System

Abstract

An object of a system according to an embodiment is to procure optimal renewable energy power without expert knowledge.SOLUTION: A system according to an embodiment includes a plan development unit, a supply and demand prediction unit, and a plan selection unit. The plan development unit develops a power procurement plan based on an input from a user. The supply and demand prediction unit performs supply and demand prediction of electric power based on the plan formulated by the plan formulation unit. The plan selection unit selects an optimal power procurement plan based on the supply and demand predicted by the supply and demand prediction unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, the process of procuring renewable energy electricity at the optimal price was complex and difficult to implement without specialized knowledge.

[0005] The system according to the embodiment aims to procure optimal renewable energy power without requiring specialized knowledge. [Means for solving the problem]

[0006] The system according to the embodiment includes a plan formulation unit, a supply and demand forecasting unit, and a plan selection unit. The plan formulation unit formulates a power procurement plan based on input from a user. The supply and demand forecasting unit predicts power supply and demand based on the plan formulated by the plan formulation unit. The plan selection unit selects an optimal power procurement plan based on the supply and demand forecasted by the supply and demand forecasting unit. [Effects of the Invention]

[0007] The system according to the embodiment can procure optimal renewable energy power without requiring specialized knowledge. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. 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.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The power procurement system according to an embodiment of the present invention utilizes a generation AI to interactively carry out the processes required for power procurement, from formulating plans to forecasting supply and demand and selecting the optimal plan. This enables businesses and households to procure optimal power without specialized knowledge.

[0029] An electric power procurement system according to an embodiment includes a planning unit, a supply and demand forecasting unit, and a plan selection unit. The planning unit formulates an electric power procurement plan based on user input. For example, if a user inputs, "I want to create an electric power procurement plan for next year," the generation AI analyzes past electricity usage data and market trends to propose an optimal procurement plan. The planning unit can also formulate a plan based on prompts containing user instructions. The supply and demand forecasting unit predicts electricity supply and demand based on the plan formulated by the planning unit. For example, if a user inputs, "I want to predict electricity demand for the next three months," the generation AI analyzes past usage data, seasonal fluctuations, economic indicators, etc., and predicts supply and demand. The supply and demand forecasting unit can also predict supply and demand based on prompts containing user instructions. The plan selection unit selects an optimal electric power procurement plan based on the supply and demand predicted by the supply and demand forecasting unit. For example, if a user inputs, "I want to keep costs down," the generation AI analyzes market electricity prices and renewable energy supply conditions to propose an optimal plan. In addition, the plan selection unit can select a plan based on prompts including user instructions from the generation AI. As a result, the power procurement system according to the embodiment enables businesses and households to procure optimal power without specialized knowledge.

[0030] The plan formulation unit can analyze the user's past power usage patterns, predict future usage patterns, and reflect them in the plan. For example, the generation AI in the plan formulation unit collects the user's power usage data from the past five years and analyzes seasonal usage patterns. For example, it predicts future usage patterns taking into account differences in power usage between summer and winter. The plan formulation unit can also have the generation AI predict future usage patterns based on past usage data and reflect them in the plan. This makes it possible to predict future usage patterns based on past usage data and reflect them in the plan.

[0031] The planning department can monitor fluctuations in market electricity prices in real time and propose the optimal procurement timing. For example, the generation AI in the planning department collects market electricity price data in real time and analyzes price fluctuation patterns. For example, the planning department can predict the optimal procurement timing based on price fluctuation data from the past year. The generation AI in the planning department can also monitor price data in real time and propose the optimal procurement timing. This allows the planning department to monitor fluctuations in market electricity prices in real time and propose the optimal procurement timing.

[0032] The planning department can propose an optimal procurement plan that combines different energy sources. For example, the generation AI in the planning department analyzes supply data for different energy sources such as solar, wind, and hydroelectric power, and proposes the optimal combination. For example, the planning department creates a plan taking into account seasonal fluctuations in supply volume. The generation AI in the planning department can also propose an optimal procurement plan that combines different energy sources. This makes it possible to propose an optimal procurement plan that combines different energy sources.

