System

Abstract

To provide a system.SOLUTION: A system includes: means for acquiring transaction price information from an electricity market; means for calculating a unit price of an electricity rate in a market-based electric plan on the basis of the acquired transaction price information; means for acquiring the power consumption of a user; means for calculating an electricity rate on the basis of the unit price of the electricity rate and the power consumption; means for notifying the user of the calculation results of the electricity rate; means for collecting data including the voice, expression, and actions of the user; means for analyzing the collected data and recognizing the feeling of the user; means for proposing an optimal time period for using household appliance on the basis of the recognized feelings; and means for notifying the user of the proposal contents.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Electricity rates based on market-linked plans fluctuate according to the trading price on the power exchange, making it difficult for users to understand their electricity rates and determine the optimal times to use home appliances to reduce costs. [Means for solving the problem]

[0005] The present invention provides a means for calculating electricity charges based on a market-linked plan, a means for receiving home appliance usage information from a user, a means for calculating optimal usage time periods based on the received home appliance usage information, and a means for proposing the calculated optimal usage time periods and electricity charges to the user, thereby enabling the user to understand the optimal usage time periods and electricity charges based on their own home appliance usage habits and efficiently reduce costs. [Brief explanation of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices 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), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0011] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0012] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 1, a 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.

[0017] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0018] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0019] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0020] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0024] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0025] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] One aspect of the present invention is a system for calculating electricity rates based on a market-linked plan. This system acquires trading price information from the power exchange and calculates electricity rates based on that information. Specifically, the system calculates the unit price of electricity based on the trading price information and uses that to calculate the electricity rate.

[0029] "Example 2"

[0030] Another aspect of the present invention is a system that receives information about the use of home appliances from a user. This system receives input information from the user and calculates the usage time periods for the home appliances based on that information. Specifically, the system has the user input the type of home appliance they want to use and the usage time, and calculates the optimal usage time period based on that information.

[0031] "Example 3"

[0032] Furthermore, one embodiment of the present invention is a system that proposes the calculated optimal usage time periods and electricity rates to users. This system presents the calculated optimal usage time periods and electricity rates to users, and uses them as reference information for users to reduce their electricity rates. Specifically, when a user inputs the type of home appliance they want to use and the usage time, for example, "I will use the air conditioner from 3:00 PM to 6:00 PM," the system presents the user with an electricity rate calculated based on the trading price at the power exchange for that time period.

[0033] The processing flow of each embodiment will be described below.

[0034] "Example 1"

[0035] Step 1: Obtain trading price information from the power exchange.

[0036] Step 2: Calculate the unit price of electricity based on the acquired transaction price information.

[0037] Step 3: Calculate the electricity bill using the calculated unit price of electricity.

[0038] "Example 2"

[0039] Step 1: Receive input from the user about the type of appliance they want to use and the usage time.

[0040] Step 2: Calculate the optimal usage time based on the received input information.

[0041] "Example 3"

[0042] Step 1: The user enters the type of appliance they want to use and the usage time.

[0043] Step 2: Calculate the electricity rate based on the trading price on the power exchange for that time period.

[0044] Step 3: Present the calculated electricity bill to the user.

[0045] Example 1

[0046] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0047] Conventional electricity rate calculation systems have difficulty reflecting trading price information from the power exchange in real time, making it impossible to provide users with optimal electricity rates. Furthermore, they lacked the functionality to suggest optimal usage times based on users' home appliance usage information, making it difficult for users to use electricity efficiently.

[0048] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0049] In this invention, the server includes means for acquiring trading price information from an electricity exchange, means for storing the acquired trading price information in a database, means for calculating the unit price of electricity based on the stored trading price information, means for calculating the user's electricity bill using the calculated unit price of electricity, means for receiving home appliance usage information from the user, means for calculating optimal usage time periods based on the received home appliance usage information, and means for proposing the calculated optimal usage time periods and electricity bill to the user. This enables calculation of electricity bills that reflect trading price information in real time, and further enables efficient power use by proposing optimal usage time periods to the user.

[0050] An "electricity exchange" is a market where electricity is bought and sold, and an institution that provides trading price information.

[0051] "Trading price information" is information relating to the price of electricity traded at the power exchange.

[0052] A "database" is a system for efficiently storing, managing, and retrieving data.

[0053] The "unit price of electricity" is the unit price set for a certain amount of electricity.

[0054] A "user" is an individual or corporation that consumes electricity and is the entity that uses this system.

[0055] "Home appliance usage information" is information about the types of home appliances used by the user and the duration of use.

[0056] The "optimal usage time period" is the time period recommended for efficient power consumption.

[0057] The "means for proposing" is a method or device for notifying the user of the calculated optimal usage time period and electricity charges.

[0058] MODE FOR CARRYING OUT THE INVENTION

[0059] This invention relates to a system for calculating electricity rates based on a market-linked plan, which acquires trading price information from the power exchange and calculates electricity rates based on that information. Specific embodiments of this system are described below.

[0060] Hardware and software used

[0061] 1. Server

[0062] Hardware: General server equipment

[0063] Software: API clients, database management systems (e.g., MySQL (registered trademark), PostgreSQL), scripting languages ​​(e.g., Python, JavaScript (registered trademark))

[0064] 2. Terminal

[0065] Hardware: The computer or smartphone you use

[0066] Software: Web browser, mobile application

[0067] 3. Users

[0068] A user is an individual or a corporation that consumes electricity and is the entity that uses this system.

[0069] Explanation of program processing

[0070] 1. Obtaining trading price information

[0071] The server sends a request to the power exchange's API to obtain the latest trading price information.

[0072] Specific operation: The server sends an HTTP GET request to the API endpoint of the power exchange and receives trading price information in JSON format.

[0073] 2. Saving of transaction price information

[0074] The server stores the acquired transaction price information in a database.

[0075] Specific operation: The server parses the received JSON data and executes an insert query into the corresponding table in the MySQL database.

[0076] 3. Calculation of electricity unit price

[0077] The server calculates the unit price of electricity based on the stored transaction price information.

[0078] Specific operation: The server executes a Python script, retrieves trading price information from the database, and calculates the unit price of electricity based on a specific algorithm.

[0079] 4. Calculation of user's electricity bill

[0080] The server calculates the electricity charge for the user using the calculated electricity unit price.

[0081] Specific operation: The server retrieves the user's power consumption data from the database and multiplies it by the calculated unit price to calculate the electricity bill.

[0082] 5. Receiving information on home appliance usage

[0083] The server receives home appliance usage information from the user.

[0084] Specific operation: The user enters the type of home appliance they want to use and the usage time through a web browser or mobile application, and sends the information to the server.

[0085] 6. Calculating the best time to use

[0086] The server calculates the optimal usage time period based on the received home appliance usage information.

[0087] Specific operation: The server executes an algorithm to calculate the optimal usage time based on the electricity rate and appliance usage information.

[0088] 7. Notification of Proposal

[0089] The server then proposes the calculated optimal usage time period and electricity rate to the user.

[0090] Specific operation: The server notifies the user's device of the calculation results, and the user confirms the proposal through a web browser or mobile application.

[0091] Examples and prompts

[0092] 1. The server obtains trading price information for "2023-10-01" from the power exchange API.

[0093] 2. The server stores the acquired trading price information in a MySQL database.

[0094] 3. The server uses a Python script to calculate the unit price of electricity for "2023-10-01" from the trading price information.

[0095] 4. The server calculates the user's electricity bill (e.g., 1,000 yen) using the user's power consumption data (e.g., 100 kWh) and the calculated unit price (e.g., 10 yen / kWh).

[0096] Prompt Sentence Examples

[0097] "Get the latest trading price information from the power exchange API and store it in a MySQL database. Then, use a Python script to calculate the unit price of electricity and calculate the electricity bill based on the user's energy consumption data."

[0098] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0099] Step 1:

[0100] Obtaining trading price information

[0101] The server sends a request to the power exchange's API to obtain the latest trading price information.

[0102] Input: Power Exchange API endpoint URL

[0103] Output: Trade price information in JSON format

[0104] Specific operation: The server sends an HTTP GET request to the API endpoint of the electricity exchange and receives trading price information in JSON format as a response. For example, use the requests library to send a request to https: / / api.electricity-market.com / prices.

[0105] Step 2:

[0106] Stores trading price information

[0107] The server stores the acquired transaction price information in a database.

[0108] Input: Trade price information in JSON format

[0109] Output: Transaction price information stored in the database

[0110] What happens: The server parses the received JSON data, extracts the required fields (e.g., datetime, price), and then uses the pymysql library to connect to a MySQL database and execute a query like INSERT INTO prices (datetime, price) VALUES ('2023-10-01 00:00:00', 10.5).

[0111] Step 3:

[0112] Calculating electricity unit prices

[0113] The server calculates the unit price of electricity based on the stored transaction price information.

[0114] Input: Transaction price information stored in the database

[0115] Output: Calculated electricity unit price

[0116] Specific operation: The server executes a Python script to obtain trading price information from the database. For example, it uses the pymysql library to execute a query such as SELECT price FROM prices WHERE datetime = '2023-10-01 00:00:00', and calculates the electricity price using a specific algorithm (e.g., average, weighted average) based on the obtained price information.

[0117] Step 4:

[0118] Calculating the user's electricity bill

[0119] The server calculates the electricity charge for the user using the calculated electricity unit price.

[0120] Input: User's power consumption data, calculated electricity rate

[0121] Output: Calculated user electricity charges

[0122] Specific operation: The server retrieves the user's power consumption data from the database. For example, using the pymysql library, it executes a query such as SELECT consumption FROM user_data WHERE user_id = 1, multiplies the retrieved power consumption data (e.g., 100 kWh) by the calculated unit price (e.g., 10 yen / kWh), and calculates the user's electricity bill (e.g., 1,000 yen).

[0123] Step 5:

[0124] Receiving home appliance usage information

[0125] The server receives home appliance usage information from the user.

[0126] Input: Home appliance usage information entered by the user (type of appliance, usage time)

[0127] Output: Appliance usage information stored on the server

[0128] Specific operation: The user inputs the type of appliances they want to use and the usage time through a web browser or mobile application, and sends the information to the server. The server then stores the received information in a database.

[0129] Step 6:

[0130] Calculating the best time to use

[0131] The server calculates the optimal usage time period based on the received home appliance usage information.

[0132] Input: Home appliance usage information, electricity rate

[0133] Output: Best time to use

[0134] Specific operation: The server runs an algorithm to calculate the optimal usage time based on the electricity rate and appliance usage information, for example, to avoid peak hours.

[0135] Step 7:

[0136] Proposal Notification

[0137] The server then proposes the calculated optimal usage time period and electricity rate to the user.

[0138] Input: Optimal usage time, calculated electricity price

[0139] Output: User notification

[0140] Specific operation: The server notifies the user of the calculation results, and the user confirms the proposal through a web browser or mobile application, for example, by sending a push notification or email.

[0141] (Application example 1)

[0142] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0143] Conventional electricity bill calculation systems based on market-linked plans only suggest optimal usage times based on the user's appliance usage information, but do not optimize power consumption in real time. Therefore, there is a need to further reduce electricity costs by obtaining trading price information from the power exchange in real time and optimizing power consumption.

[0144] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0145] In this invention, the server includes means for calculating electricity charges based on a market-linked plan, means for receiving home appliance usage information from a user, means for calculating optimal usage time periods based on the received home appliance usage information, means for proposing the calculated optimal usage time periods and electricity charges to the user, means for acquiring trading price information from an electricity exchange in real time, means for optimizing electricity consumption based on the acquired price information, and means for reducing electricity consumption during high price time periods and increasing electricity consumption during low price time periods, thereby enabling optimization of electricity consumption in real time.

[0146] A "market-linked plan" is a rate plan in which electricity rates are determined based on the trading price at the power exchange.

[0147] A "means for calculating electricity rates" is a method or device for calculating electricity rates based on a market-linked plan.

[0148] The "means for receiving home appliance usage information from a user" refers to a method or device for acquiring information such as the type of home appliance used by the user and the duration of use.

[0149] The "means for calculating the optimum usage time period" is a method or device for calculating the optimum usage time period for optimizing power consumption based on the received home appliance usage information.

[0150] The "means for making suggestions to the user" refers to a method or device for notifying the user of the calculated optimal usage time period and electricity charges.

[0151] The "means for obtaining trading price information from the power exchange in real time" refers to a method or device for obtaining trading price information from the power exchange in real time.

[0152] "Means for optimizing electricity consumption" refers to methods and devices for efficiently managing and adjusting electricity consumption based on acquired trading price information.

[0153] "Means for reducing electricity consumption during high price range hours and increasing electricity consumption during low price range hours" refers to a method or device for reducing electricity consumption during high electricity price range hours and increasing electricity consumption during low electricity price range hours.

[0154] To implement the present invention, the following system configuration and program are required.

[0155] System Configuration

[0156] 1. Server: Calculates electricity rates based on market-linked plans and obtains real-time trading price information from the power exchange.

[0157] 2. Device: Receives information on home appliance usage from the user, calculates the optimal usage time, and makes suggestions to the user.

[0158] 3. Network: A communication method for sending and receiving data between the server and the terminal.

[0159] Program processing explanation

[0160] Hardware

[0161] Server: A computer with a powerful processor and large memory capacity.

[0162] Terminal: A device with a user interface, such as a smartphone or tablet.

[0163] Network: Internet connection.

[0164] software

[0165] Python: The primary implementation language for the program.

[0166] Requests library: A library for obtaining electricity exchange trading price information from the API.

[0167] JSON Data Processing: The format for parsing the retrieved data.

[0168] Data processing and calculation

[0169] 1. The server obtains real-time trading price information from the power exchange using an API and saves the data in JSON format.

[0170] 2. The server calculates the electricity charges based on the market-linked plan. Based on the acquired trading price information, it calculates the unit price of the electricity charge and combines it with the user's electricity consumption data to calculate the total electricity charge.

[0171] 3. The device receives information about the user's use of the appliance, including the type of appliance they want to use and the duration of their use.

[0172] 4. The server calculates the optimal usage time based on the received appliance usage information. To do this, it selects the time period when the electricity trading price is low and suggests using the appliance during that time period.

[0173] 5. The device will then suggest the optimal usage time and electricity rates to the user, using the notification function to provide information to the user.

[0174] 6. The server optimizes power consumption based on the acquired price information, reducing power consumption during high-price periods and increasing power consumption during low-price periods.

[0175] Specific examples

[0176] For example, if this system is used on a factory production line, electricity costs can be reduced by reducing machine operation during times when prices on the electricity exchange are high and increasing operation during times when prices are low.

[0177] Prompt Sentence Examples

[0178] "Create a Python program that obtains real-time trading price information from the power exchange and optimizes power consumption within the factory. The program should have the ability to reduce power consumption during times when prices are high and increase power consumption during times when prices are low."

[0179] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0180] Step 1:

[0181] The server obtains trading price information from the power exchange in real time. Specifically, it obtains trading price information from the power exchange using an API and saves it in JSON format. The input is the API endpoint, and the output is the obtained trading price information.

[0182] Step 2:

[0183] The server calculates the electricity bill based on the market-linked plan. Based on the acquired transaction price information, it calculates the unit price of the electricity bill and combines it with the user's electricity consumption data to calculate the total electricity bill. The input is the transaction price information and the user's electricity consumption data, and the output is the total electricity bill.

[0184] Step 3:

[0185] The terminal receives home appliance usage information from the user. The user inputs the type of home appliance they want to use and the usage time, and the terminal receives it. The input is the user's home appliance usage information, and the output is the received home appliance usage information.

[0186] Step 4:

[0187] The server calculates the optimal usage time based on the received appliance usage information. It selects a time period when the electricity trading price is low and suggests using the appliance during that time period. The input is appliance usage information and trading price information, and the output is the optimal usage time period.

[0188] Step 5:

[0189] The terminal proposes the calculated optimal usage time and electricity rate to the user. The notification function is used to provide information to the user. The input is the optimal usage time and electricity rate, and the output is a notification to the user.

[0190] Step 6:

[0191] The server optimizes power consumption based on the acquired price information. It reduces power consumption during high-price periods and increases power consumption during low-price periods. The input is trading price information, and the output is the optimized power consumption pattern.

[0192] Example 2

[0193] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0194] Conventional electricity rate optimization systems based on home appliance usage information have difficulty efficiently processing the information entered by users and calculating the optimal usage time periods. Furthermore, there was a lack of a means to quickly provide the results of the calculations to users, resulting in low user convenience. Furthermore, there was a need for a system that could comprehensively calculate electricity rates based on market-linked plans and propose optimal usage time periods.

[0195] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0196] In this invention, the server includes means for receiving home appliance usage information from a user, means for storing the received home appliance usage information in a database, means for calculating optimal usage time periods based on the stored data, means for proposing the calculated optimal usage time periods and electricity rates to the user, means for displaying the calculation results to the user, and means for calculating electricity rates based on a market-linked plan. This makes it possible to efficiently process the home appliance usage information entered by the user and quickly provide optimal usage time periods and electricity rates.

[0197] A "market-linked plan" is a rate plan in which electricity rates are determined based on the trading price at the power exchange.

[0198] "Means for calculating electricity charges" means algorithms or software for calculating electricity charges based on market-linked plans.

[0199] "Means for receiving home appliance usage information from users" refers to interfaces or devices for receiving information such as the type of home appliance and usage time entered by the user.

[0200] "Means for storing in a database" refers to software or hardware for storing received home appliance usage information in a relational database management system (RDBMS) or similar.

[0201] "Means for calculating optimal usage times" refers to algorithms or software that use stored data to analyze past usage data and power consumption patterns to calculate optimal usage times.

[0202] "Means for proposing calculation results to users" refers to an interface or device for notifying users of the calculated optimal usage time and electricity charges.

[0203] "Means for displaying the calculation results to the user" refers to software or hardware for displaying the calculation results on the user's terminal.

[0204] MODE FOR CARRYING OUT THE INVENTION

[0205] This invention is a system that receives information on the use of home appliances from a user, calculates the optimal usage time period and electricity charges based on that information, and proposes them to the user. A specific embodiment of this system will be described below.

[0206] System configuration

[0207] This system consists of three main components: a server, a terminal, and a user.

[0208] 1. Server

[0209] The server provides a means for receiving appliance usage information from users, which is an interface accessible through a web browser or a mobile application.

[0210] The server stores the received appliance usage information in a database, which uses a relational database management system (RDBMS) such as MySQL or PostgreSQL.

[0211] The server calculates the optimal usage time based on the stored data, using programming languages ​​such as Python and R and leveraging data analysis libraries (e.g., Pandas and NumPy).

[0212] The server has a means to propose the calculated optimal usage time and electricity rate to the user. This proposal is made in real time.

[0213] The server has a means to send the calculation results to the terminal for display to the user. This transmission is securely performed using the HTTPS protocol.

[0214] 2. Terminal

[0215] The device sends the home appliance usage information entered by the user to the server. This transmission is securely transmitted using the HTTPS protocol.

[0216] The device then displays the calculation results received from the server to the user, for example, displaying "The best time to use the device is between 7:00 PM and 9:00 PM" on the mobile application screen.

[0217] 3. Users

[0218] Users use a web browser or mobile application to input the type of appliance they want to use and the time they want to use it. For example, if a user wants to use an air conditioner, they enter "air conditioner" and "from 18:00 to 22:00."

[0219] Specific examples

[0220] As a specific example, consider the case where a user wants to use an air conditioner.

[0221] 1. A user opens the mobile application and enters "air conditioning" and "18:00 to 22:00."

[0222] 2. The device sends this information to the server using the HTTPS protocol.

[0223] 3. The server stores the received information in the "user_requests" table.

[0224] 4. The server uses Python's Pandas library to analyze past usage data and power consumption patterns and calculate the optimal usage time.

[0225] 5. The server sends the calculation result "The best time is between 19:00 and 21:00" to the terminal.

[0226] 6. The device displays the result on the mobile application screen and notifies the user.

[0227] Prompt Sentence Examples

[0228] Example prompts to input to a generative AI model:

[0229] A user wants to use the air conditioner. The usage time is entered as "18:00 to 22:00." Please calculate the optimal usage time based on past usage data and power consumption patterns.

[0230] In this way, a system is realized in which the server, terminal, and user work together to calculate the optimal time periods for using home appliances and provide the results to the user.

[0231] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0232] Step 1:

[0233] The user inputs the home appliance usage information.

[0234] Users use a web browser or mobile application to input the type of appliance they want to use and the time they want to use it. For example, if a user wants to use an air conditioner, they enter "air conditioner" and "from 18:00 to 22:00." The input information is saved in an input field on the device.

[0235] Step 2:

[0236] The terminal sends the input information to the server.

[0237] The device sends the appliance usage information entered by the user to the server using the HTTPS protocol. Specifically, it converts the entered information into JSON format and sends a POST request to the server's API endpoint. The input is the type of appliance and usage time, and the output is the status of completion of transmission to the server.

[0238] Step 3:

[0239] The server stores the input information in a database.

[0240] The server stores the received appliance usage information in a database. Specifically, the server parses the received JSON data and stores it in a relational database management system (RDBMS) such as MySQL or PostgreSQL. The input is the received appliance usage information, and the output is the status of completion of saving to the database.

[0241] Step 4:

[0242] The server calculates the optimal time of day for use based on the stored data.

[0243] The server calculates the optimal usage time based on the stored data. Specifically, it uses Python's Pandas library to analyze past usage data and power consumption patterns to calculate the optimal usage time. The input is the appliance usage information and past usage data stored in the database, and the output is the optimal usage time.

[0244] Step 5:

[0245] The server sends the calculation results to the terminal.

[0246] The server sends the calculation result to the terminal. Specifically, it converts the calculated optimal usage time slot into JSON format and sends it to the terminal using the HTTPS protocol. The input is the calculation result of the optimal usage time slot, and the output is the status of completion of transmission to the terminal.

[0247] Step 6:

[0248] The terminal displays the calculation results to the user.

[0249] The terminal displays the calculation results received from the server to the user. Specifically, it displays "The best time to use the device is from 19:00 to 21:00" on the mobile application screen. The input is the calculation result of the best time to use the device received from the server, and the output is the content displayed to the user.

[0250] (Application example 2)

[0251] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0252] Conventional systems for calculating optimal usage times based on information on home appliance usage only cover information on home appliance usage, and are not compatible with optimizing charging schedules for autonomous vehicles. This makes it difficult for users to calculate optimal charging times based on autonomous vehicle operation schedules and improve energy management efficiency.

[0253] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for calculating an electricity rate based on a market-linked plan, means for receiving home appliance usage information from a user, means for calculating an optimal usage time period based on the received home appliance usage information, means for proposing the calculated optimal usage time period and electricity rate to the user, means for receiving operation schedule information from the user and calculating an optimal charging time period, and means for notifying the user of the calculated optimal charging time period. This enables the user to calculate the optimal charging time based on the operation schedule of the autonomously driving vehicle and improve the efficiency of energy management.

[0254] A "market-linked plan" is a rate plan in which electricity rates are determined based on the trading price on the electricity exchange.

[0255] A "means for calculating electricity charges" is a device or program for calculating electricity charges based on a market-linked plan.

[0256] "Home appliance usage information" is information about the type of home appliance that the user wants to use and the duration of use.

[0257] The "means for calculating the optimum use time period" is a device or program for calculating the optimum use time period of the home appliance based on the received home appliance use information.

[0258] "Means for making suggestions to users" refers to a device or program for notifying or displaying to users the calculated optimal usage time period and electricity charges.

