JP7749778B2 - system - Google Patents

Description

The technology disclosed herein relates to a system.

Patent document 1 discloses a persona chatbot control method executed 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's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Japanese Patent Application Laid-Open No. 2022-180282

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.

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 times based on the received home appliance usage information, and a means for proposing the calculated optimal usage times and electricity charges to the user. This allows users to understand the optimal usage times and electricity charges based on their own home appliance usage habits, enabling them to efficiently reduce costs.

1 is a conceptual diagram illustrating an example of the configuration of a data processing system according to a first embodiment. 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. FIG. 10 is a conceptual diagram illustrating an example of the configuration of a data processing system according to a second embodiment. 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. FIG. 10 is a conceptual diagram illustrating an example of the configuration of a data processing system according to a third embodiment. 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. FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. 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. 1 shows an emotion map onto which multiple emotions are mapped. 1 shows an emotion map onto which multiple emotions are mapped. FIG. 10 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. FIG. 10 is a sequence diagram showing a processing flow of the data processing system in Application Example 1 of Form Example 1. FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2. FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a third embodiment of the third form example. FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3. 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. 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. 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. 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. 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. 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.

Below, an example of an embodiment of a system relating to the technology disclosed herein will be described with reference to the accompanying drawings.

First, let me explain the terminology used in the following explanation.

In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, a processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose Computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

In the following embodiments, coded random access memory (RAM) is memory in which information is temporarily stored and is used as work memory by the processor.

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

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

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 just A, just B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and/or B" also applies when three or more things are expressed connected by "and/or."

[First embodiment]

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

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

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, RAM 30, and storage 32. 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).

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, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

The reception device 38 is equipped with a touch panel 38A, a microphone 38B, etc., and receives user input. The touch panel 38A detects contact with an indicator (e.g., a pen or finger) to receive user input via the indicator. The microphone 38B detects the user's voice to receive user input via 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.

The output device 40 is equipped with a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible by the user 20 (e.g., 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.

The communication I/F 44 is connected to the 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.

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

As shown in FIG. 2, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" 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.

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.

In the smart device 14, the processor 46 performs the reception output processing. 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 processing 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.

Next, we will explain the identification process performed by the identification processing unit 290 of the data processing device 12.

"Example 1"

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 electricity rates.

"Example 2"

Another aspect of the present invention is a system that receives home appliance usage information 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 wish to use and the usage time, and calculates the optimal usage time period based on that information.

"Example 3"

Another aspect 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 reducing electricity bills. Specifically, when a user inputs the type of home appliance they wish 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 them with an electricity rate calculated based on the trading price at the power exchange for that time period.

The processing flow for each example form is explained below.

"Example 1"

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

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

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

"Example 2"

Step 1: Receive input from the user about the type of appliance they wish to use and the usage time.

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

"Example 3"

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

Step 2: Calculate the electricity bill based on the trading price on the power exchange for that time period.

Step 3: Present the calculated electricity bill to the user.

(Example 1)

Next, we will explain Example 1 of Form Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

Conventional electricity rate calculation systems struggled to reflect real-time trading price information from the power exchange, making it impossible to provide users with optimal electricity rates. Furthermore, they lacked functionality for suggesting optimal usage times based on users' appliance usage information, making it difficult for users to use electricity efficiently.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

In this invention, the server includes means for acquiring trading price information from the power exchange, means for saving the acquired trading price information in a database, means for calculating the unit price of electricity based on the saved 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 the optimal usage time period based on the received home appliance usage information, and means for proposing the calculated optimal usage time period and electricity bill to the user. This makes it possible to calculate electricity bills that reflect trading price information in real time, and furthermore, by proposing the optimal usage time period to the user, efficient power use is possible.

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

"Trading price information" refers to information regarding the price of electricity traded at the power exchange.

A "database" is a system for efficiently storing, managing, and searching data.

"Electricity unit price" is the unit price set for a certain amount of electricity.

"User" refers to an individual or corporation that consumes electricity and is the entity that uses this system.

"Home appliance usage information" refers to information about the types of home appliances used by users and the duration of use.

"Optimal usage times" are the recommended times for efficient power consumption.

"Proposal means" refers to a method or device for notifying the user of the calculated optimal usage time period and electricity charges.

Form for implementing the invention

This invention is 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. A specific embodiment of this system is described below.

Hardware and software used

1. Server

Hardware: General server equipment

Software: API client, database management system (e.g., MySQL (registered trademark), PostgreSQL), scripting language (e.g., Python, JavaScript (registered trademark))

2. Device

Hardware: The computer or smartphone used by the user

Software: Web browser, mobile application

3. User

Users are individuals or corporations that consume electricity and are the entities that use this system.

Program processing explanation

1. Obtaining trading price information

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

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

2. Saving transaction price information

The server stores the acquired trading price information in a database.

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

3. Calculating electricity unit prices

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

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

4. Calculating the user's electricity bill

The server calculates the user's electricity bill using the calculated electricity unit price.

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.

5. Receiving appliance usage information

The server receives home appliance usage information from the user.

Specific operation: The user enters the type of home appliance they wish to use and the usage time via a web browser or mobile application, and sends this information to the server.

6. Calculating the optimal usage time

The server calculates the optimal usage time based on the received appliance usage information.

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

7. Proposal Notification

The server then proposes the optimal usage time and electricity rates to the user.

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.

Examples and prompts

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

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

3. The server uses a Python script to calculate the electricity rate for "2023-10-01" from the trading price information.

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).

Example prompt

"Get the latest trading price information from the power exchange API and save 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 power consumption data."

The flow of the identification process in Example 1 will be explained using Figure 11.

Step 1:

Get trading price information

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

Input: Power exchange API endpoint URL

Output: Transaction price information in JSON format

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

Step 2:

Storing transaction price information

The server stores the acquired trading price information in a database.

Input: Transaction price information in JSON format

Output: Transaction price information stored in the database

Specific behavior: The server parses the received JSON data and extracts the necessary fields (e.g., datetime, price). Then, using the pymysql library, it connects to a MySQL database and executes a query such as INSERT INTO prices (datetime, price) VALUES ('2023-10-01 00:00:00', 10.5).

Step 3:

Calculating electricity unit prices

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

Input: Transaction price information stored in the database

Output: Calculated electricity unit price

Specific operation: The server executes a Python script to obtain trading price information from the database. For example, using the pymysql library, it executes 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.

Step 4:

Calculating your electricity bill

The server calculates the user's electricity bill using the calculated electricity unit price.

Input: User power consumption data, calculated electricity rate

Output: Calculated user electricity bill

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., 100kWh) by the calculated unit price (e.g., 10 yen/kWh), and calculates the user's electricity bill (e.g., 1,000 yen).

Step 5:

Receive appliance usage information

The server receives home appliance usage information from the user.

Input: Appliance usage information entered by the user (type of appliance, usage time)

Output: Appliance usage information stored on the server

Specific operation: The user enters the type of home appliance they want to use and the usage time via a web browser or mobile application, and sends this information to the server. The server then stores the received information in a database.

Step 6:

Calculating the best time to use

The server calculates the optimal usage time based on the received appliance usage information.

Input: Appliance usage information, electricity unit price

Output: Optimal usage time

Specific operation: The server runs an algorithm to calculate the optimal usage time based on the electricity rate and appliance usage information. For example, it calculates the optimal usage time to avoid peak hours.

Step 7:

Proposal Notification

The server then proposes the optimal usage time and electricity rates to the user.

Input: Optimal usage time, calculated electricity rate

Output: User notification

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. For example, a push notification or email is sent.

(Application Example 1)

Next, we will explain Application Example 1 of Form Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

Conventional electricity billing 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 real-time trading price information from the power exchange and optimizing power consumption.

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

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 the power 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. This enables optimization of electricity consumption in real time.

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

"Means for calculating electricity charges" refers to a method or device for calculating electricity charges based on a market-linked plan.

"Means for receiving home appliance usage information from users" refers to methods or devices for obtaining information such as the types of home appliances used by users and the duration of use.

"Means for calculating optimal usage time periods" refers to a method or device that calculates optimal usage time periods for optimizing power consumption based on received home appliance usage information.

"Means for making suggestions to users" refers to methods or devices for notifying users of the calculated optimal usage time periods and electricity charges.

"Means for obtaining trading price information from an electricity exchange in real time" refers to a method or device for obtaining trading price information from an electricity exchange in real time.

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

"Means for reducing electricity consumption during high-price time periods and increasing electricity consumption during low-price time periods" refers to a method or device for reducing electricity consumption during times when the electricity trading price is high and increasing electricity consumption during times when the trading price is low.

To implement this invention, the following system configuration and program are required.

System Configuration

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

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

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

Program processing explanation

Hardware

Server: A computer with a high-performance processor and large memory capacity.

Device: A device with a user interface, such as a smartphone or tablet.

Network: Internet connection.

Software

Python: The primary implementation language for the program.

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

JSON data processing: A format for parsing retrieved data.

Data processing and calculations

1. The server obtains trading price information from the power exchange in real time. To do this, it uses an API to obtain the data and saves it in JSON format.

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

3. The device receives appliance usage information from the user, including the type of appliance they want to use and the duration of their use.

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

5. The device will then suggest the optimal usage time and electricity rates to the user. This will be done using the notification function to provide information to the user.

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.

Specific examples

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.

Example prompt

"Create a Python program that obtains real-time trading price information from the power exchange and optimizes power consumption within the factory. It should have the ability to reduce power consumption during high-price periods and increase power consumption during low-price periods."

The flow of the identification process in Application Example 1 will be explained using Figure 12.

Step 1:

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

Step 2:

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

Step 3:

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 this. The input is the user's home appliance usage information, and the output is the received home appliance usage information.

Step 4:

The server calculates the optimal usage time period 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.

Step 5:

The device then proposes the calculated optimal usage time and electricity rate to the user. It uses the notification function 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.

Step 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. The input is trading price information, and the output is the optimized power consumption pattern.

(Example 2)

Next, we will explain Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

Existing electricity rate optimization systems based on home appliance usage information struggled to efficiently process user-entered information and calculate optimal usage times. Furthermore, there was a lack of a way to quickly provide users with the results, resulting in poor user convenience. Furthermore, there was a need for a system that could comprehensively calculate electricity rates based on market-linked plans and suggest optimal usage times.

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

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 the optimal usage time period based on the stored data, means for proposing the calculated optimal usage time period and electricity rate to the user, means for displaying the calculation results to the user, and means for calculating the electricity rate 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 the optimal usage time period and electricity rate.

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

"Means for calculating electricity charges" refers to algorithms or software for calculating electricity charges based on market-linked plans.

"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.

"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.

"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.

"Means for proposing calculation results to users" refers to interfaces or devices that notify users of the calculated optimal usage times and electricity charges.

"Means for displaying calculation results to the user" refers to software or hardware for displaying calculation results on the user's device.

Form for implementing the invention

This invention is a system that receives information about home appliance usage from users, calculates optimal usage times and electricity rates based on that information, and makes recommendations to the user. A specific embodiment of this system is described below.

System Configuration

This system consists of three main elements: a server, a terminal, and a user.

1. Server

The server provides a means for receiving appliance usage information from users. This means is an interface that can be accessed through a web browser or mobile application.

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

The server calculates the optimal usage time based on the stored data. This calculation is done using programming languages such as Python and R, and utilizing data analysis libraries (e.g., Pandas, NumPy).

The server has a means of proposing the calculated optimal usage time and electricity rates to the user. This proposal is made in real time.

The server has a means of sending the calculation results to the terminal for display to the user. This transmission is securely performed using the HTTPS protocol.

2. Device

The device sends the home appliance usage information entered by the user to the server. This transmission is secure, using the HTTPS protocol.

The device displays the calculation results received from the server to the user. For example, the mobile application screen might display "The best time to use the device is between 7:00 PM and 9:00 PM."

3. User

Users use a web browser or mobile application to enter 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."

Specific examples

As a concrete example, consider the case where a user wants to use the air conditioner.

1. The user opens the mobile application and enters "air conditioner" and "from 18:00 to 22:00."

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

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

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

5. The server sends the calculation result "The best time is between 7:00 PM and 9:00 PM" to the device.

6. The device displays the results on the mobile application screen and notifies the user.

Example prompt

Example of a prompt to input to a generative AI model:

The 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.

In this way, a system is realized in which the server, terminal, and user work together to calculate the optimal usage time for home appliances and provide it to the user.

The flow of the identification process in Example 2 will be explained using Figure 13.

Step 1:

The user enters appliance usage information.

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

Step 2:

The device sends the input information to the server.

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.

Step 3:

The server saves the input information in a database.

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.

Step 4:

The server calculates the optimal usage time based on the stored data.

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.

Step 5:

The server sends the calculation results to the device.

The server sends the calculation results to the device. Specifically, it converts the calculated optimal usage time slot into JSON format and sends it to the device 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 device.

Step 6:

The terminal displays the calculation results to the user.

The device displays the calculation results received from the server to the user. Specifically, the mobile application screen displays "The optimal usage time is from 19:00 to 21:00." The input is the calculation result of the optimal usage time received from the server, and the output is the content displayed to the user.

(Application Example 2)

Next, we will explain Application Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

Existing systems that calculate optimal usage times based on home appliance usage information only consider information about home appliance usage and do not support 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.

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 electricity charges based on a market-linked plan, 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, means for proposing the calculated optimal usage time periods and electricity charges to the user, means for receiving operation schedule information from the user and calculating optimal charging time periods, and means for notifying the user of the calculated optimal charging time periods. This allows the user to calculate the optimal charging time based on the operation schedule of the autonomous vehicle and improve energy management efficiency.

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

"Means for calculating electricity charges" means a device or program for calculating electricity charges based on a market-linked plan.

"Home appliance usage information" refers to information about the type of home appliance the user wants to use and the duration of use.

"Means for calculating optimal usage time periods" refers to a device or program for calculating optimal usage time periods for home appliances based on received home appliance usage information.

"Means for making suggestions to users" refers to a device or program that notifies or displays to users the calculated optimal usage time and electricity charges.

"Scheduled operation information" refers to information such as departure time, arrival time, and destination entered by the user regarding the operation of an autonomous vehicle.

"Means for calculating the optimal charging time slot" refers to a device or program for calculating the optimal charging time slot for an autonomous vehicle based on the received operation schedule information.

"Notification means" refers to a device or program that notifies the user of the calculated optimal charging time period.

A system for implementing this invention consists of a server, a user terminal, and an autonomous vehicle. The server includes the following means:

1. A method for calculating electricity rates based on market-linked plans

2. How to receive appliance usage information from users

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

4. A method for suggesting the calculated optimal usage time and electricity rates to users

5. A means of receiving schedule information from users and calculating the optimal charging time slots

6. A method for notifying users of the calculated optimal charging time slot

Program processing explanation

The server is implemented using a programming language such as Python. It receives appliance usage information and operation schedule information sent from the user's device, and calculates the optimal usage and charging times based on this information.

Specifically, the server performs the following processes:

1. Calculating electricity charges:

The server obtains the trading price from the power exchange based on the market-linked plan and calculates the electricity bill. This calculation involves obtaining data from the power exchange via an API.

2. Receiving and processing home appliance usage information:

The system receives appliance usage information (type of appliance to be used and usage time) sent from the user's device and calculates the optimal usage time. It uses an AI model to calculate and avoid peak hours of power consumption.

3. Receiving and processing schedule information:

The system receives flight schedule 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.

4. Notice:

The user will be notified of the calculated optimal usage and charging times. Notifications will be sent to the user's device via push notification or email.

Specific examples

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

Departure time: 2023-10-01 08:00:00

Arrival time: 2023-10-01 10:00:00

Destination: Office

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).

Example of a prompt to input to a generative AI model:

The user has entered a scheduled autonomous vehicle trip. 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. Please calculate the optimal charging time based on this information.

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

The flow of the specific processing in Application Example 2 will be explained using Figure 14.

Step 1:

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

Step 2:

The server receives 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. Based on this information, the server processes the data to calculate the optimal usage time period.

Step 3:

The server obtains trading prices from the power exchange based on a market-linked plan. It then calculates electricity charges based on the obtained trading price data. Calculations involve obtaining data from the power exchange via an API and taking real-time price fluctuations into account.

Step 4:

The server calculates the optimal usage time based on the received appliance usage information and the acquired trading 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.

Step 5:

The server then proposes the calculated optimal usage time and electricity rates to the user. The proposal is sent to the user's device via push notification or email. The user can check the proposed optimal usage time and plan their home appliance usage.

Step 6:

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.

Step 7:

The server calculates the optimal charging time based on the received flight schedule information. It uses an AI model to determine the optimal charging time based on the flight schedule. Specifically, it applies an algorithm that starts charging two hours before departure.

Step 8:

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

In this way, optimal usage and charging times can be calculated based on the user's home appliance usage information and operation schedule information, making it possible to improve energy management efficiency.

(Example 3)

Next, we will explain Example 3 of Form Example 3. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

With conventional electricity bill calculation systems, it was difficult for users to know the optimal time to use their home appliances, making it difficult to effectively reduce electricity bills. Furthermore, because electricity bills were not calculated in real time based on trading prices at 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 making it difficult to use energy efficiently.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

In this invention, the server includes means for receiving home appliance usage information from the user, means for acquiring trading price data from the power 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 results to the user. This allows the user to know the optimal time period for using home appliances and the electricity rate for that time period in real time, allowing for effective savings on electricity bills.

"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.

"Means for obtaining trading price data from the power exchange" refers to devices or software that have the functionality to access the power exchange's API and obtain the latest trading price data.

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

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

"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 optimal usage times to the user.

Form for implementing the invention

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 the user. This system receives home appliance usage information from the user, acquires trading price data from the power exchange, processes the acquired data to calculate the electricity rate, and presents the results to the user.

Hardware and software used

This system uses the following hardware and software:

Server: A computer system for data processing and calculations.

Device: The device through which the user enters information (smartphone, tablet, PC, etc.).

Python: A programming language used to develop programs.

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

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

Program processing

The server receives input from the user. When the user enters the type of appliance they want to use and the duration of use, 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 appliance's power consumption.

Specific examples

For example, if a user enters "I want the air conditioner to run from 3:00 PM to 6:00 PM," the server will process it as follows:

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

2. The device sends this input data to the server.

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

4. The server receives the trading price data returned from the API and converts it into a data frame using Pandas.

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

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

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."

8. The server sends this message to the device.

9. The device displays a message to the user.

Example prompt

Example of a prompt to input to a generative AI model:

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 trading price on the power exchange for that time period and displays it to the user.

In this way, users can learn the optimal usage times and electricity rates, and use this information as reference for reducing electricity costs. The flow of the identification process in Example 3 will be explained using Figure 15.

Program processing flow

Step 1: Receive user input

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

Specific actions:

Input: The user enters "Air conditioning, 15:00-18:00" into the device's input form.

Output: The device sends this input data to the server.

Step 2: Obtaining electricity trading price data

The server retrieves the latest trading price data from the power exchange's API. This data is used to calculate the electricity rate for the specified time period.

Specific actions:

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

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

Output: The server receives the trading price data returned from the API.

Step 3: Process the data and calculate the electricity bill

The server processes the acquired trading price data using the Pandas library and calculates the electricity rate for the specified time period. To do this, it multiplies the power consumption of the appliance by the trading price.

Specific actions:

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

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

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

Output: Calculated electricity bill data.

Step 4: Presenting the calculation results

The server then presents the calculation results to the user, allowing the user to obtain reference information for reducing electricity bills.

Specific actions:

Input: Calculated electricity rate data.

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, your electricity bill will be XX yen."

Output: The server sends this message to the terminal.

Action: The device displays a message to the user.

In this way, users can learn the optimal times to use electricity and electricity rates, which can be used as reference information to reduce electricity bills.

(Application Example 3)

Next, we will explain Application Example 3 of Form Example 3. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

When it comes to electricity usage, users often have difficulty knowing the optimal time of day to use the power in order to reduce their electricity bills. Furthermore, when charging electric vehicles in particular, electricity rates vary greatly depending on the time of day, making it difficult for users to select the optimal time of day to charge. For this reason, there is a demand for a system that allows users to use electricity efficiently and reduce their electricity bills.

The specific processing performed by the specific 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 electricity charges based on a market-linked plan, means for receiving electricity usage information from the user, means for calculating optimal usage time periods based on the received electricity 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 electricity usage time periods entered by the user, and means for presenting the calculated optimal electricity 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.

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

"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.

"Electricity usage information" refers to information such as the type of electrical equipment used by the user and the duration of use.

"Means for calculating the optimal usage time period" refers to a device or program that has the function of calculating the time period with the lowest electricity rates based on the received electricity usage information.

"Means for making suggestions to users" refers to a device or program that has the function of notifying users of the calculated optimal usage time period and electricity charges.

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

"Means for calculating the time period with the cheapest electricity rates" refers to 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.

"Means for presenting estimated electricity charges to users" refers to a device or program that has the function of notifying users of the calculated optimal time periods for electricity usage and the estimated electricity charges for those times.

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

System Configuration

1. Server

The server runs a program with the following functions:

Function to calculate electricity rates based on market-linked plans

A function to receive power usage information from users

A function that calculates the optimal usage time based on received power usage information.

A function that suggests optimal usage times and electricity rates to users.

A function that calculates the cheapest electricity rate among the electricity usage periods entered by the user.

A function that displays the calculated optimal power usage times and estimated electricity charges to the user.

2. User Device

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

3. Power Exchange API

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

Program processing explanation

The server calculates electricity charges 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:

1. Receiving power usage information

Power usage information entered by the user on the device (for example, the time period during which they wish to charge) is sent to the server.

2. Calculating your electricity bill

The server uses the power exchange's API to obtain the trading price for the specified time period. It then calculates the electricity bill based on the obtained trading price.

3. Calculating the optimal usage time

The server calculates the cheapest electricity rate among the time periods entered by the user. This calculation uses real-time trading price data.

4. Presentation of results

The calculation results (optimal usage times and estimated electricity rates) are sent to the user's terminal and presented to the user.

Specific examples

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

Example prompt

Example of a prompt to input to a generative AI model:

Design a system that, when a user inputs the time period they wish to charge their electric vehicle, calculates the optimal charging time and electricity rate based on the trading price on the power exchange for that time period and presents it to the user. For example, if a user inputs "I would like to charge between 3:00 PM and 6:00 PM on October 1, 2023," the system will calculate and present the time period with the cheapest electricity rate within that time period.

The flow of the specific processing in Application Example 3 will be explained using Figure 16.

Step 1:

The user enters power usage information from the device.

Specifically, users use 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.

Input: Time of day you want to charge

Output: Power usage information sent to the server

Step 2:

The server receives power usage information.

The server receives and analyzes the power usage information sent from the user's device. The received information includes the time period during which the user wishes to charge.

Input: Power usage information submitted by the user

Output: Analyzed power usage information

Step 3:

The server obtains trading prices from the power exchange API.

The server uses the power exchange's API to obtain the trading price for the specified time period. The obtained trading price is used to calculate the electricity bill.

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

Output: Trading price for the specified time period

Step 4:

The server calculates the electricity bill.

The server calculates the electricity charges for the time period specified by the user based on the acquired transaction price. The transaction price and usage time are used for the calculation.

Input: Trading price for the specified time period, usage time

Output: Calculated electricity cost

Step 5:

The server will calculate the optimal usage time.

The server calculates the cheapest electricity rate among the time periods entered by the user. This calculation uses real-time trading price data.

Input: Trading price for the specified time period, usage time

Output: Optimal usage time

Step 6:

The server presents the calculation results to the user.

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

Input: Optimal usage time, calculated electricity rate

Output: Optimal usage times and estimated electricity rates presented to the user

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.

"Example 1"

One embodiment of the present invention provides a system incorporating an emotion engine. This system recognizes emotions from the user's voice, facial expressions, actions, 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). Alternatively, 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.

"Example 2"

In another embodiment of the present invention, a system is provided that adjusts 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 for entertainment-oriented home appliances and proposes an increase in the resulting electricity bill. On the other hand, if the user is feeling stressed, the system extends the usage time periods for relaxation-oriented home appliances and proposes an increase in the resulting electricity bill. This makes it possible to propose optimal electricity rates according to the user's emotions.

"Example 3"

One embodiment of the present invention provides a system incorporating an emotion engine. This system recognizes emotions from the user's voice, facial expressions, actions, 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). Alternatively, 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.

The processing flow for each example form is explained below.

"Example 1"

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

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 home appliances based on the results from the emotion engine.

Step 4: The system will suggest the calculated optimal usage time to the user.

"Example 2"

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

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

Step 4: The system communicates the adjusted electricity rate proposal to the user.

"Example 3"

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

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 home appliances based on the results from the emotion engine.

Step 4: The system will suggest the calculated optimal usage time to the user.