[0033] The planning unit can analyze the power supply situation for each region and formulate a plan that suits the characteristics of that region. In the planning unit, for example, the generation AI collects power supply data for each region and analyzes the supply situation. For example, the planning unit formulates a plan taking into account the power supply volume and supply stability for each region. The planning unit can also have the generation AI analyze the power supply situation for each region and formulate a plan that suits the characteristics of that region. This makes it possible to analyze the power supply situation for each region and formulate a plan that suits the characteristics of that region.

[0034] The supply and demand forecasting unit can analyze past weather data and make supply and demand forecasts based on weather conditions. For example, the generation AI in the supply and demand forecasting unit collects weather data from the past 10 years and makes supply and demand forecasts based on weather conditions. For example, it predicts electricity demand taking into account fluctuations in temperature and precipitation. The generation AI in the supply and demand forecasting unit can also make supply and demand forecasts based on past weather data. This makes it possible to make supply and demand forecasts based on past weather data.

[0035] The supply and demand forecasting unit can analyze economic indicators and make supply and demand forecasts according to the economic situation. For example, the generation AI in the supply and demand forecasting unit collects past economic indicator data and makes supply and demand forecasts according to the economic situation. For example, the power demand is predicted taking into account fluctuations in the GDP growth rate and unemployment rate. The generation AI in the supply and demand forecasting unit can also make supply and demand forecasts based on economic indicators. This makes it possible to make supply and demand forecasts based on economic indicators.

[0036] The supply and demand forecasting unit can forecast supply and demand for different time periods and predict peak electricity demand. In the supply and demand forecasting unit, for example, the generation AI forecasts supply and demand for different time periods based on past electricity usage data. For example, it forecasts peak electricity demand on weekdays and weekends. The supply and demand forecasting unit can also forecast supply and demand for different time periods and predict peak electricity demand. This makes it possible to forecast supply and demand for different time periods and predict peak electricity demand.

[0037] The supply and demand forecasting unit can perform supply and demand forecasts for different industries and provide forecasts that are tailored to the characteristics of the industries. In the supply and demand forecasting unit, for example, the generation AI collects electricity usage data from different industries and performs supply and demand forecasts that are tailored to the characteristics of the industries. For example, the forecast is made taking into account the differences in electricity demand between the manufacturing industry and the service industry. The supply and demand forecasting unit can also perform supply and demand forecasts for different industries with the generation AI and provide forecasts that are tailored to the characteristics of the industries. This makes it possible to perform supply and demand forecasts for different industries and provide forecasts that are tailored to the characteristics of the industries.

[0038] The plan selection unit can analyze the user's past electricity usage data and propose the optimal plan. For example, the generation AI in the plan selection unit collects the user's electricity usage data from the past three years and analyzes usage patterns. For example, the plan selection unit proposes the optimal plan taking into account seasonal fluctuations in usage. The generation AI in the plan selection unit can also propose the optimal plan based on past usage data. This makes it possible to propose the optimal plan based on the user's past electricity usage data.

[0039] The plan selection unit can analyze market electricity prices and propose a plan that minimizes costs. For example, the generation AI in the plan selection unit collects market electricity price data and analyzes price fluctuation patterns. For example, the generation AI can propose a plan that minimizes costs based on price fluctuation data from the past year. The plan selection unit can also propose a plan that minimizes costs based on market electricity prices using the generation AI. This makes it possible to propose a plan that minimizes costs based on market electricity prices.

[0040] The plan selection unit can propose plans for different contract periods and provide options that meet the user's needs. For example, the generation AI analyzes plans for different contract periods (e.g., 1 year, 3 years, 5 years) and proposes the optimal plan that meets the user's needs. For example, it compares the advantages and disadvantages of short-term and long-term contracts. The plan selection unit can also propose plans for different contract periods and provide options that meet the user's needs. This makes it possible to propose plans for different contract periods and provide options that meet the user's needs.