[0259] "Operation schedule information" refers to information such as departure time, arrival time, and destination that is input by the user regarding the operation of an autonomous vehicle.

[0260] The "means for calculating the optimal charging time slot" is a device or program for calculating the optimal charging time slot for an autonomously driven vehicle based on the received operation schedule information.

[0261] The "notification means" is a device or program for notifying the user of the calculated optimal charging time period.

[0262] A system for implementing the present invention includes a server, a user terminal, and an autonomous vehicle. The server includes the following means:

[0263] 1. Means for calculating electricity tariffs under market-linked plans

[0264] 2. Means of receiving information on home appliance usage from users

[0265] 3. A method for calculating optimal usage times based on received appliance usage information

[0266] 4. A method for proposing the calculated optimal usage time and electricity rates to users

[0267] 5. A means to receive schedule information from users and calculate the optimal charging time

[0268] 6. A method for notifying users of the calculated optimal charging time

[0269] Program processing explanation

[0270] The server is implemented using a programming language such as Python. It receives information about home appliance usage and operation schedules sent from user devices and calculates optimal usage and charging times based on this information.

[0271] Specifically, the server performs the following process.

[0272] 1. Calculation of electricity charges:

[0273] The server obtains the trading price of the power exchange based on the market-linked plan and calculates the electricity tariff, which is calculated by obtaining the data of the power exchange through API.

[0274] 2. Receiving and processing appliance usage information:

[0275] The system receives information about home appliance usage (the type of appliance you want to use and the time of use) sent from the user's device and calculates the optimal usage time period, using an AI model to avoid peak hours of power consumption.

[0276] 3. Receiving and processing schedule information:

[0277] The system receives scheduled flight information (departure time, arrival time, destination) sent from the user's device and calculates the optimal charging time. The calculation uses an AI model to determine the optimal charging time based on the flight schedule.

[0278] 4. Notice:

[0279] The system notifies the user of the calculated optimal usage and charging times. Notifications are sent to the user's device via push notification or email.

[0280] Specific examples

[0281] For example, a user enters the following information into a smartphone app:

[0282] Departure time: 2023-10-01 08:00:00

[0283] Arrival time: 2023-10-01 10:00:00

[0284] Destination: Office

[0285] Based on this information, the server calculates the optimal charging time and notifies the driver to start charging two hours before departure (2023-10-01 06:00:00).

[0286] Example prompts to input to a generative AI model:

[0287] A user has entered a scheduled trip for an autonomous vehicle. The departure time is 2023-10-01 08:00:00, the arrival time is 2023-10-01 10:00:00, and the destination is Office. Based on this information, please calculate the optimal charging time.

[0288] In this way, it is possible to calculate the optimal charging time based on the user's driving schedule and improve energy management efficiency.

[0289] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0290] Step 1:

[0291] Users use a smartphone app to input information about their appliance usage and scheduled trips. The information includes the type of appliance they want to use, the duration of use, departure time, arrival time, and destination. This information is then sent from the user's device to the server.

[0292] Step 2:

[0293] The server receives the appliance usage information sent from the user's device. The received information includes the type of appliance desired to be used and the usage time. The server processes the data based on this information to calculate the optimal usage time period.

[0294] Step 3:

[0295] The server obtains the trading price from the power exchange based on the market-linked plan. The server calculates the electricity charges based on the acquired trading price data. The calculation obtains the data from the power exchange through an API and takes into account real-time price fluctuations.

[0296] Step 4:

[0297] The server calculates the optimal usage time based on the received appliance usage information and acquired transaction price data. The calculation uses an AI model to avoid peak hours of electricity consumption. Specifically, it applies an algorithm to avoid peak hours of electricity consumption.

[0298] Step 5:

[0299] The server then calculates the optimal usage time and electricity rates and proposes them to the user. The proposals are sent to the user's device via push notification or email. The user can then check the proposed optimal usage time and plan their use of home appliances.

[0300] Step 6:

[0301] The server receives the scheduled vehicle information sent from the user's device. The received information includes departure time, arrival time, and destination. The server processes this information to calculate the optimal charging time slot.

[0302] Step 7:

[0303] The server calculates the optimal charging time based on the received flight schedule information. For the calculation, an AI model is used to determine the optimal charging time based on the flight schedule. Specifically, an algorithm is applied to start charging two hours before departure.

[0304] Step 8:

[0305] The server notifies the user of the calculated optimal charging time slot. The notification is sent to the user's device via push notification or email. The user can check the optimal charging time slot and plan charging for their autonomous vehicle.

[0306] In this way, it is possible to calculate optimal usage and charging time periods based on the user's home appliance usage information and operation schedule information, thereby making it possible to improve the efficiency of energy management.

[0307] Example 3

[0308] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0309] Conventional electricity bill calculation systems made it difficult for users to know the optimal time to use their home appliances, making it difficult to effectively reduce electricity bills. Furthermore, because real-time electricity bill calculations were not based on the trading price of the power exchange, it was not possible to suggest the optimal time for users to use their appliances. This often resulted in users paying high electricity bills and made it difficult to use energy efficiently.

[0310] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0311] In this invention, the server includes means for receiving home appliance usage information from a user, means for acquiring trading price data from an electricity exchange, means for processing the acquired trading price data and calculating the electricity rate for a specified time period, means for proposing the calculated optimal usage time period and electricity rate to the user, and means for presenting the calculation result to the user. This allows the user to know the optimal time period for using the home appliance and the electricity rate for that time period in real time, thereby effectively reducing electricity charges.

[0312] "Means for receiving home appliance usage information from users" refers to a device or software that provides an interface for users to input the type of home appliance they wish to use and the usage time, and has the function of sending that information to a server.

[0313] "Means for obtaining trading price data from the power exchange" refers to a device or software that has the function of accessing the API of the power exchange and obtaining the latest trading price data.

[0314] "Means for processing the acquired transaction price data and calculating the electricity charges for the specified time period" refers to a device or software that has the function of analyzing the acquired transaction price data and calculating the electricity charges for the time period specified by the user.

[0315] "Means for proposing to users the calculated optimal usage times and electricity rates" refers to a device or software that has the function of presenting to users the optimal usage times for home appliances and the electricity rates for those times based on the calculation results.

[0316] "Means for presenting calculation results to the user" refers to a device or software that has the function of visually or audibly presenting the calculated electricity charges and the optimal time periods for use to the user.

[0317] MODE FOR CARRYING OUT THE INVENTION

[0318] This invention is a system that calculates the optimal time periods for users to use home appliances and the electricity rates for those times, and then suggests these to users.The system receives information about the use of home appliances from users, acquires trading price data from the power exchange, processes the acquired data to calculate the electricity rate, and presents the results to the user.

[0319] Hardware and software used

[0320] The system uses the following hardware and software:

[0321] Server: A computer system for performing data processing and calculations.

[0322] Terminal: The device through which a user enters information (e.g., smartphone, tablet, PC).

[0323] Python: A programming language used to develop programs.

[0324] Pandas library: A Python library used for data processing and analysis.

[0325] Requests library: A Python library used to retrieve data from the power exchange API.

[0326] Program processing

[0327] The server receives input from the user. When the user inputs the type of appliance they want to use and the time they want to use it, the server retrieves the latest trading price data from the power exchange's API. Based on this data, the server calculates the electricity rate for the specified time period. The calculation is done by multiplying the electricity trading price by the power consumption of the appliance.

[0328] Specific examples

[0329] For example, if a user enters "I want the air conditioning to run from 3pm to 6pm," the server will do the following:

[0330] 1. The user enters "Air conditioner, 15:00-18:00" into the device's input form.

[0331] 2. The device sends this input data to the server.

[0332] 3. The server uses the Requests library to send a request to the power exchange API.

[0333] 4. The server receives the quote data returned from the API and converts it into a data frame using Pandas.

[0334] 5. The server extracts the trading prices for the specified time period (15:00-18:00) from the data frame.

[0335] 6. The server calculates the electricity bill by multiplying the air conditioner's power consumption (for example, 1.5kW) by the transaction price.

[0336] 7. The server generates the calculation result: "If you use the air conditioner from 3:00 pm to 6:00 pm, the electricity bill will be XX yen."

[0337] 8. The server sends this message to the terminal.

[0338] 9. The terminal displays a message to the user.

[0339] Prompt Sentence Examples

[0340] Example prompts to input to a generative AI model:

[0341] If a user inputs that they want to use the air conditioner from 3:00 PM to 6:00 PM, create a program that calculates the electricity rate based on the electricity exchange price for that time period and displays it to the user.

[0342] In this way, the user can know the optimal usage time period and electricity charges, and can use this information as reference information to reduce electricity charges. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0343] Program processing flow

[0344] Step 1: Accepting User Input

[0345] The user uses the terminal to input the type of appliance they want to use and the time they want to use it. For example, the user can input "I want to use the air conditioner from 3:00 PM to 6:00 PM."

[0346] Specific behavior:

[0347] Input: The user enters "Air conditioner, 15:00-18:00" into the input form on the device.

[0348] Output: The terminal sends this input data to the server.

[0349] Step 2: Obtaining electricity trading price data

[0350] The server retrieves the latest trading price data from the power exchange API, which is used to calculate the electricity price for the specified time period.

[0351] Specific behavior:

[0352] Input: The server receives the home appliance usage information sent by the user.

[0353] Data processing: The server uses the Requests library to send requests to the power exchange API.

[0354] Output: The server receives the quote data returned from the API.

[0355] Step 3: Process the data and calculate the electricity cost

[0356] The server processes the acquired trading price data using the Pandas library and calculates the electricity rate for the specified time period by multiplying the power consumption of the appliances by the trading price.

[0357] Specific behavior:

[0358] Input: The server receives the trading price data obtained from the API.

[0359] Data processing: The server uses Pandas to convert the trading price data into a data frame.

[0360] Data calculation: The server extracts the trading price for the specified time period (15:00-18:00) from the data frame, multiplies it by the air conditioner's power consumption (for example, 1.5 kW), and calculates the electricity bill.

[0361] Output: Calculated electricity price data.

[0362] Step 4: Presenting the calculation results

[0363] The server then presents the calculation results to the user, who can then use this information to help reduce their electricity bills.

[0364] Specific behavior:

[0365] Input: Calculated electricity price data.

[0366] Data processing: The server generates the calculation results in a format such as "If you use the air conditioner from 3:00 pm to 6:00 pm, the electricity bill will be XX yen."

[0367] Output: The server sends this message to the terminal.

[0368] Action: The terminal displays a message to the user.

[0369] In this way, users can learn the optimal time periods for use and electricity rates, which can be used as reference information to reduce electricity charges.

[0370] (Application example 3)

[0371] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0372] When using electricity, it is difficult for users to know the optimal time to use it in order to reduce their electricity bills. Furthermore, when charging electric vehicles, electricity rates vary greatly depending on the time of day, making it difficult for users to select the optimal time to charge. Therefore, there is a need for a system that helps users use electricity efficiently and reduce their electricity bills.

[0373] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0374] In this invention, the server includes means for calculating electricity charges based on a market-linked plan, means for receiving power usage information from a user, means for calculating optimal usage time periods based on the received power usage information, means for proposing the calculated optimal usage time periods and electricity charges to the user, means for calculating the time period with the lowest electricity charge among the power usage time periods input by the user, and means for presenting the calculated optimal power usage time periods and estimated electricity charges to the user. This allows the user to know the optimal usage time periods for reducing electricity charges.

[0375] A "market-linked plan" is a rate plan in which electricity rates are determined based on the trading price at the power exchange.

[0376] "Means for calculating electricity charges" refers to a device or program that has the function of calculating the charges for electricity used by a user based on the trading price at the electricity exchange.

[0377] "Power usage information" refers to information such as the type of power equipment used by the user and the duration of use.

[0378] The "means for calculating the optimal usage time period" is a device or program that has the function of calculating the time period when the electricity rate is lowest based on the received power usage information.

[0379] The "means for making suggestions to the user" is a device or program that has the function of notifying the user of the calculated optimal usage time period and electricity charges.

[0380] "Power usage time period" refers to the time period during which a user plans to use power equipment.

[0381] The "means for calculating the time period with the cheapest electricity rates" is a device or program that has the function of calculating the time period with the cheapest electricity rates among the electricity usage time periods entered by the user.

[0382] The "means for presenting the user with the estimated electricity charges" is a device or program that has the function of notifying the user of the calculated optimal time period for electricity use and the estimated electricity charges for that time period.

[0383] A system for implementing the present invention is for optimizing charging of electric vehicles. The system includes a server, a user terminal, and an API of a power exchange. A specific embodiment of the system will be described below.

[0384] System Configuration

[0385] 1. Server

[0386] The server runs a program that has the following functions:

[0387] Ability to calculate electricity rates based on market-linked plans

[0388] A function to receive power usage information from users

[0389] A function that calculates the optimal time period for use based on received power usage information

[0390] A function that suggests the optimal usage time and electricity charges to users

[0391] A function that calculates the time period with the cheapest electricity rates among the electricity usage time periods entered by the user.

[0392] A function that displays the calculated optimal power usage time and estimated electricity charges to the user

[0393] 2. User Device

[0394] The user terminal is a device such as a smartphone or tablet that provides an interface for users to input power usage information. The user inputs the time period during which they wish to charge, and the information is sent to the server.

[0395] 3. Power Exchange API

[0396] The API of the power exchange is an interface for obtaining trading prices from the power exchange. The server obtains real-time electricity trading prices through this API and uses them to calculate electricity charges.

[0397] Program processing explanation

[0398] The server calculates the electricity bill based on the electricity usage information received from the user's device and the trading price obtained from the power exchange's API. Specifically, the process is carried out as follows:

[0399] 1. Receiving electricity usage information

[0400] Power usage information (for example, the time period during which the user wishes to charge) entered from the terminal is sent to the server.

[0401] 2. Calculating electricity charges

[0402] The server uses the API of the power exchange to obtain the trading price for the specified time period, and then calculates the electricity fee based on the obtained trading price.

[0403] 3. Calculating the best time to use

[0404] The server calculates the cheapest electricity rate among the user-entered time slots, using real-time market price data.

[0405] 4. Presentation of results

[0406] The calculation results (optimal usage time and estimated electricity charges) are sent to the user's terminal and presented to the user.

[0407] Specific examples

[0408] For example, if a user inputs "I want to charge between 3:00 p.m. and 6:00 p.m. on October 1, 2023," the server will retrieve the trading price for that time period and calculate and present the time period with the cheapest electricity rates.

[0409] Prompt Sentence Examples

[0410] Example prompts to input to a generative AI model:

[0411] When a user inputs the charging time period for their electric vehicle, the system calculates the optimal charging time period and electricity rate based on the trading price on the power exchange for that time period, and presents these to the user. For example, if a user inputs "I want to charge between 3:00 PM and 6:00 PM on October 1, 2023," the system calculates and presents the time period with the cheapest electricity rate within that time period.

[0412] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0413] Step 1:

[0414] The user inputs power usage information from the device.

[0415] Specifically, the user uses a device such as a smartphone or tablet to input the time period during which they would like to charge (for example, "October 1, 2023, from 3:00 PM to 6:00 PM"). The input information is then sent from the device to the server.

[0416] Input: Time period you want to charge

[0417] Output: Power usage information sent to the server

[0418] Step 2:

[0419] A server receives the power usage information.

[0420] The server receives and analyzes the power usage information sent from the user's device, including the time period during which the user wishes to charge.

[0421] Input: Power usage information sent by the user

[0422] Output: Analyzed power usage information

[0423] Step 3:

[0424] The server obtains the trading price from the power exchange API.

[0425] The server uses the API of the power exchange to obtain the trading price for the specified time period, which is then used to calculate the electricity bill.

[0426] Input: Analyzed power usage information (time period you want to charge)

[0427] Output: Trading price for the specified time period

[0428] Step 4:

[0429] The server calculates the electricity bill.

[0430] The server calculates the electricity charges for the time period specified by the user based on the acquired transaction price. The calculation uses the transaction price and the usage time.

[0431] Input: Trading price for the specified time period, usage time

[0432] Output: Calculated electricity cost

[0433] Step 5:

[0434] The server calculates the optimal time to use it.

[0435] The server calculates the cheapest electricity rate among the user-entered time slots, using real-time market price data.

[0436] Input: Trading price for the specified time period, usage time

[0437] Output: Best time to use

[0438] Step 6:

[0439] The server presents the calculation results to the user.

[0440] The server then sends the calculated optimal usage time and estimated electricity charges to the user's terminal and presents them to the user, who can then select the optimal charging time based on the presented information.

[0441] Input: Optimal usage time, calculated electricity price

[0442] Output: The best time to use electricity and the estimated electricity price are presented to the user.

[0443] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0444] "Example 1"

[0445] As one embodiment of the present invention, a system incorporating an emotion engine is provided. This system recognizes emotions from the user's voice, facial expressions, behavior, etc., and suggests optimal times to use home appliances based on those emotions. For example, if the user is expressing joy, the system suggests using home appliances that are highly entertaining (e.g., televisions or game consoles). If the user is feeling stressed, the system suggests using home appliances that contribute to relaxation (e.g., massage chairs or aroma diffusers). This makes it possible to suggest optimal times to use home appliances based on the user's emotions.

[0446] "Example 2"

[0447] Another embodiment of the present invention provides a system for adjusting electricity rate proposals based on the recognition results of an emotion engine. This system optimizes electricity rate proposals according to the user's emotions. For example, if the user is expressing joy, the system extends the usage time periods of entertainment-oriented home appliances and proposes an increase in the resulting electricity rate. On the other hand, if the user is feeling stressed, the system extends the usage time periods of relaxation-oriented home appliances and proposes an increase in the resulting electricity rate. This makes it possible to propose optimal electricity rates according to the user's emotions.

[0448] "Example 3"

[0449] As one embodiment of the present invention, a system incorporating an emotion engine is provided. This system recognizes emotions from the user's voice, facial expressions, behavior, etc., and suggests optimal times to use home appliances based on those emotions. For example, if the user is expressing joy, the system suggests using home appliances that are highly entertaining (e.g., televisions or game consoles). If the user is feeling stressed, the system suggests using home appliances that contribute to relaxation (e.g., massage chairs or aroma diffusers). This makes it possible to suggest optimal times to use home appliances based on the user's emotions.

[0450] The processing flow of each embodiment will be described below.

[0451] "Example 1"

[0452] Step 1: The system activates an emotion engine that recognizes emotions from the user's voice, facial expressions, behavior, etc.

[0453] Step 2: The emotion engine recognizes the user's emotions and communicates the results to the system. Step 3: The system calculates the optimal time to use the appliances based on the results from the emotion engine.

[0454] Step 4: The system will suggest the optimal usage time slot to the user.

[0455] "Example 2"

[0456] Step 1: The system activates an emotion engine that recognizes emotions from the user's voice, facial expressions, behavior, etc.

[0457] Step 2: The emotion engine recognizes the user's emotion and communicates the result to the system. Step 3: The system adjusts the electricity rate proposal based on the result from the emotion engine.

[0458] Step 4: The system will communicate the adjusted electricity tariff proposal to the user.

[0459] "Example 3"

[0460] Step 1: The system activates an emotion engine that recognizes emotions from the user's voice, facial expressions, behavior, etc.

[0461] Step 2: The emotion engine recognizes the user's emotions and communicates the results to the system. Step 3: The system calculates the optimal time to use the appliances based on the results from the emotion engine.

[0462] Step 4: The system will suggest the optimal usage time slot to the user.

[0463] Example 1

[0464] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0465] Conventional electricity rate calculation systems calculate electricity rates based on the trading price in the electricity market, but do not suggest optimal times to use home appliances based on the user's emotions or behavior. As a result, it is not possible to promote the use of home appliances in accordance with the user's emotions, making it difficult to improve user satisfaction.

[0466] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0467] In this invention, the server includes means for acquiring transaction price information from the electricity market, means for analyzing the acquired transaction price information and calculating the unit price of electricity, means for acquiring the user's power consumption, means for calculating the electricity charge based on the unit price of electricity and the power consumption, means for notifying the user of the calculation results, means for collecting the user's voice, facial expression, and behavior, means for analyzing the collected data and recognizing the user's emotions, means for proposing optimal times for using home appliances based on the recognized emotions, and means for notifying the user of the proposed content. This makes it possible to suggest optimal times for using home appliances according to the user's emotions and improve user satisfaction.

[0468] An "electricity market" is a market where electricity is bought and sold, and where the trading price of electricity is determined.

[0469] "Trading price information" is data related to the buying and selling prices of electricity in the electricity market.

[0470] The "unit price of electricity" is a price indicating the charge for a certain amount of electricity, and is calculated based on transaction price information.

[0471] "Power consumption amount" is data indicating the amount of power consumed by a user over a certain period of time.

[0472] "Electricity charge" is a charge calculated by multiplying the amount of power consumed by the unit price of electricity.

[0473] "User's voice, facial expressions, and actions" refers to information such as the voice, facial expressions, and body movements of the user.

[0474] "Means for recognizing emotions" refers to technologies and devices that analyze a user's voice, facial expressions, and behavior to identify the user's emotions.

[0475] "Optimal time to use home appliances" refers to the most suitable time to use home appliances based on the user's emotions and behavior.

[0476] "Means for notifying users of proposals" refers to technologies and devices for communicating calculation results and proposals to users.

[0477] This system calculates electricity rates based on market-linked plans and proposes optimal times for using home appliances based on user sentiment. This system is composed of multiple components, including a server and a terminal.

[0478] First, the server obtains trading price information from the electricity market. Trading price information is data on the buying and selling prices of electricity in the electricity market, and is obtained through an API. For example, an HTTP request is sent and price data is received in JSON format. The server analyzes this data and uses the Python pandas library to calculate the average price over a certain period (e.g., every hour). Specifically, the server converts the JSON data into a pandas DataFrame and calculates the average price for each hour using the resample('H').mean() method.

[0479] Next, the server obtains the power consumption data from the user's smart meter. The smart meter data is sent from the IoT device using the MQTT protocol. The server connects to the MQTT broker and receives the user's power consumption data. Based on the received data, the electricity bill is calculated by multiplying the electricity rate by the amount of power consumed. For example, if the unit price is 10 yen / kWh and the amount of power consumed is 5kWh, the electricity bill will be 50 yen. The calculation result is notified to the user by sending an email using the SMTP protocol or a push notification using Firebase Cloud Messaging.

[0480] Meanwhile, the device collects the user's voice, facial expressions, and behavior. To do this, it uses a camera and microphone, and uses the Google® Cloud Speech-to-Text API for voice recognition and OpenCV for facial recognition. The device captures the user's face with the camera and extracts facial feature points using OpenCV. It also records audio with the microphone and converts it into text using the Google Cloud Speech-to-Text API. The device uses the Microsoft® Azure® Emotion API to analyze the collected data and recognize the user's emotions. The device sends the facial feature point data to the Microsoft Azure Emotion API and obtains an emotion label (e.g., joy, sadness, anger).

[0481] Based on the recognized emotions, the device will suggest optimal times to use home appliances. For example, if the user is expressing joy, it will suggest using the TV. If the user is feeling stressed, it will suggest using appliances that contribute to relaxation (e.g., a massage chair or aroma diffuser). Suggestions are sent via push notifications using Firebase Cloud Messaging or spoken through the voice assistant.

[0482] For example, if a user consumes 5 kWh of electricity in one hour and the trading price for that time period is 10 yen / kWh, the electricity bill will be 50 yen. Also, if the user smiles at the camera, the system recognizes that the user is expressing a happy emotion and suggests using the television.