(Example 1)

Next, we will explain Example 1 of Form Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

Conventional electricity bill calculation systems calculate electricity bills 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, they are unable to promote the use of home appliances in accordance with the user's emotions, making it difficult to improve user satisfaction.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

In this invention, the server includes means for acquiring trading price information from the electricity market, means for analyzing the acquired trading price information and calculating the unit price of electricity, means for acquiring the user's power consumption, means for calculating the electricity bill 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 expressions, 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 based on the user's emotions, thereby improving user satisfaction.

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

"Trading price information" refers to data regarding the buying and selling prices of electricity in the electricity market.

"Electricity unit price" refers to the price per unit of electricity, calculated based on transaction price information.

"Power consumption" is data that indicates the amount of electricity consumed by a user over a certain period of time.

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

"User's voice, facial expressions, and actions" refers to information such as the voice, facial expressions, and body movements made by the user.

"Means for recognizing emotions" refers to technology or devices that analyze a user's voice, facial expressions, and behavior to identify the user's emotions.

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

"Means for notifying users of proposal content" refers to technology or devices used to communicate calculation results and proposal content to users.

This invention is a system that calculates electricity rates based on market-linked plans and suggests optimal times for using home appliances based on the user's emotions. This system consists of multiple components, including a server and a terminal.

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 Python's pandas library to calculate the average price over a certain period (e.g., every hour). Specifically, it converts the JSON data into a pandas DataFrame and calculates the average price for each hour using the resample('H').mean() method.

Next, the server obtains power consumption data from the user's smart meter. 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.

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 its camera and extracts facial feature points using OpenCV. It also records audio with its microphone and converts it into text using the Google Cloud Speech-to-Text API. To analyze the collected data and recognize the user's emotions, it uses the Microsoft® Azure® Emotion API. The device sends facial feature point data to the Microsoft Azure Emotion API and obtains emotion labels (e.g., joy, sadness, anger).

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 television. 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.

For example, if a user consumes 5kWh 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.

Examples of prompts include, "Calculate the hourly electricity bill based on 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."

The flow of the identification process in Example 1 will be explained using Figure 17.

Step 1:

The server obtains trading price information from the electricity market.

Input: Power market API endpoint.

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.

Output: Retrieved trading price information (JSON data).

Step 2:

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

Input: Trading price information (JSON data).

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.

Output: Average hourly price (unit price of electricity).

Step 3:

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

Input: Smart meter data (MQTT message).

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

Output: User power consumption data.

Step 4:

The server calculates the electricity bill based on the unit price of electricity and the amount of electricity consumed.

Input: Electricity unit price and power consumption data.

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

Output: Calculated electricity cost.

Step 5:

The server notifies the user of the calculation results.

Input: Calculated electricity cost.

Specific behavior: The server sends emails using the SMTP protocol or push notifications using Firebase Cloud Messaging.

Output: The electricity charge notified to the user.

Step 6:

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

Input: User's voice, facial expressions, and actions.

Specific operations: 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 to text using the Google Cloud Speech-to-Text API.

Output: User's voice, facial expression, and behavioral data.

Step 7:

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

Input: User voice, facial expression, and behavioral data.

Specific operation: The device sends facial feature point data to the Microsoft Azure Emotion API and obtains an emotion label (e.g., happy, sad, or angry).

Output: Recognized emotion label.

Step 8:

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

Input: Recognized emotion label.

Specific operation: Based on the emotion label, the device selects the most suitable appliance from a pre-defined list and suggests usage times.

Output: Suggested appliance usage times.

Step 9:

The device will notify the user of the suggestions.

Input: Suggested appliance usage time period.

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

Output: The suggestion notified to the user.

(Application Example 1)

Next, we will explain Application Example 1 of Form Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

Conventional electricity bill calculation systems were limited to calculating electricity rates based on market-linked plans and suggesting optimal usage times based on information about home appliance usage from users. However, because they did not consider the user's emotional state when making suggestions about home appliance usage, they were unable to improve user satisfaction or comfort. Furthermore, because they did not make suggestions about home appliance usage based on emotions, energy management that responded to the user's psychological needs was not possible.

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

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 times based on the received home appliance usage information, means for proposing the calculated optimal usage times 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.

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

"Means for calculating electricity charges" means a device or software for calculating electricity charges based on a market-linked plan.

"Means for receiving home appliance usage information from users" refers to an interface that allows users to input the type of home appliance they wish to use and the usage time.

"Means for calculating optimal usage time periods" refers to a device or software that calculates the most efficient usage time periods based on the received home appliance usage information.

"Means for making suggestions to users" refers to an interface that notifies users of the calculated optimal usage time and electricity charges.

"Means for recognizing user emotions" refers to devices or software that detect emotions from the user's voice, facial expressions, behavior, etc.

"Means for suggesting optimal home appliance usage" refers to a device or software that suggests optimal home appliance usage to a user based on recognized emotions.

The following system configuration is described as an embodiment of this invention.

System Configuration

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 times based on the received home appliance usage information, means for suggesting the calculated optimal usage times and electricity charges to the user, means for recognizing the user's emotions, and means for suggesting optimal home appliance usage based on the recognized emotions.

Program processing

The server first obtains trading price information from the power exchange. This information is obtained via an API. It then calculates the electricity bill based on the obtained trading price information. The calculated electricity bill is used to calculate the optimal usage time based on the appliance usage information entered by the user.

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 suggests the optimal use of home appliances.

Hardware and software used

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

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

Specific examples

For example, if a user is using a smartphone, the app uses the smartphone's camera to recognize their 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.

Example prompt

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

The flow of the specific processing in Application Example 1 is explained using Figure 18.

Step 1:

The server obtains trading price information from the power exchange.

Input: Power Exchange API endpoint

Data processing: Send API requests and obtain trading price information.

Output: Trading price information (JSON format)

Step 2:

The server calculates electricity charges based on the acquired transaction price information.

Input: Transaction price information

Data calculation: Extracts current electricity unit prices from trading price information and calculates electricity charges based on usage.

Output: Calculated electricity cost

Step 3:

Users enter home appliance usage information using their smartphones.

Input: Type of appliance, usage time

Data processing: Send the entered information to the server.

Output: Appliance usage information

Step 4:

The server calculates the optimal usage time based on the received appliance usage information.

Input: Appliance usage information, transaction price information

Data calculation: Combining appliance usage information and transaction price information to calculate the most cost-effective usage times.

Output: Optimal usage time

Step 5:

The server then proposes the optimal usage time and electricity rates to the user.

Input: Optimal usage time, calculated electricity rate

Data processing: Generate proposals and send them to the user's device.

Output: Recommendations (optimal usage time and electricity rates)

Step 6:

The user uses their smartphone camera to recognize their face.

Input: User's face image

Data processing: Send facial images to the server

Output: Facial image data

Step 7:

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

Input: Facial image data

Data calculation: Analyze facial image data and recognize emotions

Output: Recognized emotion

Step 8:

The server then suggests the optimal use of home appliances based on the recognized emotions.

Input: Recognized emotion

Data calculation: Generate home appliance usage suggestions based on emotions

Output: Home appliance usage suggestion

Step 9:

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

Input: Appliance usage proposals, transaction price information

Data calculation: Calculate the usage costs of proposed appliances

Output: Usage cost notification

(Example 2)

Next, we will explain Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

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

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

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 the user, means for calculating optimal usage time periods 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 periods and electricity charges to the user. This makes it possible to propose optimal home appliance usage and adjust electricity charges according to the user's emotions.

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

"Means for calculating electricity charges" is a function for calculating electricity charges based on electricity consumption and a market-linked plan.

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

"Means for calculating optimal usage times" is a function that calculates the optimal usage times for home appliances based on received home appliance usage information, taking into account power consumption patterns and past data.

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

"Means for adjusting electricity rate proposals" is a function for optimizing electricity rate proposals based on recognized user emotions.

"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.

Form for implementing the invention

This invention is a system that receives information about home appliance usage from users, calculates optimal usage times 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.

Hardware and software used

Hardware

Device: The device on which the user provides input information (e.g., smartphone, tablet, PC)

Server: Server for processing and storing data

Software

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

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

Specific system operation

1. Collection of user input information

Users use the terminal to input the type of appliance they want to use and the desired usage time.

Specific actions involve the user entering information such as "air conditioner" and "2:00 PM to 4:00 PM" into the application's input form.

2. Submitting input information

The device sends the information entered by the user to the server.

Specific operations involve the device sending input information to the server using an HTTP request.

3. Calculating appliance usage times

The server calculates the optimal usage time based on the type of appliance and usage time received.

Specifically, the server runs an algorithm that takes into account past usage data and power consumption patterns to calculate the optimal usage time period.

For example, the server might calculate that the best time to use the air conditioner is between 2:00 PM and 4:00 PM.

4. Emotion Recognition Using an Emotion Engine

The server uses an emotion engine to recognize the user's emotions.

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

5. Proposal for adjusting electricity rates

The server adjusts the electricity rate suggestions based on the emotion engine's recognition results.

Specific behavior is that if the user expresses happiness, the server will extend the usage time of entertainment-oriented home appliances (e.g., televisions) and suggest an increase in the resulting electricity bill.

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

6. Notifying users of results

The server sends the calculation results and suggestions to the device.

The device will notify the user of the received information.

Specific actions include the device informing the user via push notifications and in-app messages 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."

Specific examples

User input

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

Server processing

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

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).

Example prompt for a generative AI model

"The user inputs that they want to use the air conditioner from 2:00 PM to 4:00 PM. Calculate the optimal usage time, and if the user's emotion is joy, suggest extending the usage time of entertainment appliances."

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.

The flow of the identification process in Example 2 will be explained using Figure 19.

Step 1:

Collecting user input information

Users use the terminal to input the type of appliance they want to use and the desired usage time.

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

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

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

Step 2:

Submit input information

The device sends the information entered by the user to the server.

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

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

Specific operation: The device generates an HTTP request and sends the entered data to the server.

Step 3:

Calculating appliance usage times

The server calculates the optimal usage time based on the type of appliance and usage time received.

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

Output: Data on optimal usage times.

Specific operation: The server runs an algorithm that takes into account past usage data and power consumption patterns to calculate the optimal usage time. For example, it may calculate that the optimal time to use the air conditioner is between 2:00 PM and 4:00 PM.

Step 4:

Emotion recognition using an emotion engine

The server uses an emotion engine to recognize the user's emotions.

Input: User's facial image and voice data.

Output: User's emotional state (e.g., happy, stressed).

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

Step 5:

Proposed adjustment of electricity rates

The server adjusts the electricity rate suggestions based on the emotion engine's recognition results.

Input: User's emotional state and optimal usage time data.

Output: Proposed adjusted electricity rates.

Specific behavior: If the user is expressing happiness, 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).

Step 6:

Notifying users of results

The server sends the calculation results and suggestions to the device.

The device will notify the user of the received information.

Input: Data suggesting optimal usage times and adjusted electricity rates.

Output: Notification message to the user.

Specific behavior: 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."

(Application Example 2)

Next, we will explain Application Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

Conventional electricity rate optimization systems based on appliance usage information were unable to take into account the user's emotional state, making it difficult to increase user satisfaction. Furthermore, when optimizing the usage times of in-store appliances, adjustments were not made based on the customer's emotions, preventing an improved customer experience.

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

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 times based on the received home appliance usage information, means for proposing the calculated optimal usage times 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 charges based on the recognized emotions. This makes it possible to propose optimal home appliance usage times and electricity charges according to the user's emotional state, thereby increasing user satisfaction. Furthermore, by adjusting the usage times of home appliances in the store according to the customer's emotions, the customer experience can be improved.

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

"Means for calculating electricity charges" means a device or program for calculating electricity charges based on a market-linked plan.

"Means for receiving home appliance usage information" refers to a device or program for receiving information such as the type of home appliance and usage time from the user as input.

"Means for calculating optimal usage time periods" refers to a device or program for calculating optimal usage time periods for home appliances based on received home appliance usage information.

"Means for making suggestions to users" refers to a device or program that notifies or displays to users the calculated optimal usage time and electricity charges.

An "emotion recognition engine" is an algorithm or program that recognizes a user's emotions.

"Means for recognizing emotions" refers to a device or program for detecting a user's emotions using an emotion recognition engine.

"Means for adjusting electricity rate proposals" is a device or program for changing the content of electricity rate proposals based on recognized user sentiment.

The following system configuration is described as an embodiment of this invention.

System Configuration

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

Program processing

1. Enter your appliance usage information:

The user device receives input from the user about the type of appliance and the amount of time it has been used. This information is sent to the server.

2. Emotion recognition:

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

3. Calculating the best time to use:

The server uses an electricity rate calculation engine to calculate the optimal usage time based on the received home appliance usage information. The electricity rate calculation engine calculates electricity rates based on a market-linked plan.

4. Proposed electricity rate adjustment:

The server adjusts electricity rate 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.

5. Notice to Users:

The server then notifies the user's device of the calculated optimal usage time period and the adjusted electricity rate. The user can then adjust their home appliance usage accordingly.

Hardware and software used

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

Software: Emotion recognition engine (EmotionEngine), electricity bill calculation engine (ElectricityPricing)

Specific examples

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.

Example prompt

Develop an application that optimizes the use of lighting and air conditioning in stores and adjusts electricity rates based on customer emotions. If a customer is happy, extend the use of entertainment appliances, and if they are stressed, extend the use of relaxation appliances.

In this way, it becomes possible to propose optimal times for using home appliances and electricity rates based on the user's emotional state, thereby increasing user satisfaction. Furthermore, by adjusting the usage times of home appliances in the store based on the customer's emotions, the customer experience can be improved.

The flow of the specific processing in Application Example 2 is explained using Figure 20.

Step 1:

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

Step 2:

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.

Step 3:

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

Step 4:

The server adjusts electricity rate suggestions based on the recognized user emotions. Specifically, the server uses emotional state data from the emotion recognition engine to adjust the suggestions 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 optimal use time periods, and the output data is the adjusted electricity rate suggestions.

Step 5:

The server notifies the user's device of the calculated optimal usage time period and adjusted electricity charges. Specifically, the server sends data on the optimal usage time period and adjusted electricity charges to the user's device, allowing the user to check it. The input data is the optimal usage time period and adjusted electricity charges, and the output data is the suggested information displayed on the user's device.

In this way, it becomes possible to propose optimal times for using home appliances and electricity rates based on the user's emotional state, thereby increasing user satisfaction. Furthermore, by adjusting the usage times of home appliances in the store based on the customer's emotions, the customer experience can be improved.

(Example 3)

Next, we will explain Example 3 of Form Example 3. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

Conventional electricity bill calculation systems could suggest optimal times for users to use home appliances, but they did not make suggestions based on the user's emotions. As a result, they were 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 bills were not calculated based on trading prices at the power exchange, meaning users were unable to obtain reference information for reducing their electricity bills.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

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 the user, means for calculating optimal usage times based on the received home appliance usage information, means for proposing the calculated optimal usage times and electricity charges to the user, means for recognizing the user's emotions, and means for proposing optimal home appliance usage times based on the recognized emotions. This makes it possible to propose optimal home appliance usage times based on the user's emotional state and calculate electricity charges based on the trading price at the power exchange.

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

"Means for calculating electricity charges" refers to a device or program that has the function of calculating electricity charges according to usage time based on the trading price at the electricity exchange and the power consumption of home appliances.

"Means for receiving home appliance usage information from users" refers to a device or program that provides an interface for users to input the type of home appliance they wish to use and the usage time, and receives that information.

"Means for calculating optimal usage time periods" refers to a device or program that has the function of calculating the optimal time periods for using home appliances based on the received home appliance usage information and the trading price at the electricity exchange.

"Means for making suggestions to users" refers to a device or program that notifies or displays to users the calculated optimal usage time and electricity charges.

"Means for recognizing user emotions" refers to a device or program that has the function of analyzing the user's voice, facial expressions, behavior, etc., and determining the user's emotional state.

"Means for suggesting optimal times to use home appliances based on emotions" refers to 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.

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 makes suggestions to the user. It also has a function that recognizes the user's emotions and suggests times for using home appliances based on those emotions.

Hardware and software used

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

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

Specific system operation

1. User input

The user uses the device to enter the type of appliance they want to use and the usage time.

Example: User enters "Use the air conditioner from 3pm to 6pm."

2. Obtaining Power Exchange Data

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

Example: The server uses an API to retrieve electricity trading price data from 3:00 PM to 6:00 PM.

3. Calculating your electricity bill

The electricity charges for the specified time period are calculated based on the electricity trading price obtained by the server.

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

4. Emotion recognition

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.

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 feeling stressed.

5. Optimal usage time suggestions

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

Example: The server determines that the user is feeling stressed and suggests using a home appliance that will promote relaxation (e.g., a massage chair).

6. Presentation to the user

The server sends the calculation results and suggestions to the user's device and presents them to the user.

Example: The server displays calculated electricity bills and suggestions for using relaxation appliances on the user's smartphone.

Example prompt

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

In this way, the system can suggest optimal times to use home appliances and electricity rates based on the user's input and emotions. The flow of the identification process in Example 3 will be explained using Figure 21.

Step 1:

The user uses the device to enter the type of appliance they want to use and the usage time.

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

Specific operation: 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.

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

Step 2:

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

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

Specific operation: The server sends a request to the power exchange data acquisition API to obtain power trading price data for the specified time period.

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

Step 3:

The electricity charges for the specified time period are calculated based on the electricity trading price obtained by the server.

Input: Obtained electricity trading price data and the power consumption of the home appliance (e.g., 1.5kW power consumption of an air conditioner).

Specific operation: The server calculates the electricity bill from 3:00 PM to 6:00 PM using the air conditioner's power consumption and the acquired electricity trading price.

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

Step 4:

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.

Input: User voice, facial expression, and behavioral data.

Specific operation: When the user speaks into the device's camera, the device sends audio and video data to the emotion recognition engine.

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

Step 5:

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

Input: Calculated electricity bill and user's emotional state.

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., a massage chair).

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

Step 6:

The server sends the calculation results and suggestions to the user's device and presents them to the user.

Input: Calculation results and suggestions.

Specific operation: The server sends the calculation results and suggestions to the user's device.

Output: The user will receive a notification on their smartphone and the suggestions will be displayed when they open the app.

(Application Example 3)

Next, we will explain Application Example 3 of Form Example 3. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

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

The specific processing by the specific 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 electricity charges based on a market-linked plan, means for receiving home appliance usage information from the user, means for calculating optimal usage times based on the received home appliance usage information, means for proposing the calculated optimal usage times and electricity charges to the user, means for recognizing the user's emotions, means for proposing optimal home appliance usage times based on the recognized emotions, means for checking the usage status of home appliances in the store in real time, and means for optimal environmental settings according to the customer's emotions. This makes it possible to optimize electricity charges, propose optimal home appliance usage times according to the user's or customer's emotions, and provide a comfortable environment.

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

"Means for calculating electricity charges" means a device or software for calculating electricity charges based on a market-linked plan.

"Means for receiving home appliance usage information from users" refers to an interface that allows users to input the type of home appliance they wish to use and the usage time.

"Means for calculating optimal usage time periods" refers to a device or software that calculates optimal usage time periods based on received home appliance usage information, taking into account electricity trading prices.

"Means for making suggestions to users" refers to an interface that notifies users of the calculated optimal usage time and electricity charges.

"Means for recognizing user emotions" refers to devices or software that analyze emotions from the user's voice, facial expressions, behavior, etc.

"Means for suggesting optimal times to use home appliances based on emotions" refers to a device or software that calculates and suggests optimal times to use home appliances based on the user's recognized emotions.

"Means for checking the usage status of home appliances within a store in real time" refers to a device or software that monitors the usage status of home appliances within a store in real time and notifies the manager.

"Means for optimal environmental settings according to customer emotions" refers to devices or software that analyze customer emotions and, based on that, optimize environmental settings such as lighting, air conditioning, and music in the store.

The following system configuration is described as an embodiment of this invention.

System Configuration

1. Hardware:

Smartphone (iOS/ANDROID (registered trademark))

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

Head-mounted display (e.g., Oculus Rift)

Robots (e.g., Pepper)

2. Software:

Facial recognition API (e.g., Microsoft Azure Face API)

Emotion recognition API (e.g. Affectiva)

Electricity trading price API (e.g., EPEX SPOT API)

Database (e.g., Firebase)

Program processing

The server first receives information about home appliance usage from the user. The user enters the type of appliance they wish to use and the usage time via 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.

Furthermore, the server uses facial recognition APIs 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.

Based on the recognized emotions, the server recalculates the optimal times 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).

Specific examples

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

Example prompt

Enter customer facial expression data and recognize their emotions. Then, retrieve the current electricity price from the electricity trading price API and calculate the optimal time to use 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.

In this way, it is possible to optimize electricity rates, suggest optimal times to use home appliances based on the emotions of users and customers, and provide a comfortable environment.

The flow of the specific processing in Application Example 3 will be explained using Figure 22.

Step 1:

The user uses a smartphone app to input the type of appliance they wish to use and the amount of time they will be using it. The information entered is the type of appliance and the amount of time it will be used. This information is then sent to the server.

Step 2:

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' usage times.

Step 3:

The server then proposes the calculated optimal usage time and electricity rates to the user. The proposal includes the optimal usage time and the electricity rate for that time. This information is displayed on the smartphone app.

Step 4:

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, which are sent to the server.

Step 5:

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.

Step 6:

The server recalculates the optimal time periods for using home 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, it will suggest using appliances that will contribute to relaxation.

Step 7:

The server then proposes the recalculated optimal usage times and electricity rates to the user. The proposals include the optimal usage times based on the user's emotions and the electricity rates for those times. This information is displayed on a smartphone app, smart glasses, a head-mounted display, and a robot.

Step 8:

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 appliances, and the output is the real-time usage status. This allows store managers to check the usage status of home appliances through a smartphone app.

Step 9:

The server optimizes environmental settings such as lighting, air conditioning, and music to provide optimal environmental settings 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.

These steps will not only optimize electricity rates, but also suggest optimal times to use home appliances based on the emotions of users and customers, and provide a comfortable environment.

The specific processing unit 290 transmits the results 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 results of the specific processing. The microphone 38B acquires audio indicating the user input regarding the results 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.

Data generation model 58 is what is known as generative AI (artificial intelligence). An example of data generation model 58 is generative AI such as ChatGPT (registered trademark) (Internet search <URL: https://openai.com/blog/chatgpt>). Data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing an instruction is input to 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. Data generation model 58 performs inference on the input inference data in accordance with the instructions indicated by the prompt, and outputs the inference results in the form of data such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization.

Another example of generative AI is Gemini (registered trademark) (Internet search <URL: https://gemini.google.com/?hl=ja>).

In the above embodiment, an example was given in which the specific processing was performed by the data processing device 12, but the technology disclosed herein is not limited to this, and the specific processing may also be performed by the smart device 14.

[Second embodiment]

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

As shown in FIG. 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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, RAM 30, and storage 32. 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).

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, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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.

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 user 20's surroundings (e.g., an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

The communication I/F 44 is connected to the 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 manner.

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

The specific processing program 56 is an example of a "program" 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 the specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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.

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.

Next, we will explain the identification process performed by the identification processing unit 290 of the data processing device 12.

"Example 1"

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, it calculates the unit price of electricity based on the trading price information and uses that to calculate electricity rates.

"Example 2"

Another aspect of the present invention is a system that receives home appliance usage information 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 wish to use and the usage time, and calculates the optimal usage time period based on that information.

"Example 3"

Another aspect 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 reducing electricity bills. Specifically, when a user inputs the type of home appliance they wish 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 them with an electricity rate calculated based on the trading price at the power exchange for that time period.

The processing flow for each example form is explained below.

"Example 1"

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

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

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

"Example 2"

Step 1: Receive input from the user about the type of appliance they wish to use and the usage time.

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

"Example 3"

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

Step 2: Calculate the electricity bill based on the trading price on the power exchange for that time period.

Step 3: Present the calculated electricity bill to the user.

(Example 1)

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

Conventional electricity rate calculation systems struggled to reflect real-time trading price information from the power exchange, making it impossible to provide users with optimal electricity rates. Furthermore, they lacked functionality for suggesting optimal usage times based on users' appliance usage information, making it difficult for users to use electricity efficiently.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

In this invention, the server includes means for acquiring trading price information from the power exchange, means for saving the acquired trading price information in a database, means for calculating the unit price of electricity based on the saved 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 the optimal usage time period based on the received home appliance usage information, and means for proposing the calculated optimal usage time period and electricity bill to the user. This makes it possible to calculate electricity bills that reflect trading price information in real time, and furthermore, by proposing the optimal usage time period to the user, efficient power use is possible.

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

"Trading price information" refers to information regarding the price of electricity traded at the power exchange.

A "database" is a system for efficiently storing, managing, and searching data.

"Electricity unit price" is the unit price set for a certain amount of electricity.

"User" refers to an individual or corporation that consumes electricity and is the entity that uses this system.

"Home appliance usage information" refers to information about the types of home appliances used by users and the duration of use.

"Optimal usage times" are the recommended times for efficient power consumption.

