices and performs specific functions.

[0483] "Instantaneous power consumption information" is data that shows the amount of power a power appliance is consuming at a given point in time in real time.

[0484] "Consumption patterns" refer to patterns or trends that indicate how users or power appliances use electricity.

[0485] A "rate plan" is a detailed plan regarding electricity rates provided by electricity suppliers, and it serves as the basis for determining the optimal timing for electricity usage for electricity consumers.

[0486] "Optimal electricity usage plan" refers to an electricity usage schedule calculated based on electricity consumption data and pricing plans, with the aim of improving the efficiency of electricity consumption and reducing costs.

[0487] A "predictive maintenance algorithm" is a computational method that analyzes past usage data and operating patterns of equipment to predict the likelihood of future deterioration or failure.

[0488] "Power equipment" refers to all kinds of devices and equipment that operate using electricity.

[0489] The "repair period" refers to the time when deterioration of electrical equipment is expected, and by performing preventative maintenance at this time, malfunctions can be prevented before they occur.

[0490] "Users" refers to consumers and operators who use power systems and related equipment.

[0491] This invention is a system designed to optimize power consumption and prevent the deterioration of power equipment, and it operates primarily through the cooperation of servers, terminals, and users.

[0492] Server Role

[0493] The server, acting as an information processing device, receives instantaneous power consumption information transmitted from terminals and analyzes it using a generation AI algorithm. Data analysis includes data processing using Python and identification of consumption patterns using machine learning with TensorFlow. The server also obtains pricing plan data from power suppliers and calculates the optimal power usage schedule based on this data. Furthermore, a predictive maintenance algorithm monitors the deterioration of connected power equipment and notifies the user of repair timing.

[0494] Terminal role

[0495] The terminal is a device that monitors the power consumption of each power device in real time. The terminal uses a current sensor to measure instantaneous power and transmits the data to a server via Wi-Fi. When the terminal detects abnormal power consumption, it notifies the user with an alert. Equipped with a predictive maintenance algorithm, it learns the normal operating patterns of power devices, so it can react immediately when an anomaly occurs and notify the user.

[0496] User roles

[0497] Users receive optimization suggestions and anomaly notifications from the server via their devices, and adjust power consumption settings accordingly. Users can easily implement specific power-saving measures using a smartphone application. They can also obtain questions and support through a chatbot powered by a generative AI model.

[0498] For example, the server might suggest optimizing the use of air conditioning by shifting it from daytime to nighttime during the summer based on the analyzed data. A possible prompt might be, "What advice do you have for avoiding peak electricity rates?"

[0499] Through this configuration, the system aims to support efficient power consumption and prevent equipment degradation.

[0500] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0501] Step 1:

[0502] The terminal collects instantaneous power consumption information in real time from each connected power device. The input is raw data obtained through power sensors. This data is processed internally and converted into a timestamped dataset. The output is ready-to-send statistical information to the server. Specifically, the terminal is configured to send data to the server at regular intervals.

[0503] Step 2:

[0504] The server receives instantaneous power consumption information sent from the terminal. The input is timestamped power consumption data sent from the terminal. The server applies a generative AI model to analyze this data and identify consumption patterns. The output is a report based on each user's consumption patterns. Specifically, the server uses Python and a machine learning framework to analyze the data and understand consumption trends.

[0505] Step 3:

[0506] The server retrieves pricing plan data obtained from power suppliers. The input is the latest information regarding pricing plans. The server combines this information with consumption patterns reports to calculate the optimal power usage schedule. The output is an optimized usage schedule. The server generates the schedule and suggests specific strategies for the user to achieve maximum cost-effectiveness.

[0507] Step 4:

[0508] The server uses a predictive maintenance algorithm to monitor the degradation of connected power equipment. Inputs are power consumption data and equipment status information. The algorithm analyzes the data and detects signs of equipment degradation. Outputs are alerts indicating when repairs are needed. This information is communicated to the user, who is then given a specific maintenance schedule.

[0509] Step 5:

[0510] Users receive optimization suggestions and degradation alerts sent from the server via their devices. Input consists of reports and notifications from the server. Users use the received information to adjust power consumption settings. Output is optimized consumption behavior. Users can easily change settings using a smartphone app.

[0511] Step 6:

[0512] Users can use a chatbot powered by a generative AI model to ask questions and receive support regarding the system. The input consists of the user's questions and prompts. The generative AI model uses a predetermined algorithm to quickly respond to questions and provide appropriate advice. The output is real-time answers and support information. For example, a user can ask, "What are the cheapest times of day for electricity?" and receive an answer from the server.

[0513] (Application Example 1)

[0514] 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."

[0515] Modern cities require the efficient use of energy, and optimizing electricity consumption in homes and businesses is particularly important. However, there is a challenge in improving the efficiency of energy management due to a lack of optimization suggestions and warning notifications tailored to individual consumption patterns and equipment degradation. Therefore, a system is needed that provides consumers with suggestions and warnings based on real-time data to promote the efficient use of electricity.

[0516] 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.

[0517] In this invention, the server includes means for an information processing device to analyze instantaneous power consumption information acquired from multiple measuring devices and identify consumption behavior; means for calculating an optimal usage schedule based on a rate plan acquired from a power supplier; means for monitoring the deterioration of connected equipment using a preventive maintenance algorithm and notifying users of the timing of maintenance; and means for supplying a program that operates on a mobile communication terminal to acquire information in real time and provide efficient consumption suggestions based on the analysis results. This makes it possible to provide efficient power consumption suggestions to residents of urban areas in real time.

[0518] An "information processing device" is a device that analyzes power consumption information acquired from multiple measuring devices and identifies consumption behavior.

[0519] A "measuring device" is a device that acquires instantaneous power consumption information from each piece of equipment in real time.

[0520] A "power supplier" is an energy supply company that provides electricity and presents pricing plans.

[0521] A "rate plan" refers to the pricing structure for electricity usage presented by electricity suppliers, and is used to calculate the optimal schedule based on the user's consumption trends.

[0522] A "preventive maintenance algorithm" is an algorithm used to monitor equipment deterioration and identify the appropriate timing for maintenance.

[0523] "Deterioration" refers to the phenomenon where the performance of equipment declines with use, and it serves as an indicator for determining the appropriate timing for maintenance.

[0524] A "mobile communication terminal" is a portable communication device that can acquire information in real time and provide efficient consumption suggestions based on the analysis results.

[0525] "Means for supplying programs" refers to means for providing a method for executing efficient consumption suggestions based on analysis on a mobile communication terminal.

[0526] This invention is a system that collects power consumption information using multiple measuring devices, and an information processing device analyzes that data to identify consumption behavior and provide an efficient power usage schedule. The server calculates the optimal power usage schedule based on the analysis results and proposes it to the user.

[0527] Specifically, the server receives power consumption data acquired in real time from measuring devices and performs analysis using a generative AI model. Machine learning software frameworks such as TensorFlow and PyTorch are used for the analysis. This makes it possible to identify patterns in consumption behavior and detect anomalies.