[0483] Examples of prompts include, "Calculate the hourly electricity bill based on the trading price information obtained from the power exchange," and "Recognize the user's emotions from their voice and facial expressions, and suggest the optimal times to use home appliances based on those emotions."

[0484] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0485] Step 1:

[0486] The server obtains trading price information from the electricity market.

[0487] Input: Power market API endpoint.

[0488] Specific operation: The server sends an HTTP request to an endpoint such as "https: / / api.electricity-market.com / prices" and receives trading price information in JSON format.

[0489] Output: Retrieved trading price information (JSON data).

[0490] Step 2:

[0491] The server analyzes the acquired transaction price information and calculates the unit price of electricity.

[0492] Input: Transaction price information (JSON data).

[0493] Specific operation: The server converts the JSON data into a pandas DataFrame and calculates the average price for each hour using the resample('H').mean() method.

[0494] Output: Average price per hour (unit price of electricity).

[0495] Step 3:

[0496] The server obtains power consumption data from the user's smart meter.

[0497] Input: Smart meter data (MQTT message).

[0498] Specific operation: The server connects to the MQTT broker and receives the user's power consumption data.

[0499] Output: User energy consumption data.

[0500] Step 4:

[0501] The server calculates the electricity bill based on the unit price of electricity and the amount of power consumed.

[0502] Input: Electricity unit price, power consumption data.

[0503] Specific operation: The server executes the formula Charge = Unit Price Power Consumption to calculate the electricity charge.

[0504] Output: The calculated electricity cost.

[0505] Step 5:

[0506] The server notifies the user of the calculation results.

[0507] Input: Calculated electricity cost.

[0508] What happens: The server sends emails using the SMTP protocol or push notifications using Firebase Cloud Messaging.

[0509] Output: The electricity price notified to the user.

[0510] Step 6:

[0511] The device collects the user's voice, facial expressions, and behavior.

[0512] Input: User voice, facial expressions, and actions.

[0513] What it does: The device captures the user's face with the camera and extracts facial feature points using OpenCV, and records audio with the microphone and converts it to text using the Google Cloud Speech-to-Text API.

[0514] Output: User voice, facial expression, and behavioral data.

[0515] Step 7:

[0516] The device analyzes the collected data and recognizes the user's emotions.

[0517] Input: User voice, facial expression, and behavioral data.

[0518] What it does: The device sends facial feature point data to the Microsoft Azure Emotion API to obtain an emotion label (e.g., joy, sadness, anger).

[0519] Output: Recognized emotion label.

[0520] Step 8:

[0521] The device will suggest optimal times to use home appliances based on the emotions it recognizes.

[0522] Input: Recognized emotion label.

[0523] Specific operation: Based on the emotion label, the device selects the most suitable appliance from a pre-defined list and suggests the best time to use it.

[0524] Output: Suggested appliance usage times.

[0525] Step 9:

[0526] The device will notify the user of the suggestions.

[0527] Input: Proposed appliance usage time slots.

[0528] What happens: The device will send a push notification using Firebase Cloud Messaging or speak the suggestion through your voice assistant.

[0529] Output: The suggestion that was notified to the user.

[0530] (Application example 1)

[0531] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0532] Conventional electricity bill calculation systems are limited to calculating electricity rates based on market-linked plans and suggesting optimal usage times based on user information on home appliance usage. However, because they do not consider the user's emotional state when suggesting home appliance usage, they are unable to improve user satisfaction or comfort. Furthermore, because they do not suggest home appliance usage based on emotions, energy management that responds to the user's psychological needs has not been realized.

[0533] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0534] In this invention, the server includes means for calculating electricity charges based on a market-linked plan, means for receiving home appliance usage information from a user, means for calculating optimal usage time periods based on the received home appliance usage information, means for proposing the calculated optimal usage time periods and electricity charges to the user, means for recognizing the user's emotions, and means for proposing optimal home appliance usage based on the recognized emotions. This makes it possible to suggest optimal home appliance usage based on the user's emotional state, thereby improving user satisfaction and comfort.

[0535] A "market-linked plan" is a rate plan in which electricity rates are determined based on the trading price at the power exchange.

[0536] A "means for calculating electricity charges" is a device or software for calculating electricity charges based on a market-linked plan.

[0537] The "means for receiving home appliance usage information from the user" is an interface that allows the user to input the type of home appliance they wish to use and the usage time.

[0538] The "means for calculating the optimum usage time period" is a device or software that calculates the most efficient usage time period based on the received home appliance usage information.

[0539] The "means for making suggestions to the user" is an interface for notifying the user of the calculated optimal usage time period and electricity charges.

[0540] "Means for recognizing user emotions" refers to a device or software for detecting emotions from the user's voice, facial expressions, behavior, etc.

[0541] The "means for suggesting the optimum use of home appliances" is a device or software for suggesting the optimum use of home appliances to a user based on the recognized emotions.

[0542] The following system configuration will be described as an embodiment of the present invention.

[0543] System Configuration

[0544] The server includes means for calculating an electricity charge based on a market-linked plan, means for receiving home appliance usage information from a user, means for calculating an optimum usage time period based on the received home appliance usage information, means for proposing the calculated optimum usage time period and electricity charge to the user, means for recognizing the user's emotions, and means for proposing the optimum use of the home appliance based on the recognized emotions.

[0545] Program processing

[0546] The server first obtains trading price information from the power exchange. This information is obtained through an API. Next, it calculates the electricity rate based on the obtained trading price information. The calculated electricity rate is used to calculate the optimal usage time based on the appliance usage information entered by the user.

[0547] To recognize the user's emotions, the server uses emotion recognition software. This software detects emotions from the user's voice, facial expressions, and behavior. Based on the recognized emotions, the server then suggests the optimal use of home appliances.

[0548] Hardware and software used

[0549] Hardware: Servers, user devices (smartphones, tablets, etc.)

[0550] Software: Emotion recognition software, power exchange API, home appliance control software

[0551] Specific examples

[0552] For example, if a user is using a smartphone, the app uses the smartphone's camera to recognize the user's face. If the app recognizes the user's emotion as "stress," the server will suggest using a "massage chair" and an "aroma diffuser." Based on the current electricity price, the server will calculate the cost of using these appliances and notify the user.

[0553] Prompt Sentence Examples

[0554] Design a smartphone app that recognizes the user's emotions and suggests optimal times to use home appliances. The app retrieves electricity price information from the power exchange and calculates electricity bills based on it. If the user is feeling stressed, it suggests using appliances that contribute to relaxation (e.g., a massage chair or aroma diffuser).

[0555] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0556] Step 1:

[0557] The server acquires trading price information from the power exchange.

[0558] Input: Power Exchange API endpoint

[0559] Data processing: Send an API request and obtain trading price information

[0560] Output: Trading price information (JSON format)

[0561] Step 2:

[0562] The server calculates the electricity bill based on the acquired transaction price information.

[0563] Input: Transaction price information

[0564] Data calculation: Extracts the current electricity price from trading price information and calculates electricity charges based on usage

[0565] Output: Calculated electricity cost

[0566] Step 3:

[0567] A user inputs home appliance usage information using a smartphone.

[0568] Input: Type of appliance, usage time

[0569] Data processing: Send the entered information to the server

[0570] Output: Home appliance usage information

[0571] Step 4:

[0572] The server calculates the optimal usage time period based on the received home appliance usage information.

[0573] Input: Home appliance usage information, transaction price information

[0574] Data calculation: Combining appliance usage information and trading price information to calculate the most cost-effective usage time period

[0575] Output: Best time to use

[0576] Step 5:

[0577] The server then proposes the calculated optimal usage time period and electricity rate to the user.

[0578] Input: Optimal usage time, calculated electricity price

[0579] Data processing: Generate proposals and send them to the user's device

[0580] Output: Proposal content (optimal usage time and electricity rate)

[0581] Step 6:

[0582] The user uses the smartphone camera to have their face recognized.

[0583] Input: User's face image

[0584] Data processing: Send the face image to the server

[0585] Output: Face image data

[0586] Step 7:

[0587] The server uses emotion recognition software to recognize the user's emotions.

[0588] Input: Facial image data

[0589] Data calculation: Analyzes facial image data and recognizes emotions

[0590] Output: Recognized emotion

[0591] Step 8:

[0592] The server suggests optimal use of home appliances based on the recognized emotions.

[0593] Input: Recognized emotion

[0594] Data Computing: Generating Emotion-Based Appliance Usage Suggestions

[0595] Output: Home appliance usage suggestion

[0596] Step 9:

[0597] The server calculates the cost of using the proposed appliances and notifies the user.

[0598] Input: Home appliance usage proposals, transaction price information

[0599] Data calculation: Calculate the usage cost of the proposed appliances

[0600] Output: Usage cost notification

[0601] Example 2

[0602] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0603] Conventional electricity rate calculation systems can calculate optimal usage times based on the user's home appliance usage information, but they have the problem of not being able to propose electricity rates that take the user's emotional state into account. This makes it difficult to propose optimal home appliance usage based on the user's emotions, making it difficult to improve user satisfaction.

[0604] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0605] In this invention, the server includes means for calculating an electricity charge based on a market-linked plan, means for receiving home appliance usage information from a user, means for calculating an optimal usage time period based on the received home appliance usage information, means for recognizing the user's emotions, means for adjusting the proposed electricity charge based on the recognized emotions, and means for proposing the calculated optimal usage time period and electricity charge to the user. This makes it possible to propose optimal home appliance usage and adjust the electricity charge according to the user's emotions.

[0606] A "market-linked plan" is a rate plan in which electricity rates are determined based on the trading price at the power exchange.

[0607] The "means for calculating electricity charges" is a function for calculating electricity charges based on electricity consumption and a market-linked plan.

[0608] "Means for receiving information on home appliance usage from users" is a function for inputting the type of home appliance and usage time entered by the user into the system.

[0609] The "means for calculating the optimal usage time period" is a function for calculating the optimal usage time period for a home appliance based on the received home appliance usage information, taking into account power consumption patterns and past data.

[0610] "Means for recognizing user emotions" refers to a function for analyzing and recognizing the user's emotional state.

[0611] The "means for adjusting electricity rate proposals" is a function for optimizing the content of electricity rate proposals based on the recognized user sentiment.

[0612] The "means for proposing the calculated optimal usage time period and electricity charges to the user" is a function for notifying the user of the calculated optimal usage time period and adjusted electricity charges.

[0613] MODE FOR CARRYING OUT THE INVENTION

[0614] This invention is a system that receives information on the use of home appliances from users, calculates the optimal usage time based on that information, and adjusts electricity rate proposals based on the user's emotions. This system is implemented using the following hardware and software.

[0615] Hardware and software used

[0616] Hardware

[0617] Device: The device on which a user provides input information (e.g., smartphone, tablet, PC)

[0618] Server: A server for processing and storing data.

[0619] software

[0620] Emotion engine: Software for recognizing user emotions (e.g., emotion recognition API)

[0621] Database: A database for storing information on home appliance usage and user emotion data (e.g., MySQL, PostgreSQL)

[0622] Specific operation of the system

[0623] 1. Collecting user input information

[0624] The user uses the terminal to input the type of home appliance they want to use and the desired usage time.

[0625] Specifically, the user enters information such as "air conditioner" and "from 2:00 p.m. to 4:00 p.m." into the application's input form.

[0626] 2. Sending input information

[0627] The terminal transmits the information entered by the user to the server.

[0628] Specifically, the terminal uses an HTTP request to send input information to the server.

[0629] 3. Calculating the usage time of home appliances

[0630] The server calculates the optimal time to use the appliance based on the type and usage time of the appliance received.

[0631] Specifically, the server runs an algorithm that takes into account past usage data and power consumption patterns to calculate the optimal usage times.

[0632] For example, the server may calculate that the best time to use the air conditioner is between 2:00 p.m. and 4:00 p.m.

[0633] 4. Emotion Recognition by Emotion Engine

[0634] The server uses an emotion engine to recognize the user's emotion.

[0635] Specifically, the server inputs the user's facial image and voice data into the emotion engine to obtain the user's emotional state (e.g., joy, stress).

[0636] 5. Proposal for adjusting electricity charges

[0637] The server adjusts the electricity rate proposal based on the recognition results of the emotion engine.

[0638] Specifically, if the user expresses joy, the server will extend the usage time of entertainment-oriented home appliances (e.g., television) and suggest an increase in the resulting electricity bill.

[0639] On the other hand, if the user is feeling stressed, the system will suggest extending the usage time of home appliances that contribute to relaxation (e.g., massage chairs).

[0640] 6. Notifying Users of Results

[0641] The server sends the calculation results and the proposal to the terminal.

[0642] The terminal notifies the user of the received information.

[0643] Specifically, the device will use push notifications and in-app messages to inform the user that "The best time to use the air conditioner is between 2:00 PM and 4:00 PM. We also recommend extending the TV usage time."

[0644] Specific examples

[0645] User Input

[0646] A user uses a smartphone to enter, "I want to use the air conditioner from 2:00 PM to 4:00 PM."

[0647] Server Processing

[0648] The server calculates the times when the air conditioner will be used and suggests the optimal times based on past data and power consumption patterns.

[0649] If the emotion engine recognizes the user's emotion as "joy," it will suggest extending the usage time of entertainment-oriented home appliances (e.g., television).

[0650] Example prompts for generative AI models

[0651] "A user inputs that they want to use the air conditioner from 2 PM to 4 PM. Calculate the optimal usage time, and if the user's emotion is joy, suggest extending the usage time of the appliances that are most entertaining."

[0652] This system not only allows users to use their home appliances efficiently, but also allows them to receive optimal electricity rate suggestions based on their emotions.

[0653] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0654] Step 1:

[0655] Collecting user input

[0656] The user uses the terminal to input the type of home appliance they want to use and the desired usage time.

[0657] Input: The user enters information such as "air conditioner" and "2:00 PM to 4:00 PM" into the application's input form.

[0658] Output: Data on the type of appliance and usage time entered.

[0659] Specific operation: The user opens the smartphone application, enters the type of home appliance and usage time into the input form, and presses the submit button.

[0660] Step 2:

[0661] Sending input information

[0662] The terminal transmits the information entered by the user to the server.

[0663] Input: Data on the type of appliance and usage time entered by the user.

[0664] Output: Data on the type of appliance and usage time sent to the server.

[0665] Specific operation: The terminal generates an HTTP request and sends the entered data to the server.

[0666] Step 3:

[0667] Calculating the usage time of home appliances

[0668] The server calculates the optimal time to use the appliance based on the type and usage time of the appliance received.

[0669] Input: Data on the type of appliance and usage time sent to the server.

[0670] Output: Optimal usage time period data.

[0671] How it works: The server runs an algorithm that takes into account past usage data and power consumption patterns to calculate the optimal time to use the air conditioner. For example, it might calculate that the best time to use the air conditioner is between 2:00 PM and 4:00 PM.

[0672] Step 4:

[0673] Emotion recognition by emotion engine

[0674] The server uses an emotion engine to recognize the user's emotion.

[0675] Input: User's facial image and voice data.

[0676] Output: The user's emotional state (e.g., happy, stressed).

[0677] Specific operation: The server inputs the user's facial image and voice data into the emotion engine to obtain the user's emotional state.

[0678] Step 5:

[0679] Proposal adjustment of electricity charges

[0680] The server adjusts the electricity rate proposal based on the recognition results of the emotion engine.

[0681] Input: Data on the user's emotional state and optimal usage times.

[0682] Output: Adjusted electricity tariff proposal data.

[0683] Specific operation: If the user shows emotions of joy, the server will suggest extending the usage time of entertainment appliances (e.g., televisions) and reducing the resulting electricity bill increase. On the other hand, if the user is feeling stressed, the server will suggest extending the usage time of relaxation appliances (e.g., massage chairs).

[0684] Step 6:

[0685] Notifying users of the results

[0686] The server sends the calculation results and the proposal to the terminal.

[0687] The terminal notifies the user of the received information.

[0688] Input: Optimal usage time and adjusted electricity rate suggestion data.

[0689] Output: A notification message to the user.

[0690] What it does: The device will use push notifications and in-app messages to inform the user that "The best time to use the air conditioner is between 2:00 PM and 4:00 PM. We also recommend extending the TV usage time."

[0691] (Application example 2)

[0692] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0693] Conventional electricity rate optimization systems based on home appliance usage information were unable to consider the user's emotional state, making it difficult to increase user satisfaction. Also, when optimizing the usage times of home appliances in stores, adjustments were not made based on the customer's emotions, making it difficult to improve the customer experience.

[0694] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0695] In this invention, the server includes means for calculating electricity charges based on a market-linked plan, means for receiving home appliance usage information from a user, means for calculating optimal usage time periods based on the received home appliance usage information, means for proposing the calculated optimal usage time periods and electricity charges to the user, means for recognizing the user's emotions using an emotion recognition engine, and means for adjusting the proposed electricity charge based on the recognized emotion. This makes it possible to propose optimal home appliance usage time periods and electricity charges according to the user's emotional state, thereby increasing user satisfaction. Furthermore, adjusting the usage time periods of home appliances in a store according to the customer's emotions can improve the customer experience.

[0696] A "market-linked plan" is a rate plan in which electricity rates are determined based on the trading price at the power exchange.

[0697] A "means for calculating electricity charges" is a device or program for calculating electricity charges based on a market-linked plan.

[0698] The "means for receiving home appliance usage information" is a device or program for receiving information such as the type of home appliance and usage time from the user as input.

[0699] The "means for calculating the optimum usage time period" is a device or program for calculating the optimum usage time period of the home appliance based on the received home appliance usage information.

[0700] "Means for making suggestions to users" refers to a device or program for notifying or displaying to users the calculated optimal usage time period and electricity charges.

[0701] An "emotion recognition engine" is an algorithm or program for recognizing a user's emotions.

[0702] The "means for recognizing emotions" is a device or program for detecting the emotions of a user using an emotion recognition engine.

[0703] A "means for adjusting an electricity rate offer" is a device or program for modifying an electricity rate offer based on recognized user sentiment.

[0704] The following system configuration will be described as an embodiment of the present invention.

[0705] System Configuration

[0706] This system consists of a server, a user terminal, an emotion recognition engine, an electricity rate calculation engine, and a home appliance control device. The server receives information on home appliance usage from users, calculates the optimal usage time, recognizes the user's emotions using the emotion recognition engine, and adjusts the electricity rate proposal.

[0707] Program processing

[0708] 1. Enter your appliance usage information:

[0709] The user terminal receives input from the user about the type of appliance and the usage time, and this information is sent to the server.

[0710] 2. Emotion recognition:

[0711] The server recognizes the user's emotions using an emotion recognition engine, which analyzes the user's image and audio data acquired from cameras and sensors to determine the user's emotional state.

[0712] 3. Calculate the best time of day:

[0713] The server calculates the optimal usage time period based on the received home appliance usage information using an electricity rate calculation engine, which calculates the electricity rate based on a market-linked plan.

[0714] 4. Proposed adjustment of electricity tariff:

[0715] The server adjusts the electricity bill suggestions based on the user's recognized emotions. For example, if the user is happy, it will extend the use of entertainment appliances, and if the user is stressed, it will extend the use of relaxation appliances.

[0716] 5. Notice to Users:

[0717] The server then notifies the user device of the calculated optimal usage time and the adjusted electricity rate, allowing the user to adjust their home appliance usage accordingly.

[0718] Hardware and software used

[0719] Hardware: Cameras, sensors, user devices (smartphones, smart glasses, etc.)

[0720] Software: Emotion recognition engine (EmotionEngine), electricity price calculation engine (ElectricityPricing)

[0721] Specific examples

[0722] For example, consider a scenario in which the usage times of lighting and air conditioning in a store are optimized. The store manager uses a smartphone to input the usage times of lighting and air conditioning. The server uses cameras in the store to recognize the customer's emotions and calculates the optimal usage times. If the customer is happy, the lighting usage time is extended to increase entertainment. If the customer is stressed, the air conditioning usage time is extended to promote relaxation.

[0723] Prompt Sentence Examples

[0724] Develop an application that optimizes lighting and air conditioning usage in stores and adjusts electricity tariff suggestions based on customer emotions: if a customer is happy, extend the use of entertainment appliances, and if a customer is stressed, extend the use of relaxation appliances.

[0725] In this way, it becomes possible to propose optimal times for using home appliances and electricity rates according to the user's emotional state, thereby increasing user satisfaction. Also, by adjusting the usage times of home appliances in the store according to the customer's emotions, it is possible to improve the customer experience.

[0726] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0727] Step 1:

[0728] The user terminal receives the type of home appliance and usage time from the user as input. Specifically, the user uses a smartphone or smart glasses to input the type of home appliance (e.g., lighting, air conditioner, etc.) they want to use and the usage time. This information is sent from the user terminal to the server. The input data is the type of home appliance and usage time, and the output data is the home appliance usage information sent to the server.

[0729] Step 2:

[0730] The server uses an emotion recognition engine to recognize the user's emotions. Specifically, the server inputs the user's image and voice data acquired by the camera or sensor into the emotion recognition engine to determine the user's emotional state. The input data is the user's image and voice data, and the output data is the recognized emotional state of the user.

[0731] Step 3:

[0732] The server uses an electricity rate calculation engine to calculate the optimal usage time based on the received home appliance usage information. Specifically, the server inputs the home appliance usage information and market-linked plan data into the electricity rate calculation engine to calculate the optimal usage time. The input data is the home appliance usage information and market-linked plan data, and the output data is the optimal usage time.

[0733] Step 4:

[0734] The server adjusts the electricity rate proposal based on the recognized user emotion. Specifically, the server uses the emotional state data from the emotion recognition engine to adjust the proposal of the electricity rate calculation engine. For example, if the user is happy, the server extends the use time of entertainment appliances, and if the user is stressed, the server extends the use time of relaxation appliances. The input data is the user's emotional state and the optimal use time period, and the output data is the adjusted electricity rate proposal.

[0735] Step 5:

[0736] The server notifies the user terminal of the calculated optimal usage time slot and the adjusted electricity rate. Specifically, the server sends the data of the optimal usage time slot and the adjusted electricity rate to the user terminal so that the user can check it. The input data is the optimal usage time slot and the adjusted electricity rate, and the output data is the suggested information displayed on the user terminal.

[0737] In this way, it becomes possible to propose optimal times for using home appliances and electricity rates according to the user's emotional state, thereby increasing user satisfaction. Also, by adjusting the usage times of home appliances in the store according to the customer's emotions, it is possible to improve the customer experience.

[0738] Example 3

[0739] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0740] Conventional electricity rate calculation systems can suggest optimal times for users to use home appliances, but do not make suggestions based on the user's emotions. As a result, they are unable to suggest optimal times to use home appliances based on the user's emotional state, making it difficult to improve user satisfaction. Furthermore, electricity rates are not calculated based on trading prices at the power exchange, meaning users are unable to obtain reference information for reducing their electricity bills.

[0741] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0742] In this invention, the server includes means for calculating an electricity rate based on a market-linked plan, means for receiving home appliance usage information from a user, means for calculating optimal usage time periods based on the received home appliance usage information, means for proposing the calculated optimal usage time periods and electricity rates to the user, means for recognizing the user's emotions, and means for proposing optimal home appliance usage time periods based on the recognized emotions. This makes it possible to propose optimal home appliance usage time periods according to the user's emotional state and calculate electricity rates based on the trading price at the power exchange.

[0743] A "market-linked plan" is a rate plan in which electricity rates are determined based on the trading price at the power exchange.

[0744] A "means for calculating electricity charges" is a device or program that has the function of calculating electricity charges according to the duration of use based on the trading price at the power exchange and the power consumption of home appliances.