"Proposal means" refers to a method or device for notifying the user of the calculated optimal usage time period and electricity charges.

Form for implementing the invention

This invention is 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. A specific embodiment of this system is described below.

Hardware and software used

1. Server

Hardware: General server equipment

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

2. Device

Hardware: The computer or smartphone used by the user

Software: Web browser, mobile application

3. User

Users are individuals or corporations that consume electricity and are the entities that use this system.

Program processing explanation

1. Obtaining trading price information

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

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

2. Saving transaction price information

The server stores the acquired trading price information in a database.

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

3. Calculating electricity unit prices

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

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

4. Calculating the user's electricity bill

The server calculates the user's electricity bill using the calculated electricity unit price.

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.

5. Receiving appliance usage information

The server receives home appliance usage information from the user.

Specific operation: The user enters the type of home appliance they wish to use and the usage time via a web browser or mobile application, and sends this information to the server.

6. Calculating the optimal usage time

The server calculates the optimal usage time based on the received appliance usage information.

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

7. Proposal Notification

The server then proposes the optimal usage time and electricity rates to the user.

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.

Examples and prompts

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

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

3. The server uses a Python script to calculate the electricity rate for "2023-10-01" from the trading price information.

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).

Example prompt

"Get the latest trading price information from the power exchange's API and save it in a MySQL database. Then, use a Python script to calculate the electricity rate and calculate the electricity bill based on the user's power consumption data."

The flow of the identification process in Example 1 will be explained using Figure 11.

Step 1:

Get trading price information

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

Input: Power exchange API endpoint URL

Output: Transaction price information in JSON format

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

Step 2:

Storing transaction price information

The server stores the acquired trading price information in a database.

Input: Transaction price information in JSON format

Output: Transaction price information stored in the database

Specific behavior: The server parses the received JSON data and extracts the necessary fields (e.g., datetime, price). Then, using the pymysql library, it connects to a MySQL database and executes a query such as INSERT INTO prices (datetime, price) VALUES ('2023-10-01 00:00:00', 10.5).

Step 3:

Calculating electricity unit prices

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

Input: Trading price information stored in the database

Output: Calculated electricity unit price

Specific operation: The server executes a Python script to obtain trading price information from the database. For example, using the pymysql library, it executes 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.

Step 4:

Calculating your electricity bill

The server calculates the user's electricity bill using the calculated electricity unit price.

Input: User power consumption data, calculated electricity rate

Output: Calculated user electricity bill

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., 100kWh) by the calculated unit price (e.g., 10 yen/kWh), and calculates the user's electricity bill (e.g., 1,000 yen).

Step 5:

Receive appliance usage information

The server receives home appliance usage information from the user.

Input: Appliance usage information entered by the user (type of appliance, usage time)

Output: Appliance usage information stored on the server

Specific operation: The user enters the type of home appliance they want to use and the usage time via a web browser or mobile application, and sends this information to the server. The server then stores the received information in a database.

Step 6:

Calculating the best time to use

The server calculates the optimal usage time based on the received appliance usage information.

Input: Appliance usage information, electricity unit price

Output: Optimal usage time

Specific operation: The server runs an algorithm to calculate the optimal usage time based on the electricity rate and appliance usage information. For example, it calculates the optimal usage time to avoid peak hours.

Step 7:

Proposal Notification

The server then proposes the optimal usage time and electricity rates to the user.

Input: Optimal usage time, calculated electricity rate

Output: User notification

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. For example, a push notification or email is sent.

(Application Example 1)

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

Conventional electricity billing 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 real-time trading price information from the power exchange and optimizing power consumption.

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

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 the power 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. This enables optimization of electricity consumption in real time.

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

"Means for calculating electricity charges" refers to a method or device for calculating electricity charges based on a market-linked plan.

"Means for receiving home appliance usage information from users" refers to methods or devices for obtaining information such as the types of home appliances used by users and the duration of use.

"Means for calculating optimal usage time periods" refers to a method or device that calculates optimal usage time periods for optimizing power consumption based on received home appliance usage information.

"Means for making suggestions to users" refers to methods or devices for notifying users of the calculated optimal usage time periods and electricity charges.

"Means for obtaining trading price information from an electricity exchange in real time" refers to a method or device for obtaining trading price information from an electricity exchange in real time.

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

"Means for reducing electricity consumption during high-price time periods and increasing electricity consumption during low-price time periods" refers to a method or device for reducing electricity consumption during time periods when the electricity trading price is high and increasing electricity consumption during time periods when the trading price is low.

To implement this invention, the following system configuration and program are required.

System Configuration

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

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

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

Program processing explanation

Hardware

Server: A computer with a high-performance processor and large memory capacity.

Device: A device with a user interface, such as a smartphone or tablet.

Network: Internet connection.

Software

Python: The primary implementation language for the program.

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

JSON data processing: A format for parsing retrieved data.

Data processing and calculations

1. The server obtains trading price information from the power exchange in real time. To do this, it uses an API to obtain the data and saves it in JSON format.

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

3. The device receives appliance usage information from the user, including the type of appliance they want to use and the duration of their use.

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

5. The device will then suggest the optimal usage time and electricity rates to the user. This will be done using the notification function to provide information to the user.

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.

Specific examples

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.

Example prompt

"Create a Python program that obtains real-time trading price information from the power exchange and optimizes power consumption within the factory. It should have the ability to reduce power consumption during high-price periods and increase power consumption during low-price periods."

The flow of the specific processing in Application Example 1 will be explained using Figure 12.

Step 1:

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

Step 2:

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

Step 3:

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 this. The input is the user's home appliance usage information, and the output is the received home appliance usage information.

Step 4:

The server calculates the optimal usage time period 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.

Step 5:

The device then proposes the calculated optimal usage time and electricity rate to the user. It uses the notification function 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.

Step 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. The input is trading price information, and the output is the optimized power consumption pattern.

(Example 2)

Next, we will explain Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

Existing electricity rate optimization systems based on home appliance usage information struggled to efficiently process user-entered information and calculate optimal usage times. Furthermore, there was a lack of a way to quickly provide users with the results, resulting in poor user convenience. Furthermore, there was a need for a system that could comprehensively calculate electricity rates based on market-linked plans and suggest optimal usage times.

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

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 the optimal usage time period based on the stored data, means for proposing the calculated optimal usage time period and electricity rate to the user, means for displaying the calculation results to the user, and means for calculating the electricity rate 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 the optimal usage time period and electricity rate.

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

"Means for calculating electricity charges" refers to algorithms or software for calculating electricity charges based on market-linked plans.

"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.

"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.

"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.

"Means for proposing calculation results to users" refers to interfaces or devices that notify users of the calculated optimal usage times and electricity charges.

"Means for displaying calculation results to the user" refers to software or hardware for displaying calculation results on the user's device.

Form for implementing the invention

This invention is a system that receives information about home appliance usage from users, calculates optimal usage times and electricity rates based on that information, and makes recommendations to the user. A specific embodiment of this system is described below.

System Configuration

This system consists of three main elements: a server, a terminal, and a user.

1. Server

The server provides a means for receiving appliance usage information from users. This means is an interface that can be accessed through a web browser or mobile application.

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

The server calculates the optimal usage time based on the stored data. This calculation is done using programming languages such as Python and R, and utilizing data analysis libraries (e.g., Pandas, NumPy).

The server has a means of proposing the calculated optimal usage time and electricity rates to the user. This proposal is made in real time.

The server has a means of sending the calculation results to the terminal for display to the user. This transmission is securely performed using the HTTPS protocol.

2. Device

The device sends the home appliance usage information entered by the user to the server. This transmission is secure, using the HTTPS protocol.

The device displays the calculation results received from the server to the user. For example, the mobile application screen might display "The best time to use the device is between 7:00 PM and 9:00 PM."

3. User

Users use a web browser or mobile application to enter 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."

Specific examples

As a concrete example, consider the case where a user wants to use the air conditioner.

1. The user opens the mobile application and enters "air conditioner" and "18:00 to 22:00."

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

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

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

5. The server sends the calculation result "The best time is between 7:00 PM and 9:00 PM" to the device.

6. The device displays the results on the mobile application screen and notifies the user.

Example prompt

Example of a prompt to input to a generative AI model:

The 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.

In this way, a system is realized in which the server, terminal, and user work together to calculate the optimal usage time for home appliances and provide it to the user.

The flow of the identification process in Example 2 will be explained using Figure 13.

Step 1:

The user enters appliance usage information.

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

Step 2:

The device sends the input information to the server.

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.

Step 3:

The server saves the input information in a database.

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.

Step 4:

The server calculates the optimal usage time based on the stored data.

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.

Step 5:

The server sends the calculation results to the device.

The server sends the calculation results to the device. Specifically, it converts the calculated optimal usage time slot into JSON format and sends it to the device 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 device.

Step 6:

The terminal displays the calculation results to the user.

The device displays the calculation results received from the server to the user. Specifically, the mobile application screen displays "The optimal usage time is from 19:00 to 21:00." The input is the calculation result of the optimal usage time received from the server, and the output is the content displayed to the user.

(Application Example 2)

Next, we will explain Application Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

Existing systems that calculate optimal usage times based on home appliance usage information only consider information about home appliance usage and do not support 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.

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 electricity charges based on a market-linked plan, 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, means for proposing the calculated optimal usage time periods and electricity charges to the user, means for receiving operation schedule information from the user and calculating optimal charging time periods, and means for notifying the user of the calculated optimal charging time periods. This allows the user to calculate the optimal charging time based on the operation schedule of the autonomous vehicle and improve energy management efficiency.

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

"Means for calculating electricity charges" means a device or program for calculating electricity charges based on a market-linked plan.

"Home appliance usage information" refers to information about the type of home appliance the user wants to use and the duration of use.

"Means for calculating optimal usage time periods" refers to a device or program for calculating optimal usage time periods for home appliances based on received home appliance usage information.

"Means for making suggestions to users" refers to a device or program that notifies or displays to users the calculated optimal usage time and electricity charges.

"Scheduled operation information" refers to information such as departure time, arrival time, and destination entered by the user regarding the operation of an autonomous vehicle.

"Means for calculating the optimal charging time slot" refers to a device or program for calculating the optimal charging time slot for an autonomous vehicle based on the received operation schedule information.

"Notification means" refers to a device or program that notifies the user of the calculated optimal charging time period.

A system for implementing this invention consists of a server, a user terminal, and an autonomous vehicle. The server includes the following means:

1. A method for calculating electricity rates based on market-linked plans

2. How to receive appliance usage information from users

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

4. A method for suggesting the calculated optimal usage time and electricity rates to users

5. A means of receiving schedule information from users and calculating the optimal charging time slots

6. A method for notifying users of the calculated optimal charging time slot

Program processing explanation

The server is implemented using a programming language such as Python. It receives appliance usage information and operation schedule information sent from the user's device, and calculates the optimal usage and charging times based on this information.

Specifically, the server performs the following processes:

1. Calculating electricity charges:

The server obtains the trading price from the power exchange based on the market-linked plan and calculates the electricity bill. This calculation involves obtaining data from the power exchange via an API.

2. Receiving and processing home appliance usage information:

The system receives appliance usage information (type of appliance to be used and usage time) sent from the user's device and calculates the optimal usage time. It uses an AI model to calculate and avoid peak hours of power consumption.

3. Receiving and processing schedule information:

The system receives flight schedule 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.

4. Notice:

The user will be notified of the calculated optimal usage and charging times. Notifications will be sent to the user's device via push notification or email.

Specific examples

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

Departure time: 2023-10-01 08:00:00

Arrival time: 2023-10-01 10:00:00

Destination: Office

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).

Example of a prompt to input to a generative AI model:

The user has entered a scheduled autonomous vehicle trip. 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. Please calculate the optimal charging time based on this information.

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

The flow of the specific processing in Application Example 2 will be explained using Figure 14.

Step 1:

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

Step 2:

The server receives 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. Based on this information, the server processes the data to calculate the optimal usage time period.

Step 3:

The server obtains trading prices from the power exchange based on a market-linked plan. It then calculates electricity charges based on the obtained trading price data. Calculations involve obtaining data from the power exchange via an API and taking real-time price fluctuations into account.

Step 4:

The server calculates the optimal usage time based on the received appliance usage information and acquired trading 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.

Step 5:

The server then proposes the calculated optimal usage time and electricity rates to the user. The proposal is sent to the user's device via push notification or email. The user can check the proposed optimal usage time and plan their home appliance usage.

Step 6:

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.

Step 7:

The server calculates the optimal charging time based on the received flight schedule information. It uses an AI model to determine the optimal charging time based on the flight schedule. Specifically, it applies an algorithm that starts charging two hours before departure.

Step 8:

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

In this way, optimal usage and charging times can be calculated based on the user's home appliance usage information and operation schedule information, making it possible to improve energy management efficiency.

(Example 3)

Next, we will explain Example 3 of Form Example 3. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

With conventional electricity bill calculation systems, it was difficult for users to know the optimal time to use their home appliances, making it difficult to effectively reduce electricity bills. Furthermore, because electricity bills were not calculated in real time based on trading prices at 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 making it difficult to use energy efficiently.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

In this invention, the server includes means for receiving home appliance usage information from the user, means for acquiring trading price data from the power 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 results to the user. This allows the user to know the optimal time period for using home appliances and the electricity rate for that time period in real time, allowing for effective savings on electricity bills.

"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.

"Means for obtaining trading price data from the power exchange" refers to devices or software that have the functionality to access the power exchange's API and obtain the latest trading price data.

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

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

"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 optimal usage times to the user.

Form for implementing the invention

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 the user. This system receives home appliance usage information from the user, acquires trading price data from the power exchange, processes the acquired data to calculate the electricity rate, and presents the results to the user.

Hardware and software used

This system uses the following hardware and software:

Server: A computer system for data processing and calculations.

Device: The device through which the user enters information (smartphone, tablet, PC, etc.).

Python: A programming language used to develop programs.

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

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

Program processing

The server receives input from the user. When the user enters the type of appliance they want to use and the duration of use, 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 appliance's power consumption.

Specific examples

For example, if a user enters "I want the air conditioner to run from 3:00 PM to 6:00 PM," the server will process it as follows:

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

2. The device sends this input data to the server.

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

4. The server receives the trading price data returned from the API and converts it into a data frame using Pandas.

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

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

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."

8. The server sends this message to the device.

9. The device displays a message to the user.

Example prompt

Example of a prompt to input to a generative AI model:

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 trading price on the power exchange for that time period and displays it to the user.

In this way, users can learn the optimal usage times and electricity rates, and use this information as reference for reducing electricity costs. The flow of the identification process in Example 3 will be explained using Figure 15.

Program processing flow

Step 1: Receive user input

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

Specific actions:

Input: The user enters "Air conditioning, 15:00-18:00" into the device's input form.

Output: The device sends this input data to the server.

Step 2: Obtaining electricity trading price data

The server retrieves the latest trading price data from the power exchange's API. This data is used to calculate the electricity rate for the specified time period.

Specific actions:

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

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

Output: The server receives the trading price data returned from the API.

Step 3: Process the data and calculate the electricity bill

The server processes the acquired trading price data using the Pandas library and calculates the electricity rate for the specified time period. To do this, it multiplies the power consumption of the appliance by the trading price.

Specific actions:

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

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

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

Output: Calculated electricity bill data.

Step 4: Presenting the calculation results

The server then presents the calculation results to the user, allowing the user to obtain reference information for reducing electricity bills.

Specific actions:

Input: Calculated electricity rate data.

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, your electricity bill will be XX yen."

Output: The server sends this message to the terminal.

Action: The device displays a message to the user.

In this way, users can learn the optimal times to use electricity and electricity rates, which can be used as reference information to reduce electricity bills.

(Application Example 3)

Next, we will explain Application Example 3 of Form Example 3. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

When it comes to electricity usage, users often have difficulty knowing the optimal time of day to use the power in order to reduce their electricity bills. Furthermore, when charging electric vehicles in particular, electricity rates vary greatly depending on the time of day, making it difficult for users to select the optimal time of day to charge. For this reason, there is a demand for a system that allows users to use electricity efficiently and reduce their electricity bills.

The specific processing performed by the specific 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 electricity charges based on a market-linked plan, means for receiving electricity usage information from the user, means for calculating optimal usage time periods based on the received electricity 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 electricity usage time periods entered by the user, and means for presenting the calculated optimal electricity 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.

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

"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.

"Electricity usage information" refers to information such as the type of electrical equipment used by the user and the duration of use.

"Means for calculating the optimal usage time period" refers to a device or program that has the function of calculating the time period with the lowest electricity rates based on the received electricity usage information.

"Means for making suggestions to users" refers to a device or program that has the function of notifying users of the calculated optimal usage time period and electricity charges.

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

"Means for calculating the time period with the cheapest electricity rates" refers to 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.

"Means for presenting estimated electricity charges to users" refers to a device or program that has the function of notifying users of the calculated optimal time periods for electricity usage and the estimated electricity charges for those times.

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

System Configuration

1. Server

The server runs a program with the following functions:

Function to calculate electricity rates based on market-linked plans

A function to receive power usage information from users

A function that calculates the optimal usage time based on received power usage information.

A function that suggests optimal usage times and electricity rates to users.

A function that calculates the cheapest electricity rate among the electricity usage periods entered by the user.

A function that displays the calculated optimal power usage times and estimated electricity charges to the user.

2. User Device

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

3. Power Exchange API

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

Program processing explanation

The server calculates electricity charges 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:

1. Receiving power usage information

Power usage information entered by the user on the device (for example, the time period during which they wish to charge) is sent to the server.

2. Calculating your electricity bill

The server uses the power exchange's API to obtain the trading price for the specified time period. It then calculates the electricity bill based on the obtained trading price.

3. Calculating the optimal usage time

The server calculates the cheapest electricity rate among the time periods entered by the user. This calculation uses real-time trading price data.

4. Presentation of results

The calculation results (optimal usage times and estimated electricity rates) are sent to the user's terminal and presented to the user.

Specific examples

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

Example prompt

Example of a prompt to input to a generative AI model:

Design a system that, when a user inputs the time period they wish to charge their electric vehicle, calculates the optimal charging time and electricity rate based on the trading price on the power exchange for that time period and presents it to the user. For example, if a user inputs "I would like to charge between 3:00 PM and 6:00 PM on October 1, 2023," the system will calculate and present the time period with the cheapest electricity rate within that time period.

The flow of the specific processing in Application Example 3 will be explained using Figure 16.

Step 1:

The user enters power usage information from the device.

Specifically, users use 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.

Input: Time of day you want to charge

Output: Power usage information sent to the server

Step 2:

The server receives power usage information.

The server receives and analyzes the power usage information sent from the user's device. The received information includes the time period during which the user wishes to charge.

Input: Power usage information submitted by the user

Output: Analyzed power usage information

Step 3:

The server obtains trading prices from the power exchange API.

The server uses the power exchange's API to obtain the trading price for the specified time period. The obtained trading price is used to calculate the electricity bill.

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

Output: Trading price for the specified time period

Step 4:

The server calculates the electricity bill.

The server calculates the electricity charges for the time period specified by the user based on the acquired transaction price. The transaction price and usage time are used for the calculation.

Input: Trading price for the specified time period, usage time

Output: Calculated electricity cost

Step 5:

The server will calculate the optimal usage time.

The server calculates the cheapest electricity rate among the time periods entered by the user. This calculation uses real-time trading price data.

Input: Trading price for the specified time period, usage time

Output: Optimal usage time

Step 6:

The server presents the calculation results to the user.

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

Input: Optimal usage time, calculated electricity rate

Output: Optimal usage times and estimated electricity rates presented to the user

It is also possible to further combine an emotion engine that estimates the user's emotion. 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.

"Example 1"

One embodiment of the present invention provides a system incorporating an emotion engine. This system recognizes emotions from the user's voice, facial expressions, actions, 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). Alternatively, 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.

"Example 2"

In another embodiment of the present invention, a system is provided that adjusts 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 for entertainment-oriented home appliances and proposes an increase in the resulting electricity bill. On the other hand, if the user is feeling stressed, the system extends the usage time periods for relaxation-oriented home appliances and proposes an increase in the resulting electricity bill. This makes it possible to propose optimal electricity rates according to the user's emotions.

"Example 3"

One embodiment of the present invention provides a system incorporating an emotion engine. This system recognizes emotions from the user's voice, facial expressions, actions, 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). Alternatively, 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.

The processing flow for each example form is explained below.

"Example 1"

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

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 home appliances based on the results from the emotion engine.

Step 4: The system will suggest the calculated optimal usage time to the user.

"Example 2"

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

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

Step 4: The system communicates the adjusted electricity rate proposal to the user.

"Example 3"

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

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 home appliances based on the results from the emotion engine.

Step 4: The system will suggest the calculated optimal usage time to the user.

(Example 1)

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

Conventional electricity bill calculation systems calculate electricity bills 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, they are unable to promote the use of home appliances in accordance with the user's emotions, making it difficult to improve user satisfaction.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

In this invention, the server includes means for acquiring trading price information from the electricity market, means for analyzing the acquired trading price information and calculating the unit price of electricity, means for acquiring the user's power consumption, means for calculating the electricity bill 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 expressions, 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 based on the user's emotions, thereby improving user satisfaction.

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

"Trading price information" refers to data regarding the buying and selling prices of electricity in the electricity market.

"Electricity unit price" refers to the price per unit of electricity, calculated based on transaction price information.

"Power consumption" is data that indicates the amount of electricity consumed by a user over a certain period of time.

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

"User's voice, facial expressions, and actions" refers to information such as the voice, facial expressions, and body movements made by the user.

"Means for recognizing emotions" refers to technology or devices that analyze a user's voice, facial expressions, and behavior to identify the user's emotions.

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

"Means for notifying users of proposal content" refers to technology or devices used to communicate calculation results and proposal content to users.

This invention is a system that calculates electricity rates based on market-linked plans and suggests optimal times for using home appliances based on the user's emotions. This system consists of multiple components, including a server and a terminal.

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 Python's pandas library to calculate the average price over a certain period (e.g., every hour). Specifically, it converts the JSON data into a pandas DataFrame and calculates the average price for each hour using the resample('H').mean() method.

Next, the server obtains power consumption data from the user's smart meter. 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.

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).

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 television. 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.

For example, if a user consumes 5kWh 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.

Examples of prompts include, "Calculate the hourly electricity bill based on 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."

The flow of the identification process in Example 1 will be explained using Figure 17.

Step 1:

The server obtains trading price information from the electricity market.

Input: Power market API endpoint.

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.

Output: Retrieved trading price information (JSON data).

Step 2:

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

Input: Trading price information (JSON data).

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.

Output: Average hourly price (unit price of electricity).

Step 3:

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

Input: Smart meter data (MQTT message).

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

Output: User power consumption data.

Step 4:

The server calculates the electricity bill based on the unit price of electricity and the amount of electricity consumed.

Input: Electricity unit price and power consumption data.

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

Output: Calculated electricity cost.

Step 5:

The server notifies the user of the calculation results.

Input: Calculated electricity cost.

Specific behavior: The server sends emails using the SMTP protocol or push notifications using Firebase Cloud Messaging.

Output: The electricity charge notified to the user.

Step 6:

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

Input: User's voice, facial expressions, and actions.

Specific operations: 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 to text using the Google Cloud Speech-to-Text API.

Output: User's voice, facial expression, and behavioral data.

Step 7:

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

Input: User voice, facial expression, and behavioral data.

Specific operation: The device sends facial feature point data to the Microsoft Azure Emotion API and obtains an emotion label (e.g., happy, sad, or angry).

Output: Recognized emotion label.

Step 8:

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

Input: Recognized emotion label.

Specific operation: Based on the emotion label, the device selects the most suitable appliance from a pre-defined list and suggests usage times.

Output: Suggested appliance usage times.

Step 9:

The device will notify the user of the suggestions.

Input: Suggested appliance usage time period.

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

Output: The suggestion notified to the user.

(Application Example 1)

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

Conventional electricity bill calculation systems were limited to calculating electricity rates based on market-linked plans and suggesting optimal usage times based on information about home appliance usage from users. However, because they did not consider the user's emotional state when making suggestions about home appliance usage, they were unable to improve user satisfaction or comfort. Furthermore, because they did not make suggestions about home appliance usage based on emotions, energy management that responded to the user's psychological needs was not possible.

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

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 times based on the received home appliance usage information, means for proposing the calculated optimal usage times 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.

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

"Means for calculating electricity charges" means a device or software for calculating electricity charges based on a market-linked plan.

"Means for receiving home appliance usage information from users" refers to an interface that allows users to input the type of home appliance they wish to use and the usage time.

"Means for calculating optimal usage time periods" refers to a device or software that calculates the most efficient usage time periods based on the received home appliance usage information.

"Means for making suggestions to users" refers to an interface that notifies users of the calculated optimal usage time and electricity charges.

"Means for recognizing user emotions" refers to devices or software that detect emotions from the user's voice, facial expressions, behavior, etc.