[0041] The plan selection unit can compare plans from different electricity suppliers and select the most suitable provider. For example, the generation AI collects plans from different electricity suppliers and compares prices and supply stability. For example, the plan selection unit selects the most suitable provider based on past supply performance and price fluctuations. The generation AI can also compare plans from different electricity suppliers and select the most suitable provider. This makes it possible to compare plans from different electricity suppliers and select the most suitable provider.

[0042] The system analyzes information on renewable energy suppliers and proposes the most suitable supplier. For example, the generation AI in the system collects data on renewable energy suppliers and analyzes supply volume and prices. For example, the system proposes the most suitable supplier based on past supply performance and price fluctuations. The generation AI in the system can also analyze information on renewable energy suppliers and propose the most suitable supplier. This makes it possible to analyze information on renewable energy suppliers and propose the most suitable supplier.

[0043] The system monitors the renewable energy supply situation in real time and proposes the optimal procurement timing. For example, the generation AI in the system collects renewable energy supply data in real time and analyzes the supply situation. For example, it proposes procurement when the supply volume increases. The system can also monitor the renewable energy supply situation in real time and propose the optimal procurement timing. This makes it possible to monitor the renewable energy supply situation in real time and propose the optimal procurement timing.

[0044] The system proposes an optimal procurement plan that combines different renewable energy sources. For example, the generation AI in the system analyzes supply data for different renewable energy sources, such as solar, wind, and hydropower, and proposes the optimal combination. For example, the system creates a plan taking into account seasonal fluctuations in supply volume. The generation AI in the system can also propose an optimal procurement plan that combines different renewable energy sources. This makes it possible to propose an optimal procurement plan that combines different renewable energy sources.

[0045] The system analyzes the renewable energy supply situation in each region and formulates a procurement plan that suits the characteristics of that region. For example, the generation AI in the system collects renewable energy supply data for each region and analyzes the supply situation. For example, the system formulates a plan that takes into account the renewable energy supply volume and supply stability in each region. The generation AI in the system can also analyze the renewable energy supply situation in each region and formulate a procurement plan that suits the characteristics of that region. This makes it possible to analyze the renewable energy supply situation in each region and formulate a procurement plan that suits the characteristics of that region.

[0046] The system analyzes past electricity usage data and suggests optimal usage methods. For example, the generation AI of the system collects the user's electricity usage data from the past three years and analyzes usage patterns. For example, the system can suggest optimal usage methods taking into account seasonal fluctuations in usage. The generation AI of the system can also suggest optimal usage methods based on past electricity usage data. This allows the system to analyze past electricity usage data and suggest optimal usage methods.

[0047] The system analyzes the equipment's operating status and proposes efficient ways of using it. For example, the system uses a generation AI to collect equipment operating data and analyze the operating status. For example, the system proposes efficient ways of using it based on the equipment's operating rate and operating time. The system can also use a generation AI to analyze the equipment's operating status and propose efficient ways of using it. This makes it possible to analyze the equipment's operating status and propose efficient ways of using it.

[0048] The system proposes usage methods for different time periods and optimizes power usage during peak hours. For example, the system's generating AI proposes usage methods for different time periods based on past power usage data. For example, it optimizes power usage during peak hours on weekdays and weekends. The system can also propose usage methods for different time periods and optimize power usage during peak hours. This makes it possible to propose usage methods for different time periods and optimize power usage during peak hours.