[0528] Next, the device uses this analysis result to notify the user in real time of suggestions for optimizing consumption. Firebase Cloud Messaging is used for notifications, and since the device is a mobile communication device such as a smartphone or tablet, it offers high convenience.

[0529] This system can optimize power usage by suggesting efficient times to use air conditioners, for example, during peak summer usage periods when air conditioner use is concentrated. Specifically, a prompt message such as, "Generate optimization suggestions for efficiency based on real-time power consumption data and create alerts to prevent malfunctions," will be sent to the customer, suggesting a concrete usage schedule.

[0530] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0531] Step 1:

[0532] The server acquires real-time power consumption data from multiple measuring devices. This input data represents instantaneous power consumption information for each piece of equipment. The server aggregates this data and stores it in a database, processing it into a format that can be referenced.

[0533] Step 2:

[0534] The server inputs aggregated power consumption data into a generating AI model. Here, machine learning algorithms using TensorFlow and PyTorch are employed for analysis. The output includes each user's consumption pattern and indicators of potential anomalies. This analysis allows for the identification of specific consumption behaviors.

[0535] Step 3:

[0536] The server receives pricing plan information from power suppliers and calculates the optimal power usage schedule based on the analysis results. This calculation uses pricing plan and consumption pattern data as input, and the result is output as an efficient schedule.

[0537] Step 4:

[0538] The device receives schedules and anomaly detection information provided by the server. Based on this information, it performs actions to notify the user in real time. Specifically, push notifications are sent to smartphones via Firebase Cloud Messaging.

[0539] Step 5:

[0540] Users check notifications received on their devices and refer to the suggested power usage schedule. Furthermore, if they receive a warning about an anomaly, they can consider appropriate actions based on the content of the warning, thereby achieving optimal power usage.

[0541] 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.

[0542] The system of the present invention consists of a server with information processing capabilities, multiple terminals, and an emotion engine that recognizes the user's emotions. In addition to optimizing power consumption, this system aims to improve the user experience by providing interactions that take into account the user's emotional state.

[0543] Server operation

[0544] The server analyzes power consumption data transmitted from the terminal to identify the user's consumption patterns. Furthermore, it applies the latest pricing plans from the power supplier to calculate the optimal power usage schedule. An emotion engine acquires the user's emotional data, and based on the analysis results, the server adjusts the content and presentation of suggestions. For example, for a user experiencing stress, it provides suggestions in simpler and more considerate language.

[0545] Terminal operation

[0546] The terminal monitors the power consumption of each electrical device in real time and immediately notifies the user of any abnormalities. It also acquires emotional data based on the user's voice commands and camera input and provides it to the emotion engine. Based on the emotional state, it dynamically adjusts the timing of optimization suggestions and alerts to provide appropriate user support.

[0547] How the emotion engine works

[0548] The emotion engine evaluates the user's emotional state in real time based on their voice and facial expression data. Based on this evaluation, it supports suggestions and feedback to reduce the user's emotional burden. For example, if the emotion engine recognizes user frustration, the device will modify its suggestions and explain things in a gentler tone.

[0549] User interaction

[0550] Users can receive reports and notifications from the server on their devices, allowing them to view details and suggestions regarding power consumption. Furthermore, they can provide emotional data via voice input and touchscreen to assist the emotional engine. Receiving emotionally sensitive suggestions allows users to manage their power consumption in a more relaxed state.

[0551] For example, if a user is using a high-power device during a busy morning, the device reports this to the server. The server then gently suggests shifting usage to off-peak hours to reduce user stress. In this way, the system integrates seamlessly into the user's life, enabling more personalized optimization of power consumption.

[0552] The following describes the processing flow.

[0553] Step 1:

[0554] The terminal collects real-time power consumption data from each electrical device and periodically sends it to the server. This data includes instantaneous consumption and usage time.

[0555] Step 2:

[0556] The device acquires voice input and camera footage from the user and sends this data to the emotion engine. Here, data is collected to understand the user's emotional state.

[0557] Step 3:

[0558] The server analyzes power consumption data received from the terminal to identify the user's consumption patterns. This provides basic information for optimizing power usage.

[0559] Step 4:

[0560] The server references the latest pricing plans obtained from the power company and combines them with consumption patterns to calculate the optimal power usage schedule. This schedule is designed to maximize cost savings.

[0561] Step 5:

[0562] The emotion engine analyzes audio and video data provided by the device to evaluate the user's emotional state. Based on the emotional state, it instructs the server to adjust the suggestions.

[0563] Step 6:

[0564] The server receives feedback from the emotion engine and adjusts power usage optimization suggestions according to the user's emotions. For example, if the user is stressed, the suggestions will be made in a more considerate tone.

[0565] Step 7:

[0566] The device notifies the user with tailored suggestions and alerts. The content and timing of these notifications are optimized to take into account the user's emotional state.

[0567] Step 8:

[0568] Users review the suggestions through their devices and adjust their power usage schedules as needed. They also receive feedback based on the emotion engine's evaluation, which they use to improve their power management.

[0569] Step 9:

[0570] When a user changes their power usage pattern according to the suggestion, the device collects the resulting data again and sends it to the server to help optimize it for the next time.

[0571] Through this process, the system optimizes power consumption in real time and improves the user experience by incorporating emotionally sensitive elements.

[0572] (Example 2)

[0573] 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."

[0574] In modern society, the efficient use of electricity is a crucial issue. However, simply proposing optimization of electricity usage fails to consider the emotional state of users, making it difficult to improve the quality of the user experience. Furthermore, unexpected problems due to insufficient monitoring of electrical equipment deterioration and malfunctions are also a major issue. Therefore, there is a need for a system that optimizes electricity consumption while considering user emotions and also taking equipment maintenance into account.

[0575] 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.

[0576] In this invention, the server includes means for analyzing instantaneous power consumption data acquired from terminal devices by an information processing device and identifying consumption patterns, means for calculating an optimal power usage schedule based on rate plans acquired from power supply organizations, means for evaluating the user's emotional state and adjusting the content and presentation method of suggestions accordingly, and means for monitoring the deterioration of connected electrical equipment using preventive maintenance techniques and notifying the user of maintenance timing. This makes it possible to optimize power consumption and detect deterioration early, taking into account the user's emotions.

[0577] An "information processing device" is a computer system that collects, analyzes, and processes data, primarily functioning as a server to provide users with necessary suggestions and information.

[0578] A "terminal device" is a device that communicates with a user and an information processing device, and is responsible for data collection and alert notifications.

[0579] "Instantaneous power consumption data" refers to data that shows the power usage status of each electrical device at any given moment, and is data that can be monitored in real time.

[0580] A "consumption pattern" refers to a characteristic usage trend that shows how users and devices utilize electricity within a specific period of time.

[0581] A "pricing plan" is a plan provided by an electricity supplier that outlines pricing based on electricity consumption and time of day.