"Means for suggesting optimal home appliance usage" refers to a device or software that suggests optimal home appliance usage to a user based on recognized emotions.

The following system configuration is described as an embodiment of this invention.

System Configuration

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 times based on the received home appliance usage information, means for suggesting the calculated optimal usage times and electricity charges to the user, means for recognizing the user's emotions, and means for suggesting optimal home appliance usage based on the recognized emotions.

Program processing

The server first obtains trading price information from the power exchange. This information is obtained via an API. It then calculates the electricity bill based on the obtained trading price information. The calculated electricity bill is used to calculate the optimal usage time based on the appliance usage information entered by the user.

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 suggests the optimal use of home appliances.

Hardware and software used

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

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

Specific examples

For example, if a user is using a smartphone, the app uses the smartphone's camera to recognize their 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.

Example prompt

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

The flow of the specific processing in Application Example 1 is explained using Figure 18.

Step 1:

The server obtains trading price information from the power exchange.

Input: Power Exchange API endpoint

Data processing: Send API requests and obtain trading price information.

Output: Trading price information (JSON format)

Step 2:

The server calculates electricity charges based on the acquired transaction price information.

Input: Transaction price information

Data calculation: Extracts current electricity unit prices from trading price information and calculates electricity charges based on usage.

Output: Calculated electricity cost

Step 3:

Users enter home appliance usage information using their smartphones.

Input: Type of appliance, usage time

Data processing: Send the entered information to the server.

Output: Appliance usage information

Step 4:

The server calculates the optimal usage time based on the received appliance usage information.

Input: Appliance usage information, transaction price information

Data calculation: Combining appliance usage information and transaction price information to calculate the most cost-effective usage times.

Output: Optimal usage time

Step 5:

The server then proposes the optimal usage time and electricity rates to the user.

Input: Optimal usage time, calculated electricity rate

Data processing: Generate proposals and send them to the user's device.

Output: Recommendations (optimal usage time and electricity rates)

Step 6:

The user uses their smartphone camera to recognize their face.

Input: User's face image

Data processing: Send facial images to the server

Output: Facial image data

Step 7:

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

Input: Facial image data

Data calculation: Analyze facial image data and recognize emotions

Output: Recognized emotion

Step 8:

The server then suggests the optimal use of home appliances based on the recognized emotions.

Input: Recognized emotion

Data calculation: Generate home appliance usage suggestions based on emotions

Output: Home appliance usage suggestion

Step 9:

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

Input: Appliance usage proposals, transaction price information

Data calculation: Calculate the usage costs of proposed appliances

Output: Usage cost notification

(Example 2)

Next, we will explain Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

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

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

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 the user, means for calculating optimal usage time periods 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 periods and electricity charges to the user. This makes it possible to propose optimal home appliance usage and adjust electricity charges according to the user's emotions.

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

"Means for calculating electricity charges" is a function for calculating electricity charges based on electricity consumption and a market-linked plan.

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

"Means for calculating optimal usage times" is a function that calculates the optimal usage times for home appliances based on received home appliance usage information, taking into account power consumption patterns and past data.

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

"Means for adjusting electricity rate proposals" is a function for optimizing electricity rate proposals based on recognized user emotions.

"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.

Form for implementing the invention

This invention is a system that receives information about home appliance usage from users, calculates optimal usage times 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.

Hardware and software used

Hardware

Device: The device on which the user provides input information (e.g., smartphone, tablet, PC)

Server: Server for processing and storing data

Software

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

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

Specific system operation

1. Collection of user input information

Users use the terminal to input the type of appliance they want to use and the desired usage time.

Specific actions involve the user entering information such as "air conditioner" and "2:00 PM to 4:00 PM" into the application's input form.

2. Submitting input information

The device sends the information entered by the user to the server.

Specific operations involve the device sending input information to the server using an HTTP request.

3. Calculating appliance usage times

The server calculates the optimal usage time based on the type of appliance and usage time received.

Specifically, the server runs an algorithm that takes into account past usage data and power consumption patterns to calculate the optimal usage time period.

For example, the server might calculate that the best time to use the air conditioner is between 2:00 PM and 4:00 PM.

4. Emotion Recognition Using an Emotion Engine

The server uses an emotion engine to recognize the user's emotions.

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

5. Proposal for adjusting electricity rates

The server adjusts the electricity rate suggestions based on the emotion engine's recognition results.

Specific behavior is that if the user expresses happiness, the server will extend the usage time of entertainment-oriented appliances (e.g., televisions) and suggest an increase in the resulting electricity bill.

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

6. Notifying users of results

The server sends the calculation results and suggestions to the device.

The device will notify the user of the received information.

Specific actions include the device informing the user via push notifications and in-app messages 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."

Specific examples

User input

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

Server processing

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

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).

Example prompt for a generative AI model

"The user inputs that they want to use the air conditioner from 2:00 PM to 4:00 PM. Calculate the optimal usage time, and if the user's emotion is joy, suggest extending the usage time of entertainment appliances."

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.

The flow of the identification process in Example 2 will be explained using Figure 19.

Step 1:

Collecting user input information

Users use the terminal to input the type of appliance they want to use and the desired usage time.

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

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

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

Step 2:

Submit input information

The device sends the information entered by the user to the server.

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

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

Specific operation: The device generates an HTTP request and sends the entered data to the server.

Step 3:

Calculating appliance usage times

The server calculates the optimal usage time based on the type of appliance and usage time received.

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

Output: Data on optimal usage times.

Specific operation: The server runs an algorithm that takes into account past usage data and power consumption patterns to calculate the optimal usage time. For example, it may calculate that the optimal time to use the air conditioner is between 2:00 PM and 4:00 PM.

Step 4:

Emotion recognition using an emotion engine

The server uses an emotion engine to recognize the user's emotions.

Input: User's facial image and voice data.

Output: User's emotional state (e.g., happy, stressed).

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

Step 5:

Proposed adjustment of electricity rates

The server adjusts the electricity rate suggestions based on the emotion engine's recognition results.

Input: User's emotional state and optimal usage time data.

Output: Proposed adjusted electricity rates.

Specific behavior: If the user is expressing happiness, 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).

Step 6:

Notifying users of results

The server sends the calculation results and suggestions to the device.

The device will notify the user of the received information.

Input: Data suggesting optimal usage times and adjusted electricity rates.

Output: Notification message to the user.

Specific behavior: 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."

(Application Example 2)

Next, we will explain Application Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

Conventional electricity rate optimization systems based on appliance usage information were unable to take into account the user's emotional state, making it difficult to increase user satisfaction. Furthermore, when optimizing the usage times of in-store appliances, adjustments were not made based on the customer's emotions, preventing an improved customer experience.

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

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 times based on the received home appliance usage information, means for proposing the calculated optimal usage times 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 charges based on the recognized emotions. This makes it possible to propose optimal home appliance usage times and electricity charges according to the user's emotional state, thereby increasing user satisfaction. Furthermore, by adjusting the usage times of home appliances in the store according to the customer's emotions, the customer experience can be improved.

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

"Means for calculating electricity charges" means a device or program for calculating electricity charges based on a market-linked plan.

"Means for receiving home appliance usage information" refers to a device or program for receiving information such as the type of home appliance and usage time from the user as input.

"Means for calculating optimal usage time periods" refers to a device or program for calculating optimal usage time periods for home appliances based on received home appliance usage information.

"Means for making suggestions to users" refers to a device or program that notifies or displays to users the calculated optimal usage time and electricity charges.

An "emotion recognition engine" is an algorithm or program that recognizes a user's emotions.

"Means for recognizing emotions" refers to a device or program for detecting a user's emotions using an emotion recognition engine.

"Means for adjusting electricity rate proposals" is a device or program for changing the content of electricity rate proposals based on recognized user sentiment.

The following system configuration is described as an embodiment of this invention.

System Configuration

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

Program processing

1. Enter your appliance usage information:

The user device receives input from the user about the type of appliance and the amount of time it has been used. This information is sent to the server.

2. Emotion recognition:

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

3. Calculating the best time to use:

The server uses an electricity rate calculation engine to calculate the optimal usage time based on the received home appliance usage information. The electricity rate calculation engine calculates electricity rates based on a market-linked plan.

4. Proposed electricity rate adjustment:

The server adjusts electricity rate 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.

5. Notice to Users:

The server then notifies the user's device of the calculated optimal usage time period and the adjusted electricity rate. The user can then adjust their home appliance usage accordingly.

Hardware and software used

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

Software: Emotion recognition engine (EmotionEngine), electricity bill calculation engine (ElectricityPricing)

Specific examples

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.

Example prompt

Develop an application that optimizes the use of lighting and air conditioning in stores and adjusts electricity rates based on customer emotions. If a customer is happy, extend the use of entertainment appliances, and if they are stressed, extend the use of relaxation appliances.

In this way, it becomes possible to propose optimal times for using home appliances and electricity rates based on the user's emotional state, thereby increasing user satisfaction. Furthermore, by adjusting the usage times of home appliances in the store based on the customer's emotions, the customer experience can be improved.

The flow of the specific processing in Application Example 2 is explained using Figure 20.

Step 1:

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

Step 2:

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.

Step 3:

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

Step 4:

The server adjusts electricity rate suggestions based on the recognized user emotions. Specifically, the server uses emotional state data from the emotion recognition engine to adjust the suggestions 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 optimal use time periods, and the output data is the adjusted electricity rate suggestions.

Step 5:

The server notifies the user's device of the calculated optimal usage time period and adjusted electricity charges. Specifically, the server sends data on the optimal usage time period and adjusted electricity charges to the user's device, allowing the user to check it. The input data is the optimal usage time period and adjusted electricity charges, and the output data is the suggested information displayed on the user's device.

In this way, it becomes possible to propose optimal times for using home appliances and electricity rates based on the user's emotional state, thereby increasing user satisfaction. Furthermore, by adjusting the usage times of home appliances in the store based on the customer's emotions, the customer experience can be improved.

(Example 3)

Next, we will explain Example 3 of Form Example 3. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

Conventional electricity bill calculation systems could suggest optimal times for users to use home appliances, but they did not make suggestions based on the user's emotions. As a result, they were 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 bills were not calculated based on trading prices at the power exchange, meaning users were unable to obtain reference information for reducing their electricity bills.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

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 the user, means for calculating optimal usage times based on the received home appliance usage information, means for proposing the calculated optimal usage times and electricity charges to the user, means for recognizing the user's emotions, and means for proposing optimal home appliance usage times based on the recognized emotions. This makes it possible to propose optimal home appliance usage times based on the user's emotional state and calculate electricity charges based on the trading price at the power exchange.

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

"Means for calculating electricity charges" refers to a device or program that has the function of calculating electricity charges according to usage time based on the trading price at the electricity exchange and the power consumption of home appliances.

"Means for receiving home appliance usage information from users" refers to a device or program that provides an interface for users to input the type of home appliance they wish to use and the usage time, and receives that information.

"Means for calculating optimal usage time periods" refers to a device or program that has the function of calculating the optimal time periods for using home appliances based on the received home appliance usage information and the trading price at the electricity exchange.

"Means for making suggestions to users" refers to a device or program that notifies or displays to users the calculated optimal usage time and electricity charges.

"Means for recognizing user emotions" refers to a device or program that has the function of analyzing the user's voice, facial expressions, behavior, etc., and determining the user's emotional state.

"Means for suggesting optimal times to use home appliances based on emotions" refers to 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.

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 makes suggestions to the user. It also has a function that recognizes the user's emotions and suggests times for using home appliances based on those emotions.

Hardware and software used

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

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

Specific system operation

1. User input

The user uses the device to enter the type of appliance they want to use and the usage time.

Example: User enters "Use the air conditioner from 3pm to 6pm."

2. Obtaining Power Exchange Data

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

Example: The server uses an API to retrieve electricity trading price data from 3:00 PM to 6:00 PM.

3. Calculating your electricity bill

The electricity charges for the specified time period are calculated based on the electricity trading price obtained by the server.

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

4. Emotion recognition

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.

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 feeling stressed.

5. Optimal usage time suggestions

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

Example: The server determines that the user is feeling stressed and suggests using a home appliance that will promote relaxation (e.g., a massage chair).

6. Presentation to the user

The server sends the calculation results and suggestions to the user's device and presents them to the user.

Example: The server displays calculated electricity bills and suggestions for using relaxation appliances on the user's smartphone.

Example prompt

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

In this way, the system can suggest optimal times to use home appliances and electricity rates based on the user's input and emotions. The flow of the identification process in Example 3 will be explained using Figure 21.

Step 1:

The user uses the device to enter the type of appliance they want to use and the usage time.

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

Specific operation: 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.

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

Step 2:

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

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

Specific operation: The server sends a request to the power exchange data acquisition API to obtain power trading price data for the specified time period.

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

Step 3:

The electricity charges for the specified time period are calculated based on the electricity trading price obtained by the server.

Input: Obtained electricity trading price data and the power consumption of the home appliance (e.g., 1.5kW power consumption of an air conditioner).

Specific operation: The server calculates the electricity bill from 3:00 PM to 6:00 PM using the air conditioner's power consumption and the acquired electricity trading price.

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

Step 4:

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.

Input: User voice, facial expression, and behavioral data.

Specific operation: When the user speaks into the device's camera, the device sends audio and video data to the emotion recognition engine.

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

Step 5:

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

Input: Calculated electricity bill and user's emotional state.

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., a massage chair).

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

Step 6:

The server sends the calculation results and suggestions to the user's device and presents them to the user.

Input: Calculation results and suggestions.

Specific operation: The server sends the calculation results and suggestions to the user's device.

Output: The user will receive a notification on their smartphone and the suggestions will be displayed when they open the app.

(Application Example 3)

Next, we will explain Application Example 3 of Form Example 3. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

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

The specific processing by the specific 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 electricity charges based on a market-linked plan, means for receiving home appliance usage information from the user, means for calculating optimal usage times based on the received home appliance usage information, means for proposing the calculated optimal usage times and electricity charges to the user, means for recognizing the user's emotions, means for proposing optimal home appliance usage times based on the recognized emotions, means for checking the usage status of home appliances in the store in real time, and means for optimal environmental settings according to the customer's emotions. This makes it possible to optimize electricity charges, propose optimal home appliance usage times according to the user's or customer's emotions, and provide a comfortable environment.

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

"Means for calculating electricity charges" means a device or software for calculating electricity charges based on a market-linked plan.

"Means for receiving home appliance usage information from users" refers to an interface that allows users to input the type of home appliance they wish to use and the usage time.

"Means for calculating optimal usage time periods" refers to a device or software that calculates optimal usage time periods based on received home appliance usage information, taking into account electricity trading prices.

"Means for making suggestions to users" refers to an interface that notifies users of the calculated optimal usage time and electricity charges.

"Means for recognizing user emotions" refers to devices or software that analyze emotions from the user's voice, facial expressions, behavior, etc.

"Means for suggesting optimal times to use home appliances based on emotions" refers to a device or software that calculates and suggests optimal times to use home appliances based on the user's recognized emotions.

"Means for checking the usage status of home appliances within a store in real time" refers to a device or software that monitors the usage status of home appliances within a store in real time and notifies the manager.

"Means for optimal environmental settings according to customer emotions" refers to devices or software that analyze customer emotions and, based on that, optimize environmental settings such as lighting, air conditioning, and music in the store.

The following system configuration is described as an embodiment of this invention.

System Configuration

1. Hardware:

Smartphone (iOS/Android)

Smart glasses (e.g., Google Glass)

Head-mounted display (e.g., Oculus Rift)

Robots (e.g., Pepper)

2. Software:

Facial recognition API (e.g., Microsoft Azure Face API)

Emotion recognition API (e.g. Affectiva)

Electricity trading price API (e.g., EPEX SPOT API)

Database (e.g., Firebase)

Program processing

The server first receives information about home appliance usage from the user. The user enters the type of appliance they wish to use and the usage time via 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.

Furthermore, the server uses facial recognition APIs 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.

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

Specific examples

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

Example prompt

Enter customer facial expression data and recognize their emotions. Then, retrieve the current electricity price from the electricity trading price API and calculate the optimal time to use 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.

In this way, it is possible to optimize electricity rates, suggest optimal times to use home appliances based on the emotions of users and customers, and provide a comfortable environment.

The flow of the specific processing in Application Example 3 is explained using Figure 22.

Step 1:

The user uses a smartphone app to input the type of appliance they wish to use and the amount of time they will be using it. The information entered is the type of appliance and the amount of time it will be used. This information is then sent to the server.

Step 2:

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. The server uses this data to calculate the electricity charges for the appliances' usage times.

Step 3:

The server then proposes the calculated optimal usage time and electricity rates to the user. The proposal includes the optimal usage time and the electricity rate for that time. This information is displayed on the smartphone app.

Step 4:

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, which are sent to the server.

Step 5:

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.

Step 6:

The server recalculates the optimal time periods for using home 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, it will suggest using appliances that will contribute to relaxation.

Step 7:

The server then proposes the recalculated optimal usage times and electricity rates to the user. The proposals include the optimal usage times based on the user's emotions and the electricity rates for those times. This information is displayed on a smartphone app, smart glasses, a head-mounted display, and a robot.

Step 8:

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 appliances, and the output is the real-time usage status. This allows store managers to check the usage status of home appliances through a smartphone app.

Step 9:

The server optimizes environmental settings such as lighting, air conditioning, and music to provide optimal environmental settings 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.

These steps will not only optimize electricity rates, but also suggest optimal times to use home appliances based on the emotions of users and customers, and provide a comfortable environment.

The specific processing unit 290 transmits the results 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 results of the specific processing. The microphone 238 acquires audio indicating the user input regarding the results 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.

Data generation model 58 is what is known as generative AI (artificial intelligence). An example of data generation model 58 is generative AI such as ChatGPT (Internet search <URL: https://openai.com/blog/chatgpt>). Data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing an instruction is input to 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. Data generation model 58 performs inference on the input inference data in accordance with the instructions indicated by the prompt, and outputs the inference results in the form of data such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization.

Another example of generative AI is Gemini (Internet search <URL: https://gemini.google.com/?hl=ja>).

In the above embodiment, an example was given in which the specific processing was performed by the data processing device 12, but the technology disclosed herein is not limited to this, and the specific processing may also be performed by the smart glasses 214.

[Third embodiment]

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

As shown in FIG. 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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, RAM 30, and storage 32.

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).

The headset 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, RAM 48, and storage 50. The processor 46, RAM 48, and 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.

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.

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 user 20's surroundings (e.g., an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

The communication I/F 44 is connected to the 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 manner.

Figure 6 shows an example of the main functions of the data processing device 12 and headset terminal 314. As shown in Figure 6, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32.

The specific processing program 56 is an example of a "program" 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 the specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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.

In the headset terminal 314, 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.

Next, we will explain the identification process performed by the identification processing unit 290 of the data processing device 12.

"Example 1"

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, it calculates the unit price of electricity based on the trading price information and uses that to calculate electricity rates.

"Example 2"

Another aspect of the present invention is a system that receives home appliance usage information 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 wish to use and the usage time, and calculates the optimal usage time period based on that information.

"Example 3"

Another aspect 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 reducing electricity bills. Specifically, when a user inputs the type of home appliance they wish 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 them with an electricity rate calculated based on the trading price at the power exchange for that time period.

The processing flow for each example form is explained below.

"Example 1"

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

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

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

"Example 2"

Step 1: Receive input from the user about the type of appliance they wish to use and the usage time.

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

"Example 3"

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

Step 2: Calculate the electricity bill based on the trading price on the power exchange for that time period.

Step 3: Present the calculated electricity bill to the user.

(Example 1)

Next, Example 1 of Form Example 1 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

Conventional electricity rate calculation systems struggled to reflect real-time trading price information from the power exchange, making it impossible to provide users with optimal electricity rates. Furthermore, they lacked functionality for suggesting optimal usage times based on users' appliance usage information, making it difficult for users to use electricity efficiently.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

In this invention, the server includes means for acquiring trading price information from the power exchange, means for saving the acquired trading price information in a database, means for calculating the unit price of electricity based on the saved 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 the optimal usage time period based on the received home appliance usage information, and means for proposing the calculated optimal usage time period and electricity bill to the user. This makes it possible to calculate electricity bills that reflect trading price information in real time, and furthermore, by proposing the optimal usage time period to the user, efficient power use is possible.

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

"Trading price information" refers to information regarding the price of electricity traded at the power exchange.

A "database" is a system for efficiently storing, managing, and searching data.

"Electricity unit price" is the unit price set for a certain amount of electricity.

"User" refers to an individual or corporation that consumes electricity and is the entity that uses this system.

"Home appliance usage information" refers to information about the types of home appliances used by users and the duration of use.

"Optimal usage times" are the recommended times for efficient power consumption.

"Proposal means" refers to a method or device for notifying the user of the calculated optimal usage time period and electricity charges.

Form for implementing the invention

This invention is 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. A specific embodiment of this system is described below.

Hardware and software used

1. Server

Hardware: General server equipment

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

2. Device

Hardware: The computer or smartphone used by the user

Software: Web browser, mobile application

3. User

Users are individuals or corporations that consume electricity and are the entities that use this system.

Program processing explanation

1. Obtaining trading price information

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

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

2. Saving transaction price information

The server stores the acquired trading price information in a database.

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

3. Calculating electricity unit prices

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

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

4. Calculating the user's electricity bill

The server calculates the user's electricity bill using the calculated electricity unit price.

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.

5. Receiving appliance usage information

The server receives home appliance usage information from the user.

Specific operation: The user enters the type of home appliance they wish to use and the usage time via a web browser or mobile application, and sends this information to the server.

6. Calculating the optimal usage time

The server calculates the optimal usage time based on the received appliance usage information.

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

7. Proposal Notification

The server then proposes the optimal usage time and electricity rates to the user.

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.

Examples and prompts

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

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

3. The server uses a Python script to calculate the electricity rate for "2023-10-01" from the trading price information.

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).

Example prompt

"Get the latest trading price information from the power exchange's API and save it in a MySQL database. Then, use a Python script to calculate the electricity rate and calculate the electricity bill based on the user's power consumption data."

The flow of the identification process in Example 1 will be explained using Figure 11.

Step 1:

Get trading price information

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

Input: Power exchange API endpoint URL

Output: Transaction price information in JSON format

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

Step 2:

Storing transaction price information

The server stores the acquired trading price information in a database.

Input: Transaction price information in JSON format

Output: Transaction price information stored in the database

Specific behavior: The server parses the received JSON data and extracts the necessary fields (e.g., datetime, price). Then, using the pymysql library, it connects to a MySQL database and executes a query such as INSERT INTO prices (datetime, price) VALUES ('2023-10-01 00:00:00', 10.5).

Step 3:

Calculating electricity unit prices

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

Input: Trading price information stored in the database

Output: Calculated electricity unit price

Specific operation: The server executes a Python script to obtain trading price information from the database. For example, using the pymysql library, it executes 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.

Step 4:

Calculating your electricity bill

The server calculates the user's electricity bill using the calculated electricity unit price.

Input: User power consumption data, calculated electricity rate

Output: Calculated user electricity bill

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., 100kWh) by the calculated unit price (e.g., 10 yen/kWh), and calculates the user's electricity bill (e.g., 1,000 yen).

Step 5:

Receive appliance usage information

The server receives home appliance usage information from the user.

Input: Appliance usage information entered by the user (type of appliance, usage time)

Output: Appliance usage information stored on the server

Specific operation: The user enters the type of home appliance they want to use and the usage time via a web browser or mobile application, and sends this information to the server. The server then stores the received information in a database.

Step 6:

Calculating the best time to use

The server calculates the optimal usage time based on the received appliance usage information.

Input: Appliance usage information, electricity unit price

Output: Optimal usage time

Specific operation: The server runs an algorithm to calculate the optimal usage time based on the electricity rate and appliance usage information. For example, it calculates the optimal usage time to avoid peak hours.

Step 7:

Proposal Notification

The server then proposes the optimal usage time and electricity rates to the user.

Input: Optimal usage time, calculated electricity rate

Output: User notification

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. For example, a push notification or email is sent.

(Application Example 1)

Next, we will explain Application Example 1 of Form Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

Conventional electricity billing 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 real-time trading price information from the power exchange and optimizing power consumption.

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

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 the power 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. This enables optimization of electricity consumption in real time.

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

"Means for calculating electricity charges" refers to a method or device for calculating electricity charges based on a market-linked plan.

"Means for receiving home appliance usage information from users" refers to methods or devices for obtaining information such as the types of home appliances used by users and the duration of use.

"Means for calculating optimal usage time periods" refers to a method or device that calculates optimal usage time periods for optimizing power consumption based on received home appliance usage information.

"Means for making suggestions to users" refers to methods or devices for notifying users of the calculated optimal usage time periods and electricity charges.

"Means for obtaining trading price information from an electricity exchange in real time" refers to a method or device for obtaining trading price information from an electricity exchange in real time.