[0049] The system proposes usage methods for each different piece of equipment and performs optimization according to the characteristics of the equipment. For example, the generation AI collects usage data for different pieces of equipment and proposes usage methods according to the characteristics of the equipment. For example, the system optimizes the usage patterns of air conditioners and lighting. The generation AI can also propose usage methods for each different piece of equipment and perform optimization according to the characteristics of the equipment. This makes it possible to propose usage methods for each different piece of equipment and perform optimization according to the characteristics of the equipment.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The power procurement system can also analyze the user's health data and propose a power usage plan based on their health condition. For example, if a user provides health checkup data, the generating AI can use that data to propose a power usage pattern that is optimal for their health condition. For example, it can create a plan to reduce power usage at night and promote daytime activity. It can also propose a power usage plan to reduce stress based on the health data. This makes it possible to propose a power usage plan that takes the user's health condition into consideration.

[0052] The power procurement system can also analyze the user's lifestyle data and propose a power usage plan that suits their lifestyle. For example, when a user provides data on their daily activities, the generating AI uses that data to propose a power usage pattern that is optimal for that lifestyle. For example, for a user who frequently works from home, a plan that optimizes daytime power usage can be created. The system can also propose a power usage plan that maximizes energy efficiency based on the lifestyle data. This makes it possible to propose a power usage plan that takes the user's lifestyle into consideration.

[0053] The power procurement system can also analyze the user's environmental awareness data and propose an environmentally friendly power usage plan. For example, if the user is interested in environmental protection, the generation AI will propose a plan that prioritizes the use of renewable energy. For example, it will create a plan that maximizes the use of solar and wind power. It can also propose a power usage plan that minimizes the carbon footprint based on the environmental awareness data. This makes it possible to propose a power usage plan that takes the user's environmental awareness into account.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The planning unit formulates a power procurement plan based on input from the user. For example, if a user inputs, "I would like to formulate a power procurement plan for next year," the generation AI analyzes past power usage data and market trends and proposes an optimal procurement plan. The planning unit can also formulate a plan based on prompts containing user instructions. Step 2: The supply and demand forecasting unit forecasts electricity supply and demand based on the plan formulated by the planning unit. For example, if a user inputs, "I want you to predict electricity demand for the next three months," the generation AI will analyze past usage data, seasonal fluctuations, economic indicators, etc., and make a supply and demand forecast. The supply and demand forecasting unit can also make supply and demand forecasts based on prompts containing user instructions. Step 3: The plan selection unit selects the optimal power procurement plan based on the supply and demand predicted by the supply and demand forecasting unit. For example, if a user inputs "I want to keep costs down," the generation AI will analyze market electricity prices and the renewable energy supply situation and propose the optimal plan. The plan selection unit can also select a plan based on prompts containing user instructions.

[0056] (Example 2) The power procurement system according to an embodiment of the present invention utilizes a generation AI to interactively carry out the processes required for power procurement, from formulating plans to forecasting supply and demand and selecting the optimal plan. This enables businesses and households to procure optimal power without specialized knowledge.

[0057] An electric power procurement system according to an embodiment includes a planning unit, a supply and demand forecasting unit, and a plan selection unit. The planning unit formulates an electric power procurement plan based on user input. For example, if a user inputs, "I want to create an electric power procurement plan for next year," the generation AI analyzes past electricity usage data and market trends to propose an optimal procurement plan. The planning unit can also formulate a plan based on prompts containing user instructions. The supply and demand forecasting unit predicts electricity supply and demand based on the plan formulated by the planning unit. For example, if a user inputs, "I want to predict electricity demand for the next three months," the generation AI analyzes past usage data, seasonal fluctuations, economic indicators, etc., and predicts supply and demand. The supply and demand forecasting unit can also predict supply and demand based on prompts containing user instructions. The plan selection unit selects an optimal electric power procurement plan based on the supply and demand predicted by the supply and demand forecasting unit. For example, if a user inputs, "I want to keep costs down," the generation AI analyzes market electricity prices and renewable energy supply conditions to propose an optimal plan. In addition, the plan selection unit can select a plan based on prompts including user instructions from the generation AI. As a result, the power procurement system according to the embodiment enables businesses and households to procure optimal power without specialized knowledge.