[0582] A "power usage schedule" is a specific schedule of times and amounts of power usage planned to optimize power consumption and reduce costs.

[0583] "User emotional state" refers to the user's current psychological situation and emotions as judged from factors such as voice and facial expressions, and is information used to adjust the interface.

[0584] "Preventive maintenance" refers to a method of monitoring the condition of equipment and planning in advance the necessary maintenance and actions to prevent failures.

[0585] "Monitoring" refers to observation and recording conducted to monitor the status of target equipment or systems and to detect abnormalities or deterioration.

[0586] The system of the present invention consists of a server equipped with information processing functions, multiple terminal devices, and an emotion engine that recognizes the user's emotions.

[0587] The server receives real-time power consumption data transmitted from terminals and uses a database management system to analyze this data. Python libraries are used for analysis to identify consumption patterns. This allows for the calculation of power usage schedules optimized for the pricing plans provided by power suppliers. Furthermore, using user sentiment data obtained from the sentiment engine, the server adjusts suggestions to achieve more personalized interactions.

[0588] The device is equipped with smart meters and sensors to monitor the power consumption of each electrical appliance. If any anomalies are detected, the device immediately alerts the user. The device also provides voice commands and camera input data to an emotion engine. As a simple example, at night when the user is relaxed, the device can send emotion data to the server in real time, allowing the user to receive suggestions that are appropriate for a quiet time.

[0589] Users can view reports and notifications received from the server via their device, gaining a detailed understanding of their power consumption. They can also provide subjective emotional data through voice and touch input, supporting the system's adaptive responses. A specific prompt might read, "I would like to receive information on my current power consumption and emotionally sensitive optimization suggestions." In this way, it's possible to optimize user power consumption and provide emotionally responsive support.

[0590] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0591] Step 1:

[0592] The server receives power consumption data transmitted from terminals. The input is power consumption data monitored by each terminal, and the output is stored in a database. The server uses a data processing program to organize and store instantaneous power consumption in real time.

[0593] Step 2:

[0594] The server analyzes accumulated power consumption data. The input is historical power consumption data stored in a database, and the output identifies consumption patterns. The server uses a Python data analysis library to calculate consumption trends and peak times within a specific period.

[0595] Step 3:

[0596] The server calculates the optimal power usage schedule by applying rate plans from power suppliers. The input is analyzed consumption patterns and rate plan information, and the output is a recommended power usage schedule. The server uses mathematical optimization techniques to generate a cost-effective schedule.

[0597] Step 4:

[0598] The server retrieves user emotion data from the emotion engine and adjusts the suggestions based on it. The input is the user's emotional state provided by the emotion engine via the terminal, and the output is user-friendly suggestions. The server uses an emotion analysis model to create messages that take into account stress and relaxation levels.

[0599] Step 5:

[0600] The terminal monitors the power consumption of each electrical device in real time and detects anomalies. Input is real-time data from smart meters and sensors, and output is an alert generated when an anomaly is detected. The terminal processes the data using a microcontroller and immediately notifies the user.

[0601] Step 6:

[0602] The device transmits the user's voice data and camera footage to the emotion engine. The input is the voice and video data recorded by the device, and the output is the storage of this information in the emotion engine's database. The device uses voice recognition software to format the data and transfers it to the emotion engine via the network.

[0603] Step 7:

[0604] The user receives optimization suggestions from the server via their device and modifies their behavior accordingly. Inputs are optimization suggestions and schedules provided by the server, and output is optimized power consumption behavior for the user. The user responds using voice and touch controls, leveraging the device's interface.

[0605] In this way, a series of processing steps work in an interconnected manner, resulting in user-friendly power consumption and emotional management.

[0606] (Application Example 2)

[0607] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0608] The challenge lies in simultaneously achieving efficient electricity consumption in cities and optimal use of public facilities and transportation based on individual emotional states. Conventional systems focused on optimizing electricity consumption but failed to consider users' emotional states, limiting their ability to reduce user stress and improve comfort. Furthermore, there was a problem with the difficulty in providing timely and appropriate information regarding the use of public facilities and transportation within cities.

[0609] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0610] In this invention, the server includes an information processing device that analyzes instantaneous power consumption data acquired from multiple computer devices and identifies consumption characteristics, a means for calculating an optimal power usage plan based on rate plans acquired from power supply organizations, a means for monitoring the deterioration of connected electrical equipment using a preventive maintenance algorithm and notifying users of maintenance timing, and an emotion recognition engine that analyzes the user's voice and facial expression data and evaluates their emotional state, and a means for suggesting optimal routes and methods for using public facilities and transportation based on the emotional state. This enables efficient power consumption in cities and lifestyle improvements that take into account individual emotions.

[0611] An "information processing device" is an electronic device that analyzes data acquired from multiple computer devices and performs specified tasks as needed.

[0612] A "computer device" is an electronic device that has the function of collecting, processing, and communicating data.

[0613] "Instantaneous power consumption data" refers to information that shows the amount of electricity used at a specific point in time.

[0614] "Consumption characteristics" refer to features that indicate patterns and trends in electricity usage.

[0615] A "power supply organization" is an organization or company that generates electricity and supplies it to consumers.

[0616] A "pricing plan" is a pricing structure for electricity usage presented by an electricity supplier.

[0617] A "power usage plan" is a schedule or guideline created to optimize power consumption.

[0618] A "preventive maintenance algorithm" is a computational method that monitors the status of connected equipment and prevents failures from occurring before they happen.

[0619] "Maintenance time" refers to the time when connected electrical equipment requires repair or adjustment.

[0620] An "emotion recognition engine" is a system that evaluates a user's emotional state from voice and facial expression data.

[0621] "Emotional state" refers to information that indicates the user's psychological and emotional state.

[0622] "Public facilities" are buildings and equipment installed for use by the general public.

[0623] "Transportation" refers to means of transport and systems that support the movement of users.

[0624] The "optimal route or method" refers to a path or technique that efficiently achieves a goal while minimizing stress and burden.

[0625] The system implementing this invention comprises a server, multiple terminals, and an emotion recognition engine. The server primarily functions as an information processing device, acquiring instantaneous power consumption data from multiple computer devices. Using this data, it identifies consumption characteristics and calculates an optimal power usage plan based on rate plans obtained from power supply organizations. Furthermore, it monitors the status of connected electrical equipment using a preventative maintenance algorithm and notifies users of maintenance schedules.

[0626] On the other hand, the terminal is a device operated by the user themselves, and it is a system that monitors the power consumption of each electrical device in real time and notifies the user if an abnormal condition is detected. This terminal has a camera and microphone built in, and uses these to transmit the user's voice and facial expression data to the emotion recognition engine. The emotion recognition engine analyzes the received data and evaluates the user's emotional state.

[0627] Based on the assessment of emotional state, the server proposes the optimal way to use public facilities and transportation. This proposal includes an approach aimed at reducing user stress, selecting the best route and method. For example, if a user is using crowded transportation and experiencing stress, the server will suggest an alternative route and notify the user of this information via their device. This allows users to move through urban spaces comfortably and efficiently.