[0745] The "means for receiving home appliance usage information from the user" refers to a device or program that provides an interface for the user to input the type of home appliance they wish to use and the usage time, and receives that information.

[0746] The "means for calculating the optimal usage time period" is a device or program that has the function of calculating the optimal time period for using the home appliance based on the received home appliance usage information and the trading price at the power exchange.

[0747] "Means for making suggestions to users" refers to a device or program for notifying or displaying to users the calculated optimal usage time period and electricity charges.

[0748] A "means for recognizing a user's emotions" is a device or program that has the function of analyzing the user's voice, facial expression, behavior, etc., and determining the user's emotional state.

[0749] The "means for suggesting optimal times to use home appliances based on emotions" is a device or program that has the function of suggesting optimal times to use home appliances based on the recognized emotional state of the user.

[0750] This invention is a system that calculates the optimal time periods for users to use home appliances and the electricity charges for those times, and then suggests these to the user.It also has a function to recognize the user's emotions and suggest times for using home appliances that correspond to those emotions.

[0751] Hardware and software used

[0752] Hardware: Servers, user devices (smartphones, tablets, PCs, etc.)

[0753] Software: Emotion recognition engine, power exchange data acquisition API, generative AI model

[0754] Specific operation of the system

[0755] 1. User Input

[0756] The user uses the terminal to input the type of appliance they want to use and the usage time.

[0757] Example: User types "Use air conditioning from 3pm to 6pm."

[0758] 2. Obtaining Power Exchange Data

[0759] The server calls the power exchange data acquisition API and obtains the power trading price for the specified time period.

[0760] Example: A server uses an API to retrieve electricity trading price data from 3pm to 6pm.

[0761] 3. Calculating electricity charges

[0762] The electricity charges for the specified time period are calculated based on the electricity trading price acquired by the server.

[0763] Example: The server calculates the electricity bill from 3:00 PM to 6:00 PM using the power consumption of the air conditioner and the acquired electricity trading price.

[0764] 4. Emotion recognition

[0765] The user's voice, facial expressions, and actions are input into the emotion recognition engine via the device's camera and microphone, and the user's emotions are recognized.

[0766] Example: When a user speaks into the device's camera, the emotion recognition engine analyzes the user's tone of voice and facial expressions and determines that the user is stressed.

[0767] 5. Recommendations for optimal usage times

[0768] The server suggests optimal times to use home appliances based on the electricity bill calculated by the server and the user's emotions.

[0769] Example: The server determines that the user is feeling stressed and suggests using a home appliance that contributes to relaxation (e.g., a massage chair).

[0770] 6. Presentation to Users

[0771] The server sends the calculation results and suggestions to the user's terminal and presents them to the user.

[0772] Example: The server calculates electricity charges and displays suggestions for using relaxation appliances on the user's smartphone.

[0773] Prompt Sentence Examples

[0774] "Calculate the electricity cost if the air conditioner is used from 3:00 PM to 6:00 PM, and suggest the use of appliances that contribute to relaxation when the user is feeling stressed."

[0775] In this way, the system can propose optimal times for using home appliances and electricity rates based on the user's input and emotions. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0776] Step 1:

[0777] The user uses the terminal to input the type of appliance they want to use and the usage time.

[0778] Input: The user inputs the type of appliance (e.g., air conditioner) and the usage time (e.g., 3:00 PM to 6:00 PM).

[0779] Specific behavior: The user opens the app on their device, accesses a form to enter the type of appliance and usage time, and enters the required information.

[0780] Output: The entered data on the type of appliance and usage time is sent to the server.

[0781] Step 2:

[0782] The server calls the power exchange data acquisition API and obtains the power trading price for the specified time period.

[0783] Input: The usage time entered by the user (e.g., 3:00 PM to 6:00 PM).

[0784] Specific operation: The server sends a request to the power exchange data acquisition API to acquire power exchange price data for the specified time period.

[0785] Output: The acquired electricity trading price data is saved on the server.

[0786] Step 3:

[0787] The electricity charges for the specified time period are calculated based on the electricity trading price acquired by the server.

[0788] Input: Obtained electricity trading price data and power consumption of home appliances (e.g., air conditioner power consumption 1.5kW).

[0789] Specific operation: The server calculates the electricity charges from 3:00 PM to 6:00 PM using the power consumption of the air conditioner and the acquired electricity trading price.

[0790] Output: The calculated electricity cost (e.g. 3 hours x 1.5kW x 20 yen = 90 yen) will be obtained.

[0791] Step 4:

[0792] The user's voice, facial expressions, and actions are input into the emotion recognition engine via the device's camera and microphone, and the user's emotions are recognized.

[0793] Input: User voice, facial expression, and behavioral data.

[0794] How it works: When a user speaks into the device's camera, the device sends audio and video data to an emotion recognition engine.

[0795] Output: The emotion recognition engine analyzes the user's emotional state (e.g., feeling stressed).

[0796] Step 5:

[0797] The server suggests optimal times to use home appliances based on the electricity bill calculated by the server and the user's emotions.

[0798] Input: Calculated electricity price and user's emotional state.

[0799] Specific operation: The server integrates the user's emotional data and electricity bill data and suggests the use of home appliances that contribute to relaxation (e.g., massage chairs).

[0800] Output: The suggestion is displayed as "Using the air conditioner from 3:00 PM to 6:00 PM will cost 90 yen, but since you are feeling stressed, we recommend using the massage chair."

[0801] Step 6:

[0802] The server sends the calculation results and suggestions to the user's terminal and presents them to the user.

[0803] Input: Calculation results and suggestions.

[0804] Specific operation: The server sends the calculation results and proposals to the user device.

[0805] Output: The user will receive a notification on their smartphone and will see the suggestions when they open the app.

[0806] (Application example 3)

[0807] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0808] Conventional energy management systems can optimize electricity rates based on electricity trading prices, but they cannot suggest optimal times to use home appliances based on user emotions. Furthermore, in physical stores, it is difficult to provide a comfortable environment that reflects customer emotions, making it difficult to improve customer satisfaction.

[0809] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means for calculating an electricity rate based on a market-linked plan, means for receiving home appliance usage information from a user, means for calculating optimal usage time periods based on the received home appliance usage information, means for proposing the calculated optimal usage time periods and electricity rates to the user, means for recognizing the user's emotions, means for proposing optimal home appliance usage time periods based on the recognized emotions, means for checking the usage status of home appliances in the store in real time, and means for setting optimal environments according to the emotions of customers. This makes it possible to optimize electricity rates, propose optimal home appliance usage time periods according to the emotions of users and customers, and provide a comfortable environment.

[0810] A "market-linked plan" is a rate plan in which electricity rates are determined based on the trading price at the power exchange.

[0811] "Means for calculating electricity charges" means a device or software for calculating electricity charges under a market-linked plan.

[0812] The "means for receiving home appliance usage information from the user" is an interface that allows the user to input the type of home appliance they wish to use and the usage time.

[0813] The "means for calculating the optimum usage time period" is a device or software that calculates the optimum usage time period based on the received home appliance usage information while taking into account the electricity trading price.

[0814] The "means for making suggestions to the user" is an interface for notifying the user of the calculated optimal usage time period and electricity charges.

[0815] A "means for recognizing user emotions" is a device or software that analyzes emotions from the user's voice, facial expressions, behavior, etc.

[0816] The "means for proposing optimal times to use home appliances based on emotions" is a device or software that calculates and proposes optimal times to use home appliances based on the user's recognized emotions.

[0817] The "means for checking the usage status of home appliances in a store in real time" refers to a device or software for monitoring the usage status of home appliances in a store in real time and notifying the manager.

[0818] The "means for optimally setting the environment in response to customer emotions" refers to a device or software that analyzes customer emotions and, based on that, optimizes the lighting, air conditioning, music, and other environmental settings within the store.

[0819] The following system configuration will be described as an embodiment of the present invention.

[0820] System Configuration

[0821] 1. Hardware:

[0822] Smartphone (iOS / ANDROID (registered trademark))

[0823] Smart glasses (e.g., Google Glass (registered trademark))

[0824] Head-mounted displays (e.g., Oculus Rift)

[0825] Robots (e.g. Pepper)

[0826] 2. Software:

[0827] Facial recognition API (e.g. Microsoft Azure Face API)

[0828] Emotion recognition API (e.g. Affectiva)

[0829] Power trading price API (e.g. EPEX SPOT API)

[0830] Database (e.g. Firebase)

[0831] Program processing

[0832] The server first receives information about the user's appliance usage. The user inputs the type of appliance they want to use and the time of use through a smartphone app. The server then uses an electricity trading price API to obtain real-time electricity prices. Based on this, the server calculates the optimal usage time and electricity rates and makes suggestions to the user.

[0833] In addition, the server uses face recognition and emotion recognition APIs to recognize the user's emotions. Smart glasses, head-mounted displays, and robots capture the user's facial expressions and voice and send them to the server. The server analyzes this data and recognizes the user's emotions.

[0834] Based on the recognized emotions, the server recalculates the optimal time to use home appliances and suggests them to the user. For example, if the user is feeling stressed, it will suggest using home appliances that contribute to relaxation (e.g., a massage chair or aroma diffuser).

[0835] Specific examples

[0836] As a concrete example, consider an energy management system in a brick-and-mortar store. The store manager uses a smartphone app to check the usage status of home appliances in the store and the optimization information of electricity charges in real time. Store staff wearing smart glasses analyze customers' facial expressions, and if the customer is relaxed, they change the lighting in the store to warm colors and play relaxation music.

[0837] Prompt Sentence Examples

[0838] Input customer facial expression data and recognize their emotions. Then, retrieve the current electricity price from the electricity trading price API and calculate the optimal usage time for home appliances based on the customer's emotions. Display the calculation results on a smartphone app, smart glasses, a head-mounted display, or a robot.

[0839] In this way, it is possible to optimize electricity charges, propose optimal times for using home appliances based on the emotions of users and customers, and provide a comfortable environment.

[0840] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0841] Step 1:

[0842] The user uses a smartphone app to input the type of appliance they want to use and the amount of time they want to use it. The input information is the type of appliance and the amount of time they want to use it. This information is then sent to the server.

[0843] Step 2:

[0844] The server uses the electricity trading price API to obtain real-time electricity prices. The input is the current time, and the output is the electricity price corresponding to that time. Based on this data, the server calculates the electricity charges for the appliances during their usage time.

[0845] Step 3:

[0846] The server then proposes the optimal time slots and electricity rates to the user. The proposal details the optimal time slots and electricity rates for those times. This information is displayed on the smartphone app.

[0847] Step 4:

[0848] When a user uses smart glasses, a head-mounted display, or a robot, these devices capture the user's facial expressions and voice. The input is the user's facial expression data and voice data, and this data is sent to the server.

[0849] Step 5:

[0850] The server uses a facial recognition API and an emotion recognition API to analyze the user's emotions. The input is facial expression data and voice data, and the output is analyzed emotion data. The server recognizes the user's emotions based on this data.

[0851] Step 6:

[0852] The server recalculates the optimal time periods for using appliances based on the recognized emotions. The input is emotion data and appliance usage information, and the output is the optimal usage time period based on the emotion. For example, if the user is feeling stressed, the server suggests using appliances that contribute to relaxation.

[0853] Step 7:

[0854] The server then recalculates the optimal usage time and electricity rates and proposes them to the user. The proposals include optimal usage time and electricity rates for each emotion. This information is displayed on a smartphone app, smart glasses, a head-mounted display, and a robot.

[0855] Step 8:

[0856] To check the usage status of home appliances in the store in real time, the server uses a database to monitor the usage status of home appliances. The input is the usage data of the home appliances and the output is the real-time usage status. This allows the store manager to check the usage status of home appliances through a smartphone app.

[0857] Step 9:

[0858] The server optimizes lighting, air conditioning, music, and other environmental settings to optimize the environment according to the customer's emotions. The input is the customer's emotional data, and the output is the optimized environmental settings. For example, if the customer is relaxing, the lighting is changed to warm colors and relaxation music is played.

[0859] Through the above steps, it is possible to optimize electricity charges, propose optimal times for using home appliances based on the emotions of users and customers, and provide a comfortable environment.

[0860] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0861] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0862] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0863] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0864] [Second embodiment]

[0865] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0866] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0867] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0868] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0869] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0870] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0871] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0872] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0873] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0874] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0875] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0876] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0877] "Example 1"

[0878] One aspect of the present invention is a system for calculating electricity rates based on a market-linked plan. This system acquires trading price information from the power exchange and calculates electricity rates based on that information. Specifically, the system calculates the unit price of electricity based on the trading price information and uses that to calculate the electricity rate.

[0879] "Example 2"

[0880] Another aspect of the present invention is a system that receives information about the use of home appliances from a user. This system receives input information from the user and calculates the usage time periods for the home appliances based on that information. Specifically, the system has the user input the type of home appliance they want to use and the usage time, and calculates the optimal usage time period based on that information.

[0881] "Example 3"

[0882] Furthermore, one embodiment of the present invention is a system that proposes the calculated optimal usage time periods and electricity rates to users. This system presents the calculated optimal usage time periods and electricity rates to users, and uses them as reference information for users to reduce their electricity rates. Specifically, when a user inputs the type of home appliance they want to use and the usage time, for example, "I will use the air conditioner from 3:00 PM to 6:00 PM," the system presents the user with an electricity rate calculated based on the trading price at the power exchange for that time period.

[0883] The processing flow of each embodiment will be described below.

[0884] "Example 1"

[0885] Step 1: Obtain trading price information from the power exchange.

[0886] Step 2: Calculate the unit price of electricity based on the acquired transaction price information.

[0887] Step 3: Calculate the electricity bill using the calculated unit price of electricity.

[0888] "Example 2"

[0889] Step 1: Receive input from the user about the type of appliance they want to use and the usage time.

[0890] Step 2: Calculate the optimal usage time based on the received input information.

[0891] "Example 3"

[0892] Step 1: The user enters the type of appliance they want to use and the usage time.

[0893] Step 2: Calculate the electricity rate based on the trading price on the power exchange for that time period.

[0894] Step 3: Present the calculated electricity bill to the user.

[0895] Example 1

[0896] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0897] Conventional electricity rate calculation systems have difficulty reflecting trading price information from the power exchange in real time, making it impossible to provide users with optimal electricity rates. Furthermore, they lacked the functionality to suggest optimal usage times based on users' home appliance usage information, making it difficult for users to use electricity efficiently.

[0898] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0899] In this invention, the server includes means for acquiring trading price information from an electricity exchange, means for storing the acquired trading price information in a database, means for calculating the unit price of electricity based on the stored trading price information, means for calculating the user's electricity bill using the calculated unit price of electricity, means for receiving home appliance usage information from the user, means for calculating optimal usage time periods based on the received home appliance usage information, and means for proposing the calculated optimal usage time periods and electricity bill to the user. This enables calculation of electricity bills that reflect trading price information in real time, and further enables efficient power use by proposing optimal usage time periods to the user.

[0900] An "electricity exchange" is a market where electricity is bought and sold, and an institution that provides trading price information.

[0901] "Trading price information" is information relating to the price of electricity traded at the power exchange.

[0902] A "database" is a system for efficiently storing, managing, and retrieving data.

[0903] The "unit price of electricity" is the unit price set for a certain amount of electricity.

[0904] A "user" is an individual or corporation that consumes electricity and is the entity that uses this system.

[0905] "Home appliance usage information" is information about the types of home appliances used by the user and the duration of use.

[0906] The "optimal usage time period" is the time period recommended for efficient power consumption.

[0907] The "means for proposing" is a method or device for notifying the user of the calculated optimal usage time period and electricity charges.

[0908] MODE FOR CARRYING OUT THE INVENTION

[0909] This invention relates to a system for calculating electricity rates based on a market-linked plan, which acquires trading price information from the power exchange and calculates electricity rates based on that information. Specific embodiments of this system are described below.

[0910] Hardware and software used

[0911] 1. Server

[0912] Hardware: General server equipment

[0913] Software: API clients, database management systems (e.g., MySQL, PostgreSQL), scripting languages ​​(e.g., Python, JavaScript)

[0914] 2. Terminal

[0915] Hardware: The computer or smartphone you use

[0916] Software: Web browser, mobile application

[0917] 3. Users

[0918] A user is an individual or a corporation that consumes electricity and is the entity that uses this system.

[0919] Explanation of program processing

[0920] 1. Obtaining trading price information

[0921] The server sends a request to the power exchange's API to obtain the latest trading price information.

[0922] Specific operation: The server sends an HTTP GET request to the API endpoint of the power exchange and receives trading price information in JSON format.

[0923] 2. Saving of transaction price information

[0924] The server stores the acquired transaction price information in a database.

[0925] Specific operation: The server parses the received JSON data and executes an insert query into the corresponding table in the MySQL database.

[0926] 3. Calculation of electricity unit price

[0927] The server calculates the unit price of electricity based on the stored transaction price information.

[0928] Specific operation: The server executes a Python script, retrieves trading price information from the database, and calculates the unit price of electricity based on a specific algorithm.

[0929] 4. Calculation of user's electricity bill

[0930] The server calculates the electricity charge for the user using the calculated electricity unit price.

[0931] Specific operation: The server retrieves the user's power consumption data from the database and multiplies it by the calculated unit price to calculate the electricity bill.

[0932] 5. Receiving information on home appliance usage

[0933] The server receives home appliance usage information from the user.

[0934] Specific operation: The user enters the type of home appliance they want to use and the usage time through a web browser or mobile application, and sends the information to the server.

[0935] 6. Calculating the best time to use

[0936] The server calculates the optimal usage time period based on the received home appliance usage information.

[0937] Specific operation: The server executes an algorithm to calculate the optimal usage time based on the electricity rate and appliance usage information.

[0938] 7. Notification of Proposal

[0939] The server then proposes the calculated optimal usage time period and electricity rate to the user.

[0940] Specific operation: The server notifies the user's device of the calculation results, and the user confirms the proposal through a web browser or mobile application.

[0941] Examples and prompts

[0942] 1. The server obtains trading price information for "2023-10-01" from the power exchange API.

[0943] 2. The server stores the acquired trading price information in a MySQL database.

[0944] 3. The server uses a Python script to calculate the unit price of electricity for "2023-10-01" from the trading price information.

[0945] 4. The server calculates the user's electricity bill (e.g., 1,000 yen) using the user's power consumption data (e.g., 100 kWh) and the calculated unit price (e.g., 10 yen / kWh).

[0946] Prompt Sentence Examples

[0947] "Get the latest trading price information from the power exchange API and store it in a MySQL database. Then, use a Python script to calculate the unit price of electricity and calculate the electricity bill based on the user's energy consumption data."

[0948] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0949] Step 1:

[0950] Obtaining trading price information

[0951] The server sends a request to the power exchange's API to obtain the latest trading price information.

[0952] Input: Power Exchange API endpoint URL

[0953] Output: Trade price information in JSON format

[0954] Specific operation: The server sends an HTTP GET request to the API endpoint of the electricity exchange and receives trading price information in JSON format as a response. For example, use the requests library to send a request to https: / / api.electricity-market.com / prices.

[0955] Step 2:

[0956] Stores trading price information

[0957] The server stores the acquired transaction price information in a database.

[0958] Input: Trade price information in JSON format

[0959] Output: Transaction price information stored in the database

[0960] What happens: The server parses the received JSON data, extracts the required fields (e.g., datetime, price), and then uses the pymysql library to connect to a MySQL database and execute a query like INSERT INTO prices (datetime, price) VALUES ('2023-10-01 00:00:00', 10.5).

[0961] Step 3:

[0962] Calculating electricity unit prices

[0963] The server calculates the unit price of electricity based on the stored transaction price information.

[0964] Input: Transaction price information stored in the database

[0965] Output: Calculated electricity unit price

[0966] Specific operation: The server executes a Python script to obtain trading price information from the database. For example, it uses the pymysql library to execute a query such as SELECT price FROM prices WHERE datetime = '2023-10-01 00:00:00', and calculates the electricity price using a specific algorithm (e.g., average, weighted average) based on the obtained price information.

[0967] Step 4:

[0968] Calculating the user's electricity bill

[0969] The server calculates the electricity charge for the user using the calculated electricity unit price.

[0970] Input: User's power consumption data, calculated electricity rate

[0971] Output: Calculated user electricity charges

[0972] Specific operation: The server retrieves the user's power consumption data from the database. For example, using the pymysql library, it executes a query such as SELECT consumption FROM user_data WHERE user_id = 1, multiplies the retrieved power consumption data (e.g., 100 kWh) by the calculated unit price (e.g., 10 yen / kWh), and calculates the user's electricity bill (e.g., 1,000 yen).

[0973] Step 5:

[0974] Receiving home appliance usage information

[0975] The server receives home appliance usage information from the user.

[0976] Input: Home appliance usage information entered by the user (type of appliance, usage time)

[0977] Output: Appliance usage information stored on the server

[0978] Specific operation: The user inputs the type of appliances they want to use and the usage time through a web browser or mobile application, and sends the information to the server. The server then stores the received information in a database.

[0979] Step 6:

[0980] Calculating the best time to use

[0981] The server calculates the optimal usage time period based on the received home appliance usage information.

[0982] Input: Home appliance usage information, electricity rate

[0983] Output: Best time to use

[0984] Specific operation: The server runs an algorithm to calculate the optimal usage time based on the electricity rate and appliance usage information, for example, to avoid peak hours.

[0985] Step 7:

[0986] Proposal Notification

[0987] The server then proposes the calculated optimal usage time period and electricity rate to the user.

[0988] Input: Optimal usage time, calculated electricity price

[0989] Output: User notification

[0990] Specific operation: The server notifies the user of the calculation results, and the user confirms the proposal through a web browser or mobile application, for example, by sending a push notification or email.

[0991] (Application example 1)

[0992] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0993] Conventional electricity bill calculation systems based on market-linked plans only suggest optimal usage times based on the user's appliance usage information, but do not optimize power consumption in real time. Therefore, there is a need to further reduce electricity costs by obtaining trading price information from the power exchange in real time and optimizing power consumption.

[0994] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0995] In this invention, the server includes means for calculating electricity charges based on a market-linked plan, means for receiving home appliance usage information from a user, means for calculating optimal usage time periods based on the received home appliance usage information, means for proposing the calculated optimal usage time periods and electricity charges to the user, means for acquiring trading price information from an electricity exchange in real time, means for optimizing electricity consumption based on the acquired price information, and means for reducing electricity consumption during high price time periods and increasing electricity consumption during low price time periods, thereby enabling optimization of electricity consumption in real time.

[0996] A "market-linked plan" is a rate plan in which electricity rates are determined based on the trading price at the power exchange.

[0997] A "means for calculating electricity rates" is a method or device for calculating electricity rates based on a market-linked plan.

[0998] The "means for receiving home appliance usage information from a user" refers to a method or device for acquiring information such as the type of home appliance used by the user and the duration of use.

[0999] The "means for calculating the optimum usage time period" is a method or device for calculating the optimum usage time period for optimizing power consumption based on the received home appliance usage information.

[1000] The "means for making suggestions to the user" refers to a method or device for notifying the user of the calculated optimal usage time period and electricity charges.

[1001] The "means for obtaining trading price information from the power exchange in real time" refers to a method or device for obtaining trading price information from the power exchange in real time.

[1002] "Means for optimizing electricity consumption" refers to methods and devices for efficiently managing and adjusting electricity consumption based on acquired trading price information.