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

"Means for reducing electricity consumption during high-price time periods and increasing electricity consumption during low-price time periods" refers to a method or device for reducing electricity consumption during times when the electricity trading price is high and increasing electricity consumption during times when the trading price is low.

To implement this invention, the following system configuration and program are required.

System Configuration

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

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

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

Program processing explanation

Hardware

Server: A computer with a high-performance processor and large memory capacity.

Device: A device with a user interface, such as a smartphone or tablet.

Network: Internet connection.

Software

Python: The primary implementation language for the program.

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

JSON data processing: A format for parsing retrieved data.

Data processing and calculations

1. The server obtains trading price information from the power exchange in real time. To do this, it uses an API to obtain the data and saves it in JSON format.

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

3. The device receives appliance usage information from the user, including the type of appliance they want to use and the duration of their use.

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

5. The device will then suggest the optimal usage time and electricity rates to the user. This will be done using the notification function to provide information to the user.

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.

Specific examples

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.

Example prompt

"Create a Python program that obtains real-time trading price information from the power exchange and optimizes power consumption within the factory. It should have the ability to reduce power consumption during high-price periods and increase power consumption during low-price periods."

The flow of the specific processing in Application Example 1 will be explained using Figure 12.

Step 1:

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

Step 2:

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

Step 3:

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 this. The input is the user's home appliance usage information, and the output is the received home appliance usage information.

Step 4:

The server calculates the optimal usage time period 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.

Step 5:

The device then proposes the calculated optimal usage time and electricity rate to the user. It uses the notification function 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.

Step 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. The input is trading price information, and the output is the optimized power consumption pattern.

(Example 2)

Next, Example 2 of Form Example 2 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

Existing electricity rate optimization systems based on home appliance usage information struggled to efficiently process user-entered information and calculate optimal usage times. Furthermore, there was a lack of a way to quickly provide users with the results, resulting in poor user convenience. Furthermore, there was a need for a system that could comprehensively calculate electricity rates based on market-linked plans and suggest optimal usage times.

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

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 the optimal usage time period based on the stored data, means for proposing the calculated optimal usage time period and electricity rate to the user, means for displaying the calculation results to the user, and means for calculating the electricity rate 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 the optimal usage time period and electricity rate.

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

"Means for calculating electricity charges" refers to algorithms or software for calculating electricity charges based on market-linked plans.

"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.

"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.

"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.

"Means for proposing calculation results to users" refers to interfaces or devices that notify users of the calculated optimal usage times and electricity charges.

"Means for displaying calculation results to the user" refers to software or hardware for displaying calculation results on the user's device.

Form for implementing the invention

This invention is a system that receives information about home appliance usage from users, calculates optimal usage times and electricity rates based on that information, and makes recommendations to the user. A specific embodiment of this system is described below.

System Configuration

This system consists of three main elements: a server, a terminal, and a user.

1. Server

The server provides a means for receiving appliance usage information from users. This means is an interface that can be accessed through a web browser or mobile application.

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

The server calculates the optimal usage time based on the stored data. This calculation is done using programming languages such as Python and R, and utilizing data analysis libraries (e.g., Pandas, NumPy).

The server has a means of proposing the calculated optimal usage time and electricity rates to the user. This proposal is made in real time.

The server has a means of sending the calculation results to the terminal for display to the user. This transmission is securely performed using the HTTPS protocol.

2. Device

The device sends the home appliance usage information entered by the user to the server. This transmission is secure, using the HTTPS protocol.

The device displays the calculation results received from the server to the user. For example, the mobile application screen might display "The best time to use the device is between 7:00 PM and 9:00 PM."

3. User

Users use a web browser or mobile application to enter 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."

Specific examples

As a concrete example, consider the case where a user wants to use the air conditioner.

1. The user opens the mobile application and enters "air conditioner" and "from 18:00 to 22:00."

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

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

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

5. The server sends the calculation result "The best time is between 7:00 PM and 9:00 PM" to the device.

6. The device displays the results on the mobile application screen and notifies the user.

Example prompt

Example of a prompt to input to a generative AI model:

The 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.

In this way, a system is realized in which the server, terminal, and user work together to calculate the optimal usage time for home appliances and provide it to the user.

The flow of the identification process in Example 2 will be explained using Figure 13.

Step 1:

The user enters appliance usage information.

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

Step 2:

The device sends the input information to the server.

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.

Step 3:

The server saves the input information in a database.

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.

Step 4:

The server calculates the optimal usage time based on the stored data.

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.

Step 5:

The server sends the calculation results to the device.

The server sends the calculation results to the device. Specifically, it converts the calculated optimal usage time slot into JSON format and sends it to the device 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 device.

Step 6:

The terminal displays the calculation results to the user.

The device displays the calculation results received from the server to the user. Specifically, the mobile application screen displays "The optimal usage time is from 19:00 to 21:00." The input is the calculation result of the optimal usage time received from the server, and the output is the content displayed to the user.

(Application Example 2)

Next, we will explain Application Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

Existing systems that calculate optimal usage times based on home appliance usage information only consider information about home appliance usage and do not support 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.

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 electricity charges based on a market-linked plan, 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, means for proposing the calculated optimal usage time periods and electricity charges to the user, means for receiving operation schedule information from the user and calculating optimal charging time periods, and means for notifying the user of the calculated optimal charging time periods. This allows the user to calculate the optimal charging time based on the operation schedule of the autonomous vehicle and improve energy management efficiency.

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

"Means for calculating electricity charges" means a device or program for calculating electricity charges based on a market-linked plan.

"Home appliance usage information" refers to information about the type of home appliance the user wants to use and the duration of use.

"Means for calculating optimal usage time periods" refers to a device or program for calculating optimal usage time periods for home appliances based on received home appliance usage information.

"Means for making suggestions to users" refers to a device or program that notifies or displays to users the calculated optimal usage time and electricity charges.

"Scheduled operation information" refers to information such as departure time, arrival time, and destination entered by the user regarding the operation of an autonomous vehicle.

"Means for calculating the optimal charging time slot" refers to a device or program for calculating the optimal charging time slot for an autonomous vehicle based on the received operation schedule information.

"Notification means" refers to a device or program that notifies the user of the calculated optimal charging time period.

A system for implementing this invention consists of a server, a user terminal, and an autonomous vehicle. The server includes the following means:

1. A method for calculating electricity rates based on market-linked plans

2. How to receive appliance usage information from users

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

4. A method for suggesting the calculated optimal usage time and electricity rates to users

5. A means of receiving schedule information from users and calculating the optimal charging time slots

6. A method for notifying users of the calculated optimal charging time slot

Program processing explanation

The server is implemented using a programming language such as Python. It receives appliance usage information and operation schedule information sent from the user's device, and calculates the optimal usage and charging times based on this information.

Specifically, the server performs the following processes:

1. Calculating electricity charges:

The server obtains the trading price from the power exchange based on the market-linked plan and calculates the electricity bill. This calculation involves obtaining data from the power exchange via an API.

2. Receiving and processing home appliance usage information:

The system receives appliance usage information (type of appliance to be used and usage time) sent from the user's device and calculates the optimal usage time. It uses an AI model to calculate and avoid peak hours of power consumption.

3. Receiving and processing schedule information:

The system receives flight schedule 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.

4. Notice:

The user will be notified of the calculated optimal usage and charging times. Notifications will be sent to the user's device via push notification or email.

Specific examples

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

Departure time: 2023-10-01 08:00:00

Arrival time: 2023-10-01 10:00:00

Destination: Office

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).

Example of a prompt to input to a generative AI model:

The user has entered a scheduled autonomous vehicle trip. 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. Please calculate the optimal charging time based on this information.

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

The flow of the specific processing in Application Example 2 will be explained using Figure 14.

Step 1:

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

Step 2:

The server receives 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. Based on this information, the server processes the data to calculate the optimal usage time period.

Step 3:

The server obtains trading prices from the power exchange based on a market-linked plan. It then calculates electricity charges based on the obtained trading price data. Calculations involve obtaining data from the power exchange via an API and taking real-time price fluctuations into account.

Step 4:

The server calculates the optimal usage time based on the received appliance usage information and the acquired trading 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.

Step 5:

The server then proposes the calculated optimal usage time and electricity rates to the user. The proposal is sent to the user's device via push notification or email. The user can check the proposed optimal usage time and plan their home appliance usage.

Step 6:

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.

Step 7:

The server calculates the optimal charging time based on the received flight schedule information. It uses an AI model to determine the optimal charging time based on the flight schedule. Specifically, it applies an algorithm that starts charging two hours before departure.

Step 8:

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

In this way, optimal usage and charging times can be calculated based on the user's home appliance usage information and operation schedule information, making it possible to improve energy management efficiency.

(Example 3)

Next, we will explain Example 3 of Form Example 3. In the following explanation, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

With conventional electricity bill calculation systems, it was difficult for users to know the optimal time to use their home appliances, making it difficult to effectively reduce electricity bills. Furthermore, because electricity bills were not calculated in real time based on trading prices at 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 making it difficult to use energy efficiently.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

In this invention, the server includes means for receiving home appliance usage information from the user, means for acquiring trading price data from the power 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 results to the user. This allows the user to know the optimal time period for using home appliances and the electricity rate for that time period in real time, allowing for effective savings on electricity bills.

"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.

"Means for obtaining trading price data from the power exchange" refers to devices or software that have the functionality to access the power exchange's API and obtain the latest trading price data.

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

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

"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 optimal usage times to the user.

Form for implementing the invention

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 the user. This system receives home appliance usage information from the user, acquires trading price data from the power exchange, processes the acquired data to calculate the electricity rate, and presents the results to the user.

Hardware and software used

This system uses the following hardware and software:

Server: A computer system for data processing and calculations.

Device: The device through which the user enters information (smartphone, tablet, PC, etc.).

Python: A programming language used to develop programs.

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

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

Program processing

The server receives input from the user. When the user enters the type of appliance they want to use and the duration of use, 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 appliance's power consumption.

Specific examples

For example, if a user enters "I want the air conditioner to run from 3:00 PM to 6:00 PM," the server will process it as follows:

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

2. The device sends this input data to the server.

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

4. The server receives the trading price data returned from the API and converts it into a data frame using Pandas.

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

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

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."

8. The server sends this message to the device.

9. The device displays a message to the user.

Example prompt

Example of a prompt to input to a generative AI model:

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 trading price on the power exchange for that time period and displays it to the user.

In this way, users can learn the optimal usage times and electricity rates, and use this information as reference for reducing electricity costs. The flow of the identification process in Example 3 will be explained using Figure 15.

Program processing flow

Step 1: Receive user input

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

Specific actions:

Input: The user enters "Air conditioning, 15:00-18:00" into the device's input form.

Output: The device sends this input data to the server.

Step 2: Obtaining electricity trading price data

The server retrieves the latest trading price data from the power exchange's API. This data is used to calculate the electricity rate for the specified time period.

Specific actions:

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

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

Output: The server receives the trading price data returned from the API.

Step 3: Process the data and calculate the electricity bill

The server processes the acquired trading price data using the Pandas library and calculates the electricity rate for the specified time period. To do this, it multiplies the power consumption of the appliance by the trading price.

Specific actions:

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

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

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

Output: Calculated electricity bill data.

Step 4: Presenting the calculation results

The server then presents the calculation results to the user, allowing the user to obtain reference information for reducing electricity bills.

Specific actions:

Input: Calculated electricity rate data.

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, your electricity bill will be XX yen."

Output: The server sends this message to the terminal.

Action: The device displays a message to the user.

In this way, users can learn the optimal times to use electricity and electricity rates, which can be used as reference information to reduce electricity bills.

(Application Example 3)

Next, we will explain Application Example 3 of Form Example 3. In the following explanation, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

When it comes to electricity usage, users often have difficulty knowing the optimal time of day to use the power in order to reduce their electricity bills. Furthermore, when charging electric vehicles in particular, electricity rates vary greatly depending on the time of day, making it difficult for users to select the optimal time of day to charge. For this reason, there is a demand for a system that allows users to use electricity efficiently and reduce their electricity bills.

The specific processing performed by the specific 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 electricity charges based on a market-linked plan, means for receiving electricity usage information from the user, means for calculating optimal usage time periods based on the received electricity 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 electricity usage time periods entered by the user, and means for presenting the calculated optimal electricity 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.

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

"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.

"Electricity usage information" refers to information such as the type of electrical equipment used by the user and the duration of use.

"Means for calculating the optimal usage time period" refers to a device or program that has the function of calculating the time period with the lowest electricity rates based on the received electricity usage information.

"Means for making suggestions to users" refers to a device or program that has the function of notifying users of the calculated optimal usage time period and electricity charges.

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

"Means for calculating the time period with the cheapest electricity rates" refers to 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.

"Means for presenting estimated electricity charges to users" refers to a device or program that has the function of notifying users of the calculated optimal time periods for electricity usage and the estimated electricity charges for those times.

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

System Configuration

1. Server

The server runs a program with the following functions:

Function to calculate electricity rates based on market-linked plans

A function to receive power usage information from users

A function that calculates the optimal usage time based on received power usage information.

A function that suggests optimal usage times and electricity rates to users.

A function that calculates the cheapest electricity rate among the electricity usage periods entered by the user.

A function that displays the calculated optimal power usage times and estimated electricity charges to the user.

2. User Device

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

3. Power Exchange API

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

Program processing explanation

The server calculates electricity charges 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:

1. Receiving power usage information

Power usage information entered by the user on the device (for example, the time period during which they wish to charge) is sent to the server.

2. Calculating your electricity bill

The server uses the power exchange's API to obtain the trading price for the specified time period. It then calculates the electricity bill based on the obtained trading price.

3. Calculating the optimal usage time

The server calculates the cheapest electricity rate among the time periods entered by the user. This calculation uses real-time trading price data.

4. Presentation of results

The calculation results (optimal usage times and estimated electricity rates) are sent to the user's terminal and presented to the user.

Specific examples

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

Example prompt

Example of a prompt to input to a generative AI model:

Design a system that, when a user inputs the time period they wish to charge their electric vehicle, calculates the optimal charging time and electricity rate based on the trading price on the power exchange for that time period and presents it to the user. For example, if a user inputs "I would like to charge between 3:00 PM and 6:00 PM on October 1, 2023," the system will calculate and present the time period with the cheapest electricity rate within that time period.

The flow of the specific processing in Application Example 3 will be explained using Figure 16.

Step 1:

The user enters power usage information from the device.

Specifically, users use 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.

Input: Time of day you want to charge

Output: Power usage information sent to the server

Step 2:

The server receives power usage information.

The server receives and analyzes the power usage information sent from the user's device. The received information includes the time period during which the user wishes to charge.

Input: Power usage information submitted by the user

Output: Analyzed power usage information

Step 3:

The server obtains trading prices from the power exchange API.

The server uses the power exchange's API to obtain the trading price for the specified time period. The obtained trading price is used to calculate the electricity bill.

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

Output: Trading price for the specified time period

Step 4:

The server calculates the electricity bill.

The server calculates the electricity charges for the time period specified by the user based on the acquired transaction price. The transaction price and usage time are used for the calculation.

Input: Trading price for the specified time period, usage time

Output: Calculated electricity cost

Step 5:

The server will calculate the optimal usage time.

The server calculates the cheapest electricity rate among the time periods entered by the user. This calculation uses real-time trading price data.

Input: Trading price for the specified time period, usage time

Output: Optimal usage time

Step 6:

The server presents the calculation results to the user.

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

Input: Optimal usage time, calculated electricity rate

Output: Optimal usage times and estimated electricity rates presented to the user

It is also possible to further combine an emotion engine that estimates the user's emotion. 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.

"Example 1"

One embodiment of the present invention provides a system incorporating an emotion engine. This system recognizes emotions from the user's voice, facial expressions, actions, 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). Alternatively, 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.

"Example 2"

In another embodiment of the present invention, a system is provided that adjusts 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 for entertainment-oriented home appliances and proposes an increase in the resulting electricity bill. On the other hand, if the user is feeling stressed, the system extends the usage time periods for relaxation-oriented home appliances and proposes an increase in the resulting electricity bill. This makes it possible to propose optimal electricity rates according to the user's emotions.

"Example 3"

One embodiment of the present invention provides a system incorporating an emotion engine. This system recognizes emotions from the user's voice, facial expressions, actions, 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). Alternatively, 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.

The processing flow for each example form is explained below.

"Example 1"

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

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 home appliances based on the results from the emotion engine.

Step 4: The system will suggest the calculated optimal usage time to the user.

"Example 2"

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

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

Step 4: The system communicates the adjusted electricity rate proposal to the user.

"Example 3"

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

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 home appliances based on the results from the emotion engine.

Step 4: The system will suggest the calculated optimal usage time to the user.

(Example 1)

Next, Example 1 of Form Example 1 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

Conventional electricity bill calculation systems calculate electricity bills 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, they are unable to promote the use of home appliances in accordance with the user's emotions, making it difficult to improve user satisfaction.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

In this invention, the server includes means for acquiring trading price information from the electricity market, means for analyzing the acquired trading price information and calculating the unit price of electricity, means for acquiring the user's power consumption, means for calculating the electricity bill 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 expressions, 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 based on the user's emotions, thereby improving user satisfaction.

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

"Trading price information" refers to data regarding the buying and selling prices of electricity in the electricity market.

"Electricity unit price" refers to the price per unit of electricity, calculated based on transaction price information.

"Power consumption" is data that indicates the amount of electricity consumed by a user over a certain period of time.

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

"User's voice, facial expressions, and actions" refers to information such as the voice, facial expressions, and body movements made by the user.

"Means for recognizing emotions" refers to technology or devices that analyze a user's voice, facial expressions, and behavior to identify the user's emotions.

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

"Means for notifying users of proposal content" refers to technology or devices used to communicate calculation results and proposal content to users.

This invention is a system that calculates electricity rates based on market-linked plans and suggests optimal times for using home appliances based on the user's emotions. This system consists of multiple components, including a server and a terminal.

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 Python's pandas library to calculate the average price over a certain period (e.g., every hour). Specifically, it converts the JSON data into a pandas DataFrame and calculates the average price for each hour using the resample('H').mean() method.

Next, the server obtains power consumption data from the user's smart meter. 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.

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).

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 television. 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.

For example, if a user consumes 5kWh 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.

Examples of prompts include, "Calculate the hourly electricity bill based on 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."

The flow of the identification process in Example 1 will be explained using Figure 17.

Step 1:

The server obtains trading price information from the electricity market.

Input: Power market API endpoint.

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.

Output: Retrieved trading price information (JSON data).

Step 2:

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

Input: Trading price information (JSON data).

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.

Output: Average hourly price (unit price of electricity).

Step 3:

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

Input: Smart meter data (MQTT message).

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

Output: User power consumption data.

Step 4:

The server calculates the electricity bill based on the unit price of electricity and the amount of electricity consumed.

Input: Electricity unit price and power consumption data.

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

Output: Calculated electricity cost.

Step 5:

The server notifies the user of the calculation results.

Input: Calculated electricity cost.

Specific behavior: The server sends emails using the SMTP protocol or push notifications using Firebase Cloud Messaging.

Output: The electricity charge notified to the user.

Step 6:

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

Input: User's voice, facial expressions, and actions.

Specific operations: 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 to text using the Google Cloud Speech-to-Text API.

Output: User's voice, facial expression, and behavioral data.

Step 7:

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

Input: User voice, facial expression, and behavioral data.

Specific operation: The device sends facial feature point data to the Microsoft Azure Emotion API and obtains an emotion label (e.g., happy, sad, or angry).

Output: Recognized emotion label.

Step 8:

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

Input: Recognized emotion label.

Specific operation: Based on the emotion label, the device selects the most suitable appliance from a pre-defined list and suggests usage times.

Output: Suggested appliance usage times.

Step 9:

The device will notify the user of the suggestions.

Input: Suggested appliance usage time period.

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

Output: The suggestion notified to the user.

(Application Example 1)

Next, we will explain Application Example 1 of Form Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

Conventional electricity bill calculation systems were limited to calculating electricity rates based on market-linked plans and suggesting optimal usage times based on information about home appliance usage from users. However, because they did not consider the user's emotional state when making suggestions about home appliance usage, they were unable to improve user satisfaction or comfort. Furthermore, because they did not make suggestions about home appliance usage based on emotions, energy management that responded to the user's psychological needs was not possible.

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

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 times based on the received home appliance usage information, means for proposing the calculated optimal usage times 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.

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

"Means for calculating electricity charges" means a device or software for calculating electricity charges based on a market-linked plan.

"Means for receiving home appliance usage information from users" refers to an interface that allows users to input the type of home appliance they wish to use and the usage time.

"Means for calculating optimal usage time periods" refers to a device or software that calculates the most efficient usage time periods based on the received home appliance usage information.

"Means for making suggestions to users" refers to an interface that notifies users of the calculated optimal usage time and electricity charges.

"Means for recognizing user emotions" refers to devices or software that detect emotions from the user's voice, facial expressions, behavior, etc.

"Means for suggesting optimal home appliance usage" refers to a device or software that suggests optimal home appliance usage to a user based on recognized emotions.

The following system configuration is described as an embodiment of this invention.

System Configuration

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 times based on the received home appliance usage information, means for suggesting the calculated optimal usage times and electricity charges to the user, means for recognizing the user's emotions, and means for suggesting optimal home appliance usage based on the recognized emotions.

Program processing

The server first obtains trading price information from the power exchange. This information is obtained via an API. It then calculates the electricity bill based on the obtained trading price information. The calculated electricity bill is used to calculate the optimal usage time based on the appliance usage information entered by the user.

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 suggests the optimal use of home appliances.

Hardware and software used

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

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

Specific examples

For example, if a user is using a smartphone, the app uses the smartphone's camera to recognize their 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.

Example prompt

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

The flow of the specific processing in Application Example 1 is explained using Figure 18.

Step 1:

The server obtains trading price information from the power exchange.

Input: Power Exchange API endpoint

Data processing: Send API requests and obtain trading price information.

Output: Trading price information (JSON format)

Step 2:

The server calculates electricity charges based on the acquired transaction price information.

Input: Transaction price information

Data calculation: Extracts current electricity unit prices from trading price information and calculates electricity charges based on usage.

Output: Calculated electricity cost

Step 3:

Users enter home appliance usage information using their smartphones.

Input: Type of appliance, usage time

Data processing: Send the entered information to the server.

Output: Appliance usage information

Step 4:

The server calculates the optimal usage time based on the received appliance usage information.

Input: Appliance usage information, transaction price information

Data calculation: Combining appliance usage information and transaction price information to calculate the most cost-effective usage times.

Output: Optimal usage time

Step 5:

The server then proposes the optimal usage time and electricity rates to the user.

Input: Optimal usage time, calculated electricity rate

Data processing: Generate proposals and send them to the user's device.

Output: Recommendations (optimal usage time and electricity rates)

Step 6:

The user uses their smartphone camera to recognize their face.

Input: User's face image

Data processing: Send facial images to the server

Output: Facial image data

Step 7:

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

Input: Facial image data

Data calculation: Analyze facial image data and recognize emotions

Output: Recognized emotion

Step 8:

The server then suggests the optimal use of home appliances based on the recognized emotions.

Input: Recognized emotion

Data calculation: Generate home appliance usage suggestions based on emotions

Output: Home appliance usage suggestion

Step 9:

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

Input: Appliance usage proposals, transaction price information

Data calculation: Calculate the usage costs of proposed appliances

Output: Usage cost notification

(Example 2)

Next, Example 2 of Form Example 2 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

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

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

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 the user, means for calculating optimal usage time periods 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 periods and electricity charges to the user. This makes it possible to propose optimal home appliance usage and adjust electricity charges according to the user's emotions.

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

"Means for calculating electricity charges" is a function for calculating electricity charges based on electricity consumption and a market-linked plan.

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

"Means for calculating optimal usage times" is a function that calculates the optimal usage times for home appliances based on received home appliance usage information, taking into account power consumption patterns and past data.

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

"Means for adjusting electricity rate proposals" is a function for optimizing electricity rate proposals based on recognized user emotions.

"Means for proposing the calculated optimal usage time periods and electricity charges to the user" is a function for notifying the user of the calculated optimal usage time periods and adjusted electricity charges.

Form for implementing the invention

This invention is a system that receives information about home appliance usage from users, calculates optimal usage times 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.

Hardware and software used

Hardware

Device: The device on which the user provides input information (e.g., smartphone, tablet, PC)

Server: Server for processing and storing data

Software

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

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

Specific system operation

1. Collection of user input information

Users use the terminal to input the type of appliance they want to use and the desired usage time.

Specific actions involve the user entering information such as "air conditioner" and "2:00 PM to 4:00 PM" into the application's input form.

2. Submitting input information

The device sends the information entered by the user to the server.

Specific operations involve the device sending input information to the server using an HTTP request.

3. Calculating appliance usage times

The server calculates the optimal usage time based on the type of appliance and usage time received.

Specifically, the server runs an algorithm that takes into account past usage data and power consumption patterns to calculate the optimal usage time period.