[0628] An example of a prompt might be, "If the user is experiencing mild frustration, suggest an alternative route that allows them to relax without using regular public transport." This prompt facilitates the data analysis and decision-making process in the generative AI model.

[0629] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0630] Step 1:

[0631] The server collects instantaneous power consumption data from each computer. Using this data, it applies specific algorithms to identify consumption characteristics. The input is power consumption data from each electrical device, and the output is an analysis of consumption patterns. This analysis includes, for example, using statistical methods to extract peak times and consumption trends.

[0632] Step 2:

[0633] The server calculates the optimal power usage plan by matching consumption characteristics with the latest rate plans obtained from the power supplier. The input includes consumption characteristics and rate plans, and the output generates a power usage schedule proposed to the user. This schedule applies optimization techniques to minimize consumption costs.

[0634] Step 3:

[0635] The terminal uses internal sensors to monitor the status of connected electrical equipment in real time and notifies the user of any abnormalities. Data input includes measured power usage, and abnormalities are detected by analyzing this data. The output is an alert message presented to the user.

[0636] Step 4:

[0637] The device's camera and microphone are used to collect user voice and facial expression data, which is then sent to an emotion recognition engine. The input includes real-time user data, and the output is the emotion recognition engine's evaluation of the user's emotional state. This process utilizes a machine learning model to identify the user's emotions.

[0638] Step 5:

[0639] Based on the evaluation results of the emotion recognition engine, the server proposes optimal routes and methods for using public facilities and transportation, taking into account the user's emotional state. Inputs include evaluated emotion data and urban traffic information, and output is specific travel suggestions for the user. These suggestions include a decision-making process aimed at reducing user stress and improving comfort.

[0640] Step 6:

[0641] The user receives suggestions from the server via their terminal and adjusts power management and actions accordingly. The input is the suggestion information from the server, and the output is the action options selected by the user. A specific example of this action would be when the user chooses to travel via an alternative route suggested.

[0642] Through this series of processes, servers, terminals, and users work together to create a system that improves efficiency and comfort in urban environments.

[0643] 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.

[0644] 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.

[0645] 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.

[0646] [Fourth Embodiment]

[0647] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0648] 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.

[0649] 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).

[0650] 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.

[0651] 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.

[0652] 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).

[0653] 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.

[0654] 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.

[0655] 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.

[0656] 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.

[0657] 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.

[0658] 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.

[0659] 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".

[0660] The system of the present invention mainly comprises three elements: a server, a terminal, and a user. This system is designed to optimize power consumption, reduce costs, and prevent the deterioration of power equipment.

[0661] Server operation

[0662] The server receives instantaneous power consumption data transmitted from multiple terminals. This data is analyzed using a generating AI algorithm to identify user consumption patterns. This analysis is used not only to understand power usage trends but also to detect anomalies in consumption. The server also obtains the latest pricing plans provided by power suppliers and calculates the optimal power usage schedule for each user.

[0663] Furthermore, based on the analysis results, the server generates and notifies the user of power consumption optimization suggestions. This includes specific schedule suggestions to avoid peak hours and recommended actions to improve energy efficiency.

[0664] Terminal operation

[0665] The terminal monitors the power consumption of each connected electrical device in real time. The terminal sends the collected data to the server and immediately notifies the user of any abnormal power consumption.

[0666] The terminal further executes a preventative maintenance algorithm and monitors the condition of electrical equipment. When signs of deterioration are detected, it notifies the user of the appropriate maintenance time, preventing failures before they occur.

[0667] User interaction

[0668] Users receive reports sent from the server via their terminals. These reports include a detailed analysis of past power consumption and suggestions for improving energy efficiency. Users can use the system interface to view power consumption details and adjust settings as needed.

[0669] Furthermore, users can ask questions to the server through a chatbot powered by AI generation when they encounter any unclear points or problems. The chatbot responds immediately and provides appropriate support.

[0670] For example, if the air conditioner's power consumption is high during certain times of the summer, the server uses that data to notify the user to use the air conditioner during off-peak hours. The terminal also detects if the air conditioner's filter is clogged and sends a notification to the user prompting them to clean it. In this way, the entire system works together to enable efficient power consumption and equipment management.

[0671] The following describes the processing flow.

[0672] Step 1:

[0673] The terminal monitors the power consumption data of each connected electrical device in real time and periodically sends this data to the server.

[0674] Step 2:

[0675] The server analyzes the received instantaneous power consumption data using a generated AI algorithm to identify the user's power consumption patterns. For example, it can identify peak usage trends and signs of abnormal consumption.

[0676] Step 3:

[0677] The server retrieves the latest pricing plans from the power company and calculates the optimal power usage schedule by comparing it with the user's consumption patterns. This schedule is updated in real time to reflect changes in pricing plans.

[0678] Step 4:

[0679] Based on the calculation results, the server generates suggestions for improving energy efficiency and notifies the user. Specific examples include methods to reduce power consumption and time periods when usage should be reviewed.

[0680] Step 5:

[0681] The terminal runs a preventative maintenance algorithm and monitors the deterioration status of electrical equipment. When a warning sign is detected, the terminal notifies the user of the need for maintenance.

[0682] Step 6:

[0683] Users access reports sent from the server by operating a terminal or dedicated application. These reports include historical consumption data and optimization suggestions.

[0684] Step 7:

[0685] Users receive support when they have questions or problems through an AI-powered chatbot within the system. This bot responds to user inquiries and provides appropriate solutions.

[0686] (Example 1)

[0687] 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".

[0688] In modern electricity consumption, inefficient use can occur due to improper use of equipment and fluctuations in electricity rates. Furthermore, equipment failures due to deterioration are unpredictable, leading to sudden power outages and high repair costs. It is necessary to solve these problems and achieve efficient electricity use and extend the lifespan of equipment.

[0689] 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.

[0690] In this invention, the server includes means for an information processing device to analyze instantaneous power consumption information obtained from multiple terminal devices and identify consumption patterns, means for calculating an optimal power usage schedule based on a rate plan obtained from a power supplier, and means for monitoring the deterioration of connected power equipment using a predictive maintenance algorithm and notifying users of the repair timing. This enables efficient power use, prevention of equipment failures, and cost reduction.

[0691] An "information processing device" is a central device that collects and analyzes data acquired from multiple terminal devices and performs specific functions.

[0692] "Instantaneous power consumption information" is data that shows the amount of power a power appliance is consuming at a given point in time in real time.

[0693] "Consumption patterns" refer to patterns or trends that indicate how users or power appliances use electricity.

[0694] A "rate plan" is a detailed plan regarding electricity rates provided by electricity suppliers, and it serves as the basis for determining the optimal timing for electricity usage for electricity consumers.

[0695] "Optimal electricity usage plan" refers to an electricity usage schedule calculated based on electricity consumption data and pricing plans, with the aim of improving the efficiency of electricity consumption and reducing costs.