[1003] "Means for reducing electricity consumption during high price range hours and increasing electricity consumption during low price range hours" refers to a method or device for reducing electricity consumption during high electricity price range hours and increasing electricity consumption during low electricity price range hours.

[1004] To implement the present invention, the following system configuration and program are required.

[1005] System Configuration

[1006] 1. Server: Calculates electricity rates based on market-linked plans and obtains real-time trading price information from the power exchange.

[1007] 2. Device: Receives information on home appliance usage from the user, calculates the optimal usage time, and makes suggestions to the user.

[1008] 3. Network: A communication method for sending and receiving data between the server and the terminal.

[1009] Program processing explanation

[1010] Hardware

[1011] Server: A computer with a powerful processor and large memory capacity.

[1012] Terminal: A device with a user interface, such as a smartphone or tablet.

[1013] Network: Internet connection.

[1014] software

[1015] Python: The primary implementation language for the program.

[1016] Requests library: A library for obtaining electricity exchange trading price information from the API.

[1017] JSON Data Processing: The format for parsing the retrieved data.

[1018] Data processing and calculation

[1019] 1. The server obtains real-time trading price information from the power exchange using an API and saves the data in JSON format.

[1020] 2. The server calculates the electricity charges based on the market-linked plan. Based on the acquired trading price information, it calculates the unit price of the electricity charge and combines it with the user's electricity consumption data to calculate the total electricity charge.

[1021] 3. The device receives information about the user's use of the appliance, including the type of appliance they want to use and the duration of their use.

[1022] 4. The server calculates the optimal usage time based on the received appliance usage information. To do this, it selects the time period when the electricity trading price is low and suggests using the appliance during that time period.

[1023] 5. The device will then suggest the optimal usage time and electricity rates to the user, using the notification function to provide information to the user.

[1024] 6. The server optimizes power consumption based on the acquired price information, reducing power consumption during high-price periods and increasing power consumption during low-price periods.

[1025] Specific examples

[1026] For example, if this system is used on a factory production line, electricity costs can be reduced by reducing machine operation during times when prices on the electricity exchange are high and increasing operation during times when prices are low.

[1027] Prompt Sentence Examples

[1028] "Create a Python program that obtains real-time trading price information from the power exchange and optimizes power consumption within the factory. The program should have the ability to reduce power consumption during times when prices are high and increase power consumption during times when prices are low."

[1029] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1030] Step 1:

[1031] The server obtains trading price information from the power exchange in real time. Specifically, it obtains trading price information from the power exchange using an API and saves it in JSON format. The input is the API endpoint, and the output is the obtained trading price information.

[1032] Step 2:

[1033] The server calculates the electricity bill based on the market-linked plan. Based on the acquired transaction price information, it calculates the unit price of the electricity bill and combines it with the user's electricity consumption data to calculate the total electricity bill. The input is the transaction price information and the user's electricity consumption data, and the output is the total electricity bill.

[1034] Step 3:

[1035] The terminal receives home appliance usage information from the user. The user inputs the type of home appliance they want to use and the usage time, and the terminal receives it. The input is the user's home appliance usage information, and the output is the received home appliance usage information.

[1036] Step 4:

[1037] The server calculates the optimal usage time based on the received appliance usage information. It selects a time period when the electricity trading price is low and suggests using the appliance during that time period. The input is appliance usage information and trading price information, and the output is the optimal usage time period.

[1038] Step 5:

[1039] The terminal proposes the calculated optimal usage time and electricity rate to the user. The notification function is used to provide information to the user. The input is the optimal usage time and electricity rate, and the output is a notification to the user.

[1040] Step 6:

[1041] The server optimizes power consumption based on the acquired price information. It reduces power consumption during high-price periods and increases power consumption during low-price periods. The input is trading price information, and the output is the optimized power consumption pattern.

[1042] Example 2

[1043] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1044] Conventional electricity rate optimization systems based on home appliance usage information have difficulty efficiently processing the information entered by users and calculating the optimal usage time periods. Furthermore, there was a lack of a means to quickly provide the results of the calculations to users, resulting in low user convenience. Furthermore, there was a need for a system that could comprehensively calculate electricity rates based on market-linked plans and propose optimal usage time periods.

[1045] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1046] In this invention, the server includes means for receiving home appliance usage information from a user, means for storing the received home appliance usage information in a database, means for calculating optimal usage time periods based on the stored data, means for proposing the calculated optimal usage time periods and electricity rates to the user, means for displaying the calculation results to the user, and means for calculating electricity rates based on a market-linked plan. This makes it possible to efficiently process the home appliance usage information entered by the user and quickly provide optimal usage time periods and electricity rates.

[1047] A "market-linked plan" is a rate plan in which electricity rates are determined based on the trading price at the power exchange.

[1048] "Means for calculating electricity charges" means algorithms or software for calculating electricity charges based on market-linked plans.

[1049] "Means for receiving home appliance usage information from users" refers to interfaces or devices for receiving information such as the type of home appliance and usage time entered by the user.

[1050] "Means for storing in a database" refers to software or hardware for storing received home appliance usage information in a relational database management system (RDBMS) or similar.

[1051] "Means for calculating optimal usage times" refers to algorithms or software that use stored data to analyze past usage data and power consumption patterns to calculate optimal usage times.

[1052] "Means for proposing calculation results to users" refers to an interface or device for notifying users of the calculated optimal usage time and electricity charges.

[1053] "Means for displaying the calculation results to the user" refers to software or hardware for displaying the calculation results on the user's terminal.

[1054] MODE FOR CARRYING OUT THE INVENTION

[1055] This invention is a system that receives information on the use of home appliances from a user, calculates the optimal usage time period and electricity charges based on that information, and proposes them to the user. A specific embodiment of this system will be described below.

[1056] System configuration

[1057] This system consists of three main components: a server, a terminal, and a user.

[1058] 1. Server

[1059] The server provides a means for receiving appliance usage information from users, which is an interface accessible through a web browser or a mobile application.

[1060] The server stores the received appliance usage information in a database, which uses a relational database management system (RDBMS) such as MySQL or PostgreSQL.

[1061] The server calculates the optimal usage time based on the stored data, using programming languages ​​such as Python and R and leveraging data analysis libraries (e.g., Pandas and NumPy).

[1062] The server has a means to propose the calculated optimal usage time and electricity rate to the user. This proposal is made in real time.

[1063] The server has a means to send the calculation results to the terminal for display to the user. This transmission is securely performed using the HTTPS protocol.

[1064] 2. Terminal

[1065] The device sends the home appliance usage information entered by the user to the server. This transmission is securely transmitted using the HTTPS protocol.

[1066] The device then displays the calculation results received from the server to the user, for example, displaying "The best time to use the device is between 7:00 PM and 9:00 PM" on the mobile application screen.

[1067] 3. Users

[1068] Users use a web browser or mobile application to input the type of appliance they want to use and the time they want to use it. For example, if a user wants to use an air conditioner, they enter "air conditioner" and "from 18:00 to 22:00."

[1069] Specific examples

[1070] As a specific example, consider the case where a user wants to use an air conditioner.

[1071] 1. A user opens the mobile application and enters "air conditioning" and "18:00 to 22:00."

[1072] 2. The device sends this information to the server using the HTTPS protocol.

[1073] 3. The server stores the received information in the "user_requests" table.

[1074] 4. The server uses Python's Pandas library to analyze past usage data and power consumption patterns and calculate the optimal usage time.

[1075] 5. The server sends the calculation result "The best time is between 19:00 and 21:00" to the terminal.

[1076] 6. The device displays the result on the mobile application screen and notifies the user.

[1077] Prompt Sentence Examples

[1078] Example prompts to input to a generative AI model:

[1079] A user wants to use the air conditioner. The usage time is entered as "18:00 to 22:00." Please calculate the optimal usage time based on past usage data and power consumption patterns.

[1080] In this way, a system is realized in which the server, terminal, and user work together to calculate the optimal time periods for using home appliances and provide the results to the user.

[1081] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1082] Step 1:

[1083] The user inputs the home appliance usage information.

[1084] Users use a web browser or mobile application to input the type of appliance they want to use and the time they want to use it. For example, if a user wants to use an air conditioner, they enter "air conditioner" and "from 18:00 to 22:00." The input information is saved in an input field on the device.

[1085] Step 2:

[1086] The terminal sends the input information to the server.

[1087] The device sends the appliance usage information entered by the user to the server using the HTTPS protocol. Specifically, it converts the entered information into JSON format and sends a POST request to the server's API endpoint. The input is the type of appliance and usage time, and the output is the status of completion of transmission to the server.

[1088] Step 3:

[1089] The server stores the input information in a database.

[1090] The server stores the received appliance usage information in a database. Specifically, the server parses the received JSON data and stores it in a relational database management system (RDBMS) such as MySQL or PostgreSQL. The input is the received appliance usage information, and the output is the status of completion of saving to the database.

[1091] Step 4:

[1092] The server calculates the optimal time of day for use based on the stored data.

[1093] The server calculates the optimal usage time based on the stored data. Specifically, it uses Python's Pandas library to analyze past usage data and power consumption patterns to calculate the optimal usage time. The input is the appliance usage information and past usage data stored in the database, and the output is the optimal usage time.

[1094] Step 5:

[1095] The server sends the calculation results to the terminal.

[1096] The server sends the calculation result to the terminal. Specifically, it converts the calculated optimal usage time slot into JSON format and sends it to the terminal using the HTTPS protocol. The input is the calculation result of the optimal usage time slot, and the output is the status of completion of transmission to the terminal.

[1097] Step 6:

[1098] The terminal displays the calculation results to the user.

[1099] The terminal displays the calculation results received from the server to the user. Specifically, it displays "The best time to use the device is from 19:00 to 21:00" on the mobile application screen. The input is the calculation result of the best time to use the device received from the server, and the output is the content displayed to the user.

[1100] (Application example 2)

[1101] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1102] Conventional systems for calculating optimal usage times based on information on home appliance usage only cover information on home appliance usage, and are not compatible with optimizing charging schedules for autonomous vehicles. This makes it difficult for users to calculate optimal charging times based on autonomous vehicle operation schedules and improve energy management efficiency.

[1103] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for calculating an electricity rate based on a market-linked plan, means for receiving home appliance usage information from a user, means for calculating an optimal usage time period based on the received home appliance usage information, means for proposing the calculated optimal usage time period and electricity rate to the user, means for receiving operation schedule information from the user and calculating an optimal charging time period, and means for notifying the user of the calculated optimal charging time period. This enables the user to calculate the optimal charging time based on the operation schedule of the autonomously driving vehicle and improve the efficiency of energy management.

[1104] A "market-linked plan" is a rate plan in which electricity rates are determined based on the trading price on the electricity exchange.

[1105] A "means for calculating electricity charges" is a device or program for calculating electricity charges based on a market-linked plan.

[1106] "Home appliance usage information" is information about the type of home appliance that the user wants to use and the duration of use.

[1107] The "means for calculating the optimum use time period" is a device or program for calculating the optimum use time period of the home appliance based on the received home appliance use information.

[1108] "Means for making suggestions to users" refers to a device or program for notifying or displaying to users the calculated optimal usage time period and electricity charges.

[1109] "Operation schedule information" refers to information such as departure time, arrival time, and destination that is input by the user regarding the operation of an autonomous vehicle.

[1110] The "means for calculating the optimal charging time slot" is a device or program for calculating the optimal charging time slot for an autonomously driven vehicle based on the received operation schedule information.

[1111] The "notification means" is a device or program for notifying the user of the calculated optimal charging time period.

[1112] A system for implementing the present invention includes a server, a user terminal, and an autonomous vehicle. The server includes the following means:

[1113] 1. Means for calculating electricity tariffs under market-linked plans

[1114] 2. Means of receiving information on home appliance usage from users

[1115] 3. A method for calculating optimal usage times based on received appliance usage information

[1116] 4. A method for proposing the calculated optimal usage time and electricity rates to users

[1117] 5. A means to receive schedule information from users and calculate the optimal charging time

[1118] 6. A method for notifying users of the calculated optimal charging time

[1119] Program processing explanation

[1120] The server is implemented using a programming language such as Python. It receives information about home appliance usage and operation schedules sent from user devices and calculates optimal usage and charging times based on this information.

[1121] Specifically, the server performs the following process.

[1122] 1. Calculation of electricity charges:

[1123] The server obtains the trading price of the power exchange based on the market-linked plan and calculates the electricity tariff, which is calculated by obtaining the data of the power exchange through API.

[1124] 2. Receiving and processing appliance usage information:

[1125] The system receives information about home appliance usage (the type of appliance you want to use and the time of use) sent from the user's device and calculates the optimal usage time period, using an AI model to avoid peak hours of power consumption.

[1126] 3. Receiving and processing schedule information:

[1127] The system receives scheduled flight information (departure time, arrival time, destination) sent from the user's device and calculates the optimal charging time. The calculation uses an AI model to determine the optimal charging time based on the flight schedule.

[1128] 4. Notice:

[1129] The system notifies the user of the calculated optimal usage and charging times. Notifications are sent to the user's device via push notification or email.

[1130] Specific examples

[1131] For example, a user enters the following information into a smartphone app:

[1132] Departure time: 2023-10-01 08:00:00

[1133] Arrival time: 2023-10-01 10:00:00

[1134] Destination: Office

[1135] Based on this information, the server calculates the optimal charging time and notifies the driver to start charging two hours before departure (2023-10-01 06:00:00).

[1136] Example prompts to input to a generative AI model:

[1137] A user has entered a scheduled trip for an autonomous vehicle. The departure time is 2023-10-01 08:00:00, the arrival time is 2023-10-01 10:00:00, and the destination is Office. Based on this information, please calculate the optimal charging time.

[1138] In this way, it is possible to calculate the optimal charging time based on the user's driving schedule and improve energy management efficiency.

[1139] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1140] Step 1:

[1141] Users use a smartphone app to input information about their appliance usage and scheduled trips. The information includes the type of appliance they want to use, the duration of use, departure time, arrival time, and destination. This information is then sent from the user's device to the server.

[1142] Step 2:

[1143] The server receives the appliance usage information sent from the user's device. The received information includes the type of appliance desired to be used and the usage time. The server processes the data based on this information to calculate the optimal usage time period.

[1144] Step 3:

[1145] The server obtains the trading price from the power exchange based on the market-linked plan. The server calculates the electricity charges based on the acquired trading price data. The calculation obtains the data from the power exchange through an API and takes into account real-time price fluctuations.

[1146] Step 4:

[1147] The server calculates the optimal usage time based on the received appliance usage information and acquired transaction price data. The calculation uses an AI model to avoid peak hours of electricity consumption. Specifically, it applies an algorithm to avoid peak hours of electricity consumption.

[1148] Step 5:

[1149] The server then calculates the optimal usage time and electricity rates and proposes them to the user. The proposals are sent to the user's device via push notification or email. The user can then check the proposed optimal usage time and plan their use of home appliances.

[1150] Step 6:

[1151] The server receives the scheduled vehicle information sent from the user's device. The received information includes departure time, arrival time, and destination. The server processes this information to calculate the optimal charging time slot.

[1152] Step 7:

[1153] The server calculates the optimal charging time based on the received flight schedule information. For the calculation, an AI model is used to determine the optimal charging time based on the flight schedule. Specifically, an algorithm is applied to start charging two hours before departure.

[1154] Step 8:

[1155] The server notifies the user of the calculated optimal charging time slot. The notification is sent to the user's device via push notification or email. The user can check the optimal charging time slot and plan charging for their autonomous vehicle.

[1156] In this way, it is possible to calculate optimal usage and charging time periods based on the user's home appliance usage information and operation schedule information, thereby making it possible to improve the efficiency of energy management.

[1157] Example 3

[1158] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1159] Conventional electricity bill calculation systems made it difficult for users to know the optimal time to use their home appliances, making it difficult to effectively reduce electricity bills. Furthermore, because real-time electricity bill calculations were not based on the trading price of the power exchange, it was not possible to suggest the optimal time for users to use their appliances. This often resulted in users paying high electricity bills and made it difficult to use energy efficiently.

[1160] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1161] In this invention, the server includes means for receiving home appliance usage information from a user, means for acquiring trading price data from an electricity exchange, means for processing the acquired trading price data and calculating the electricity rate for a specified time period, means for proposing the calculated optimal usage time period and electricity rate to the user, and means for presenting the calculation result to the user. This allows the user to know the optimal time period for using the home appliance and the electricity rate for that time period in real time, thereby effectively reducing electricity charges.

[1162] "Means for receiving home appliance usage information from users" refers to a device or software that provides an interface for users to input the type of home appliance they wish to use and the usage time, and has the function of sending that information to a server.

[1163] "Means for obtaining trading price data from the power exchange" refers to a device or software that has the function of accessing the API of the power exchange and obtaining the latest trading price data.

[1164] "Means for processing the acquired transaction price data and calculating the electricity charges for the specified time period" refers to a device or software that has the function of analyzing the acquired transaction price data and calculating the electricity charges for the time period specified by the user.

[1165] "Means for proposing to users the calculated optimal usage times and electricity rates" refers to a device or software that has the function of presenting to users the optimal usage times for home appliances and the electricity rates for those times based on the calculation results.

[1166] "Means for presenting calculation results to the user" refers to a device or software that has the function of visually or audibly presenting the calculated electricity charges and the optimal time periods for use to the user.

[1167] MODE FOR CARRYING OUT THE INVENTION

[1168] This invention is a system that calculates the optimal time periods for users to use home appliances and the electricity rates for those times, and then suggests these to users.The system receives information about the use of home appliances from users, acquires trading price data from the power exchange, processes the acquired data to calculate the electricity rate, and presents the results to the user.

[1169] Hardware and software used

[1170] The system uses the following hardware and software:

[1171] Server: A computer system for performing data processing and calculations.

[1172] Terminal: The device through which a user enters information (e.g., smartphone, tablet, PC).

[1173] Python: A programming language used to develop programs.

[1174] Pandas library: A Python library used for data processing and analysis.

[1175] Requests library: A Python library used to retrieve data from the power exchange API.

[1176] Program processing

[1177] The server receives input from the user. When the user inputs the type of appliance they want to use and the time they want to use it, the server retrieves the latest trading price data from the power exchange's API. Based on this data, the server calculates the electricity rate for the specified time period. The calculation is done by multiplying the electricity trading price by the power consumption of the appliance.

[1178] Specific examples

[1179] For example, if a user enters "I want the air conditioning to run from 3pm to 6pm," the server will do the following:

[1180] 1. The user enters "Air conditioner, 15:00-18:00" into the device's input form.

[1181] 2. The device sends this input data to the server.

[1182] 3. The server uses the Requests library to send a request to the power exchange API.

[1183] 4. The server receives the quote data returned from the API and converts it into a data frame using Pandas.

[1184] 5. The server extracts the trading prices for the specified time period (15:00-18:00) from the data frame.

[1185] 6. The server calculates the electricity bill by multiplying the air conditioner's power consumption (for example, 1.5kW) by the transaction price.

[1186] 7. The server generates the calculation result: "If you use the air conditioner from 3:00 pm to 6:00 pm, the electricity bill will be XX yen."

[1187] 8. The server sends this message to the terminal.

[1188] 9. The terminal displays a message to the user.

[1189] Prompt Sentence Examples

[1190] Example prompts to input to a generative AI model:

[1191] If a user inputs that they want to use the air conditioner from 3:00 PM to 6:00 PM, create a program that calculates the electricity rate based on the electricity exchange price for that time period and displays it to the user.

[1192] In this way, the user can know the optimal usage time period and electricity charges, and can use this information as reference information to reduce electricity charges. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1193] Program processing flow

[1194] Step 1: Accepting User Input

[1195] The user uses the terminal to input the type of appliance they want to use and the time they want to use it. For example, the user can input "I want to use the air conditioner from 3:00 PM to 6:00 PM."

[1196] Specific behavior:

[1197] Input: The user enters "Air conditioner, 15:00-18:00" into the input form on the device.

[1198] Output: The terminal sends this input data to the server.

[1199] Step 2: Obtaining electricity trading price data

[1200] The server retrieves the latest trading price data from the power exchange API, which is used to calculate the electricity price for the specified time period.

[1201] Specific behavior:

[1202] Input: The server receives the home appliance usage information sent by the user.

[1203] Data processing: The server uses the Requests library to send requests to the power exchange API.

[1204] Output: The server receives the quote data returned from the API.

[1205] Step 3: Process the data and calculate the electricity cost

[1206] The server processes the acquired trading price data using the Pandas library and calculates the electricity rate for the specified time period by multiplying the power consumption of the appliances by the trading price.

[1207] Specific behavior:

[1208] Input: The server receives the trading price data obtained from the API.

[1209] Data processing: The server uses Pandas to convert the trading price data into a data frame.

[1210] Data calculation: The server extracts the trading price for the specified time period (15:00-18:00) from the data frame, multiplies it by the air conditioner's power consumption (for example, 1.5 kW), and calculates the electricity bill.

[1211] Output: Calculated electricity price data.

[1212] Step 4: Presenting the calculation results

[1213] The server then presents the calculation results to the user, who can then use this information to help reduce their electricity bills.

[1214] Specific behavior:

[1215] Input: Calculated electricity price data.

[1216] Data processing: The server generates the calculation results in a format such as "If you use the air conditioner from 3:00 pm to 6:00 pm, the electricity bill will be XX yen."

[1217] Output: The server sends this message to the terminal.

[1218] Action: The terminal displays a message to the user.

[1219] In this way, users can learn the optimal time periods for use and electricity rates, which can be used as reference information to reduce electricity charges.

[1220] (Application example 3)

[1221] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1222] When using electricity, it is difficult for users to know the optimal time to use it in order to reduce their electricity bills. Furthermore, when charging electric vehicles, electricity rates vary greatly depending on the time of day, making it difficult for users to select the optimal time to charge. Therefore, there is a need for a system that helps users use electricity efficiently and reduce their electricity bills.

[1223] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1224] In this invention, the server includes means for calculating electricity charges based on a market-linked plan, means for receiving power usage information from a user, means for calculating optimal usage time periods based on the received power usage information, means for proposing the calculated optimal usage time periods and electricity charges to the user, means for calculating the time period with the lowest electricity charge among the power usage time periods input by the user, and means for presenting the calculated optimal power usage time periods and estimated electricity charges to the user. This allows the user to know the optimal usage time periods for reducing electricity charges.

[1225] A "market-linked plan" is a rate plan in which electricity rates are determined based on the trading price at the power exchange.

[1226] "Means for calculating electricity charges" refers to a device or program that has the function of calculating the charges for electricity used by a user based on the trading price at the electricity exchange.

[1227] "Power usage information" refers to information such as the type of power equipment used by the user and the duration of use.

[1228] The "means for calculating the optimal usage time period" is a device or program that has the function of calculating the time period when the electricity rate is lowest based on the received power usage information.

[1229] The "means for making suggestions to the user" is a device or program that has the function of notifying the user of the calculated optimal usage time period and electricity charges.

[1230] "Power usage time period" refers to the time period during which a user plans to use power equipment.

[1231] The "means for calculating the time period with the cheapest electricity rates" is a device or program that has the function of calculating the time period with the cheapest electricity rates among the electricity usage time periods entered by the user.

[1232] The "means for presenting the user with the estimated electricity charges" is a device or program that has the function of notifying the user of the calculated optimal time period for electricity use and the estimated electricity charges for that time period.

[1233] A system for implementing the present invention is for optimizing charging of electric vehicles. The system includes a server, a user terminal, and an API of a power exchange. A specific embodiment of the system will be described below.