[0058] The plan formulation unit can analyze the user's past power usage patterns, predict future usage patterns, and reflect them in the plan. For example, the generation AI in the plan formulation unit collects the user's power usage data from the past five years and analyzes seasonal usage patterns. For example, it predicts future usage patterns taking into account differences in power usage between summer and winter. The plan formulation unit can also have the generation AI predict future usage patterns based on past usage data and reflect them in the plan. This makes it possible to predict future usage patterns based on past usage data and reflect them in the plan.

[0059] The planning department can monitor fluctuations in market electricity prices in real time and propose the optimal procurement timing. For example, the generation AI in the planning department collects market electricity price data in real time and analyzes price fluctuation patterns. For example, the planning department can predict the optimal procurement timing based on price fluctuation data from the past year. The generation AI in the planning department can also monitor price data in real time and propose the optimal procurement timing. This allows the planning department to monitor fluctuations in market electricity prices in real time and propose the optimal procurement timing.

[0060] The planning unit can use the emotion estimation function to consider the user's emotional state and propose a plan to reduce stress. For example, the planning unit uses the generation AI to analyze the user's emotional state in real time and propose a plan to simplify the electricity procurement plan during times of high stress. For example, it avoids complicated procedures and presents simple options. The planning unit can also use the emotion estimation function to analyze the user's emotional state and propose a plan to reduce stress. This makes it possible to consider the user's emotional state and propose a plan to reduce stress.

[0061] The planning department can propose an optimal procurement plan that combines different energy sources. For example, the generation AI in the planning department analyzes supply data for different energy sources such as solar, wind, and hydroelectric power, and proposes the optimal combination. For example, the planning department creates a plan taking into account seasonal fluctuations in supply volume. The generation AI in the planning department can also propose an optimal procurement plan that combines different energy sources. This makes it possible to propose an optimal procurement plan that combines different energy sources.

[0062] The planning unit can analyze the power supply situation for each region and formulate a plan that suits the characteristics of that region. In the planning unit, for example, the generation AI collects power supply data for each region and analyzes the supply situation. For example, the planning unit formulates a plan taking into account the power supply volume and supply stability for each region. The planning unit can also have the generation AI analyze the power supply situation for each region and formulate a plan that suits the characteristics of that region. This makes it possible to analyze the power supply situation for each region and formulate a plan that suits the characteristics of that region.

[0063] The plan formulation unit can use the emotion estimation function to propose a plan that will give the user the most peace of mind. For example, the generation AI in the plan formulation unit uses the emotion estimation function to propose a plan that will give the user a sense of security. For example, the plan formulation unit can create a new plan based on a plan that the user has previously felt a sense of security about. The generation AI in the plan formulation unit can also use the emotion estimation function to analyze the user's emotional state and propose a plan that will give the user the most peace of mind. This makes it possible to propose a plan that will give the user the most peace of mind.

[0064] The supply and demand forecasting unit can analyze past weather data and make supply and demand forecasts based on weather conditions. For example, the generation AI in the supply and demand forecasting unit collects weather data from the past 10 years and makes supply and demand forecasts based on weather conditions. For example, it predicts electricity demand taking into account fluctuations in temperature and precipitation. The generation AI in the supply and demand forecasting unit can also make supply and demand forecasts based on past weather data. This makes it possible to make supply and demand forecasts based on past weather data.

[0065] The supply and demand forecasting unit can analyze economic indicators and make supply and demand forecasts according to the economic situation. For example, the generation AI in the supply and demand forecasting unit collects past economic indicator data and makes supply and demand forecasts according to the economic situation. For example, the power demand is predicted taking into account fluctuations in the GDP growth rate and unemployment rate. The generation AI in the supply and demand forecasting unit can also make supply and demand forecasts based on economic indicators. This makes it possible to make supply and demand forecasts based on economic indicators.