[0696] A "predictive maintenance algorithm" is a computational method that analyzes past usage data and operating patterns of equipment to predict the likelihood of future deterioration or failure.

[0697] "Power equipment" refers to all kinds of devices and equipment that operate using electricity.

[0698] The "repair period" refers to the time when deterioration of electrical equipment is expected, and by performing preventative maintenance at this time, malfunctions can be prevented before they occur.

[0699] "Users" refers to consumers and operators who use power systems and related equipment.

[0700] This invention is a system designed to optimize power consumption and prevent the deterioration of power equipment, and it operates primarily through the cooperation of servers, terminals, and users.

[0701] Server Role

[0702] The server, acting as an information processing device, receives instantaneous power consumption information transmitted from terminals and analyzes it using a generation AI algorithm. Data analysis includes data processing using Python and identification of consumption patterns using machine learning with TensorFlow. The server also obtains pricing plan data from power suppliers and calculates the optimal power usage schedule based on this data. Furthermore, a predictive maintenance algorithm monitors the deterioration of connected power equipment and notifies the user of repair timing.

[0703] Terminal role

[0704] The terminal is a device that monitors the power consumption of each power device in real time. The terminal uses a current sensor to measure instantaneous power and transmits the data to a server via Wi-Fi. When the terminal detects abnormal power consumption, it notifies the user with an alert. Equipped with a predictive maintenance algorithm, it learns the normal operating patterns of power devices, so it can react immediately when an anomaly occurs and notify the user.

[0705] User roles

[0706] Users receive optimization suggestions and anomaly notifications from the server via their devices, and adjust power consumption settings accordingly. Users can easily implement specific power-saving measures using a smartphone application. They can also obtain questions and support through a chatbot powered by a generative AI model.

[0707] For example, the server might suggest optimizing the use of air conditioning by shifting it from daytime to nighttime during the summer based on the analyzed data. A possible prompt might be, "What advice do you have for avoiding peak electricity rates?"

[0708] Through this configuration, the system aims to support efficient power consumption and prevent equipment degradation.

[0709] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0710] Step 1:

[0711] The terminal collects instantaneous power consumption information in real time from each connected power device. The input is raw data obtained through power sensors. This data is processed internally and converted into a timestamped dataset. The output is ready-to-send statistical information to the server. Specifically, the terminal is configured to send data to the server at regular intervals.

[0712] Step 2:

[0713] The server receives instantaneous power consumption information sent from the terminal. The input is timestamped power consumption data sent from the terminal. The server applies a generative AI model to analyze this data and identify consumption patterns. The output is a report based on each user's consumption patterns. Specifically, the server uses Python and a machine learning framework to analyze the data and understand consumption trends.

[0714] Step 3:

[0715] The server retrieves pricing plan data obtained from power suppliers. The input is the latest information regarding pricing plans. The server combines this information with consumption patterns reports to calculate the optimal power usage schedule. The output is an optimized usage schedule. The server generates the schedule and suggests specific strategies for the user to achieve maximum cost-effectiveness.

[0716] Step 4:

[0717] The server uses a predictive maintenance algorithm to monitor the degradation of connected power equipment. Inputs are power consumption data and equipment status information. The algorithm analyzes the data and detects signs of equipment degradation. Outputs are alerts indicating when repairs are needed. This information is communicated to the user, who is then given a specific maintenance schedule.

[0718] Step 5:

[0719] Users receive optimization suggestions and degradation alerts sent from the server via their devices. Input consists of reports and notifications from the server. Users use the received information to adjust power consumption settings. Output is optimized consumption behavior. Users can easily change settings using a smartphone app.

[0720] Step 6:

[0721] Users can use a chatbot powered by a generative AI model to ask questions and receive support regarding the system. The input consists of the user's questions and prompts. The generative AI model uses a predetermined algorithm to quickly respond to questions and provide appropriate advice. The output is real-time answers and support information. For example, a user can ask, "What are the cheapest times of day for electricity?" and receive an answer from the server.

[0722] (Application Example 1)

[0723] 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".

[0724] Modern cities require the efficient use of energy, and optimizing electricity consumption in homes and businesses is particularly important. However, there is a challenge in improving the efficiency of energy management due to a lack of optimization suggestions and warning notifications tailored to individual consumption patterns and equipment degradation. Therefore, a system is needed that provides consumers with suggestions and warnings based on real-time data to promote the efficient use of electricity.

[0725] 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.

[0726] In this invention, the server includes means for an information processing device to analyze instantaneous power consumption information acquired from multiple measuring devices and identify consumption behavior; means for calculating an optimal usage schedule based on a rate plan acquired from a power supplier; means for monitoring the deterioration of connected equipment using a preventive maintenance algorithm and notifying users of the timing of maintenance; and means for supplying a program that operates on a mobile communication terminal to acquire information in real time and provide efficient consumption suggestions based on the analysis results. This makes it possible to provide efficient power consumption suggestions to residents of urban areas in real time.

[0727] An "information processing device" is a device that analyzes power consumption information acquired from multiple measuring devices and identifies consumption behavior.

[0728] A "measuring device" is a device that acquires instantaneous power consumption information from each piece of equipment in real time.

[0729] A "power supplier" is an energy supply company that provides electricity and presents pricing plans.

[0730] A "rate plan" refers to the pricing structure for electricity usage presented by electricity suppliers, and is used to calculate the optimal schedule based on the user's consumption trends.

[0731] A "preventive maintenance algorithm" is an algorithm used to monitor equipment deterioration and identify the appropriate timing for maintenance.

[0732] "Deterioration" refers to the phenomenon where the performance of equipment declines with use, and it serves as an indicator for determining the appropriate timing for maintenance.

[0733] A "mobile communication terminal" is a portable communication device that can acquire information in real time and provide efficient consumption suggestions based on the analysis results.

[0734] "Means for supplying programs" refers to means for providing a method for executing efficient consumption suggestions based on analysis on a mobile communication terminal.

[0735] This invention is a system that collects power consumption information using multiple measuring devices, and an information processing device analyzes that data to identify consumption behavior and provide an efficient power usage schedule. The server calculates the optimal power usage schedule based on the analysis results and proposes it to the user.

[0736] Specifically, the server receives power consumption data acquired in real time from measuring devices and performs analysis using a generative AI model. Machine learning software frameworks such as TensorFlow and PyTorch are used for the analysis. This makes it possible to identify patterns in consumption behavior and detect anomalies.

[0737] Next, the device uses this analysis result to notify the user in real time of suggestions for optimizing consumption. Firebase Cloud Messaging is used for notifications, and since the device is a mobile communication device such as a smartphone or tablet, it offers high convenience.

[0738] This system can optimize power usage by suggesting efficient times to use air conditioners, for example, during peak summer usage periods when air conditioner use is concentrated. Specifically, a prompt message such as, "Generate optimization suggestions for efficiency based on real-time power consumption data and create alerts to prevent malfunctions," will be sent to the customer, suggesting a concrete usage schedule.