[1234] System Configuration

[1235] 1. Server

[1236] The server runs a program that has the following functions:

[1237] Ability to calculate electricity rates based on market-linked plans

[1238] A function to receive power usage information from users

[1239] A function that calculates the optimal time period for use based on received power usage information

[1240] A function that suggests the optimal usage time and electricity charges to users

[1241] A function that calculates the time period with the cheapest electricity rates among the electricity usage time periods entered by the user.

[1242] A function that displays the calculated optimal power usage time and estimated electricity charges to the user

[1243] 2. User Device

[1244] The user terminal is a device such as a smartphone or tablet that provides an interface for users to input power usage information. The user inputs the time period during which they wish to charge, and the information is sent to the server.

[1245] 3. Power Exchange API

[1246] The API of the power exchange is an interface for obtaining trading prices from the power exchange. The server obtains real-time electricity trading prices through this API and uses them to calculate electricity charges.

[1247] Program processing explanation

[1248] The server calculates the electricity bill based on the electricity usage information received from the user's device and the trading price obtained from the power exchange's API. Specifically, the process is carried out as follows:

[1249] 1. Receiving electricity usage information

[1250] Power usage information (for example, the time period during which the user wishes to charge) entered from the terminal is sent to the server.

[1251] 2. Calculating electricity charges

[1252] The server uses the API of the power exchange to obtain the trading price for the specified time period, and then calculates the electricity fee based on the obtained trading price.

[1253] 3. Calculating the best time to use

[1254] The server calculates the cheapest electricity rate among the user-entered time slots, using real-time market price data.

[1255] 4. Presentation of results

[1256] The calculation results (optimal usage time and estimated electricity charges) are sent to the user's terminal and presented to the user.

[1257] Specific examples

[1258] For example, if a user inputs "I want to charge between 3:00 p.m. and 6:00 p.m. on October 1, 2023," the server will retrieve the trading price for that time period and calculate and present the time period with the cheapest electricity rates.

[1259] Prompt Sentence Examples

[1260] Example prompts to input to a generative AI model:

[1261] When a user inputs the charging time period for their electric vehicle, the system calculates the optimal charging time period and electricity rate based on the trading price on the power exchange for that time period, and presents these to the user. For example, if a user inputs "I want to charge between 3:00 PM and 6:00 PM on October 1, 2023," the system calculates and presents the time period with the cheapest electricity rate within that time period.

[1262] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1263] Step 1:

[1264] The user inputs power usage information from the device.

[1265] Specifically, the user uses a device such as a smartphone or tablet to input the time period during which they would like to charge (for example, "October 1, 2023, from 3:00 PM to 6:00 PM"). The input information is then sent from the device to the server.

[1266] Input: Time period you want to charge

[1267] Output: Power usage information sent to the server

[1268] Step 2:

[1269] A server receives the power usage information.

[1270] The server receives and analyzes the power usage information sent from the user's device, including the time period during which the user wishes to charge.

[1271] Input: Power usage information sent by the user

[1272] Output: Analyzed power usage information

[1273] Step 3:

[1274] The server obtains the trading price from the power exchange API.

[1275] The server uses the API of the power exchange to obtain the trading price for the specified time period, which is then used to calculate the electricity bill.

[1276] Input: Analyzed power usage information (time period you want to charge)

[1277] Output: Trading price for the specified time period

[1278] Step 4:

[1279] The server calculates the electricity bill.

[1280] The server calculates the electricity charges for the time period specified by the user based on the acquired transaction price. The calculation uses the transaction price and the usage time.

[1281] Input: Trading price for the specified time period, usage time

[1282] Output: Calculated electricity cost

[1283] Step 5:

[1284] The server calculates the optimal time to use it.

[1285] The server calculates the cheapest electricity rate among the user-entered time slots, using real-time market price data.

[1286] Input: Trading price for the specified time period, usage time

[1287] Output: Best time to use

[1288] Step 6:

[1289] The server presents the calculation results to the user.

[1290] The server then sends the calculated optimal usage time and estimated electricity charges to the user's terminal and presents them to the user, who can then select the optimal charging time based on the presented information.

[1291] Input: Optimal usage time, calculated electricity price

[1292] Output: The best time to use electricity and the estimated electricity price are presented to the user.

[1293] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1294] "Example 1"

[1295] As one embodiment of the present invention, a system incorporating an emotion engine is provided. This system recognizes emotions from the user's voice, facial expressions, behavior, etc., and suggests optimal times to use home appliances based on those emotions. For example, if the user is expressing joy, the system suggests using home appliances that are highly entertaining (e.g., televisions or game consoles). If the user is feeling stressed, the system suggests using home appliances that contribute to relaxation (e.g., massage chairs or aroma diffusers). This makes it possible to suggest optimal times to use home appliances based on the user's emotions.

[1296] "Example 2"

[1297] Another embodiment of the present invention provides a system for adjusting electricity rate proposals based on the recognition results of an emotion engine. This system optimizes electricity rate proposals according to the user's emotions. For example, if the user is expressing joy, the system extends the usage time periods of entertainment-oriented home appliances and proposes an increase in the resulting electricity rate. On the other hand, if the user is feeling stressed, the system extends the usage time periods of relaxation-oriented home appliances and proposes an increase in the resulting electricity rate. This makes it possible to propose optimal electricity rates according to the user's emotions.

[1298] "Example 3"

[1299] As one embodiment of the present invention, a system incorporating an emotion engine is provided. This system recognizes emotions from the user's voice, facial expressions, behavior, etc., and suggests optimal times to use home appliances based on those emotions. For example, if the user is expressing joy, the system suggests using home appliances that are highly entertaining (e.g., televisions or game consoles). If the user is feeling stressed, the system suggests using home appliances that contribute to relaxation (e.g., massage chairs or aroma diffusers). This makes it possible to suggest optimal times to use home appliances based on the user's emotions.

[1300] The processing flow of each embodiment will be described below.

[1301] "Example 1"

[1302] Step 1: The system activates an emotion engine that recognizes emotions from the user's voice, facial expressions, behavior, etc.

[1303] Step 2: The emotion engine recognizes the user's emotions and communicates the results to the system. Step 3: The system calculates the optimal time to use the appliances based on the results from the emotion engine.

[1304] Step 4: The system will suggest the optimal usage time slot to the user.

[1305] "Example 2"

[1306] Step 1: The system activates an emotion engine that recognizes emotions from the user's voice, facial expressions, behavior, etc.

[1307] Step 2: The emotion engine recognizes the user's emotion and communicates the result to the system. Step 3: The system adjusts the electricity rate proposal based on the result from the emotion engine.

[1308] Step 4: The system will communicate the adjusted electricity tariff proposal to the user.

[1309] "Example 3"

[1310] Step 1: The system activates an emotion engine that recognizes emotions from the user's voice, facial expressions, behavior, etc.

[1311] Step 2: The emotion engine recognizes the user's emotions and communicates the results to the system. Step 3: The system calculates the optimal time to use the appliances based on the results from the emotion engine.

[1312] Step 4: The system will suggest the optimal usage time slot to the user.

[1313] Example 1

[1314] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1315] Conventional electricity rate calculation systems calculate electricity rates based on the trading price in the electricity market, but do not suggest optimal times to use home appliances based on the user's emotions or behavior. As a result, it is not possible to promote the use of home appliances in accordance with the user's emotions, making it difficult to improve user satisfaction.

[1316] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1317] In this invention, the server includes means for acquiring transaction price information from the electricity market, means for analyzing the acquired transaction price information and calculating the unit price of electricity, means for acquiring the user's power consumption, means for calculating the electricity charge based on the unit price of electricity and the power consumption, means for notifying the user of the calculation results, means for collecting the user's voice, facial expression, and behavior, means for analyzing the collected data and recognizing the user's emotions, means for proposing optimal times for using home appliances based on the recognized emotions, and means for notifying the user of the proposed content. This makes it possible to suggest optimal times for using home appliances according to the user's emotions and improve user satisfaction.

[1318] An "electricity market" is a market where electricity is bought and sold, and where the trading price of electricity is determined.

[1319] "Trading price information" is data related to the buying and selling prices of electricity in the electricity market.

[1320] The "unit price of electricity" is a price indicating the charge for a certain amount of electricity, and is calculated based on transaction price information.

[1321] "Power consumption amount" is data indicating the amount of power consumed by a user over a certain period of time.

[1322] "Electricity charge" is a charge calculated by multiplying the amount of power consumed by the unit price of electricity.

[1323] "User's voice, facial expressions, and actions" refers to information such as the voice, facial expressions, and body movements of the user.

[1324] "Means for recognizing emotions" refers to technologies and devices that analyze a user's voice, facial expressions, and behavior to identify the user's emotions.

[1325] "Optimal time to use home appliances" refers to the most suitable time to use home appliances based on the user's emotions and behavior.

[1326] "Means for notifying users of proposals" refers to technologies and devices for communicating calculation results and proposals to users.

[1327] This system calculates electricity rates based on market-linked plans and proposes optimal times for using home appliances based on user sentiment. This system is composed of multiple components, including a server and a terminal.

[1328] First, the server obtains trading price information from the electricity market. Trading price information is data on the buying and selling prices of electricity in the electricity market, and is obtained through an API. For example, an HTTP request is sent and price data is received in JSON format. The server analyzes this data and uses the Python pandas library to calculate the average price over a certain period (e.g., every hour). Specifically, the server converts the JSON data into a pandas DataFrame and calculates the average price for each hour using the resample('H').mean() method.

[1329] Next, the server obtains the power consumption data from the user's smart meter. The smart meter data is sent from the IoT device using the MQTT protocol. The server connects to the MQTT broker and receives the user's power consumption data. Based on the received data, the electricity bill is calculated by multiplying the electricity rate by the amount of power consumed. For example, if the unit price is 10 yen / kWh and the amount of power consumed is 5kWh, the electricity bill will be 50 yen. The calculation result is notified to the user by sending an email using the SMTP protocol or a push notification using Firebase Cloud Messaging.

[1330] Meanwhile, the device collects the user's voice, facial expressions, and behavior. To do this, it uses a camera and microphone, and uses the Google Cloud Speech-to-Text API for voice recognition and OpenCV for facial recognition. The device captures the user's face with the camera and extracts facial feature points using OpenCV. It also records audio with the microphone and converts it into text using the Google Cloud Speech-to-Text API. The Microsoft Azure Emotion API is used to analyze the collected data and recognize the user's emotions. The device sends the facial feature point data to the Microsoft Azure Emotion API and obtains an emotion label (e.g., joy, sadness, anger).

[1331] Based on the recognized emotions, the device will suggest optimal times to use home appliances. For example, if the user is expressing joy, it will suggest using the TV. If the user is feeling stressed, it will suggest using appliances that contribute to relaxation (e.g., a massage chair or aroma diffuser). Suggestions are sent via push notifications using Firebase Cloud Messaging or spoken through the voice assistant.

[1332] For example, if a user consumes 5 kWh of electricity in one hour and the trading price for that time period is 10 yen / kWh, the electricity bill will be 50 yen. Also, if the user smiles at the camera, the system recognizes that the user is expressing a happy emotion and suggests using the television.

[1333] Examples of prompts include, "Calculate the hourly electricity bill based on the trading price information obtained from the power exchange," and "Recognize the user's emotions from their voice and facial expressions, and suggest the optimal times to use home appliances based on those emotions."

[1334] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1335] Step 1:

[1336] The server obtains trading price information from the electricity market.

[1337] Input: Power market API endpoint.

[1338] Specific operation: The server sends an HTTP request to an endpoint such as "https: / / api.electricity-market.com / prices" and receives trading price information in JSON format.

[1339] Output: Retrieved trading price information (JSON data).

[1340] Step 2:

[1341] The server analyzes the acquired transaction price information and calculates the unit price of electricity.

[1342] Input: Transaction price information (JSON data).

[1343] Specific operation: The server converts the JSON data into a pandas DataFrame and calculates the average price for each hour using the resample('H').mean() method.

[1344] Output: Average price per hour (unit price of electricity).

[1345] Step 3:

[1346] The server obtains power consumption data from the user's smart meter.

[1347] Input: Smart meter data (MQTT message).

[1348] Specific operation: The server connects to the MQTT broker and receives the user's power consumption data.

[1349] Output: User energy consumption data.

[1350] Step 4:

[1351] The server calculates the electricity bill based on the unit price of electricity and the amount of power consumed.

[1352] Input: Electricity unit price, power consumption data.

[1353] Specific operation: The server executes the formula Charge = Unit Price Power Consumption to calculate the electricity charge.

[1354] Output: The calculated electricity cost.

[1355] Step 5:

[1356] The server notifies the user of the calculation results.

[1357] Input: Calculated electricity cost.

[1358] What happens: The server sends emails using the SMTP protocol or push notifications using Firebase Cloud Messaging.

[1359] Output: The electricity price notified to the user.

[1360] Step 6:

[1361] The device collects the user's voice, facial expressions, and behavior.

[1362] Input: User voice, facial expressions, and actions.

[1363] What it does: The device captures the user's face with the camera and extracts facial feature points using OpenCV, and records audio with the microphone and converts it to text using the Google Cloud Speech-to-Text API.

[1364] Output: User voice, facial expression, and behavioral data.

[1365] Step 7:

[1366] The device analyzes the collected data and recognizes the user's emotions.

[1367] Input: User voice, facial expression, and behavioral data.

[1368] What it does: The device sends facial feature point data to the Microsoft Azure Emotion API to obtain an emotion label (e.g., joy, sadness, anger).

[1369] Output: Recognized emotion label.

[1370] Step 8:

[1371] The device will suggest optimal times to use home appliances based on the emotions it recognizes.

[1372] Input: Recognized emotion label.

[1373] Specific operation: Based on the emotion label, the device selects the most suitable appliance from a pre-defined list and suggests the best time to use it.

[1374] Output: Suggested appliance usage times.

[1375] Step 9:

[1376] The device will notify the user of the suggestions.

[1377] Input: Proposed appliance usage time slots.

[1378] What happens: The device will send a push notification using Firebase Cloud Messaging or speak the suggestion through your voice assistant.

[1379] Output: The suggestion that was notified to the user.

[1380] (Application example 1)

[1381] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1382] Conventional electricity bill calculation systems are limited to calculating electricity rates based on market-linked plans and suggesting optimal usage times based on user information on home appliance usage. However, because they do not consider the user's emotional state when suggesting home appliance usage, they are unable to improve user satisfaction or comfort. Furthermore, because they do not suggest home appliance usage based on emotions, energy management that responds to the user's psychological needs has not been realized.

[1383] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1384] In this invention, the server includes means for calculating electricity charges based on a market-linked plan, means for receiving home appliance usage information from a user, means for calculating optimal usage time periods based on the received home appliance usage information, means for proposing the calculated optimal usage time periods and electricity charges to the user, means for recognizing the user's emotions, and means for proposing optimal home appliance usage based on the recognized emotions. This makes it possible to suggest optimal home appliance usage based on the user's emotional state, thereby improving user satisfaction and comfort.

[1385] A "market-linked plan" is a rate plan in which electricity rates are determined based on the trading price at the power exchange.

[1386] A "means for calculating electricity charges" is a device or software for calculating electricity charges based on a market-linked plan.

[1387] The "means for receiving home appliance usage information from the user" is an interface that allows the user to input the type of home appliance they wish to use and the usage time.

[1388] The "means for calculating the optimum usage time period" is a device or software that calculates the most efficient usage time period based on the received home appliance usage information.

[1389] The "means for making suggestions to the user" is an interface for notifying the user of the calculated optimal usage time period and electricity charges.

[1390] "Means for recognizing user emotions" refers to a device or software for detecting emotions from the user's voice, facial expressions, behavior, etc.

[1391] The "means for suggesting the optimum use of home appliances" is a device or software for suggesting the optimum use of home appliances to a user based on the recognized emotions.

[1392] The following system configuration will be described as an embodiment of the present invention.

[1393] System Configuration

[1394] The server includes means for calculating an electricity charge based on a market-linked plan, means for receiving home appliance usage information from a user, means for calculating an optimum usage time period based on the received home appliance usage information, means for proposing the calculated optimum usage time period and electricity charge to the user, means for recognizing the user's emotions, and means for proposing the optimum use of the home appliance based on the recognized emotions.

[1395] Program processing

[1396] The server first obtains trading price information from the power exchange. This information is obtained through an API. Next, it calculates the electricity rate based on the obtained trading price information. The calculated electricity rate is used to calculate the optimal usage time based on the appliance usage information entered by the user.

[1397] To recognize the user's emotions, the server uses emotion recognition software. This software detects emotions from the user's voice, facial expressions, and behavior. Based on the recognized emotions, the server then suggests the optimal use of home appliances.

[1398] Hardware and software used

[1399] Hardware: Servers, user devices (smartphones, tablets, etc.)

[1400] Software: Emotion recognition software, power exchange API, home appliance control software

[1401] Specific examples

[1402] For example, if a user is using a smartphone, the app uses the smartphone's camera to recognize the user's face. If the app recognizes the user's emotion as "stress," the server will suggest using a "massage chair" and an "aroma diffuser." Based on the current electricity price, the server will calculate the cost of using these appliances and notify the user.

[1403] Prompt Sentence Examples

[1404] Design a smartphone app that recognizes the user's emotions and suggests optimal times to use home appliances. The app retrieves electricity price information from the power exchange and calculates electricity bills based on it. If the user is feeling stressed, it suggests using appliances that contribute to relaxation (e.g., a massage chair or aroma diffuser).

[1405] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1406] Step 1:

[1407] The server acquires trading price information from the power exchange.

[1408] Input: Power Exchange API endpoint

[1409] Data processing: Send an API request and obtain trading price information

[1410] Output: Trading price information (JSON format)

[1411] Step 2:

[1412] The server calculates the electricity bill based on the acquired transaction price information.

[1413] Input: Transaction price information

[1414] Data calculation: Extracts the current electricity price from trading price information and calculates electricity charges based on usage

[1415] Output: Calculated electricity cost

[1416] Step 3:

[1417] A user inputs home appliance usage information using a smartphone.

[1418] Input: Type of appliance, usage time

[1419] Data processing: Send the entered information to the server

[1420] Output: Home appliance usage information

[1421] Step 4:

[1422] The server calculates the optimal usage time period based on the received home appliance usage information.

[1423] Input: Home appliance usage information, transaction price information

[1424] Data calculation: Combining appliance usage information and trading price information to calculate the most cost-effective usage time period

[1425] Output: Best time to use

[1426] Step 5:

[1427] The server then proposes the calculated optimal usage time period and electricity rate to the user.

[1428] Input: Optimal usage time, calculated electricity price

[1429] Data processing: Generate proposals and send them to the user's device

[1430] Output: Proposal content (optimal usage time and electricity rate)

[1431] Step 6:

[1432] The user uses the smartphone camera to have their face recognized.

[1433] Input: User's face image

[1434] Data processing: Send the face image to the server

[1435] Output: Face image data

[1436] Step 7:

[1437] The server uses emotion recognition software to recognize the user's emotions.

[1438] Input: Facial image data

[1439] Data calculation: Analyzes facial image data and recognizes emotions

[1440] Output: Recognized emotion

[1441] Step 8:

[1442] The server suggests optimal use of home appliances based on the recognized emotions.

[1443] Input: Recognized emotion

[1444] Data Computing: Generating Emotion-Based Appliance Usage Suggestions

[1445] Output: Home appliance usage suggestion

[1446] Step 9:

[1447] The server calculates the cost of using the proposed appliances and notifies the user.

[1448] Input: Home appliance usage proposals, transaction price information

[1449] Data calculation: Calculate the usage cost of the proposed appliances

[1450] Output: Usage cost notification

[1451] Example 2

[1452] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1453] Conventional electricity rate calculation systems can calculate optimal usage times based on the user's home appliance usage information, but they have the problem of not being able to propose electricity rates that take the user's emotional state into account. This makes it difficult to propose optimal home appliance usage based on the user's emotions, making it difficult to improve user satisfaction.

[1454] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1455] In this invention, the server includes means for calculating an electricity charge based on a market-linked plan, means for receiving home appliance usage information from a user, means for calculating an optimal usage time period based on the received home appliance usage information, means for recognizing the user's emotions, means for adjusting the proposed electricity charge based on the recognized emotions, and means for proposing the calculated optimal usage time period and electricity charge to the user. This makes it possible to propose optimal home appliance usage and adjust the electricity charge according to the user's emotions.

[1456] A "market-linked plan" is a rate plan in which electricity rates are determined based on the trading price at the power exchange.

[1457] The "means for calculating electricity charges" is a function for calculating electricity charges based on electricity consumption and a market-linked plan.

[1458] "Means for receiving information on home appliance usage from users" is a function for inputting the types of home appliances and usage times entered by users into the system.

[1459] The "means for calculating the optimal usage time period" is a function for calculating the optimal usage time period for a home appliance based on the received home appliance usage information, taking into account power consumption patterns and past data.

[1460] "Means for recognizing user emotions" refers to a function for analyzing and recognizing the user's emotional state.

[1461] The "means for adjusting electricity rate proposals" is a function for optimizing the content of electricity rate proposals based on the recognized user sentiment.

[1462] The "means for proposing the calculated optimal usage time period and electricity charges to the user" is a function for notifying the user of the calculated optimal usage time period and adjusted electricity charges.

[1463] MODE FOR CARRYING OUT THE INVENTION

[1464] This invention is a system that receives information on the use of home appliances from users, calculates the optimal usage time based on that information, and adjusts electricity rate proposals based on the user's emotions. This system is implemented using the following hardware and software.

[1465] Hardware and software used

[1466] Hardware

[1467] Device: The device on which a user provides input information (e.g., smartphone, tablet, PC)

[1468] Server: A server for processing and storing data.

[1469] software

[1470] Emotion engine: Software for recognizing user emotions (e.g., emotion recognition API)

[1471] Database: A database for storing information on home appliance usage and user emotion data (e.g., MySQL, PostgreSQL)

[1472] Specific operation of the system

[1473] 1. Collecting user input information

[1474] The user uses the terminal to input the type of home appliance they want to use and the desired usage time.

[1475] Specifically, the user enters information such as "air conditioner" and "from 2:00 p.m. to 4:00 p.m." into the application's input form.

[1476] 2. Sending input information

[1477] The terminal transmits the information entered by the user to the server.

[1478] Specifically, the terminal uses an HTTP request to send input information to the server.

[1479] 3. Calculating the usage time of home appliances

[1480] The server calculates the optimal time to use the appliance based on the type and usage time of the appliance received.

[1481] Specifically, the server runs an algorithm that takes into account past usage data and power consumption patterns to calculate the optimal usage times.

[1482] For example, the server may calculate that the best time to use the air conditioner is between 2:00 p.m. and 4:00 p.m.

[1483] 4. Emotion Recognition by Emotion Engine

[1484] The server uses an emotion engine to recognize the user's emotion.

[1485] Specifically, the server inputs the user's facial image and voice data into the emotion engine to obtain the user's emotional state (e.g., joy, stress).

[1486] 5. Proposal for adjusting electricity charges

[1487] The server adjusts the electricity rate proposal based on the recognition results of the emotion engine.

[1488] Specifically, if the user expresses joy, the server will extend the usage time of entertainment-oriented home appliances (e.g., television) and suggest an increase in the resulting electricity bill.

[1489] On the other hand, if the user is feeling stressed, the system will suggest extending the usage time of home appliances that contribute to relaxation (e.g., massage chairs).

[1490] 6. Notifying Users of Results

[1491] The server sends the calculation results and the proposal to the terminal.

[1492] The terminal notifies the user of the received information.