[0066] The supply and demand prediction unit can use the emotion estimation function to take into account the user's emotional state and make supply and demand predictions that give a sense of security. In the supply and demand prediction unit, for example, the generation AI uses the emotion estimation function to make supply and demand predictions that give the user a sense of security. For example, a new prediction is made based on a prediction method that has given a sense of security in the past. In addition, the supply and demand prediction unit can also use the emotion estimation function to analyze the user's emotional state and make supply and demand predictions that give a sense of security. This allows the supply and demand predictions to take into account the user's emotional state and give a sense of security.

[0067] The supply and demand forecasting unit can forecast supply and demand for different time periods and predict peak electricity demand. In the supply and demand forecasting unit, for example, the generation AI forecasts supply and demand for different time periods based on past electricity usage data. For example, it forecasts peak electricity demand on weekdays and weekends. The supply and demand forecasting unit can also forecast supply and demand for different time periods and predict peak electricity demand. This makes it possible to forecast supply and demand for different time periods and predict peak electricity demand.

[0068] The supply and demand forecasting unit can perform supply and demand forecasts for different industries and provide forecasts that are tailored to the characteristics of the industries. In the supply and demand forecasting unit, for example, the generation AI collects electricity usage data from different industries and performs supply and demand forecasts that are tailored to the characteristics of the industries. For example, the forecast is made taking into account the differences in electricity demand between the manufacturing industry and the service industry. The supply and demand forecasting unit can also perform supply and demand forecasts for different industries with the generation AI and provide forecasts that are tailored to the characteristics of the industries. This makes it possible to perform supply and demand forecasts for different industries and provide forecasts that are tailored to the characteristics of the industries.

[0069] The supply and demand prediction unit can use the emotion estimation function to provide the most reliable supply and demand prediction for the user. For example, the generation AI in the supply and demand prediction unit uses the emotion estimation function to provide a supply and demand prediction that the user can trust. For example, a new prediction is made based on a prediction method that has been shown to be reliable in the past. The generation AI in the supply and demand prediction unit can also use the emotion estimation function to analyze the user's emotional state and provide the most reliable supply and demand prediction. This allows the user to be provided with the most reliable supply and demand prediction.

[0070] The plan selection unit can analyze the user's past electricity usage data and propose the optimal plan. For example, the generation AI in the plan selection unit collects the user's electricity usage data from the past three years and analyzes usage patterns. For example, the plan selection unit proposes the optimal plan taking into account seasonal fluctuations in usage. The generation AI in the plan selection unit can also propose the optimal plan based on past usage data. This makes it possible to propose the optimal plan based on the user's past electricity usage data.

[0071] The plan selection unit can analyze market electricity prices and propose a plan that minimizes costs. For example, the generation AI in the plan selection unit collects market electricity price data and analyzes price fluctuation patterns. For example, the generation AI can propose a plan that minimizes costs based on price fluctuation data from the past year. The plan selection unit can also propose a plan that minimizes costs based on market electricity prices using the generation AI. This makes it possible to propose a plan that minimizes costs based on market electricity prices.

[0072] The plan selection unit can use the emotion estimation function to consider the user's emotional state and propose a plan that will provide high satisfaction. For example, the generation AI in the plan selection unit uses the emotion estimation function to propose a plan that will provide satisfaction to the user. For example, the plan selection unit can create a new plan based on a plan that has previously provided satisfaction. The generation AI can also use the emotion estimation function to analyze the user's emotional state and propose a plan that will provide high satisfaction. This makes it possible to consider the user's emotional state and propose a plan that will provide high satisfaction.

[0073] The plan selection unit can propose plans for different contract periods and provide options that meet the user's needs. For example, the generation AI analyzes plans for different contract periods (e.g., 1 year, 3 years, 5 years) and proposes the optimal plan that meets the user's needs. For example, it compares the advantages and disadvantages of short-term and long-term contracts. The plan selection unit can also propose plans for different contract periods and provide options that meet the user's needs. This makes it possible to propose plans for different contract periods and provide options that meet the user's needs.