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

[0740] Step 1:

[0741] The server acquires real-time power consumption data from multiple measuring devices. This input data represents instantaneous power consumption information for each piece of equipment. The server aggregates this data and stores it in a database, processing it into a format that can be referenced.

[0742] Step 2:

[0743] The server inputs aggregated power consumption data into a generating AI model. Here, machine learning algorithms using TensorFlow and PyTorch are employed for analysis. The output includes each user's consumption pattern and indicators of potential anomalies. This analysis allows for the identification of specific consumption behaviors.

[0744] Step 3:

[0745] The server receives pricing plan information from power suppliers and calculates the optimal power usage schedule based on the analysis results. This calculation uses pricing plan and consumption pattern data as input, and the result is output as an efficient schedule.

[0746] Step 4:

[0747] The device receives schedules and anomaly detection information provided by the server. Based on this information, it performs actions to notify the user in real time. Specifically, push notifications are sent to smartphones via Firebase Cloud Messaging.

[0748] Step 5:

[0749] Users check notifications received on their devices and refer to the suggested power usage schedule. Furthermore, if they receive a warning about an anomaly, they can consider appropriate actions based on the content of the warning, thereby achieving optimal power usage.

[0750] 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.

[0751] The system of the present invention consists of a server with information processing capabilities, multiple terminals, and an emotion engine that recognizes the user's emotions. In addition to optimizing power consumption, this system aims to improve the user experience by providing interactions that take into account the user's emotional state.

[0752] Server operation

[0753] The server analyzes power consumption data transmitted from the terminal to identify the user's consumption patterns. Furthermore, it applies the latest pricing plans from the power supplier to calculate the optimal power usage schedule. An emotion engine acquires the user's emotional data, and based on the analysis results, the server adjusts the content and presentation of suggestions. For example, for a user experiencing stress, it provides suggestions in simpler and more considerate language.

[0754] Terminal operation

[0755] The terminal monitors the power consumption of each electrical device in real time and immediately notifies the user of any abnormalities. It also acquires emotional data based on the user's voice commands and camera input and provides it to the emotion engine. Based on the emotional state, it dynamically adjusts the timing of optimization suggestions and alerts to provide appropriate user support.

[0756] How the emotion engine works

[0757] The emotion engine evaluates the user's emotional state in real time based on their voice and facial expression data. Based on this evaluation, it supports suggestions and feedback to reduce the user's emotional burden. For example, if the emotion engine recognizes user frustration, the device will modify its suggestions and explain things in a gentler tone.

[0758] User interaction

[0759] Users can receive reports and notifications from the server on their devices, allowing them to view details and suggestions regarding power consumption. Furthermore, they can provide emotional data via voice input and touchscreen to assist the emotional engine. Receiving emotionally sensitive suggestions allows users to manage their power consumption in a more relaxed state.

[0760] For example, if a user is using a high-power device during a busy morning, the device reports this to the server. The server then gently suggests shifting usage to off-peak hours to reduce user stress. In this way, the system integrates seamlessly into the user's life, enabling more personalized optimization of power consumption.

[0761] The following describes the processing flow.

[0762] Step 1:

[0763] The terminal collects real-time power consumption data from each electrical device and periodically sends it to the server. This data includes instantaneous consumption and usage time.

[0764] Step 2:

[0765] The device acquires voice input and camera footage from the user and sends this data to the emotion engine. Here, data is collected to understand the user's emotional state.

[0766] Step 3:

[0767] The server analyzes power consumption data received from the terminal to identify the user's consumption patterns. This provides basic information for optimizing power usage.

[0768] Step 4:

[0769] The server references the latest pricing plans obtained from the power company and combines them with consumption patterns to calculate the optimal power usage schedule. This schedule is designed to maximize cost savings.

[0770] Step 5:

[0771] The emotion engine analyzes audio and video data provided by the device to evaluate the user's emotional state. Based on the emotional state, it instructs the server to adjust the suggestions.

[0772] Step 6:

[0773] The server receives feedback from the emotion engine and adjusts power usage optimization suggestions according to the user's emotions. For example, if the user is stressed, the suggestions will be made in a more considerate tone.

[0774] Step 7:

[0775] The device notifies the user with tailored suggestions and alerts. The content and timing of these notifications are optimized to take into account the user's emotional state.

[0776] Step 8:

[0777] Users review the suggestions through their devices and adjust their power usage schedules as needed. They also receive feedback based on the emotion engine's evaluation, which they use to improve their power management.

[0778] Step 9:

[0779] When a user changes their power usage pattern according to the suggestion, the device collects the resulting data again and sends it to the server to help optimize it for the next time.

[0780] Through this process, the system optimizes power consumption in real time and improves the user experience by incorporating emotionally sensitive elements.

[0781] (Example 2)

[0782] 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".

[0783] In modern society, the efficient use of electricity is a crucial issue. However, simply proposing optimization of electricity usage fails to consider the emotional state of users, making it difficult to improve the quality of the user experience. Furthermore, unexpected problems due to insufficient monitoring of electrical equipment deterioration and malfunctions are also a major issue. Therefore, there is a need for a system that optimizes electricity consumption while considering user emotions and also taking equipment maintenance into account.

[0784] 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.

[0785] In this invention, the server includes means for analyzing instantaneous power consumption data acquired from terminal devices by an information processing device and identifying consumption patterns, means for calculating an optimal power usage schedule based on rate plans acquired from power supply organizations, means for evaluating the user's emotional state and adjusting the content and presentation method of suggestions accordingly, and means for monitoring the deterioration of connected electrical equipment using preventive maintenance techniques and notifying the user of maintenance timing. This makes it possible to optimize power consumption and detect deterioration early, taking into account the user's emotions.

[0786] An "information processing device" is a computer system that collects, analyzes, and processes data, primarily functioning as a server to provide users with necessary suggestions and information.

[0787] A "terminal device" is a device that communicates with a user and an information processing device, and is responsible for data collection and alert notifications.

[0788] "Instantaneous power consumption data" refers to data that shows the power usage status of each electrical device at any given moment, and is data that can be monitored in real time.

[0789] A "consumption pattern" refers to a characteristic usage trend that shows how users and devices utilize electricity within a specific period of time.

[0790] A "pricing plan" is a plan provided by an electricity supplier that outlines pricing based on electricity consumption and time of day.

[0791] A "power usage schedule" is a specific schedule of times and amounts of power usage planned to optimize power consumption and reduce costs.

[0792] "User emotional state" refers to the user's current psychological situation and emotions as judged from factors such as voice and facial expressions, and is information used to adjust the interface.

[0793] "Preventive maintenance" refers to a method of monitoring the condition of equipment and planning in advance the necessary maintenance and actions to prevent failures.

[0794] "Monitoring" refers to observation and recording conducted to monitor the status of target equipment or systems and to detect abnormalities or deterioration.