[1493] Specifically, the device will use push notifications and in-app messages to inform the user that "The best time to use the air conditioner is between 2:00 PM and 4:00 PM. We also recommend extending the TV usage time."

[1494] Specific examples

[1495] User Input

[1496] A user uses a smartphone to enter, "I want to use the air conditioner from 2:00 PM to 4:00 PM."

[1497] Server Processing

[1498] The server calculates the times when the air conditioner will be used and suggests the optimal times based on past data and power consumption patterns.

[1499] If the emotion engine recognizes the user's emotion as "joy," it will suggest extending the usage time of entertainment-oriented home appliances (e.g., television).

[1500] Example prompts for generative AI models

[1501] "A user inputs that they want to use the air conditioner from 2 PM to 4 PM. Calculate the optimal usage time, and if the user's emotion is joy, suggest extending the usage time of the appliances that are most entertaining."

[1502] This system not only allows users to use their home appliances efficiently, but also allows them to receive optimal electricity rate suggestions based on their emotions.

[1503] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1504] Step 1:

[1505] Collecting user input

[1506] The user uses the terminal to input the type of home appliance they want to use and the desired usage time.

[1507] Input: The user enters information such as "air conditioner" and "2:00 PM to 4:00 PM" into the application's input form.

[1508] Output: Data on the type of appliance and usage time entered.

[1509] Specific operation: The user opens the smartphone application, enters the type of home appliance and usage time into the input form, and presses the submit button.

[1510] Step 2:

[1511] Sending input information

[1512] The terminal transmits the information entered by the user to the server.

[1513] Input: Data on the type of appliance and usage time entered by the user.

[1514] Output: Data on the type of appliance and usage time sent to the server.

[1515] Specific operation: The terminal generates an HTTP request and sends the entered data to the server.

[1516] Step 3:

[1517] Calculating the usage time of home appliances

[1518] The server calculates the optimal time to use the appliance based on the type and usage time of the appliance received.

[1519] Input: Data on the type of appliance and usage time sent to the server.

[1520] Output: Optimal usage time period data.

[1521] How it works: The server runs an algorithm that takes into account past usage data and power consumption patterns to calculate the optimal time to use the air conditioner. For example, it might calculate that the best time to use the air conditioner is between 2:00 PM and 4:00 PM.

[1522] Step 4:

[1523] Emotion recognition by emotion engine

[1524] The server uses an emotion engine to recognize the user's emotion.

[1525] Input: User's facial image and voice data.

[1526] Output: The user's emotional state (e.g., happy, stressed).

[1527] Specific operation: The server inputs the user's facial image and voice data into the emotion engine to obtain the user's emotional state.

[1528] Step 5:

[1529] Proposal and adjustment of electricity charges

[1530] The server adjusts the electricity rate proposal based on the recognition results of the emotion engine.

[1531] Input: Data on the user's emotional state and optimal usage times.

[1532] Output: Adjusted electricity tariff proposal data.

[1533] Specific operation: If the user shows emotions of joy, the server will suggest extending the usage time of entertainment appliances (e.g., televisions) and reducing the resulting electricity bill increase. On the other hand, if the user is feeling stressed, the server will suggest extending the usage time of relaxation appliances (e.g., massage chairs).

[1534] Step 6:

[1535] Notifying users of the results

[1536] The server sends the calculation results and the proposal to the terminal.

[1537] The terminal notifies the user of the received information.

[1538] Input: Optimal usage time and adjusted electricity rate suggestion data.

[1539] Output: A notification message to the user.

[1540] What it does: The device will use push notifications and in-app messages to inform the user that "The best time to use the air conditioner is between 2:00 PM and 4:00 PM. We also recommend extending the TV usage time."

[1541] (Application example 2)

[1542] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1543] Conventional electricity rate optimization systems based on home appliance usage information were unable to consider the user's emotional state, making it difficult to increase user satisfaction. Also, when optimizing the usage times of home appliances in stores, adjustments were not made based on the customer's emotions, making it difficult to improve the customer experience.

[1544] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1545] In this invention, the server includes means for calculating electricity charges based on a market-linked plan, means for receiving home appliance usage information from a user, means for calculating optimal usage time periods based on the received home appliance usage information, means for proposing the calculated optimal usage time periods and electricity charges to the user, means for recognizing the user's emotions using an emotion recognition engine, and means for adjusting the proposed electricity charge based on the recognized emotion. This makes it possible to propose optimal home appliance usage time periods and electricity charges according to the user's emotional state, thereby increasing user satisfaction. Furthermore, adjusting the usage time periods of home appliances in a store according to the customer's emotions can improve the customer experience.

[1546] A "market-linked plan" is a rate plan in which electricity rates are determined based on the trading price at the power exchange.

[1547] A "means for calculating electricity charges" is a device or program for calculating electricity charges based on a market-linked plan.

[1548] The "means for receiving home appliance usage information" is a device or program for receiving information such as the type of home appliance and usage time from the user as input.

[1549] The "means for calculating the optimum usage time period" is a device or program for calculating the optimum usage time period of the home appliance based on the received home appliance usage information.

[1550] "Means for making suggestions to users" refers to a device or program for notifying or displaying to users the calculated optimal usage time period and electricity charges.

[1551] An "emotion recognition engine" is an algorithm or program for recognizing a user's emotions.

[1552] The "means for recognizing emotions" is a device or program for detecting the emotions of a user using an emotion recognition engine.

[1553] A "means for adjusting an electricity rate offer" is a device or program for modifying an electricity rate offer based on recognized user sentiment.

[1554] The following system configuration will be described as an embodiment of the present invention.

[1555] System Configuration

[1556] This system consists of a server, a user terminal, an emotion recognition engine, an electricity rate calculation engine, and a home appliance control device. The server receives information on home appliance usage from users, calculates the optimal usage time, recognizes the user's emotions using the emotion recognition engine, and adjusts the electricity rate proposal.

[1557] Program processing

[1558] 1. Enter your appliance usage information:

[1559] The user terminal receives input from the user about the type of appliance and the usage time, and this information is sent to the server.

[1560] 2. Emotion recognition:

[1561] The server recognizes the user's emotions using an emotion recognition engine, which analyzes the user's image and audio data acquired from cameras and sensors to determine the user's emotional state.

[1562] 3. Calculate the best time of day:

[1563] The server calculates the optimal usage time period based on the received home appliance usage information using an electricity rate calculation engine, which calculates the electricity rate based on a market-linked plan.

[1564] 4. Proposed adjustment of electricity tariff:

[1565] The server adjusts the electricity bill suggestions based on the user's recognized emotions. For example, if the user is happy, it will extend the use of entertainment appliances, and if the user is stressed, it will extend the use of relaxation appliances.

[1566] 5. Notice to Users:

[1567] The server then notifies the user device of the calculated optimal usage time and the adjusted electricity rate, allowing the user to adjust their home appliance usage accordingly.

[1568] Hardware and software used

[1569] Hardware: Cameras, sensors, user devices (smartphones, smart glasses, etc.)

[1570] Software: Emotion recognition engine (EmotionEngine), electricity price calculation engine (ElectricityPricing)

[1571] Specific examples

[1572] For example, consider a scenario in which the usage times of lighting and air conditioning in a store are optimized. The store manager uses a smartphone to input the usage times of lighting and air conditioning. The server uses cameras in the store to recognize the customer's emotions and calculates the optimal usage times. If the customer is happy, the lighting usage time is extended to increase entertainment. If the customer is stressed, the air conditioning usage time is extended to promote relaxation.

[1573] Prompt Sentence Examples

[1574] Develop an application that optimizes lighting and air conditioning usage in stores and adjusts electricity tariff suggestions based on customer emotions: if a customer is happy, extend the use of entertainment appliances, and if a customer is stressed, extend the use of relaxation appliances.

[1575] In this way, it becomes possible to propose optimal times for using home appliances and electricity rates according to the user's emotional state, thereby increasing user satisfaction. Also, by adjusting the usage times of home appliances in the store according to the customer's emotions, it is possible to improve the customer experience.

[1576] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1577] Step 1:

[1578] The user terminal receives the type of home appliance and usage time from the user as input. Specifically, the user uses a smartphone or smart glasses to input the type of home appliance (e.g., lighting, air conditioner, etc.) they want to use and the usage time. This information is sent from the user terminal to the server. The input data is the type of home appliance and usage time, and the output data is the home appliance usage information sent to the server.

[1579] Step 2:

[1580] The server uses an emotion recognition engine to recognize the user's emotions. Specifically, the server inputs the user's image and voice data acquired by the camera or sensor into the emotion recognition engine to determine the user's emotional state. The input data is the user's image and voice data, and the output data is the recognized emotional state of the user.

[1581] Step 3:

[1582] The server uses an electricity rate calculation engine to calculate the optimal usage time based on the received home appliance usage information. Specifically, the server inputs the home appliance usage information and market-linked plan data into the electricity rate calculation engine to calculate the optimal usage time. The input data is the home appliance usage information and market-linked plan data, and the output data is the optimal usage time.

[1583] Step 4:

[1584] The server adjusts the electricity rate proposal based on the recognized user emotion. Specifically, the server uses the emotional state data from the emotion recognition engine to adjust the proposal of the electricity rate calculation engine. For example, if the user is happy, the server extends the use time of entertainment appliances, and if the user is stressed, the server extends the use time of relaxation appliances. The input data is the user's emotional state and the optimal use time period, and the output data is the adjusted electricity rate proposal.

[1585] Step 5:

[1586] The server notifies the user terminal of the calculated optimal usage time slot and the adjusted electricity rate. Specifically, the server sends the data of the optimal usage time slot and the adjusted electricity rate to the user terminal so that the user can check it. The input data is the optimal usage time slot and the adjusted electricity rate, and the output data is the suggested information displayed on the user terminal.

[1587] In this way, it becomes possible to propose optimal times for using home appliances and electricity rates according to the user's emotional state, thereby increasing user satisfaction. Also, by adjusting the usage times of home appliances in the store according to the customer's emotions, it is possible to improve the customer experience.

[1588] Example 3

[1589] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1590] Conventional electricity rate calculation systems can suggest optimal times for users to use home appliances, but do not make suggestions based on the user's emotions. As a result, they are unable to suggest optimal times to use home appliances based on the user's emotional state, making it difficult to improve user satisfaction. Furthermore, electricity rates are not calculated based on trading prices at the power exchange, meaning users are unable to obtain reference information for reducing their electricity bills.

[1591] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1592] In this invention, the server includes means for calculating an electricity rate based on a market-linked plan, means for receiving home appliance usage information from a user, means for calculating optimal usage time periods based on the received home appliance usage information, means for proposing the calculated optimal usage time periods and electricity rates to the user, means for recognizing the user's emotions, and means for proposing optimal home appliance usage time periods based on the recognized emotions. This makes it possible to propose optimal home appliance usage time periods according to the user's emotional state and calculate electricity rates based on the trading price at the power exchange.

[1593] A "market-linked plan" is a rate plan in which electricity rates are determined based on the trading price at the power exchange.

[1594] A "means for calculating electricity charges" is a device or program that has the function of calculating electricity charges according to the duration of use based on the trading price at the power exchange and the power consumption of home appliances.

[1595] The "means for receiving home appliance usage information from the user" refers to a device or program that provides an interface for the user to input the type of home appliance they wish to use and the usage time, and receives that information.

[1596] The "means for calculating the optimal usage time period" is a device or program that has the function of calculating the optimal time period for using the home appliance based on the received home appliance usage information and the trading price at the power exchange.

[1597] "Means for making suggestions to users" refers to a device or program for notifying or displaying to users the calculated optimal usage time period and electricity charges.

[1598] A "means for recognizing a user's emotions" is a device or program that has the function of analyzing the user's voice, facial expression, behavior, etc., and determining the user's emotional state.

[1599] The "means for suggesting optimal times to use home appliances based on emotions" is a device or program that has the function of suggesting optimal times to use home appliances based on the recognized emotional state of the user.

[1600] This invention is a system that calculates the optimal time periods for users to use home appliances and the electricity charges for those times, and then suggests these to the user.It also has a function to recognize the user's emotions and suggest times for using home appliances that correspond to those emotions.

[1601] Hardware and software used

[1602] Hardware: Servers, user devices (smartphones, tablets, PCs, etc.)

[1603] Software: Emotion recognition engine, power exchange data acquisition API, generative AI model

[1604] Specific operation of the system

[1605] 1. User Input

[1606] The user uses the terminal to input the type of appliance they want to use and the usage time.

[1607] Example: User types "Use air conditioning from 3pm to 6pm."

[1608] 2. Obtaining Power Exchange Data

[1609] The server calls the power exchange data acquisition API and obtains the power trading price for the specified time period.

[1610] Example: A server uses an API to retrieve electricity trading price data from 3pm to 6pm.

[1611] 3. Calculating electricity charges

[1612] The electricity charges for the specified time period are calculated based on the electricity trading price acquired by the server.

[1613] Example: The server calculates the electricity bill from 3:00 PM to 6:00 PM using the power consumption of the air conditioner and the acquired electricity trading price.

[1614] 4. Emotion recognition

[1615] The user's voice, facial expressions, and actions are input into the emotion recognition engine via the device's camera and microphone, and the user's emotions are recognized.

[1616] Example: When a user speaks into the device's camera, the emotion recognition engine analyzes the user's tone of voice and facial expressions and determines that the user is stressed.

[1617] 5. Recommendations for optimal usage times

[1618] The server suggests optimal times to use home appliances based on the electricity bill calculated by the server and the user's emotions.

[1619] Example: The server determines that the user is feeling stressed and suggests using a home appliance that contributes to relaxation (e.g., a massage chair).

[1620] 6. Presentation to Users

[1621] The server sends the calculation results and suggestions to the user's terminal and presents them to the user.

[1622] Example: The server calculates electricity charges and displays suggestions for using relaxation appliances on the user's smartphone.

[1623] Prompt Sentence Examples

[1624] "Calculate the electricity cost if the air conditioner is used from 3:00 PM to 6:00 PM, and suggest the use of appliances that contribute to relaxation when the user is feeling stressed."

[1625] In this way, the system can propose optimal times for using home appliances and electricity rates based on the user's input and emotions. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1626] Step 1:

[1627] The user uses the terminal to input the type of appliance they want to use and the usage time.

[1628] Input: The user inputs the type of appliance (e.g., air conditioner) and the usage time (e.g., 3:00 PM to 6:00 PM).

[1629] Specific behavior: The user opens the app on their device, accesses a form to enter the type of appliance and usage time, and enters the required information.

[1630] Output: The entered data on the type of appliance and usage time is sent to the server.

[1631] Step 2:

[1632] The server calls the power exchange data acquisition API and obtains the power trading price for the specified time period.

[1633] Input: The usage time entered by the user (e.g., 3:00 PM to 6:00 PM).

[1634] Specific operation: The server sends a request to the power exchange data acquisition API to acquire power exchange price data for the specified time period.

[1635] Output: The acquired electricity trading price data is saved on the server.

[1636] Step 3:

[1637] The electricity charges for the specified time period are calculated based on the electricity trading price acquired by the server.

[1638] Input: Obtained electricity trading price data and power consumption of home appliances (e.g., air conditioner power consumption 1.5kW).

[1639] Specific operation: The server calculates the electricity charges from 3:00 PM to 6:00 PM using the power consumption of the air conditioner and the acquired electricity trading price.

[1640] Output: The calculated electricity cost (e.g. 3 hours x 1.5kW x 20 yen = 90 yen) will be obtained.

[1641] Step 4:

[1642] The user's voice, facial expressions, and actions are input into the emotion recognition engine via the device's camera and microphone, and the user's emotions are recognized.

[1643] Input: User voice, facial expression, and behavioral data.

[1644] How it works: When a user speaks into the device's camera, the device sends audio and video data to an emotion recognition engine.

[1645] Output: The emotion recognition engine analyzes the user's emotional state (e.g., feeling stressed).

[1646] Step 5:

[1647] The server suggests optimal times to use home appliances based on the electricity bill calculated by the server and the user's emotions.

[1648] Input: Calculated electricity price and user's emotional state.

[1649] Specific operation: The server integrates the user's emotional data and electricity bill data and suggests the use of home appliances that contribute to relaxation (e.g., massage chairs).

[1650] Output: The suggestion is displayed as "Using the air conditioner from 3:00 PM to 6:00 PM will cost 90 yen, but since you are feeling stressed, we recommend using the massage chair."

[1651] Step 6:

[1652] The server sends the calculation results and suggestions to the user's terminal and presents them to the user.

[1653] Input: Calculation results and suggestions.

[1654] Specific operation: The server sends the calculation results and proposals to the user device.

[1655] Output: The user will receive a notification on their smartphone and will see the suggestions when they open the app.

[1656] (Application example 3)

[1657] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1658] Conventional energy management systems can optimize electricity rates based on electricity trading prices, but they cannot suggest optimal times to use home appliances based on user emotions. Furthermore, in physical stores, it is difficult to provide a comfortable environment that reflects customer emotions, making it difficult to improve customer satisfaction.

[1659] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means for calculating an electricity rate based on a market-linked plan, means for receiving home appliance usage information from a user, means for calculating optimal usage time periods based on the received home appliance usage information, means for proposing the calculated optimal usage time periods and electricity rates to the user, means for recognizing the user's emotions, means for proposing optimal home appliance usage time periods based on the recognized emotions, means for checking the usage status of home appliances in the store in real time, and means for setting optimal environments according to the emotions of customers. This makes it possible to optimize electricity rates, propose optimal home appliance usage time periods according to the emotions of users and customers, and provide a comfortable environment.

[1660] A "market-linked plan" is a rate plan in which electricity rates are determined based on the trading price at the power exchange.

[1661] "Means for calculating electricity charges" means a device or software for calculating electricity charges under a market-linked plan.

[1662] The "means for receiving home appliance usage information from the user" is an interface that allows the user to input the type of home appliance they wish to use and the usage time.

[1663] The "means for calculating the optimum usage time period" is a device or software that calculates the optimum usage time period based on the received home appliance usage information while taking into account the electricity trading price.

[1664] The "means for making suggestions to the user" is an interface for notifying the user of the calculated optimal usage time period and electricity charges.

[1665] A "means for recognizing user emotions" is a device or software that analyzes emotions from the user's voice, facial expressions, behavior, etc.

[1666] The "means for proposing optimal times to use home appliances based on emotions" is a device or software that calculates and proposes optimal times to use home appliances based on the user's recognized emotions.

[1667] The "means for checking the usage status of home appliances in a store in real time" refers to a device or software for monitoring the usage status of home appliances in a store in real time and notifying the manager.

[1668] The "means for optimally setting the environment in response to customer emotions" refers to a device or software that analyzes customer emotions and, based on that, optimizes the lighting, air conditioning, music, and other environmental settings within the store.

[1669] The following system configuration will be described as an embodiment of the present invention.

[1670] System Configuration

[1671] 1. Hardware:

[1672] Smartphone (iOS / Android)

[1673] Smart glasses (e.g. Google Glass)

[1674] Head-mounted displays (e.g., Oculus Rift)

[1675] Robots (e.g. Pepper)

[1676] 2. Software:

[1677] Facial recognition API (e.g. Microsoft Azure Face API)

[1678] Emotion recognition API (e.g. Affectiva)

[1679] Power trading price API (e.g. EPEX SPOT API)

[1680] Database (e.g. Firebase)

[1681] Program processing

[1682] The server first receives information about the user's appliance usage. The user inputs the type of appliance they want to use and the time of use through a smartphone app. The server then uses an electricity trading price API to obtain real-time electricity prices. Based on this, the server calculates the optimal usage time and electricity rates and makes suggestions to the user.

[1683] In addition, the server uses face recognition and emotion recognition APIs to recognize the user's emotions. Smart glasses, head-mounted displays, and robots capture the user's facial expressions and voice and send them to the server. The server analyzes this data and recognizes the user's emotions.

[1684] Based on the recognized emotions, the server recalculates the optimal time to use home appliances and suggests them to the user. For example, if the user is feeling stressed, it will suggest using home appliances that contribute to relaxation (e.g., a massage chair or aroma diffuser).

[1685] Specific examples

[1686] As a concrete example, consider an energy management system in a brick-and-mortar store. The store manager uses a smartphone app to check the usage status of home appliances in the store and the optimization information of electricity charges in real time. Store staff wearing smart glasses analyze customers' facial expressions, and if the customer is relaxed, they change the lighting in the store to warm colors and play relaxation music.

[1687] Prompt Sentence Examples

[1688] Input customer facial expression data and recognize their emotions. Then, retrieve the current electricity price from the electricity trading price API and calculate the optimal usage time for home appliances based on the customer's emotions. Display the calculation results on a smartphone app, smart glasses, a head-mounted display, or a robot.

[1689] In this way, it is possible to optimize electricity charges, propose optimal times for using home appliances based on the emotions of users and customers, and provide a comfortable environment.

[1690] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1691] Step 1:

[1692] The user uses a smartphone app to input the type of appliance they want to use and the amount of time they want to use it. The input information is the type of appliance and the amount of time they want to use it. This information is then sent to the server.

[1693] Step 2:

[1694] The server uses the electricity trading price API to obtain real-time electricity prices. The input is the current time, and the output is the electricity price corresponding to that time. Based on this data, the server calculates the electricity charges for the appliances during their usage time.

[1695] Step 3:

[1696] The server then proposes the optimal usage time and electricity rates to the user. The proposal details the optimal usage time and the electricity rates for that time. This information is displayed on the smartphone app.

[1697] Step 4:

[1698] When a user uses smart glasses, a head-mounted display, or a robot, these devices capture the user's facial expressions and voice. The input is the user's facial expression data and voice data, and this data is sent to the server.

[1699] Step 5:

[1700] The server uses a facial recognition API and an emotion recognition API to analyze the user's emotions. The input is facial expression data and voice data, and the output is analyzed emotion data. The server recognizes the user's emotions based on this data.

[1701] Step 6:

[1702] The server recalculates the optimal time periods for using appliances based on the recognized emotions. The input is emotion data and appliance usage information, and the output is the optimal usage time periods based on the emotion. For example, if the user is feeling stressed, the server suggests using appliances that contribute to relaxation.

[1703] Step 7:

[1704] The server then recalculates the optimal usage time and electricity rates and proposes them to the user. The proposals include the optimal usage time and electricity rates for that time based on the user's emotions. This information is displayed on a smartphone app, smart glasses, a head-mounted display, and a robot.

[1705] Step 8:

[1706] To check the usage status of home appliances in the store in real time, the server uses a database to monitor the usage status of home appliances. The input is the usage data of the home appliances and the output is the real-time usage status. This allows the store manager to check the usage status of home appliances through a smartphone app.

[1707] Step 9:

[1708] The server optimizes lighting, air conditioning, music, and other environmental settings to optimize the environment according to the customer's emotions. The input is the customer's emotional data, and the output is the optimized environmental settings. For example, if the customer is relaxing, the lighting is changed to warm colors and relaxation music is played.

[1709] Through the above steps, it is possible to optimize electricity charges, propose optimal times for using home appliances based on the emotions of users and customers, and provide a comfortable environment.

[1710] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1711] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1712] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1713] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1714] [Third embodiment]

[1715] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1716] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1717] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32.

[1718] The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1719] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1720] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1721] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1722] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1723] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1724] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1725] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1726] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1727] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1728] "Example 1"

[1729] One aspect of the present invention is a system for calculating electricity rates based on a market-linked plan. This system acquires trading price information from the power exchange and calculates electricity rates based on that information. Specifically, the system calculates the unit price of electricity based on the trading price information and uses that to calculate the electricity rate.