[0074] The plan selection unit can compare plans from different electricity suppliers and select the most suitable provider. For example, the generation AI collects plans from different electricity suppliers and compares prices and supply stability. For example, the plan selection unit selects the most suitable provider based on past supply performance and price fluctuations. The generation AI can also compare plans from different electricity suppliers and select the most suitable provider. This makes it possible to compare plans from different electricity suppliers and select the most suitable provider.

[0075] The plan selection unit can use the emotion estimation function to propose a plan that gives the user the most peace of mind. For example, the generation AI in the plan selection unit uses the emotion estimation function to propose a plan that gives the user a sense of security. For example, the plan selection unit creates a new plan based on a plan that the user has previously felt secure about. The generation AI can also use the emotion estimation function to analyze the user's emotional state and propose the most secure plan. This makes it possible to propose a plan that gives the user the most peace of mind.

[0076] The system analyzes information on renewable energy suppliers and proposes the most suitable supplier. For example, the generation AI in the system collects data on renewable energy suppliers and analyzes supply volume and prices. For example, the system proposes the most suitable supplier based on past supply performance and price fluctuations. The generation AI in the system can also analyze information on renewable energy suppliers and propose the most suitable supplier. This makes it possible to analyze information on renewable energy suppliers and propose the most suitable supplier.

[0077] The system monitors the renewable energy supply situation in real time and proposes the optimal procurement timing. For example, the generation AI in the system collects renewable energy supply data in real time and analyzes the supply situation. For example, it proposes procurement when the supply volume increases. The system can also monitor the renewable energy supply situation in real time and propose the optimal procurement timing. This makes it possible to monitor the renewable energy supply situation in real time and propose the optimal procurement timing.

[0078] The system uses an emotion estimation function to consider the user's emotional state and suggest suppliers that give a sense of security. For example, the system uses the generation AI to suggest suppliers that give the user a sense of security using the emotion estimation function. For example, the system selects new suppliers based on suppliers that the user has shown a sense of security in the past. The system can also use the generation AI to analyze the user's emotional state using the emotion estimation function to suggest suppliers that give a sense of security. This makes it possible to consider the user's emotional state and suggest suppliers that give a sense of security.

[0079] The system proposes an optimal procurement plan that combines different renewable energy sources. For example, the generation AI in the system analyzes supply data for different renewable energy sources, such as solar, wind, and hydropower, and proposes the optimal combination. For example, the system creates a plan taking into account seasonal fluctuations in supply volume. The generation AI in the system can also propose an optimal procurement plan that combines different renewable energy sources. This makes it possible to propose an optimal procurement plan that combines different renewable energy sources.

[0080] The system analyzes the renewable energy supply situation in each region and formulates a procurement plan that suits the characteristics of that region. For example, the generation AI in the system collects renewable energy supply data for each region and analyzes the supply situation. For example, the system formulates a plan that takes into account the renewable energy supply volume and supply stability in each region. The generation AI in the system can also analyze the renewable energy supply situation in each region and formulate a procurement plan that suits the characteristics of that region. This makes it possible to analyze the renewable energy supply situation in each region and formulate a procurement plan that suits the characteristics of that region.

[0081] The system uses an emotion estimation function to suggest the renewable energy supplier that the user feels most comfortable with. For example, the system uses the generation AI's emotion estimation function to suggest the renewable energy supplier that the user feels comfortable with. For example, the system selects a new supplier based on suppliers that the user has shown comfort with in the past. The system can also use the generation AI's emotion estimation function to analyze the user's emotional state and suggest the renewable energy supplier that the user feels most comfortable with. This allows the system to suggest the renewable energy supplier that the user feels most comfortable with.