For example, the server might calculate that the best time to use the air conditioner is between 2:00 PM and 4:00 PM.

4. Emotion Recognition Using an Emotion Engine

The server uses an emotion engine to recognize the user's emotions.

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

5. Proposal for adjusting electricity rates

The server adjusts the electricity rate suggestions based on the emotion engine's recognition results.

Specific behavior is that if the user expresses happiness, the server will extend the usage time of entertainment-oriented home appliances (e.g., televisions) and suggest an increase in the resulting electricity bill.

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

6. Notifying users of results

The server sends the calculation results and suggestions to the device.

The device will notify the user of the received information.

Specific actions include the device informing the user via push notifications and in-app messages 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."

Specific examples

User input

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

Server processing

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

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).

Example prompt for a generative AI model

"The user inputs that they want to use the air conditioner from 2:00 PM to 4:00 PM. Calculate the optimal usage time, and if the user's emotion is joy, suggest extending the usage time of entertainment appliances."

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.

The flow of the identification process in Example 2 will be explained using Figure 19.

Step 1:

Collecting user input information

Users use the terminal to input the type of appliance they want to use and the desired usage time.

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

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

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

Step 2:

Submit input information

The device sends the information entered by the user to the server.

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

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

Specific operation: The device generates an HTTP request and sends the entered data to the server.

Step 3:

Calculating appliance usage times

The server calculates the optimal usage time based on the type of appliance and usage time received.

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

Output: Data on optimal usage times.

Specific operation: The server runs an algorithm that takes into account past usage data and power consumption patterns to calculate the optimal usage time. For example, it may calculate that the optimal time to use the air conditioner is between 2:00 PM and 4:00 PM.

Step 4:

Emotion recognition using an emotion engine

The server uses an emotion engine to recognize the user's emotions.

Input: User's facial image and voice data.

Output: User's emotional state (e.g., happy, stressed).

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

Step 5:

Proposed adjustment of electricity rates

The server adjusts the electricity rate suggestions based on the emotion engine's recognition results.

Input: User's emotional state and optimal usage time data.

Output: Proposed adjusted electricity rates.

Specific behavior: If the user is expressing happiness, 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).

Step 6:

Notifying users of results

The server sends the calculation results and suggestions to the device.

The device will notify the user of the received information.

Input: Data suggesting optimal usage times and adjusted electricity rates.

Output: Notification message to the user.

Specific behavior: 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."

(Application Example 2)

Next, we will explain Application Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

Conventional electricity rate optimization systems based on appliance usage information were unable to take into account the user's emotional state, making it difficult to increase user satisfaction. Furthermore, when optimizing the usage times of in-store appliances, adjustments were not made based on the customer's emotions, preventing an improved customer experience.

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

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 times based on the received home appliance usage information, means for proposing the calculated optimal usage times 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 charges based on the recognized emotions. This makes it possible to propose optimal home appliance usage times and electricity charges according to the user's emotional state, thereby increasing user satisfaction. Furthermore, by adjusting the usage times of home appliances in the store according to the customer's emotions, the customer experience can be improved.

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

"Means for calculating electricity charges" means a device or program for calculating electricity charges based on a market-linked plan.

"Means for receiving home appliance usage information" refers to a device or program for receiving information such as the type of home appliance and usage time from the user as input.

"Means for calculating optimal usage time periods" refers to a device or program for calculating optimal usage time periods for home appliances based on received home appliance usage information.

"Means for making suggestions to users" refers to a device or program that notifies or displays to users the calculated optimal usage time and electricity charges.

An "emotion recognition engine" is an algorithm or program that recognizes a user's emotions.

"Means for recognizing emotions" refers to a device or program for detecting a user's emotions using an emotion recognition engine.

"Means for adjusting electricity rate proposals" is a device or program for changing the content of electricity rate proposals based on recognized user sentiment.

The following system configuration is described as an embodiment of this invention.

System Configuration

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

Program processing

1. Enter your appliance usage information:

The user device receives input from the user about the type of appliance and the amount of time it has been used. This information is sent to the server.

2. Emotion recognition:

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

3. Calculating the best time to use:

The server uses an electricity rate calculation engine to calculate the optimal usage time based on the received home appliance usage information. The electricity rate calculation engine calculates electricity rates based on a market-linked plan.

4. Proposed electricity rate adjustment:

The server adjusts electricity rate 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.

5. Notice to Users:

The server then notifies the user's device of the calculated optimal usage time period and the adjusted electricity rate. The user can then adjust their home appliance usage accordingly.

Hardware and software used

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

Software: Emotion recognition engine (EmotionEngine), electricity bill calculation engine (ElectricityPricing)

Specific examples

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.

Example prompt

Develop an application that optimizes the use of lighting and air conditioning in stores and adjusts electricity rates based on customer emotions. If a customer is happy, extend the use of entertainment appliances, and if they are stressed, extend the use of relaxation appliances.

In this way, it becomes possible to propose optimal times for using home appliances and electricity rates based on the user's emotional state, thereby increasing user satisfaction. Furthermore, by adjusting the usage times of home appliances in the store based on the customer's emotions, the customer experience can be improved.

The flow of the specific processing in Application Example 2 is explained using Figure 20.

Step 1:

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

Step 2:

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.

Step 3:

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

Step 4:

The server adjusts electricity rate suggestions based on the recognized user emotions. Specifically, the server uses emotional state data from the emotion recognition engine to adjust the suggestions 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 optimal use time periods, and the output data is the adjusted electricity rate suggestions.

Step 5:

The server notifies the user's device of the calculated optimal usage time period and adjusted electricity charges. Specifically, the server sends data on the optimal usage time period and adjusted electricity charges to the user's device, allowing the user to check it. The input data is the optimal usage time period and adjusted electricity charges, and the output data is the suggested information displayed on the user's device.

In this way, it becomes possible to propose optimal times for using home appliances and electricity rates based on the user's emotional state, thereby increasing user satisfaction. Furthermore, by adjusting the usage times of home appliances in the store based on the customer's emotions, the customer experience can be improved.

(Example 3)

Next, we will explain Example 3 of Form Example 3. In the following explanation, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

Conventional electricity bill calculation systems could suggest optimal times for users to use home appliances, but they did not make suggestions based on the user's emotions. As a result, they were 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 bills were not calculated based on trading prices at the power exchange, meaning users were unable to obtain reference information for reducing their electricity bills.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

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 the user, means for calculating optimal usage times based on the received home appliance usage information, means for proposing the calculated optimal usage times and electricity charges to the user, means for recognizing the user's emotions, and means for proposing optimal home appliance usage times based on the recognized emotions. This makes it possible to propose optimal home appliance usage times based on the user's emotional state and calculate electricity charges based on the trading price at the power exchange.

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

"Means for calculating electricity charges" refers to a device or program that has the function of calculating electricity charges according to usage time based on the trading price at the electricity exchange and the power consumption of home appliances.

"Means for receiving home appliance usage information from users" refers to a device or program that provides an interface for users to input the type of home appliance they wish to use and the usage time, and receives that information.

"Means for calculating optimal usage time periods" refers to a device or program that has the function of calculating the optimal time periods for using home appliances based on the received home appliance usage information and the trading price at the electricity exchange.

"Means for making suggestions to users" refers to a device or program that notifies or displays to users the calculated optimal usage time and electricity charges.

"Means for recognizing user emotions" refers to a device or program that has the function of analyzing the user's voice, facial expressions, behavior, etc., and determining the user's emotional state.

"Means for suggesting optimal times to use home appliances based on emotions" refers to 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.

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 makes suggestions to the user. It also has a function that recognizes the user's emotions and suggests times for using home appliances based on those emotions.

Hardware and software used

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

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

Specific system operation

1. User input

The user uses the device to enter the type of appliance they want to use and the usage time.

Example: User enters "Use the air conditioner from 3pm to 6pm."

2. Obtaining Power Exchange Data

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

Example: The server uses an API to retrieve electricity trading price data from 3:00 PM to 6:00 PM.

3. Calculating your electricity bill

The electricity charges for the specified time period are calculated based on the electricity trading price obtained by the server.

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

4. Emotion recognition

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.

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 feeling stressed.

5. Optimal usage time suggestions

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

Example: The server determines that the user is feeling stressed and suggests using a home appliance that will promote relaxation (e.g., a massage chair).

6. Presentation to the user

The server sends the calculation results and suggestions to the user's device and presents them to the user.

Example: The server displays calculated electricity bills and suggestions for using relaxation appliances on the user's smartphone.

Example prompt

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

In this way, the system can suggest optimal times to use home appliances and electricity rates based on the user's input and emotions. The flow of the identification process in Example 3 will be explained using Figure 21.

Step 1:

The user uses the device to enter the type of appliance they want to use and the usage time.

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

Specific operation: 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.

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

Step 2:

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

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

Specific operation: The server sends a request to the power exchange data acquisition API to obtain power trading price data for the specified time period.

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

Step 3:

The electricity charges for the specified time period are calculated based on the electricity trading price obtained by the server.

Input: Obtained electricity trading price data and the power consumption of the home appliance (e.g., 1.5kW power consumption of an air conditioner).

Specific operation: The server calculates the electricity bill from 3:00 PM to 6:00 PM using the air conditioner's power consumption and the acquired electricity trading price.

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

Step 4:

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.

Input: User voice, facial expression, and behavioral data.

Specific operation: When the user speaks into the device's camera, the device sends audio and video data to the emotion recognition engine.

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

Step 5:

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

Input: Calculated electricity bill and user's emotional state.

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., a massage chair).

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

Step 6:

The server sends the calculation results and suggestions to the user's device and presents them to the user.

Input: Calculation results and suggestions.

Specific operation: The server sends the calculation results and suggestions to the user's device.

Output: The user will receive a notification on their smartphone and the suggestions will be displayed when they open the app.

(Application Example 3)

Next, we will explain Application Example 3 of Form Example 3. In the following explanation, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

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

The specific processing by the specific 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 electricity charges based on a market-linked plan, means for receiving home appliance usage information from the user, means for calculating optimal usage times based on the received home appliance usage information, means for proposing the calculated optimal usage times and electricity charges to the user, means for recognizing the user's emotions, means for proposing optimal home appliance usage times based on the recognized emotions, means for checking the usage status of home appliances in the store in real time, and means for optimal environmental settings according to the customer's emotions. This makes it possible to optimize electricity charges, propose optimal home appliance usage times according to the user's or customer's emotions, and provide a comfortable environment.

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

"Means for calculating electricity charges" means a device or software for calculating electricity charges based on a market-linked plan.

"Means for receiving home appliance usage information from users" refers to an interface that allows users to input the type of home appliance they wish to use and the usage time.

"Means for calculating optimal usage time periods" refers to a device or software that calculates optimal usage time periods based on received home appliance usage information, taking into account electricity trading prices.

"Means for making suggestions to users" refers to an interface that notifies users of the calculated optimal usage time and electricity charges.

"Means for recognizing user emotions" refers to devices or software that analyze emotions from the user's voice, facial expressions, behavior, etc.

"Means for suggesting optimal times to use home appliances based on emotions" refers to a device or software that calculates and suggests optimal times to use home appliances based on the user's recognized emotions.

"Means for checking the usage status of home appliances within a store in real time" refers to a device or software that monitors the usage status of home appliances within a store in real time and notifies the manager.

"Means for optimal environmental settings according to customer emotions" refers to devices or software that analyze customer emotions and, based on that, optimize environmental settings such as lighting, air conditioning, and music in the store.

The following system configuration is described as an embodiment of this invention.

System Configuration

1. Hardware:

Smartphone (iOS/Android)

Smart glasses (e.g., Google Glass)

Head-mounted display (e.g., Oculus Rift)

Robots (e.g., Pepper)

2. Software:

Facial recognition API (e.g., Microsoft Azure Face API)

Emotion recognition API (e.g. Affectiva)

Electricity trading price API (e.g., EPEX SPOT API)

Database (e.g., Firebase)

Program processing

The server first receives information about home appliance usage from the user. The user enters the type of appliance they wish to use and the usage time via 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.

Furthermore, the server uses facial recognition APIs 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.

Based on the recognized emotions, the server recalculates the optimal times 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).

Specific examples

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

Example prompt

Enter customer facial expression data and recognize their emotions. Then, retrieve the current electricity price from the electricity trading price API and calculate the optimal time to use 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.

In this way, it is possible to optimize electricity rates, suggest optimal times to use home appliances based on the emotions of users and customers, and provide a comfortable environment.

The flow of the specific processing in Application Example 3 will be explained using Figure 22.

Step 1:

The user uses a smartphone app to input the type of appliance they wish to use and the amount of time they will be using it. The information entered is the type of appliance and the amount of time it will be used. This information is then sent to the server.

Step 2:

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' usage times.

Step 3:

The server then proposes the calculated optimal usage time and electricity rates to the user. The proposal includes the optimal usage time and the electricity rate for that time. This information is displayed on the smartphone app.

Step 4:

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, which are sent to the server.

Step 5:

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.

Step 6:

The server recalculates the optimal time periods for using home 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, it will suggest using appliances that will contribute to relaxation.

Step 7:

The server then proposes the recalculated optimal usage times and electricity rates to the user. The proposals include the optimal usage times based on the user's emotions and the electricity rates for those times. This information is displayed on a smartphone app, smart glasses, a head-mounted display, and a robot.

Step 8:

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 appliances, and the output is the real-time usage status. This allows store managers to check the usage status of home appliances through a smartphone app.

Step 9:

The server optimizes environmental settings such as lighting, air conditioning, and music to provide optimal environmental settings 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.

These steps will not only optimize electricity rates, but also suggest optimal times to use home appliances based on the emotions of users and customers, and provide a comfortable environment.

The specific processing unit 290 transmits the results of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the results of the specific processing. The microphone 238 acquires audio indicating the user input regarding the results 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.

Data generation model 58 is what is known as generative AI (artificial intelligence). An example of data generation model 58 is generative AI such as ChatGPT (Internet search <URL: https://openai.com/blog/chatgpt>). Data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing an instruction is input to 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. Data generation model 58 performs inference on the input inference data in accordance with the instructions indicated by the prompt, and outputs the inference results in the form of data such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization.

Another example of generative AI is Gemini (Internet search <URL: https://gemini.google.com/?hl=ja>).

In the above embodiment, an example was given in which the specific processing was performed by the data processing device 12, but the technology disclosed herein is not limited to this, and the specific processing may also be performed by the headset-type terminal 314.

[Fourth embodiment]

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

As shown in FIG. 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

The data processing device 12 includes a computer 22, a database 24, and a communication I/F 26.

Computer 22 is an example of a "computer" according to the technology of the present disclosure. Computer 22 is equipped with a processor 28, RAM 30, and storage 32. Processor 28, RAM 30, and storage 32 are connected to bus 34. Database 24 and communication I/F 26 are also connected to bus 34. Communication I/F 26 is connected to network 54. Examples of network 54 include a WAN (Wide Area Network) and/or a LAN (Local Area Network).

The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I/F 44, and a control target 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and control target 443 are also connected to the bus 52.

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.

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 user 20's surroundings (e.g., an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

The communication I/F 44 is connected to the 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 manner.

The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32.

The specific processing program 56 is an example of a "program" 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 the specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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.

In the robot 414, the processor 46 performs reception and output processing. Storage 5

0 stores a reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output 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.

Next, we will explain the identification process performed by the identification processing unit 290 of the data processing device 12.

"Example 1"

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, it calculates the unit price of electricity based on the trading price information and uses that to calculate electricity rates.

"Example 2"

Another aspect of the present invention is a system that receives home appliance usage information 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 wish to use and the usage time, and calculates the optimal usage time period based on that information.

"Example 3"

Another aspect 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 reducing electricity bills. Specifically, when a user inputs the type of home appliance they wish 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 them with an electricity rate calculated based on the trading price at the power exchange for that time period.

The processing flow for each example form is explained below.

"Example 1"

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

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

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

"Example 2"

Step 1: Receive input from the user about the type of appliance they wish to use and the usage time.

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

"Example 3"

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

Step 2: Calculate the electricity bill based on the trading price on the power exchange for that time period.

Step 3: Present the calculated electricity bill to the user.

(Example 1)

Next, Example 1 of Form Example 1 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

Conventional electricity rate calculation systems struggled to reflect real-time trading price information from the power exchange, making it impossible to provide users with optimal electricity rates. Furthermore, they lacked functionality for suggesting optimal usage times based on users' appliance usage information, making it difficult for users to use electricity efficiently.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

In this invention, the server includes means for acquiring trading price information from the power exchange, means for saving the acquired trading price information in a database, means for calculating the unit price of electricity based on the saved 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 the optimal usage time period based on the received home appliance usage information, and means for proposing the calculated optimal usage time period and electricity bill to the user. This makes it possible to calculate electricity bills that reflect trading price information in real time, and furthermore, by proposing the optimal usage time period to the user, efficient power use is possible.

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

"Trading price information" refers to information regarding the price of electricity traded at the power exchange.

A "database" is a system for efficiently storing, managing, and searching data.

"Electricity unit price" is the unit price set for a certain amount of electricity.

"User" refers to an individual or corporation that consumes electricity and is the entity that uses this system.

"Home appliance usage information" refers to information about the types of home appliances used by users and the duration of use.

"Optimal usage times" are the recommended times for efficient power consumption.

"Proposal means" refers to a method or device for notifying the user of the calculated optimal usage time period and electricity charges.

Form for implementing the invention

This invention is 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. A specific embodiment of this system is described below.

Hardware and software used

1. Server

Hardware: General server equipment

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

2. Device

Hardware: The computer or smartphone used by the user

Software: Web browser, mobile application

3. User

Users are individuals or corporations that consume electricity and are the entities that use this system.

Program processing explanation

1. Obtaining trading price information

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

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

2. Saving transaction price information

The server stores the acquired trading price information in a database.

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

3. Calculating electricity unit prices

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

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

4. Calculating the user's electricity bill

The server calculates the user's electricity bill using the calculated electricity unit price.

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.

5. Receiving appliance usage information

The server receives home appliance usage information from the user.

Specific operation: The user enters the type of home appliance they wish to use and the usage time via a web browser or mobile application, and sends this information to the server.

6. Calculating the optimal usage time

The server calculates the optimal usage time based on the received appliance usage information.

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

7. Proposal Notification

The server then proposes the optimal usage time and electricity rates to the user.

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.

Examples and prompts

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

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

3. The server uses a Python script to calculate the electricity rate for "2023-10-01" from the trading price information.

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).

Example prompt

"Get the latest trading price information from the power exchange's API and save it in a MySQL database. Then, use a Python script to calculate the electricity rate and calculate the electricity bill based on the user's power consumption data."

The flow of the identification process in Example 1 will be explained using Figure 11.

Step 1:

Get trading price information

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

Input: Power exchange API endpoint URL

Output: Transaction price information in JSON format

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

Step 2:

Storing transaction price information

The server stores the acquired trading price information in a database.

Input: Transaction price information in JSON format

Output: Transaction price information stored in the database

Specific behavior: The server parses the received JSON data and extracts the necessary fields (e.g., datetime, price). Then, using the pymysql library, it connects to a MySQL database and executes a query such as INSERT INTO prices (datetime, price) VALUES ('2023-10-01 00:00:00', 10.5).

Step 3:

Calculating electricity unit prices

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

Input: Trading price information stored in the database

Output: Calculated electricity unit price

Specific operation: The server executes a Python script to obtain trading price information from the database. For example, using the pymysql library, it executes 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.

Step 4:

Calculating your electricity bill

The server calculates the user's electricity bill using the calculated electricity unit price.

Input: User power consumption data, calculated electricity rate

Output: Calculated user electricity bill

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., 100kWh) by the calculated unit price (e.g., 10 yen/kWh), and calculates the user's electricity bill (e.g., 1,000 yen).

Step 5:

Receive appliance usage information

The server receives home appliance usage information from the user.

Input: Appliance usage information entered by the user (type of appliance, usage time)

Output: Appliance usage information stored on the server

Specific operation: The user enters the type of home appliance they want to use and the usage time via a web browser or mobile application, and sends this information to the server. The server then stores the received information in a database.

Step 6:

Calculating the best time to use

The server calculates the optimal usage time based on the received appliance usage information.

Input: Appliance usage information, electricity unit price

Output: Optimal usage time

Specific operation: The server runs an algorithm to calculate the optimal usage time based on the electricity rate and appliance usage information. For example, it calculates the optimal usage time to avoid peak hours.

Step 7:

Proposal Notification

The server then proposes the optimal usage time and electricity rates to the user.

Input: Optimal usage time, calculated electricity rate

Output: User notification

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. For example, a push notification or email is sent.

(Application Example 1)

Next, we will explain Application Example 1 of Form Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

Conventional electricity billing 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 real-time trading price information from the power exchange and optimizing power consumption.

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

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 the power 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. This enables optimization of electricity consumption in real time.

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

"Means for calculating electricity charges" refers to a method or device for calculating electricity charges based on a market-linked plan.

"Means for receiving home appliance usage information from users" refers to methods or devices for obtaining information such as the types of home appliances used by users and the duration of use.

"Means for calculating optimal usage time periods" refers to a method or device that calculates optimal usage time periods for optimizing power consumption based on received home appliance usage information.

"Means for making suggestions to users" refers to methods or devices for notifying users of the calculated optimal usage time periods and electricity charges.

"Means for obtaining trading price information from an electricity exchange in real time" refers to a method or device for obtaining trading price information from an electricity exchange in real time.

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

"Means for reducing electricity consumption during high-price time periods and increasing electricity consumption during low-price time periods" refers to a method or device for reducing electricity consumption during times when the electricity trading price is high and increasing electricity consumption during times when the trading price is low.

To implement this invention, the following system configuration and program are required.

System Configuration

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

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

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

Program processing explanation

Hardware

Server: A computer with a high-performance processor and large memory capacity.

Device: A device with a user interface, such as a smartphone or tablet.

Network: Internet connection.

Software

Python: The primary implementation language for the program.

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

JSON data processing: A format for parsing retrieved data.

Data processing and calculations

1. The server obtains trading price information from the power exchange in real time. To do this, it uses an API to obtain the data and saves it in JSON format.

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

3. The device receives appliance usage information from the user, including the type of appliance they want to use and the duration of their use.

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

5. The device will then suggest the optimal usage time and electricity rates to the user. This will be done using the notification function to provide information to the user.

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.

Specific examples

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.

Example prompt

"Create a Python program that obtains real-time trading price information from the power exchange and optimizes power consumption within the factory. It should have the ability to reduce power consumption during high-price periods and increase power consumption during low-price periods."

The flow of the specific processing in Application Example 1 will be explained using Figure 12.

Step 1:

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

Step 2:

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

Step 3:

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 this. The input is the user's home appliance usage information, and the output is the received home appliance usage information.

Step 4:

The server calculates the optimal usage time period 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.

Step 5:

The device then proposes the calculated optimal usage time and electricity rate to the user. It uses the notification function 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.

Step 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. The input is trading price information, and the output is the optimized power consumption pattern.

(Example 2)

Next, Example 2 of Form Example 2 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

Existing electricity rate optimization systems based on home appliance usage information struggled to efficiently process user-entered information and calculate optimal usage times. Furthermore, there was a lack of a way to quickly provide users with the results, resulting in poor user convenience. Furthermore, there was a need for a system that could comprehensively calculate electricity rates based on market-linked plans and suggest optimal usage times.

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

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 the optimal usage time period based on the stored data, means for proposing the calculated optimal usage time period and electricity rate to the user, means for displaying the calculation results to the user, and means for calculating the electricity rate 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 the optimal usage time period and electricity rate.

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

"Means for calculating electricity charges" refers to algorithms or software for calculating electricity charges based on market-linked plans.

"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.

"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.

"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.

"Means for proposing calculation results to users" refers to interfaces or devices that notify users of the calculated optimal usage times and electricity charges.

"Means for displaying calculation results to the user" refers to software or hardware for displaying calculation results on the user's device.

Form for implementing the invention

This invention is a system that receives information about home appliance usage from users, calculates optimal usage times and electricity rates based on that information, and makes recommendations to the user. A specific embodiment of this system is described below.

System Configuration

This system consists of three main elements: a server, a terminal, and a user.

1. Server

The server provides a means for receiving appliance usage information from users. This means is an interface that can be accessed through a web browser or mobile application.

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

The server calculates the optimal usage time based on the stored data. This calculation is done using programming languages such as Python and R, and utilizing data analysis libraries (e.g., Pandas, NumPy).

The server has a means of proposing the calculated optimal usage time and electricity rates to the user. This proposal is made in real time.

The server has a means of sending the calculation results to the terminal for display to the user. This transmission is securely performed using the HTTPS protocol.

2. Device

The device sends the home appliance usage information entered by the user to the server. This transmission is secure, using the HTTPS protocol.

The device displays the calculation results received from the server to the user. For example, the mobile application screen might display "The best time to use the device is between 7:00 PM and 9:00 PM."