[1730] "Example 2"

[1731] Another aspect of the present invention is a system that receives information about the use of home appliances from a user. This system receives input information from the user and calculates the usage time periods for the home appliances based on that information. Specifically, the system has the user input the type of home appliance they want to use and the usage time, and calculates the optimal usage time period based on that information.

[1732] "Example 3"

[1733] Furthermore, one embodiment of the present invention is a system that proposes the calculated optimal usage time periods and electricity rates to users. This system presents the calculated optimal usage time periods and electricity rates to users, and uses them as reference information for users to reduce their electricity rates. Specifically, when a user inputs the type of home appliance they want to use and the usage time, for example, "I will use the air conditioner from 3:00 PM to 6:00 PM," the system presents the user with an electricity rate calculated based on the trading price at the power exchange for that time period.

[1734] The processing flow of each embodiment will be described below.

[1735] "Example 1"

[1736] Step 1: Obtain trading price information from the power exchange.

[1737] Step 2: Calculate the unit price of electricity based on the acquired transaction price information.

[1738] Step 3: Calculate the electricity bill using the calculated unit price of electricity.

[1739] "Example 2"

[1740] Step 1: Receive input from the user about the type of appliance they want to use and the usage time.

[1741] Step 2: Calculate the optimal usage time based on the received input information.

[1742] "Example 3"

[1743] Step 1: The user enters the type of appliance they want to use and the usage time.

[1744] Step 2: Calculate the electricity rate based on the trading price on the power exchange for that time period.

[1745] Step 3: Present the calculated electricity bill to the user.

[1746] Example 1

[1747] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1748] Conventional electricity rate calculation systems have difficulty reflecting trading price information from the power exchange in real time, making it impossible to provide users with optimal electricity rates. Furthermore, they lacked the functionality to suggest optimal usage times based on users' home appliance usage information, making it difficult for users to use electricity efficiently.

[1749] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1750] In this invention, the server includes means for acquiring trading price information from an electricity exchange, means for storing the acquired trading price information in a database, means for calculating the unit price of electricity based on the stored trading price information, means for calculating the user's electricity bill using the calculated unit price of electricity, means for receiving home appliance usage information from the user, means for calculating optimal usage time periods based on the received home appliance usage information, and means for proposing the calculated optimal usage time periods and electricity bill to the user. This enables calculation of electricity bills that reflect trading price information in real time, and further enables efficient power use by proposing optimal usage time periods to the user.

[1751] An "electricity exchange" is a market where electricity is bought and sold, and an institution that provides trading price information.

[1752] "Trading price information" is information relating to the price of electricity traded at the power exchange.

[1753] A "database" is a system for efficiently storing, managing, and retrieving data.

[1754] The "unit price of electricity" is the unit price set for a certain amount of electricity.

[1755] A "user" is an individual or corporation that consumes electricity and is the entity that uses this system.

[1756] "Home appliance usage information" is information about the types of home appliances used by the user and the duration of use.

[1757] The "optimal usage time period" is the time period recommended for efficient power consumption.

[1758] The "means for proposing" is a method or device for notifying the user of the calculated optimal usage time period and electricity charges.

[1759] MODE FOR CARRYING OUT THE INVENTION

[1760] This invention relates to a system for calculating electricity rates based on a market-linked plan, which acquires trading price information from the power exchange and calculates electricity rates based on that information. Specific embodiments of this system are described below.

[1761] Hardware and software used

[1762] 1. Server

[1763] Hardware: General server equipment

[1764] Software: API clients, database management systems (e.g., MySQL, PostgreSQL), scripting languages ​​(e.g., Python, JavaScript)

[1765] 2. Terminal

[1766] Hardware: The computer or smartphone you use

[1767] Software: Web browser, mobile application

[1768] 3. Users

[1769] A user is an individual or a corporation that consumes electricity and is the entity that uses this system.

[1770] Explanation of program processing

[1771] 1. Obtaining trading price information

[1772] The server sends a request to the power exchange's API to obtain the latest trading price information.

[1773] Specific operation: The server sends an HTTP GET request to the API endpoint of the power exchange and receives trading price information in JSON format.

[1774] 2. Saving of trading price information

[1775] The server stores the acquired transaction price information in a database.

[1776] Specific operation: The server parses the received JSON data and executes an insert query into the corresponding table in the MySQL database.

[1777] 3. Calculation of electricity unit price

[1778] The server calculates the unit price of electricity based on the stored transaction price information.

[1779] Specific operation: The server executes a Python script, retrieves trading price information from the database, and calculates the unit price of electricity based on a specific algorithm.

[1780] 4. Calculation of user's electricity bill

[1781] The server calculates the electricity charge for the user using the calculated electricity unit price.

[1782] Specific operation: The server retrieves the user's power consumption data from the database and multiplies it by the calculated unit price to calculate the electricity bill.

[1783] 5. Receiving information on home appliance usage

[1784] The server receives home appliance usage information from the user.

[1785] Specific operation: The user enters the type of home appliance they want to use and the usage time through a web browser or mobile application, and sends the information to the server.

[1786] 6. Calculating the best time to use

[1787] The server calculates the optimal usage time period based on the received home appliance usage information.

[1788] Specific operation: The server executes an algorithm to calculate the optimal usage time based on the electricity rate and appliance usage information.

[1789] 7. Notification of Proposal

[1790] The server then proposes the calculated optimal usage time period and electricity rate to the user.

[1791] Specific operation: The server notifies the user's device of the calculation results, and the user confirms the proposal through a web browser or mobile application.

[1792] Examples and prompts

[1793] 1. The server obtains trading price information for "2023-10-01" from the power exchange API.

[1794] 2. The server stores the acquired trading price information in a MySQL database.

[1795] 3. The server uses a Python script to calculate the unit price of electricity for "2023-10-01" from the trading price information.

[1796] 4. The server calculates the user's electricity bill (e.g., 1,000 yen) using the user's power consumption data (e.g., 100 kWh) and the calculated unit price (e.g., 10 yen / kWh).

[1797] Prompt Sentence Examples

[1798] "Get the latest trading price information from the power exchange API and store it in a MySQL database. Then, use a Python script to calculate the unit price of electricity and calculate the electricity bill based on the user's energy consumption data."

[1799] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1800] Step 1:

[1801] Obtaining trading price information

[1802] The server sends a request to the power exchange's API to obtain the latest trading price information.

[1803] Input: Power Exchange API endpoint URL

[1804] Output: Trade price information in JSON format

[1805] Specific operation: The server sends an HTTP GET request to the API endpoint of the electricity exchange and receives trading price information in JSON format as a response. For example, use the requests library to send a request to https: / / api.electricity-market.com / prices.

[1806] Step 2:

[1807] Stores trading price information

[1808] The server stores the acquired transaction price information in a database.

[1809] Input: Trade price information in JSON format

[1810] Output: Transaction price information stored in the database

[1811] What happens: The server parses the received JSON data, extracts the required fields (e.g., datetime, price), and then uses the pymysql library to connect to a MySQL database and execute a query like INSERT INTO prices (datetime, price) VALUES ('2023-10-01 00:00:00', 10.5).

[1812] Step 3:

[1813] Calculating electricity unit prices

[1814] The server calculates the unit price of electricity based on the stored transaction price information.

[1815] Input: Transaction price information stored in the database

[1816] Output: Calculated electricity unit price

[1817] Specific operation: The server executes a Python script to obtain trading price information from the database. For example, it uses the pymysql library to execute a query such as SELECT price FROM prices WHERE datetime = '2023-10-01 00:00:00', and calculates the electricity price using a specific algorithm (e.g., average, weighted average) based on the obtained price information.

[1818] Step 4:

[1819] Calculating the user's electricity bill

[1820] The server calculates the electricity charge for the user using the calculated electricity unit price.

[1821] Input: User's power consumption data, calculated electricity rate

[1822] Output: Calculated user electricity charges

[1823] Specific operation: The server retrieves the user's power consumption data from the database. For example, using the pymysql library, it executes a query such as SELECT consumption FROM user_data WHERE user_id = 1, multiplies the retrieved power consumption data (e.g., 100 kWh) by the calculated unit price (e.g., 10 yen / kWh), and calculates the user's electricity bill (e.g., 1,000 yen).

[1824] Step 5:

[1825] Receiving home appliance usage information

[1826] The server receives home appliance usage information from the user.

[1827] Input: Home appliance usage information entered by the user (type of appliance, usage time)

[1828] Output: Appliance usage information stored on the server

[1829] Specific operation: The user inputs the type of appliances they want to use and the usage time through a web browser or mobile application, and sends the information to the server. The server then stores the received information in a database.

[1830] Step 6:

[1831] Calculating the best time to use

[1832] The server calculates the optimal usage time period based on the received home appliance usage information.

[1833] Input: Home appliance usage information, electricity rate

[1834] Output: Best time to use

[1835] Specific operation: The server runs an algorithm to calculate the optimal usage time based on the electricity rate and appliance usage information, for example, to avoid peak hours.

[1836] Step 7:

[1837] Proposal Notification

[1838] The server then proposes the calculated optimal usage time period and electricity rate to the user.

[1839] Input: Optimal usage time, calculated electricity price

[1840] Output: User notification

[1841] Specific operation: The server notifies the user of the calculation results, and the user confirms the proposal through a web browser or mobile application, for example, by sending a push notification or email.

[1842] (Application example 1)

[1843] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1844] Conventional electricity bill calculation systems based on market-linked plans only suggest optimal usage times based on the user's appliance usage information, but do not optimize power consumption in real time. Therefore, there is a need to further reduce electricity costs by obtaining trading price information from the power exchange in real time and optimizing power consumption.

[1845] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1846] In this invention, the server includes means for calculating electricity charges based on a market-linked plan, means for receiving home appliance usage information from a user, means for calculating optimal usage time periods based on the received home appliance usage information, means for proposing the calculated optimal usage time periods and electricity charges to the user, means for acquiring trading price information from an electricity exchange in real time, means for optimizing electricity consumption based on the acquired price information, and means for reducing electricity consumption during high price time periods and increasing electricity consumption during low price time periods, thereby enabling optimization of electricity consumption in real time.

[1847] A "market-linked plan" is a rate plan in which electricity rates are determined based on the trading price at the power exchange.

[1848] A "means for calculating electricity rates" is a method or device for calculating electricity rates based on a market-linked plan.

[1849] The "means for receiving home appliance usage information from a user" refers to a method or device for acquiring information such as the type of home appliance used by the user and the duration of use.

[1850] The "means for calculating the optimum usage time period" is a method or device for calculating the optimum usage time period for optimizing power consumption based on the received home appliance usage information.

[1851] The "means for making suggestions to the user" refers to a method or device for notifying the user of the calculated optimal usage time period and electricity charges.

[1852] The "means for obtaining trading price information from the power exchange in real time" refers to a method or device for obtaining trading price information from the power exchange in real time.

[1853] "Means for optimizing electricity consumption" refers to methods and devices for efficiently managing and adjusting electricity consumption based on acquired trading price information.

[1854] "Means for reducing electricity consumption during high price periods and increasing electricity consumption during low price periods" refers to a method or device for reducing electricity consumption during periods when the electricity trading price is high and increasing electricity consumption during periods when the trading price is low.

[1855] To implement the present invention, the following system configuration and program are required.

[1856] System Configuration

[1857] 1. Server: Calculates electricity rates based on market-linked plans and obtains real-time trading price information from the power exchange.

[1858] 2. Device: Receives information on home appliance usage from the user, calculates the optimal usage time, and makes suggestions to the user.

[1859] 3. Network: A communication method for sending and receiving data between the server and the terminal.

[1860] Program processing explanation

[1861] Hardware

[1862] Server: A computer with a powerful processor and large memory capacity.

[1863] Terminal: A device with a user interface, such as a smartphone or tablet.

[1864] Network: Internet connection.

[1865] software

[1866] Python: The primary implementation language for the program.

[1867] Requests library: A library for obtaining electricity exchange trading price information from the API.

[1868] JSON Data Processing: The format for parsing the retrieved data.

[1869] Data processing and calculation

[1870] 1. The server obtains real-time trading price information from the power exchange using an API and saves the data in JSON format.

[1871] 2. The server calculates the electricity charges based on the market-linked plan. Based on the acquired trading price information, it calculates the unit price of the electricity charge and combines it with the user's electricity consumption data to calculate the total electricity charge.

[1872] 3. The device receives information about the user's use of the appliance, including the type of appliance they want to use and the duration of their use.

[1873] 4. The server calculates the optimal usage time based on the received appliance usage information. To do this, it selects the time period when the electricity trading price is low and suggests using the appliance during that time period.

[1874] 5. The device will then suggest the optimal usage time and electricity rates to the user, using the notification function to provide information to the user.

[1875] 6. The server optimizes power consumption based on the acquired price information, reducing power consumption during high-price periods and increasing power consumption during low-price periods.

[1876] Specific examples

[1877] For example, if this system is used on a factory production line, electricity costs can be reduced by reducing machine operation during times when prices on the electricity exchange are high and increasing operation during times when prices are low.

[1878] Prompt Sentence Examples

[1879] "Create a Python program that obtains real-time trading price information from the power exchange and optimizes power consumption within the factory. The program should have the ability to reduce power consumption during times when prices are high and increase power consumption during times when prices are low."

[1880] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1881] Step 1:

[1882] The server obtains trading price information from the power exchange in real time. Specifically, it obtains trading price information from the power exchange using an API and saves it in JSON format. The input is the API endpoint, and the output is the obtained trading price information.

[1883] Step 2:

[1884] The server calculates the electricity bill based on the market-linked plan. Based on the acquired transaction price information, it calculates the unit price of the electricity bill and combines it with the user's electricity consumption data to calculate the total electricity bill. The input is the transaction price information and the user's electricity consumption data, and the output is the total electricity bill.

[1885] Step 3:

[1886] The terminal receives home appliance usage information from the user. The user inputs the type of home appliance they want to use and the usage time, and the terminal receives it. The input is the user's home appliance usage information, and the output is the received home appliance usage information.

[1887] Step 4:

[1888] The server calculates the optimal usage time based on the received appliance usage information. It selects a time period when the electricity trading price is low and suggests using the appliance during that time period. The input is appliance usage information and trading price information, and the output is the optimal usage time period.

[1889] Step 5:

[1890] The terminal proposes the calculated optimal usage time and electricity rate to the user. The notification function is used to provide information to the user. The input is the optimal usage time and electricity rate, and the output is a notification to the user.

[1891] Step 6:

[1892] The server optimizes power consumption based on the acquired price information. It reduces power consumption during high-price periods and increases power consumption during low-price periods. The input is trading price information, and the output is the optimized power consumption pattern.

[1893] Example 2

[1894] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1895] Conventional electricity rate optimization systems based on home appliance usage information have difficulty efficiently processing the information entered by users and calculating the optimal usage time periods. Furthermore, there was a lack of a means to quickly provide the results of the calculations to users, resulting in low user convenience. Furthermore, there was a need for a system that could comprehensively calculate electricity rates based on market-linked plans and propose optimal usage time periods.

[1896] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1897] In this invention, the server includes means for receiving home appliance usage information from a user, means for storing the received home appliance usage information in a database, means for calculating optimal usage time periods based on the stored data, means for proposing the calculated optimal usage time periods and electricity rates to the user, means for displaying the calculation results to the user, and means for calculating electricity rates based on a market-linked plan. This makes it possible to efficiently process the home appliance usage information entered by the user and quickly provide optimal usage time periods and electricity rates.

[1898] A "market-linked plan" is a rate plan in which electricity rates are determined based on the trading price at the power exchange.

[1899] "Means for calculating electricity charges" means algorithms or software for calculating electricity charges based on market-linked plans.

[1900] "Means for receiving home appliance usage information from users" refers to interfaces or devices for receiving information such as the type of home appliance and usage time entered by the user.

[1901] "Means for storing in a database" refers to software or hardware for storing received home appliance usage information in a relational database management system (RDBMS) or similar.

[1902] "Means for calculating optimal usage times" refers to algorithms or software that use stored data to analyze past usage data and power consumption patterns to calculate optimal usage times.

[1903] "Means for proposing calculation results to users" refers to an interface or device for notifying users of the calculated optimal usage time and electricity charges.

[1904] "Means for displaying the calculation results to the user" refers to software or hardware for displaying the calculation results on the user's terminal.

[1905] MODE FOR CARRYING OUT THE INVENTION

[1906] This invention is a system that receives information on the use of home appliances from a user, calculates the optimal usage time period and electricity charges based on that information, and proposes them to the user. A specific embodiment of this system will be described below.

[1907] System configuration

[1908] This system consists of three main components: a server, a terminal, and a user.

[1909] 1. Server

[1910] The server provides a means for receiving appliance usage information from users, which is an interface accessible through a web browser or a mobile application.

[1911] The server stores the received appliance usage information in a database, which uses a relational database management system (RDBMS) such as MySQL or PostgreSQL.

[1912] The server calculates the optimal usage time based on the stored data, using programming languages ​​such as Python and R and leveraging data analysis libraries (e.g., Pandas and NumPy).

[1913] The server has a means to propose the calculated optimal usage time and electricity rate to the user. This proposal is made in real time.

[1914] The server has a means to send the calculation results to the terminal for display to the user. This transmission is securely performed using the HTTPS protocol.

[1915] 2. Terminal

[1916] The device sends the home appliance usage information entered by the user to the server. This transmission is securely transmitted using the HTTPS protocol.

[1917] The device then displays the calculation results received from the server to the user, for example, displaying "The best time to use the device is between 7:00 PM and 9:00 PM" on the mobile application screen.

[1918] 3. Users

[1919] Users use a web browser or mobile application to input the type of appliance they want to use and the time they want to use it. For example, if a user wants to use an air conditioner, they enter "air conditioner" and "from 18:00 to 22:00."

[1920] Specific examples

[1921] As a specific example, consider the case where a user wants to use an air conditioner.

[1922] 1. A user opens the mobile application and enters "air conditioning" and "18:00 to 22:00."

[1923] 2. The device sends this information to the server using the HTTPS protocol.

[1924] 3. The server stores the received information in the "user_requests" table.

[1925] 4. The server uses Python's Pandas library to analyze past usage data and power consumption patterns and calculate the optimal usage time.

[1926] 5. The server sends the calculation result "The best time is between 19:00 and 21:00" to the terminal.

[1927] 6. The device displays the result on the mobile application screen and notifies the user.

[1928] Prompt Sentence Examples

[1929] Example prompts to input to a generative AI model:

[1930] A user wants to use the air conditioner. The usage time is entered as "18:00 to 22:00." Please calculate the optimal usage time based on past usage data and power consumption patterns.

[1931] In this way, a system is realized in which the server, terminal, and user work together to calculate the optimal time periods for using home appliances and provide the results to the user.

[1932] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1933] Step 1:

[1934] The user inputs the home appliance usage information.

[1935] Users use a web browser or mobile application to input the type of appliance they want to use and the time they want to use it. For example, if a user wants to use an air conditioner, they enter "air conditioner" and "from 18:00 to 22:00." The input information is saved in an input field on the device.

[1936] Step 2:

[1937] The terminal sends the input information to the server.

[1938] The device sends the appliance usage information entered by the user to the server using the HTTPS protocol. Specifically, it converts the entered information into JSON format and sends a POST request to the server's API endpoint. The input is the type of appliance and usage time, and the output is the status of completion of transmission to the server.

[1939] Step 3:

[1940] The server stores the input information in a database.

[1941] The server stores the received appliance usage information in a database. Specifically, the server parses the received JSON data and stores it in a relational database management system (RDBMS) such as MySQL or PostgreSQL. The input is the received appliance usage information, and the output is the status of completion of saving to the database.

[1942] Step 4:

[1943] The server calculates the optimal time of day for use based on the stored data.

[1944] The server calculates the optimal usage time based on the stored data. Specifically, it uses Python's Pandas library to analyze past usage data and power consumption patterns to calculate the optimal usage time. The input is the appliance usage information and past usage data stored in the database, and the output is the optimal usage time.

[1945] Step 5:

[1946] The server sends the calculation results to the terminal.

[1947] The server sends the calculation result to the terminal. Specifically, it converts the calculated optimal usage time slot into JSON format and sends it to the terminal using the HTTPS protocol. The input is the calculation result of the optimal usage time slot, and the output is the status of completion of transmission to the terminal.

[1948] Step 6:

[1949] The terminal displays the calculation results to the user.

[1950] The terminal displays the calculation results received from the server to the user. Specifically, it displays "The best time to use the device is from 19:00 to 21:00" on the mobile application screen. The input is the calculation result of the best time to use the device received from the server, and the output is the content displayed to the user.

[1951] (Application example 2)

[1952] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1953] Conventional systems for calculating optimal usage times based on information on home appliance usage only cover information on home appliance usage, and are not compatible with optimizing charging schedules for autonomous vehicles. This makes it difficult for users to calculate optimal charging times based on autonomous vehicle operation schedules and improve energy management efficiency.

[1954] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for calculating an electricity rate based on a market-linked plan, means for receiving home appliance usage information from a user, means for calculating an optimal usage time period based on the received home appliance usage information, means for proposing the calculated optimal usage time period and electricity rate to the user, means for receiving operation schedule information from the user and calculating an optimal charging time period, and means for notifying the user of the calculated optimal charging time period. This enables the user to calculate the optimal charging time based on the operation schedule of the autonomously driving vehicle and improve the efficiency of energy management.

[1955] A "market-linked plan" is a rate plan in which electricity rates are determined based on the trading price on the electricity exchange.

[1956] A "means for calculating electricity charges" is a device or program for calculating electricity charges based on a market-linked plan.

[1957] "Home appliance usage information" is information about the type of home appliance that the user wants to use and the duration of use.

[1958] The "means for calculating the optimum use time period" is a device or program for calculating the optimum use time period of the home appliance based on the received home appliance use information.

[1959] "Means for making suggestions to users" refers to a device or program for notifying or displaying to users the calculated optimal usage time period and electricity charges.

[1960] "Operation schedule information" refers to information such as departure time, arrival time, and destination that is input by the user regarding the operation of an autonomous vehicle.

[1961] The "means for calculating the optimal charging time slot" is a device or program for calculating the optimal charging time slot for an autonomously driven vehicle based on the received operation schedule information.

[1962] The "notification means" is a device or program for notifying the user of the calculated optimal charging time period.

[1963] A system for implementing the present invention includes a server, a user terminal, and an autonomous vehicle. The server includes the following means:

[1964] 1. Means for calculating electricity tariffs under market-linked plans

[1965] 2. Means of receiving information on home appliance usage from users

[1966] 3. A method for calculating optimal usage times based on received appliance usage information

[1967] 4. A method for proposing the calculated optimal usage time and electric...

Claims

1. A means for acquiring transaction price information from an electricity market; A means for calculating a unit price of electricity for a market-linked plan based on the acquired transaction price information; A means for acquiring the amount of power consumption of a user; A means for calculating an electricity charge based on the unit price of the electricity and the amount of power consumption; A means for notifying a user of the calculation result of an electricity charge; A means for collecting data including user voice, facial expression, and behavior; A means of analyzing collected data and recognizing user emotions; A means for proposing optimal times for using home appliances based on the recognized emotion; A means of informing users of the suggestions; and A system including:

2. The system according to claim 1 , wherein the market-linked plan is a plan in which the electricity rate is determined according to a trading price in an electricity market.

3. The system according to claim 1 , wherein the home appliance usage information from the user includes a type of home appliance desired to be used and a usage time.

Images