[0795] The system of the present invention consists of a server equipped with information processing functions, multiple terminal devices, and an emotion engine that recognizes the user's emotions.

[0796] The server receives real-time power consumption data transmitted from terminals and uses a database management system to analyze this data. Python libraries are used for analysis to identify consumption patterns. This allows for the calculation of power usage schedules optimized for the pricing plans provided by power suppliers. Furthermore, using user sentiment data obtained from the sentiment engine, the server adjusts suggestions to achieve more personalized interactions.

[0797] The device is equipped with smart meters and sensors to monitor the power consumption of each electrical appliance. If any anomalies are detected, the device immediately alerts the user. The device also provides voice commands and camera input data to an emotion engine. As a simple example, at night when the user is relaxed, the device can send emotion data to the server in real time, allowing the user to receive suggestions that are appropriate for a quiet time.

[0798] Users can view reports and notifications received from the server via their device, gaining a detailed understanding of their power consumption. They can also provide subjective emotional data through voice and touch input, supporting the system's adaptive responses. A specific prompt might read, "I would like to receive information on my current power consumption and emotionally sensitive optimization suggestions." In this way, it's possible to optimize user power consumption and provide emotionally responsive support.

[0799] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0800] Step 1:

[0801] The server receives power consumption data transmitted from terminals. The input is power consumption data monitored by each terminal, and the output is stored in a database. The server uses a data processing program to organize and store instantaneous power consumption in real time.

[0802] Step 2:

[0803] The server analyzes accumulated power consumption data. The input is historical power consumption data stored in a database, and the output identifies consumption patterns. The server uses a Python data analysis library to calculate consumption trends and peak times within a specific period.

[0804] Step 3:

[0805] The server calculates the optimal power usage schedule by applying rate plans from power suppliers. The input is analyzed consumption patterns and rate plan information, and the output is a recommended power usage schedule. The server uses mathematical optimization techniques to generate a cost-effective schedule.

[0806] Step 4:

[0807] The server retrieves user emotion data from the emotion engine and adjusts the suggestions based on it. The input is the user's emotional state provided by the emotion engine via the terminal, and the output is user-friendly suggestions. The server uses an emotion analysis model to create messages that take into account stress and relaxation levels.

[0808] Step 5:

[0809] The terminal monitors the power consumption of each electrical device in real time and detects anomalies. Input is real-time data from smart meters and sensors, and output is an alert generated when an anomaly is detected. The terminal processes the data using a microcontroller and immediately notifies the user.

[0810] Step 6:

[0811] The device transmits the user's voice data and camera footage to the emotion engine. The input is the voice and video data recorded by the device, and the output is the storage of this information in the emotion engine's database. The device uses voice recognition software to format the data and transfers it to the emotion engine via the network.

[0812] Step 7:

[0813] The user receives optimization suggestions from the server via their device and modifies their behavior accordingly. Inputs are optimization suggestions and schedules provided by the server, and output is optimized power consumption behavior for the user. The user responds using voice and touch controls, leveraging the device's interface.

[0814] In this way, a series of processing steps work in an interconnected manner, resulting in user-friendly power consumption and emotional management.

[0815] (Application Example 2)

[0816] 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".

[0817] The challenge lies in simultaneously achieving efficient electricity consumption in cities and optimal use of public facilities and transportation based on individual emotional states. Conventional systems focused on optimizing electricity consumption but failed to consider users' emotional states, limiting their ability to reduce user stress and improve comfort. Furthermore, there was a problem with the difficulty in providing timely and appropriate information regarding the use of public facilities and transportation within cities.

[0818] 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.

[0819] In this invention, the server includes an information processing device that analyzes instantaneous power consumption data acquired from multiple computer devices and identifies consumption characteristics, a means for calculating an optimal power usage plan based on rate plans acquired from power supply organizations, a means for monitoring the deterioration of connected electrical equipment using a preventive maintenance algorithm and notifying users of maintenance timing, and an emotion recognition engine that analyzes the user's voice and facial expression data and evaluates their emotional state, and a means for suggesting optimal routes and methods for using public facilities and transportation based on the emotional state. This enables efficient power consumption in cities and lifestyle improvements that take into account individual emotions.

[0820] An "information processing device" is an electronic device that analyzes data acquired from multiple computer devices and performs specified tasks as needed.

[0821] A "computer device" is an electronic device that has the function of collecting, processing, and communicating data.

[0822] "Instantaneous power consumption data" refers to information that shows the amount of electricity used at a specific point in time.

[0823] "Consumption characteristics" refer to features that indicate patterns and trends in electricity usage.

[0824] A "power supply organization" is an organization or company that generates electricity and supplies it to consumers.

[0825] A "pricing plan" is a pricing structure for electricity usage presented by an electricity supplier.

[0826] A "power usage plan" is a schedule or guideline created to optimize power consumption.

[0827] A "preventive maintenance algorithm" is a computational method that monitors the status of connected equipment and prevents failures from occurring before they happen.

[0828] "Maintenance time" refers to the time when connected electrical equipment requires repair or adjustment.

[0829] An "emotion recognition engine" is a system that evaluates a user's emotional state from voice and facial expression data.

[0830] "Emotional state" refers to information that indicates the user's psychological and emotional state.

[0831] "Public facilities" are buildings and equipment installed for use by the general public.

[0832] "Transportation" refers to means of transport and systems that support the movement of users.

[0833] The "optimal route or method" refers to a path or technique that efficiently achieves a goal while minimizing stress and burden.

[0834] The system implementing this invention comprises a server, multiple terminals, and an emotion recognition engine. The server primarily functions as an information processing device, acquiring instantaneous power consumption data from multiple computer devices. Using this data, it identifies consumption characteristics and calculates an optimal power usage plan based on rate plans obtained from power supply organizations. Furthermore, it monitors the status of connected electrical equipment using a preventative maintenance algorithm and notifies users of maintenance schedules.

[0835] On the other hand, the terminal is a device operated by the user themselves, and it is a system that monitors the power consumption of each electrical device in real time and notifies the user if an abnormal condition is detected. This terminal has a camera and microphone built in, and uses these to transmit the user's voice and facial expression data to the emotion recognition engine. The emotion recognition engine analyzes the received data and evaluates the user's emotional state.

[0836] Based on the assessment of emotional state, the server proposes the optimal way to use public facilities and transportation. This proposal includes an approach aimed at reducing user stress, selecting the best route and method. For example, if a user is using crowded transportation and experiencing stress, the server will suggest an alternative route and notify the user of this information via their device. This allows users to move through urban spaces comfortably and efficiently.

[0837] An example of a prompt might be, "If the user is experiencing mild frustration, suggest an alternative route that allows them to relax without using regular public transport." This prompt facilitates the data analysis and decision-making process in the generative AI model.

[0838] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0839] Step 1:

[0840] The server collects instantaneous power consumption data from each computer. Using this data, it applies specific algorithms to identify consumption characteristics. The input is power consumption data from each electrical device, and the output is an analysis of consumption patterns. This analysis includes, for example, using statistical methods to extract peak times and consumption trends.