3. User

Users use a web browser or mobile application to enter 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."

Specific examples

As a concrete example, consider the case where a user wants to use the air conditioner.

1. The user opens the mobile application and enters "air conditioner" and "from 18:00 to 22:00."

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

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

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

5. The server sends the calculation result "The best time is between 7:00 PM and 9:00 PM" to the device.

6. The device displays the results on the mobile application screen and notifies the user.

Example prompt

Example of a prompt to input to a generative AI model:

The 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.

In this way, a system is realized in which the server, terminal, and user work together to calculate the optimal usage time for home appliances and provide it to the user.

The flow of the identification process in Example 2 will be explained using Figure 13.

Step 1:

The user enters appliance usage information.

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

Step 2:

The device sends the input information to the server.

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.

Step 3:

The server saves the input information in a database.

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.

Step 4:

The server calculates the optimal usage time based on the stored data.

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.

Step 5:

The server sends the calculation results to the device.

The server sends the calculation results to the device. Specifically, it converts the calculated optimal usage time slot into JSON format and sends it to the device 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 device.

Step 6:

The terminal displays the calculation results to the user.

The device displays the calculation results received from the server to the user. Specifically, the mobile application screen displays "The optimal usage time is from 19:00 to 21:00." The input is the calculation result of the optimal usage time received from the server, and the output is the content displayed to the user.

(Application Example 2)

Next, we will explain Application Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

Existing systems that calculate optimal usage times based on home appliance usage information only consider information about home appliance usage and do not support 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.

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 electricity charges based on a market-linked plan, 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, means for proposing the calculated optimal usage time periods and electricity charges to the user, means for receiving operation schedule information from the user and calculating optimal charging time periods, and means for notifying the user of the calculated optimal charging time periods. This allows the user to calculate the optimal charging time based on the operation schedule of the autonomous vehicle and improve energy management efficiency.

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

"Means for calculating electricity charges" means a device or program for calculating electricity charges based on a market-linked plan.

"Home appliance usage information" refers to information about the type of home appliance the user wants to use and the duration of use.

"Means for calculating optimal usage time periods" refers to a device or program for calculating optimal usage time periods for home appliances based on received home appliance usage information.

"Means for making suggestions to users" refers to a device or program that notifies or displays to users the calculated optimal usage time and electricity charges.

"Scheduled operation information" refers to information such as departure time, arrival time, and destination entered by the user regarding the operation of an autonomous vehicle.

"Means for calculating the optimal charging time slot" refers to a device or program for calculating the optimal charging time slot for an autonomous vehicle based on the received operation schedule information.

"Notification means" refers to a device or program that notifies the user of the calculated optimal charging time period.

A system for implementing this invention consists of a server, a user terminal, and an autonomous vehicle. The server includes the following means:

1. A method for calculating electricity rates based on market-linked plans

2. How to receive appliance usage information from users

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

4. A method for suggesting the calculated optimal usage time and electricity rates to users

5. A means of receiving schedule information from users and calculating the optimal charging time slots

6. A method for notifying users of the calculated optimal charging time slot

Program processing explanation

The server is implemented using a programming language such as Python. It receives appliance usage information and operation schedule information sent from the user's device, and calculates the optimal usage and charging times based on this information.

Specifically, the server performs the following processes:

1. Calculating electricity charges:

The server obtains the trading price from the power exchange based on the market-linked plan and calculates the electricity bill. This calculation involves obtaining data from the power exchange via an API.

2. Receiving and processing home appliance usage information:

The system receives appliance usage information (type of appliance to be used and usage time) sent from the user's device and calculates the optimal usage time. It uses an AI model to calculate and avoid peak hours of power consumption.

3. Receiving and processing schedule information:

The system receives flight schedule 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.

4. Notice:

The user will be notified of the calculated optimal usage and charging times. Notifications will be sent to the user's device via push notification or email.

Specific examples

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

Departure time: 2023-10-01 08:00:00

Arrival time: 2023-10-01 10:00:00

Destination: Office

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).

Example of a prompt to input to a generative AI model:

The user has entered a scheduled autonomous vehicle trip. 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. Please calculate the optimal charging time based on this information.

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

The flow of the specific processing in Application Example 2 will be explained using Figure 14.

Step 1:

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

Step 2:

The server receives 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. Based on this information, the server processes the data to calculate the optimal usage time period.

Step 3:

The server obtains trading prices from the power exchange based on a market-linked plan. It then calculates electricity charges based on the obtained trading price data. Calculations involve obtaining data from the power exchange via an API and taking real-time price fluctuations into account.

Step 4:

The server calculates the optimal usage time based on the received appliance usage information and the acquired trading 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.

Step 5:

The server then proposes the calculated optimal usage time and electricity rates to the user. The proposal is sent to the user's device via push notification or email. The user can check the proposed optimal usage time and plan their home appliance usage.

Step 6:

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.

Step 7:

The server calculates the optimal charging time based on the received flight schedule information. It uses an AI model to determine the optimal charging time based on the flight schedule. Specifically, it applies an algorithm that starts charging two hours before departure.

Step 8:

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

In this way, optimal usage and charging times can be calculated based on the user's home appliance usage information and operation schedule information, making it possible to improve energy management efficiency.

(Example 3)

Next, Example 3 of Form Example 3 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

With conventional electricity bill calculation systems, it was difficult for users to know the optimal time to use their home appliances, making it difficult to effectively reduce electricity bills. Furthermore, because electricity bills were not calculated in real time based on trading prices at 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 making it difficult to use energy efficiently.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

In this invention, the server includes means for receiving home appliance usage information from the user, means for acquiring trading price data from the power 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 results to the user. This allows the user to know the optimal time period for using home appliances and the electricity rate for that time period in real time, allowing for effective savings on electricity bills.

"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.

"Means for obtaining trading price data from the power exchange" refers to devices or software that have the functionality to access the power exchange's API and obtain the latest trading price data.

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

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

"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 optimal usage times to the user.

Form for implementing the invention

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 the user. This system receives home appliance usage information from the user, acquires trading price data from the power exchange, processes the acquired data to calculate the electricity rate, and presents the results to the user.

Hardware and software used

This system uses the following hardware and software:

Server: A computer system for data processing and calculations.

Device: The device through which the user enters information (smartphone, tablet, PC, etc.).

Python: A programming language used to develop programs.

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

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

Program processing

The server receives input from the user. When the user enters the type of appliance they want to use and the duration of use, 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 appliance's power consumption.

Specific examples

For example, if a user enters "I want the air conditioner to run from 3:00 PM to 6:00 PM," the server will process it as follows:

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

2. The device sends this input data to the server.

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

4. The server receives the trading price data returned from the API and converts it into a data frame using Pandas.

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

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

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."

8. The server sends this message to the device.

9. The device displays a message to the user.

Example prompt

Example of a prompt to input to a generative AI model:

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 trading price on the power exchange for that time period and displays it to the user.

In this way, users can learn the optimal usage times and electricity rates, and use this information as reference for reducing electricity costs. The flow of the identification process in Example 3 will be explained using Figure 15.

Program processing flow

Step 1: Receive user input

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

Specific actions:

Input: The user enters "Air conditioning, 15:00-18:00" into the device's input form.

Output: The device sends this input data to the server.

Step 2: Obtaining electricity trading price data

The server retrieves the latest trading price data from the power exchange's API. This data is used to calculate the electricity rate for the specified time period.

Specific actions:

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

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

Output: The server receives the trading price data returned from the API.

Step 3: Process the data and calculate the electricity bill

The server processes the acquired trading price data using the Pandas library and calculates the electricity rate for the specified time period. To do this, it multiplies the power consumption of the appliance by the trading price.

Specific actions:

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

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

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

Output: Calculated electricity bill data.

Step 4: Presenting the calculation results

The server then presents the calculation results to the user, allowing the user to obtain reference information for reducing electricity bills.

Specific actions:

Input: Calculated electricity rate data.

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, your electricity bill will be XX yen."

Output: The server sends this message to the terminal.

Action: The device displays a message to the user.

In this way, users can learn the optimal times to use electricity and electricity rates, which can be used as reference information to reduce electricity bills.

(Application Example 3)

Next, we will explain Application Example 3 of Form Example 3. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

When it comes to electricity usage, users often have difficulty knowing the optimal time of day to use the power in order to reduce their electricity bills. Furthermore, when charging electric vehicles in particular, electricity rates vary greatly depending on the time of day, making it difficult for users to select the optimal time of day to charge. For this reason, there is a demand for a system that allows users to use electricity efficiently and reduce their electricity bills.

The specific processing performed by the specific 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 electricity charges based on a market-linked plan, means for receiving electricity usage information from the user, means for calculating optimal usage time periods based on the received electricity 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 electricity usage time periods entered by the user, and means for presenting the calculated optimal electricity 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.

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

"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.

"Electricity usage information" refers to information such as the type of electrical equipment used by the user and the duration of use.

"Means for calculating the optimal usage time period" refers to a device or program that has the function of calculating the time period with the lowest electricity rates based on the received electricity usage information.

"Means for making suggestions to users" refers to a device or program that has the function of notifying users of the calculated optimal usage time period and electricity charges.

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

"Means for calculating the time period with the cheapest electricity rates" refers to 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.

"Means for presenting estimated electricity charges to users" refers to a device or program that has the function of notifying users of the calculated optimal time periods for electricity usage and the estimated electricity charges for those times.

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

System Configuration

1. Server

The server runs a program with the following functions:

Function to calculate electricity rates based on market-linked plans

A function to receive power usage information from users

A function that calculates the optimal usage time based on received power usage information.

A function that suggests optimal usage times and electricity rates to users.

A function that calculates the cheapest electricity rate among the electricity usage periods entered by the user.

A function that displays the calculated optimal power usage times and estimated electricity charges to the user.

2. User Device

The user terminal is a device such as a smartphone or tablet that provides an interface for users to enter power usage information. Users enter the time period they wish to charge, and that information is sent to the server.

3. Power Exchange API

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

Program processing explanation

The server calculates electricity charges 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:

1. Receiving power usage information

Power usage information entered by the user on the device (for example, the time period during which they wish to charge) is sent to the server.

2. Calculating your electricity bill

The server uses the power exchange's API to obtain the trading price for the specified time period. It then calculates the electricity bill based on the obtained trading price.

3. Calculating the optimal usage time

The server calculates the cheapest electricity rate among the time periods entered by the user. This calculation uses real-time trading price data.

4. Presentation of results

The calculation results (optimal usage times and estimated electricity rates) are sent to the user's terminal and presented to the user.

Specific examples

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

Example prompt

Example of a prompt to input to a generative AI model:

Design a system that, when a user inputs the time period they wish to charge their electric vehicle, calculates the optimal charging time and electricity rate based on the trading price on the power exchange for that time period and presents it to the user. For example, if a user inputs "I would like to charge between 3:00 PM and 6:00 PM on October 1, 2023," the system will calculate and present the time period with the cheapest electricity rate within that time period.

The flow of the specific processing in Application Example 3 will be explained using Figure 16.

Step 1:

The user enters power usage information from the device.

Specifically, users use 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.

Input: Time of day you want to charge

Output: Power usage information sent to the server

Step 2:

The server receives power usage information.

The server receives and analyzes the power usage information sent from the user's device. The received information includes the time period during which the user wishes to charge.

Input: Power usage information submitted by the user

Output: Analyzed power usage information

Step 3:

The server obtains trading prices from the power exchange API.

The server uses the power exchange's API to obtain the trading price for the specified time period. The obtained trading price is used to calculate the electricity bill.

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

Output: Trading price for the specified time period

Step 4:

The server calculates the electricity bill.

The server calculates the electricity charges for the time period specified by the user based on the acquired transaction price. The transaction price and usage time are used for the calculation.

Input: Trading price for the specified time period, usage time

Output: Calculated electricity cost

Step 5:

The server will calculate the optimal usage time.

The server calculates the cheapest electricity rate among the time periods entered by the user. This calculation uses real-time trading price data.

Input: Trading price for the specified time period, usage time

Output: Optimal usage time

Step 6:

The server presents the calculation results to the user.

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

Input: Optimal usage time, calculated electricity rate

Output: Optimal usage times and estimated electricity rates presented to the user

It is also possible to further combine an emotion engine that estimates the user's emotion. 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.

"Example 1"

One embodiment of the present invention provides a system incorporating an emotion engine. This system recognizes emotions from the user's voice, facial expressions, actions, 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). Alternatively, 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.

"Example 2"

In another embodiment of the present invention, a system is provided that adjusts 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 for entertainment-oriented home appliances and proposes an increase in the resulting electricity bill. On the other hand, if the user is feeling stressed, the system extends the usage time periods for relaxation-oriented home appliances and proposes an increase in the resulting electricity bill. This makes it possible to propose optimal electricity rates according to the user's emotions.

"Example 3"

One embodiment of the present invention provides a system incorporating an emotion engine. This system recognizes emotions from the user's voice, facial expressions, actions, 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). Alternatively, 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.

The processing flow for each example form is explained below.

"Example 1"

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

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 home appliances based on the results from the emotion engine.

Step 4: The system will suggest the calculated optimal usage time to the user.

"Example 2"

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

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

Step 4: The system communicates the adjusted electricity rate proposal to the user.

"Example 3"

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

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 home appliances based on the results from the emotion engine.

Step 4: The system will suggest the calculated optimal usage time to the user.

(Example 1)

Next, Example 1 of Form Example 1 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

Conventional electricity bill calculation systems calculate electricity bills 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, they are unable to promote the use of home appliances in accordance with the user's emotions, making it difficult to improve user satisfaction.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

In this invention, the server includes means for acquiring trading price information from the electricity market, means for analyzing the acquired trading price information and calculating the unit price of electricity, means for acquiring the user's power consumption, means for calculating the electricity bill 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 expressions, 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 based on the user's emotions, thereby improving user satisfaction.

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

"Trading price information" refers to data regarding the buying and selling prices of electricity in the electricity market.

"Electricity unit price" refers to the price per unit of electricity, calculated based on transaction price information.

"Power consumption" is data that indicates the amount of electricity consumed by a user over a certain period of time.

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

"User's voice, facial expressions, and actions" refers to information such as the voice, facial expressions, and body movements made by the user.

"Means for recognizing emotions" refers to technology or devices that analyze a user's voice, facial expressions, and behavior to identify the user's emotions.

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

"Means for notifying users of proposal content" refers to technology or devices used to communicate calculation results and proposal content to users.

This invention is a system that calculates electricity rates based on market-linked plans and suggests optimal times for using home appliances based on the user's emotions. This system consists of multiple components, including a server and a terminal.

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 Python's pandas library to calculate the average price over a certain period (e.g., every hour). Specifically, it converts the JSON data into a pandas DataFrame and calculates the average price for each hour using the resample('H').mean() method.

Next, the server obtains power consumption data from the user's smart meter. 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.

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).

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 television. 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.

For example, if a user consumes 5kWh 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.

Examples of prompts include, "Calculate the hourly electricity bill based on 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."

The flow of the identification process in Example 1 will be explained using Figure 17.

Step 1:

The server obtains trading price information from the electricity market.

Input: Power market API endpoint.

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.

Output: Retrieved trading price information (JSON data).

Step 2:

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

Input: Trading price information (JSON data).

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.

Output: Average hourly price (unit price of electricity).

Step 3:

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

Input: Smart meter data (MQTT message).

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

Output: User power consumption data.

Step 4:

The server calculates the electricity bill based on the unit price of electricity and the amount of electricity consumed.

Input: Electricity unit price and power consumption data.

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

Output: Calculated electricity cost.

Step 5:

The server notifies the user of the calculation results.

Input: Calculated electricity cost.

Specific behavior: The server sends emails using the SMTP protocol or push notifications using Firebase Cloud Messaging.

Output: The electricity charge notified to the user.

Step 6:

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

Input: User's voice, facial expressions, and actions.

Specific operations: 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 to text using the Google Cloud Speech-to-Text API.

Output: User's voice, facial expression, and behavioral data.

Step 7:

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

Input: User voice, facial expression, and behavioral data.

Specific operation: The device sends facial feature point data to the Microsoft Azure Emotion API and obtains an emotion label (e.g., happy, sad, or angry).

Output: Recognized emotion label.

Step 8:

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

Input: Recognized emotion label.

Specific operation: Based on the emotion label, the device selects the most suitable appliance from a pre-defined list and suggests usage times.

Output: Suggested appliance usage times.

Step 9:

The device will notify the user of the suggestions.

Input: Suggested appliance usage time period.

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

Output: The suggestion notified to the user.

(Application Example 1)

Next, we will explain Application Example 1 of Form Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

Conventional electricity bill calculation systems were limited to calculating electricity rates based on market-linked plans and suggesting optimal usage times based on information about home appliance usage from users. However, because they did not consider the user's emotional state when making suggestions about home appliance usage, they were unable to improve user satisfaction or comfort. Furthermore, because they did not make suggestions about home appliance usage based on emotions, energy management that responded to the user's psychological needs was not possible.

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

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 times based on the received home appliance usage information, means for proposing the calculated optimal usage times 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.

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

"Means for calculating electricity charges" means a device or software for calculating electricity charges based on a market-linked plan.

"Means for receiving home appliance usage information from users" refers to an interface that allows users to input the type of home appliance they wish to use and the usage time.

"Means for calculating optimal usage time periods" refers to a device or software that calculates the most efficient usage time periods based on the received home appliance usage information.

"Means for making suggestions to users" refers to an interface that notifies users of the calculated optimal usage time and electricity charges.

"Means for recognizing user emotions" refers to devices or software that detect emotions from the user's voice, facial expressions, behavior, etc.

"Means for suggesting optimal home appliance usage" refers to a device or software that suggests optimal home appliance usage to a user based on recognized emotions.

The following system configuration is described as an embodiment of this invention.

System Configuration

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 times based on the received home appliance usage information, means for suggesting the calculated optimal usage times and electricity charges to the user, means for recognizing the user's emotions, and means for suggesting optimal home appliance usage based on the recognized emotions.

Program processing

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

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 suggests the optimal use of home appliances.

Hardware and software used

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

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

Specific examples

For example, if a user is using a smartphone, the app uses the smartphone's camera to recognize their 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.

Example prompt

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

The flow of the specific processing in Application Example 1 is explained using Figure 18.

Step 1:

The server obtains trading price information from the power exchange.

Input: Power Exchange API endpoint

Data processing: Send API requests and obtain trading price information.

Output: Trading price information (JSON format)

Step 2:

The server calculates electricity charges based on the acquired transaction price information.

Input: Transaction price information

Data calculation: Extracts current electricity unit prices from trading price information and calculates electricity charges based on usage.

Output: Calculated electricity cost

Step 3:

Users enter home appliance usage information using their smartphones.

Input: Type of appliance, usage time

Data processing: Send the entered information to the server.

Output: Appliance usage information

Step 4:

The server calculates the optimal usage time based on the received appliance usage information.

Input: Appliance usage information, transaction price information

Data calculation: Combining appliance usage information and transaction price information to calculate the most cost-effective usage times.

Output: Optimal usage time

Step 5:

The server then proposes the optimal usage time and electricity rates to the user.

Input: Optimal usage time, calculated electricity rate

Data processing: Generate proposals and send them to the user's device.

Output: Recommendations (optimal usage time and electricity rates)

Step 6:

The user uses their smartphone camera to recognize their face.

Input: User's face image

Data processing: Send facial images to the server

Output: Facial image data

Step 7:

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

Input: Facial image data

Data calculation: Analyze facial image data and recognize emotions

Output: Recognized emotion

Step 8:

The server then suggests the optimal use of home appliances based on the recognized emotions.

Input: Recognized emotion

Data calculation: Generate home appliance usage suggestions based on emotions

Output: Home appliance usage suggestion

Step 9:

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

Input: Appliance usage proposals, transaction price information

Data calculation: Calculate the usage costs of proposed appliances

Output: Usage cost notification

(Example 2)

Next, Example 2 of Form Example 2 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

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

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

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 the user, means for calculating optimal usage time periods 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 periods and electricity charges to the user. This makes it possible to propose optimal home appliance usage and adjust electricity charges according to the user's emotions.

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

"Means for calculating electricity charges" is a function for calculating electricity charges based on electricity consumption and a market-linked plan.

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

"Means for calculating optimal usage times" is a function that calculates the optimal usage times for home appliances based on received home appliance usage information, taking into account power consumption patterns and past data.

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

"Means for adjusting electricity rate proposals" is a function for optimizing electricity rate proposals based on recognized user emotions.

"Means for proposing the calculated optimal usage time periods and electricity charges to the user" is a function for notifying the user of the calculated optimal usage time periods and adjusted electricity charges.

Form for implementing the invention

This invention is a system that receives information about home appliance usage from users, calculates optimal usage times 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.

Hardware and software used

Hardware

Device: The device on which the user provides input information (e.g., smartphone, tablet, PC)

Server: Server for processing and storing data

Software

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

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

Specific system operation

1. Collection of user input information

Users use the terminal to input the type of appliance they want to use and the desired usage time.

Specific actions involve the user entering information such as "air conditioner" and "2:00 PM to 4:00 PM" into the application's input form.

2. Submitting input information

The device sends the information entered by the user to the server.

Specific operations involve the device sending input information to the server using an HTTP request.

3. Calculating appliance usage times

The server calculates the optimal usage time based on the type of appliance and usage time received.

Specifically, the server runs an algorithm that takes into account past usage data and power consumption patterns to calculate the optimal usage time period.

For example, the server might calculate that the best time to use the air conditioner is between 2:00 PM and 4:00 PM.

4. Emotion Recognition Using an Emotion Engine

The server uses an emotion engine to recognize the user's emotions.

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

5. Proposal for adjusting electricity rates

The server adjusts the electricity rate suggestions based on the emotion engine's recognition results.

Specific behavior is that if the user expresses happiness, the server will extend the usage time of entertainment-oriented home appliances (e.g., televisions) and suggest an increase in the resulting electricity bill.

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

6. Notifying users of results

The server sends the calculation results and suggestions to the device.

The device will notify the user of the received information.

Specific actions include the device informing the user via push notifications and in-app messages 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."

Specific examples

User input

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

Server processing

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

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).

Example prompt for a generative AI model

"The user inputs that they want to use the air conditioner from 2:00 PM to 4:00 PM. Calculate the optimal usage time, and if the user's emotion is joy, suggest extending the usage time of entertainment appliances."

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.

The flow of the identification process in Example 2 will be explained using Figure 19.

Step 1:

Collecting user input information

Users use the terminal to input the type of appliance they want to use and the desired usage time.

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

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

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

Step 2:

Submit input information

The device sends the information entered by the user to the server.

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

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

Specific operation: The device generates an HTTP request and sends the entered data to the server.

Step 3:

Calculating appliance usage times

The server calculates the optimal usage time based on the type of appliance and usage time received.

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

Output: Data on optimal usage times.

Specific operation: The server runs an algorithm that takes into account past usage data and power consumption patterns to calculate the optimal usage time. For example, it may calculate that the optimal time to use the air conditioner is between 2:00 PM and 4:00 PM.

Step 4:

Emotion recognition using an emotion engine

The server uses an emotion engine to recognize the user's emotions.

Input: User's facial image and voice data.

Output: User's emotional state (e.g., happy, stressed).

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

Step 5:

Proposed adjustment of electricity rates

The server adjusts the electricity rate suggestions based on the emotion engine's recognition results.

Input: User's emotional state and optimal usage time data.

Output: Proposed adjusted electricity rates.

Specific behavior: If the user is expressing happiness, 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).

Step 6:

Notifying users of results

The server sends the calculation results and suggestions to the device.

The device will notify the user of the received information.

Input: Data suggesting optimal usage times and adjusted electricity rates.

Output: Notification message to the user.

Specific behavior: 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."

(Application Example 2)

Next, we will explain Application Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

Conventional electricity rate optimization systems based on appliance usage information were unable to take into account the user's emotional state, making it difficult to increase user satisfaction. Furthermore, when optimizing the usage times of in-store appliances, adjustments were not made based on the customer's emotions, preventing an improved customer experience.

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

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 times based on the received home appliance usage information, means for proposing the calculated optimal usage times 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 charges based on the recognized emotions. This makes it possible to propose optimal home appliance usage times and electricity charges according to the user's emotional state, thereby increasing user satisfaction. Furthermore, by adjusting the usage times of home appliances in the store according to the customer's emotions, the customer experience can be improved.

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

"Means for calculating electricity charges" means a device or program for calculating electricity charges based on a market-linked plan.

"Means for receiving home appliance usage information" refers to a device or program for receiving information such as the type of home appliance and usage time from the user as input.

"Means for calculating optimal usage time periods" refers to a device or program for calculating optimal usage time periods for home appliances based on received home appliance usage information.

"Means for making suggestions to users" refers to a device or program that notifies or displays to users the calculated optimal usage time and electricity charges.

An "emotion recognition engine" is an algorithm or program that recognizes a user's emotions.