[0841] Step 2:

[0842] The server calculates the optimal power usage plan by matching consumption characteristics with the latest rate plans obtained from the power supplier. The input includes consumption characteristics and rate plans, and the output generates a power usage schedule proposed to the user. This schedule applies optimization techniques to minimize consumption costs.

[0843] Step 3:

[0844] The terminal uses internal sensors to monitor the status of connected electrical equipment in real time and notifies the user of any abnormalities. Data input includes measured power usage, and abnormalities are detected by analyzing this data. The output is an alert message presented to the user.

[0845] Step 4:

[0846] The device's camera and microphone are used to collect user voice and facial expression data, which is then sent to an emotion recognition engine. The input includes real-time user data, and the output is the emotion recognition engine's evaluation of the user's emotional state. This process utilizes a machine learning model to identify the user's emotions.

[0847] Step 5:

[0848] Based on the evaluation results of the emotion recognition engine, the server proposes optimal routes and methods for using public facilities and transportation, taking into account the user's emotional state. Inputs include evaluated emotion data and urban traffic information, and output is specific travel suggestions for the user. These suggestions include a decision-making process aimed at reducing user stress and improving comfort.

[0849] Step 6:

[0850] The user receives suggestions from the server via their terminal and adjusts power management and actions accordingly. The input is the suggestion information from the server, and the output is the action options selected by the user. A specific example of this action would be when the user chooses to travel via an alternative route suggested.

[0851] Through this series of processes, servers, terminals, and users work together to create a system that improves efficiency and comfort in urban environments.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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."

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0873] The following is further disclosed regarding the embodiments described above.

[0874] (Claim 1)

[0875] The information processing device analyzes instantaneous power consumption data acquired from multiple terminal devices and identifies consumption patterns.

[0876] A means of calculating the optimal electricity usage schedule based on the rate plan obtained from the electricity supplier,

[0877] A means of monitoring the deterioration of connected electrical equipment using a preventative maintenance algorithm and notifying the user of the maintenance timing,

[0878] A system that includes this.

[0879] (Claim 2)

[0880] The system according to claim 1, wherein the information processing device has means for sending a power consumption optimization proposal to the user based on the analysis results.

[0881] (Claim 3)

[0882] The system according to claim 1, wherein the terminal device has means for monitoring the power consumption of each electrical device in real time and notifying the user of an alert when an abnormality is detected.

[0883] "Example 1"

[0884] (Claim 1)

[0885] The information processing device analyzes instantaneous power consumption information acquired from multiple terminal devices and identifies the consumption pattern.

[0886] A means for calculating the optimal electricity usage plan based on the rate plan obtained from the electricity supplier,

[0887] A means of monitoring the deterioration of connected power equipment using a predictive maintenance algorithm and notifying users of the repair timing,

[0888] A means of notifying the user of generated optimization suggestions and warnings regarding abnormal consumption via a terminal device,

[0889] A means of detecting changes in users' consumption trends using generative machine learning models and recommending corrective measures,

[0890] A system that includes this.

[0891] (Claim 2)

[0892] The system according to claim 1, comprising means for sending an optimization proposal for power consumption to the user based on the analysis results and the pricing plan.

[0893] (Claim 3)

[0894] The system according to claim 1, comprising a terminal device that monitors the power consumption of each power device in real time and means for notifying the user of a warning when an abnormality is detected.

[0895] "Application Example 1"

[0896] (Claim 1)

[0897] The information processing device analyzes instantaneous power consumption information acquired from multiple measuring devices and includes means for identifying consumption behavior.

[0898] A means of calculating the optimal usage schedule based on the rate plan obtained from the electricity supplier,

[0899] A means of monitoring the deterioration of connected equipment using a preventive maintenance algorithm and notifying users of the timing of maintenance,

[0900] A means for supplying a program that operates on a mobile communication terminal to acquire information in real time and provide efficient consumption suggestions based on the analysis results,

[0901] A system that includes this.

[0902] (Claim 2)

[0903] The system according to claim 1, comprising means for transmitting efficient consumption suggestions to residents of urban areas based on analysis results.

[0904] (Claim 3)

[0905] The system according to claim 1, wherein the terminal device has means for monitoring the consumption of each piece of equipment in real time and notifying the user of an abnormality if an abnormality is detected.

[0906] "Example 2 of combining an emotion engine"

[0907] (Claim 1)

[0908] The information processing device analyzes instantaneous power consumption data acquired from multiple terminal devices and identifies consumption patterns.

[0909] A means of calculating the optimal electricity usage schedule based on rate plans obtained from electricity supply companies,

[0910] A means of evaluating the user's emotional state and adjusting the content and presentation method of suggestions based on that evaluation,

[0911] A means of monitoring the deterioration of connected electrical equipment using preventive maintenance methods and notifying the maintenance schedule,

[0912] A system that includes this.

[0913] (Claim 2)

[0914] The system according to claim 1, wherein the information processing device comprises means for transmitting a power consumption optimization suggestion based on the analysis results and the user's emotional state.

[0915] (Claim 3)

[0916] The system according to claim 1, wherein the terminal device has means for monitoring the power consumption of each electrical device in real time and notifying an alert when an abnormality is detected.

[0917] "Application example 2 when combining with an emotional engine"

[0918] (Claim 1)

[0919] The information processing device includes means for analyzing instantaneous power consumption data acquired from multiple computer devices and identifying consumption characteristics,

[0920] A means for calculating the optimal electricity usage plan based on rate plans obtained from electricity supply organizations,

[0921] A means of monitoring the deterioration of connected electrical equipment using a preventative maintenance algorithm and notifying users of the maintenance schedule,

[0922] The emotion recognition engine analyzes the user's voice and facial expression data and provides means for evaluating their emotional state.

[0923] A means of suggesting the optimal route and method for using public facilities and transportation based on emotional state,

[0924] A system that includes this.

[0925] (Claim 2)

[0926] The system according to claim 1, wherein the information processing device has means for transmitting to the user suggestions for optimizing power consumption and suggestions corresponding to their emotional state, based on the analysis results.

[0927] (Claim 3)

[0928] The system according to claim 1, wherein the computer device has means for monitoring the power consumption of each electrical device in real time and notifying the user if an abnormality is detected. [Explanation of Symbols]

[0929] 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. The information processing device analyzes instantaneous power consumption data acquired from multiple terminal devices and identifies consumption patterns. A means of calculating the optimal electricity usage schedule based on the rate plan obtained from the electricity supplier, A means of monitoring the deterioration of connected electrical equipment using a preventative maintenance algorithm and notifying the user of the maintenance timing, A system that includes this.

2. The system according to claim 1, wherein the information processing device comprises means for sending power consumption optimization suggestions to the user based on the analysis results.

3. The system according to claim 1, wherein the terminal device has means for monitoring the power consumption of each electrical device in real time and notifying the user of an alert when an abnormality is detected.

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