"Means for recognizing emotions" refers to a device or program for detecting a user's emotions using an emotion recognition engine.

"Means for adjusting electricity rate proposals" is a device or program for changing the content of electricity rate proposals based on recognized user sentiment.

The following system configuration is described as an embodiment of this invention.

System Configuration

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

Program processing

1. Enter your appliance usage information:

The user device receives input from the user about the type of appliance and the amount of time it has been used. This information is sent to the server.

2. Emotion recognition:

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

3. Calculating the best time to use:

The server uses an electricity rate calculation engine to calculate the optimal usage time based on the received home appliance usage information. The electricity rate calculation engine calculates electricity rates based on a market-linked plan.

4. Proposed electricity rate adjustment:

The server adjusts electricity rate 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.

5. Notice to Users:

The server then notifies the user's device of the calculated optimal usage time period and the adjusted electricity rate. The user can then adjust their home appliance usage accordingly.

Hardware and software used

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

Software: Emotion recognition engine (EmotionEngine), electricity bill calculation engine (ElectricityPricing)

Specific examples

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.

Example prompt

Develop an application that optimizes the use of lighting and air conditioning in stores and adjusts electricity rates based on customer emotions. If a customer is happy, extend the use of entertainment appliances, and if they are stressed, extend the use of relaxation appliances.

In this way, it becomes possible to propose optimal times for using home appliances and electricity rates based on the user's emotional state, thereby increasing user satisfaction. Furthermore, by adjusting the usage times of home appliances in the store based on the customer's emotions, the customer experience can be improved.

The flow of the specific processing in Application Example 2 is explained using Figure 20.

Step 1:

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

Step 2:

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.

Step 3:

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

Step 4:

The server adjusts electricity rate suggestions based on the recognized user emotions. Specifically, the server uses emotional state data from the emotion recognition engine to adjust the suggestions 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 optimal use time periods, and the output data is the adjusted electricity rate suggestions.

Step 5:

The server notifies the user's device of the calculated optimal usage time period and adjusted electricity charges. Specifically, the server sends data on the optimal usage time period and adjusted electricity charges to the user's device, allowing the user to check it. The input data is the optimal usage time period and adjusted electricity charges, and the output data is the suggested information displayed on the user's device.

In this way, it becomes possible to propose optimal times for using home appliances and electricity rates based on the user's emotional state, thereby increasing user satisfaction. Furthermore, by adjusting the usage times of home appliances in the store based on the customer's emotions, the customer experience can be improved.

(Example 3)

Next, Example 3 of Form Example 3 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

Conventional electricity bill calculation systems could suggest optimal times for users to use home appliances, but they did not make suggestions based on the user's emotions. As a result, they were 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 bills were not calculated based on trading prices at the power exchange, meaning users were unable to obtain reference information for reducing their electricity bills.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

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 the user, means for calculating optimal usage times based on the received home appliance usage information, means for proposing the calculated optimal usage times and electricity charges to the user, means for recognizing the user's emotions, and means for proposing optimal home appliance usage times based on the recognized emotions. This makes it possible to propose optimal home appliance usage times based on the user's emotional state and calculate electricity charges based on the trading price at the power exchange.

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

"Means for calculating electricity charges" refers to a device or program that has the function of calculating electricity charges according to usage time based on the trading price at the electricity exchange and the power consumption of home appliances.

"Means for receiving home appliance usage information from users" refers to a device or program that provides an interface for users to input the type of home appliance they wish to use and the usage time, and receives that information.

"Means for calculating optimal usage time periods" refers to a device or program that has the function of calculating the optimal time periods for using home appliances based on the received home appliance usage information and the trading price at the electricity exchange.

"Means for making suggestions to users" refers to a device or program that notifies or displays to users the calculated optimal usage time and electricity charges.

"Means for recognizing user emotions" refers to a device or program that has the function of analyzing the user's voice, facial expressions, behavior, etc., and determining the user's emotional state.

"Means for suggesting optimal times to use home appliances based on emotions" refers to 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.

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 makes suggestions to the user. It also has a function that recognizes the user's emotions and suggests times for using home appliances based on those emotions.

Hardware and software used

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

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

Specific system operation

1. User input

The user uses the device to enter the type of appliance they want to use and the usage time.

Example: User enters "Use the air conditioner from 3pm to 6pm."

2. Obtaining Power Exchange Data

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

Example: The server uses an API to retrieve electricity trading price data from 3:00 PM to 6:00 PM.

3. Calculating your electricity bill

The electricity charges for the specified time period are calculated based on the electricity trading price obtained by the server.

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

4. Emotion recognition

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.

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 feeling stressed.

5. Optimal usage time suggestions

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

Example: The server determines that the user is feeling stressed and suggests using a home appliance that will promote relaxation (e.g., a massage chair).

6. Presentation to the user

The server sends the calculation results and suggestions to the user's device and presents them to the user.

Example: The server displays calculated electricity bills and suggestions for using relaxation appliances on the user's smartphone.

Example prompt

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

In this way, the system can suggest optimal times to use home appliances and electricity rates based on the user's input and emotions. The flow of the identification process in Example 3 will be explained using Figure 21.

Step 1:

The user uses the device to enter the type of appliance they want to use and the usage time.

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

Specific operation: 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.

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

Step 2:

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

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

Specific operation: The server sends a request to the power exchange data acquisition API to obtain power trading price data for the specified time period.

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

Step 3:

The electricity charges for the specified time period are calculated based on the electricity trading price obtained by the server.

Input: Obtained electricity trading price data and the power consumption of the home appliance (e.g., 1.5kW power consumption of an air conditioner).

Specific operation: The server calculates the electricity bill from 3:00 PM to 6:00 PM using the air conditioner's power consumption and the acquired electricity trading price.

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

Step 4:

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.

Input: User voice, facial expression, and behavioral data.

Specific operation: When the user speaks into the device's camera, the device sends audio and video data to the emotion recognition engine.

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

Step 5:

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

Input: Calculated electricity bill and user's emotional state.

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., a massage chair).

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

Step 6:

The server sends the calculation results and suggestions to the user's device and presents them to the user.

Input: Calculation results and suggestions.

Specific operation: The server sends the calculation results and suggestions to the user's device.

Output: The user will receive a notification on their smartphone and the suggestions will be displayed when they open the app.

(Application Example 3)

Next, we will explain Application Example 3 of Form Example 3. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

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

The specific processing by the specific 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 electricity charges based on a market-linked plan, means for receiving home appliance usage information from the user, means for calculating optimal usage times based on the received home appliance usage information, means for proposing the calculated optimal usage times and electricity charges to the user, means for recognizing the user's emotions, means for proposing optimal home appliance usage times based on the recognized emotions, means for checking the usage status of home appliances in the store in real time, and means for optimal environmental settings according to the customer's emotions. This makes it possible to optimize electricity charges, propose optimal home appliance usage times according to the user's or customer's emotions, and provide a comfortable environment.

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

"Means for calculating electricity charges" means a device or software for calculating electricity charges based on a market-linked plan.

"Means for receiving home appliance usage information from users" refers to an interface that allows users to input the type of home appliance they wish to use and the usage time.

"Means for calculating optimal usage time periods" refers to a device or software that calculates optimal usage time periods based on received home appliance usage information, taking into account electricity trading prices.

"Means for making suggestions to users" refers to an interface that notifies users of the calculated optimal usage time and electricity charges.

"Means for recognizing user emotions" refers to devices or software that analyze emotions from the user's voice, facial expressions, behavior, etc.

"Means for suggesting optimal times to use home appliances based on emotions" refers to a device or software that calculates and suggests optimal times to use home appliances based on the user's recognized emotions.

"Means for checking the usage status of home appliances within a store in real time" refers to a device or software that monitors the usage status of home appliances within a store in real time and notifies the manager.

"Means for optimal environmental settings according to customer emotions" refers to devices or software that analyze customer emotions and, based on that, optimize environmental settings such as lighting, air conditioning, and music in the store.

The following system configuration is described as an embodiment of this invention.

System Configuration

1. Hardware:

Smartphone (iOS/Android)

Smart glasses (e.g., Google Glass)

Head-mounted display (e.g., Oculus Rift)

Robots (e.g., Pepper)

2. Software:

Facial recognition API (e.g., Microsoft Azure Face API)

Emotion recognition API (e.g. Affectiva)

Electricity trading price API (e.g., EPEX SPOT API)

Database (e.g., Firebase)

Program processing

The server first receives information about home appliance usage from the user. The user enters the type of appliance they wish to use and the usage time via 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.

Furthermore, the server uses facial recognition APIs 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.

Based on the recognized emotions, the server recalculates the optimal times 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).

Specific examples

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

Example prompt

Enter customer facial expression data and recognize their emotions. Then, retrieve the current electricity price from the electricity trading price API and calculate the optimal time to use 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.

In this way, it is possible to optimize electricity rates, suggest optimal times to use home appliances based on the emotions of users and customers, and provide a comfortable environment.

The flow of the specific processing in Application Example 3 will be explained using Figure 22.

Step 1:

The user uses a smartphone app to input the type of appliance they wish to use and the amount of time they will be using it. The information entered is the type of appliance and the amount of time it will be used. This information is then sent to the server.

Step 2:

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' usage times.

Step 3:

The server then proposes the calculated optimal usage time and electricity rates to the user. The proposal includes the optimal usage time and the electricity rate for that time. This information is displayed on the smartphone app.

Step 4:

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, which are sent to the server.

Step 5:

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.

Step 6:

The server recalculates the optimal time periods for using home 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, it will suggest using appliances that will contribute to relaxation.

Step 7:

The server then proposes the recalculated optimal usage times and electricity rates to the user. The proposals include the optimal usage times based on the user's emotions and the electricity rates for those times. This information is displayed on a smartphone app, smart glasses, a head-mounted display, and a robot.

Step 8:

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 appliances, and the output is the real-time usage status. This allows store managers to check the usage status of home appliances through a smartphone app.

Step 9:

The server optimizes environmental settings such as lighting, air conditioning, and music to provide optimal environmental settings 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.

These steps will not only optimize electricity rates, but also suggest optimal times to use home appliances based on the emotions of users and customers, and provide a comfortable environment.

The specific processing unit 290 transmits the results of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the results of the specific processing. The microphone 238 acquires audio indicating the user input regarding the results 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.

Data generation model 58 is what is known as generative AI (artificial intelligence). An example of data generation model 58 is generative AI such as ChatGPT (Internet search <URL: https://openai.com/blog/chatgpt>). Data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing an instruction is input to 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. Data generation model 58 performs inference on the input inference data in accordance with the instructions indicated by the prompt, and outputs the inference results in the form of data such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization.

Another example of generative AI is Gemini (Internet search <URL: https://gemini.google.com/?hl=ja>).

In the above embodiment, an example was given in which the specific processing was performed by the data processing device 12, but the technology disclosed herein is not limited to this, and the specific processing may also be performed by the robot 414.

The emotion identification model 59, which serves as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see Figure 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

Figure 9 shows an emotion map 400 on which multiple emotions are mapped. In emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both emotions and mental states. Emotions that are generally generated from reactions that occur in the brain are arranged on the left side of the concentric circles. Emotions that are generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged above and below the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the top side of the concentric circles, and the emotion of "discomfort" is arranged on the bottom side. In this way, emotion map 400 maps multiple emotions based on the structure by which emotions are generated, with emotions that tend to occur simultaneously being mapped close together.

These emotions are distributed in the 3 o'clock direction on emotion map 400, and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness takes precedence over internal sensations, resulting in a sense of calm.

The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible (expressed in behavior) the emotion becomes.

Here, human emotions are based on various balances such as posture and blood sugar levels, and when these balances deviate from the ideal, it indicates discomfort, and when they approach the ideal, it indicates pleasure. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and remaining battery life, so that when these balances deviate from the ideal, it indicates discomfort, and when they approach the ideal, it indicates pleasure. Emotion maps may be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and emotional brain physiological signal analysis systems, Tokushima University, doctoral dissertation: https://ci.nii.ac.jp/naid/500000375379). The left half of the emotion map is lined with emotions belonging to an area called "reaction," where sensation is dominant. The right half of the emotion map is lined with emotions belonging to an area called "situation," where situational awareness is dominant.

The emotion map defines two emotions that encourage learning. One is the negative emotion around the middle of "repentance" or "reflection" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot experiences positive feelings such as "I want more" or "I want to know more."

The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are close to each other have similar values, as in the emotion map 900 shown in Figure 10. Figure 10 shows an example in which multiple emotions, such as "relieved," "calm," and "reassuring," have similar emotion values.

In the above embodiment, an example was given in which a specific process is performed by a single computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may also be performed by multiple computers, including computer 22.

In the above embodiment, an example was described in which the specific processing program 56 is stored in the storage 32, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

It is not necessary to store the entire specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entire specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

The hardware resources that execute specific processes can include the following types of processors. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource that executes specific processes by executing software, i.e., a program. Processors also include FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), and ASICs (Application Specific Integrated Circuits), which are specially designed to execute specific processes.

Specialized electrical circuits are processors with specialized circuit configurations. All processors have built-in or connected memory, and all processors use memory to perform specific processes.

The hardware resource that executes the specific processing may be composed of one of these various processors, or may be composed of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). The hardware resource that executes the specific processing may also be a single processor.

As an example of a configuration using a single processor, first, there is a configuration in which one processor is configured using a combination of one or more CPUs and software, and this processor functions as a hardware resource that executes specific processing. Second, there is a configuration in which a processor is used to realize the functions of an entire system, including multiple hardware resources that execute specific processing, on a single IC chip, as typified by SoC (System-on-a-chip). In this way, specific processing is realized using one or more of the various processors listed above as hardware resources.

More specifically, the hardware structure of these various processors can be an electrical circuit that combines circuit elements such as semiconductor devices. Furthermore, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps can be added, or the processing order can be rearranged, all within the scope of the spirit of the invention.

The above description and illustrations are a detailed explanation of the technology disclosed herein and are merely an example of the technology disclosed herein. For example, the above description of the configuration, functions, actions, and effects is an example of the configuration, functions, actions, and effects of the technology disclosed herein. Therefore, the above description may be used within the scope of the gist of the technology disclosed herein.

It goes without saying that unnecessary parts may be deleted, new elements may be added, or elements may be substituted in the contents and illustrations. Furthermore, in order to avoid confusion and to facilitate understanding of the parts relating to the technology of this disclosure, explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of this disclosure have been omitted from the descriptions and illustrations shown above.

All publications, patent applications, and technical standards mentioned in this specification are incorporated by reference herein to the same extent as if each individual publication, patent application, and technical standard was specifically and individually indicated to be incorporated by reference.

The following is further disclosed regarding the above embodiments.

(Claim 1)
The system includes a means for calculating an electricity charge based on a market-linked plan, a means for receiving home appliance usage information from a user, a means for calculating an optimal usage time period based on the received home appliance usage information, and a means for proposing the calculated optimal usage time period and electricity charge to the user.

(Claim 2)
The system according to claim 1 , wherein the market-linked plan is a plan in which electricity rates are determined according to trading prices on an electricity exchange.

(Claim 3)
The system according to claim 1 , wherein the home appliance usage information from the user includes the type of home appliance desired to be used and the usage time.

(Claim 4)
The system of claim 1 , further comprising an emotion engine that recognizes the user's emotion.

(Claim 5)
The system according to claim 4 , wherein the emotion engine recognizes emotions from the user's voice, facial expressions, actions, etc.

(Claim 6)
The system according to claim 4 , wherein calculation of optimal usage times and electricity rate proposals are adjusted based on the recognition results of the emotion engine.

"Example 1"
(Claim 1)
A means for obtaining trading price information from an electricity exchange;
A means for storing the acquired transaction price information in a database;
A means for calculating the unit price of electricity based on the stored transaction price information;
a means for calculating the electricity charge of the user using the calculated unit price of the electricity charge;
means for receiving home appliance usage information from a user;
A means for calculating an optimal usage time period based on the received home appliance usage information;
The system includes a means for proposing the calculated optimal usage time period and electricity rate to the user.

(Claim 2)
The system according to claim 1 , wherein the market-linked plan is a plan in which electricity rates are determined according to trading prices on an electricity exchange.

(Claim 3)
The system according to claim 1 , wherein the home appliance usage information from the user includes the type of home appliance desired to be used and the usage time.

"Application Example 1"
(Claim 1)
a means for calculating an electricity rate based on a market-linked plan;
A means for receiving home appliance usage information from a user;
A means for calculating an optimal usage time period based on the received home appliance usage information;
A means of proposing the calculated optimal usage time and electricity rates to the user;
A means for obtaining real-time trading price information from the power exchange;
means for optimizing power consumption based on the obtained price information;
A system including means for reducing power consumption during high price periods and increasing power consumption during low price periods.

(Claim 2)
The system according to claim 1 , wherein the market-linked plan is a plan in which electricity rates are determined according to trading prices on an electricity exchange.

(Claim 3)
The system according to claim 1 , wherein the home appliance usage information from the user includes the type of home appliance desired to be used and the usage time.

"Example 2"
(Claim 1)
a means for calculating an electricity rate based on a market-linked plan;
A means for receiving home appliance usage information from a user;
A means for storing the received home appliance usage information in a database;
A means for calculating the optimal usage time period based on the stored data;
A means of proposing the calculated optimal usage time and electricity rates to the user;
The system includes a means for displaying the results of the calculation to the user.

(Claim 2)
The system according to claim 1 , wherein the market-linked plan is a plan in which electricity rates are determined according to trading prices on an electricity exchange.

(Claim 3)
The system according to claim 1 , wherein the home appliance usage information from the user includes the type of home appliance desired to be used and the usage time.

"Application Example 2"
(Claim 1)
a means for calculating an electricity rate based on a market-linked plan;
A means for receiving home appliance usage information from a user;
A means for calculating an optimal usage time period based on the received home appliance usage information;
A means of proposing the calculated optimal usage time and electricity rates to the user;
A means for receiving schedule information from users and calculating the optimal charging time slot;
The system includes a means for notifying the user of the calculated optimal charging time period.

(Claim 2)
The system according to claim 1 , wherein the market-linked plan is a plan in which electricity rates are determined according to trading prices on an electricity exchange.

(Claim 3)
The system according to claim 1 , wherein the home appliance usage information from the user includes the type of home appliance desired to be used and the usage time.

(Claim 4)
The system of claim 1 , wherein the schedule information from the user includes a departure time, an arrival time, and a destination.

"Example 3"
(Claim 1)
A means for receiving home appliance usage information from a user;
A means for obtaining trading price data from an electricity exchange;
A means for processing the acquired transaction price data and calculating the electricity rate for a specified time period;
A means of proposing the calculated optimal usage time and electricity rates to the user;
a means for presenting the results of the calculation to the user;
A system including:

(Claim 2)
The system according to claim 1 , wherein the market-linked plan is a plan in which electricity rates are determined according to trading prices on an electricity exchange.

(Claim 3)
The system according to claim 1 , wherein the home appliance usage information from the user includes the type of home appliance desired to be used and the usage time.

"Application example 3"
(Claim 1)
a means for calculating an electricity rate based on a market-linked plan;
a means for receiving power usage information from a user;
A means for calculating an optimal usage time period based on the received power usage information;
A means of proposing the calculated optimal usage time and electricity rates to the user;
A means for calculating the time period with the lowest electricity rates among the power usage time periods entered by the user;
The system includes a means for presenting the calculated optimal power usage time period and the estimated electricity charges to the user.

(Claim 2)
The system according to claim 1 , wherein the market-linked plan is a plan in which electricity rates are determined according to trading prices on an electricity exchange.

(Claim 3)
The system according to claim 1 , wherein the power usage information from the user includes the type of power device desired to be used and the time of use.

"Example 1 of combining emotion engines"
(Claim 1)
A means for acquiring transaction price information from an electricity market;
A means for analyzing the acquired transaction price information and calculating the unit price of electricity;
A means for acquiring the amount of power consumed by the user;
A means for calculating electricity charges based on the unit price of electricity and the amount of power consumed;
a means for notifying the user of the results of the calculation;
A means of collecting user feedback, expressions, and behavior,
A means of analyzing the collected data and recognizing the user's emotions;
A means for suggesting optimal times to use home appliances based on the recognized emotions;
The system includes a means for notifying the user of the suggestions.

(Claim 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.

(Claim 3)
The system according to claim 1 , wherein the home appliance usage information from the user includes the type of home appliance desired to be used and the usage time.

"Application example 1 when combining emotion engines"
(Claim 1)
a means for calculating an electricity rate based on a market-linked plan;
A means for receiving home appliance usage information from a user;
A means for calculating an optimal usage time period based on the received home appliance usage information;
A means of proposing the calculated optimal usage time and electricity rates to the user;
a means of recognizing a user's emotions;
The system includes a means for suggesting optimal use of home appliances based on the recognized emotions.

(Claim 2)
The system according to claim 1 , wherein the market-linked plan is a plan in which electricity rates are determined according to trading prices on an electricity exchange.

(Claim 3)
The system according to claim 1 , wherein the home appliance usage information from the user includes the type of home appliance desired to be used and the usage time.

"Example 2 when combining emotion engines"
(Claim 1)
a means for calculating an electricity rate based on a market-linked plan;
A means for receiving home appliance usage information from a user;
A means for calculating an optimal usage time period based on the received home appliance usage information;
a means of recognizing a user's emotions;
a means for adjusting electricity rate proposals based on the perceived sentiment;
A means of proposing the calculated optimal usage time and electricity rates to the user;
A system including:

(Claim 2)
The system according to claim 1 , wherein the market-linked plan is a plan in which electricity rates are determined according to trading prices on an electricity exchange.

(Claim 3)
The system according to claim 1 , wherein the home appliance usage information from the user includes the type of home appliance desired to be used and the usage time.

"Application example 2 when combining emotion engines"
(Claim 1)
a means for calculating an electricity rate based on a market-linked plan;
A means for receiving home appliance usage information from a user;
A means for calculating an optimal usage time period based on the received home appliance usage information;
A means of proposing the calculated optimal usage time and electricity rates to the user;
means for recognizing a user's emotion using an emotion recognition engine;
a means for adjusting electricity rate proposals based on the perceived sentiment;
A system including:

(Claim 2)
The system according to claim 1 , wherein the market-linked plan is a plan in which electricity rates are determined according to trading prices on an electricity exchange.

(Claim 3)
The system according to claim 1 , wherein the home appliance usage information from the user includes the type of home appliance desired to be used and the usage time.

"Example 3 when combining emotion engines"
(Claim 1)
a means for calculating an electricity rate based on a market-linked plan;
A means for receiving home appliance usage information from a user;
A means for calculating an optimal usage time period based on the received home appliance usage information;
A means of proposing the calculated optimal usage time and electricity rates to the user;
a means of recognizing a user's emotions;
A means for suggesting optimal times to use home appliances based on the recognized emotions;
A system including:

(Claim 2)
The system according to claim 1 , wherein the market-linked plan is a plan in which electricity rates are determined according to trading prices on an electricity exchange.

(Claim 3)
The system according to claim 1 , wherein the home appliance usage information from the user includes the type of home appliance desired to be used and the usage time.

"Application example 3 when combining emotion engines"
(Claim 1)
a means for calculating an electricity rate based on a market-linked plan;
A means for receiving home appliance usage information from a user;
A means for calculating an optimal usage time period based on the received home appliance usage information;
A means of proposing the calculated optimal usage time and electricity rates to the user;
a means of recognizing a user's emotions;
A means for suggesting optimal times to use home appliances based on the recognized emotions;
A way to check the usage status of home appliances in the store in real time,
A means of optimally setting the environment according to the customer's emotions,
A system including:

(Claim 2)
The system according to claim 1 , wherein the market-linked plan is a plan in which electricity rates are determined according to trading prices on an electricity exchange.

(Claim 3)
The system according to claim 1 , wherein the home appliance usage information from the user includes the type of home appliance desired to be used and the usage time.

10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset type terminal 414 Robot

Claims (3)

A means for acquiring transaction price information from an electricity market;
A means for calculating the unit price of the electricity rate of the market-linked plan based on the acquired transaction price information;
A means for acquiring the amount of power consumed by the 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 the user of the calculated electricity charges;
A means for collecting data including user voice, facial expression, and behavior;
A means of analyzing the collected data and recognizing the user's emotions;
a means for calculating optimal usage time periods for home appliances based on the recognized emotion and adjusting the proposed electricity rate, so that if the recognized emotion is joy, the means proposes extending the usage time periods for entertainment-oriented home appliances and increasing the electricity rate, and if the recognized emotion is stress, the means proposes extending the usage time periods for home appliances that contribute to relaxation and increasing the electricity rate;
means for notifying a user of the proposed adjusted electricity rate ;
A system including: The system of claim 1, wherein the market-linked plan is a plan in which electricity rates are determined based on trading prices in the electricity market. The system according to claim 1 , wherein the home appliance usage information from the user includes the type of home appliance desired to be used and the usage time.