[0082] The system analyzes past electricity usage data and suggests optimal usage methods. For example, the generation AI of the system collects the user's electricity usage data from the past three years and analyzes usage patterns. For example, the system can suggest optimal usage methods taking into account seasonal fluctuations in usage. The generation AI of the system can also suggest optimal usage methods based on past electricity usage data. This allows the system to analyze past electricity usage data and suggest optimal usage methods.

[0083] The system analyzes the equipment's operating status and proposes efficient ways of using it. For example, the system uses a generation AI to collect equipment operating data and analyze the operating status. For example, the system proposes efficient ways of using it based on the equipment's operating rate and operating time. The system can also use a generation AI to analyze the equipment's operating status and propose efficient ways of using it. This makes it possible to analyze the equipment's operating status and propose efficient ways of using it.

[0084] The system uses the emotion estimation function to consider the user's emotional state and suggest usage methods that will reduce stress. For example, the generation AI uses the emotion estimation function to suggest usage methods that will reduce stress for the user. For example, the system may avoid usage patterns that have caused stress in the past. The generation AI may also use the emotion estimation function to analyze the user's emotional state and suggest usage methods that will reduce stress. This allows the system to consider the user's emotional state and suggest usage methods that will reduce stress.

[0085] The system proposes usage methods for different time periods and optimizes power usage during peak hours. For example, the system's generating AI proposes usage methods for different time periods based on past power usage data. For example, it optimizes power usage during peak hours on weekdays and weekends. The system can also propose usage methods for different time periods and optimize power usage during peak hours. This makes it possible to propose usage methods for different time periods and optimize power usage during peak hours.

[0086] The system proposes usage methods for each different piece of equipment and performs optimization according to the characteristics of the equipment. For example, the generation AI collects usage data for different pieces of equipment and proposes usage methods according to the characteristics of the equipment. For example, the system optimizes the usage patterns of air conditioners and lighting. The generation AI can also propose usage methods for each different piece of equipment and perform optimization according to the characteristics of the equipment. This makes it possible to propose usage methods for each different piece of equipment and perform optimization according to the characteristics of the equipment.

[0087] The system uses the emotion estimation function to suggest usage methods that will give the user the most peace of mind. For example, the generation AI uses the emotion estimation function to suggest usage methods that will give the user a sense of security. For example, the system can suggest new usage methods based on usage patterns that have shown security in the past. The generation AI can also use the emotion estimation function to analyze the user's emotional state and suggest usage methods that will give the user the most peace of mind. This makes it possible to suggest usage methods that will give the user the most peace of mind.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The power procurement system can also analyze the user's health data and propose a power usage plan based on their health condition. For example, if a user provides health checkup data, the generating AI can use that data to propose a power usage pattern that is optimal for their health condition. For example, it can create a plan to reduce power usage at night and promote daytime activity. It can also propose a power usage plan to reduce stress based on the health data. This makes it possible to propose a power usage plan that takes the user's health condition into consideration.

[0090] The power procurement system can also analyze the user's lifestyle data and propose a power usage plan that suits their lifestyle. For example, when a user provides data on their daily activities, the generating AI uses that data to propose a power usage pattern that is optimal for that lifestyle. For example, for a user who frequently works from home, a plan that optimizes daytime power usage can be created. The system can also propose a power usage plan that maximizes energy efficiency based on the lifestyle data. This makes it possible to propose a power usage plan that takes the user's lifestyle into consideration.

[0091] The power procurement system can also analyze the user's environmental awareness data and propose an environmentally friendly power usage plan. For example, if the user is interested in environmental protection, the generation AI will propose a plan that prioritizes the use of renewable energy. For example, it will create a plan that maximizes the use of solar and wind power. It can also propose a power usage plan that minimizes the carbon footprint based on the environmental awareness data. This makes it possible to propose a power usage plan that takes the user's environmental awareness into account.

[0092] The power procurement system can also estimate the user's emotional state and propose a power usage plan based on that emotion. For example, if the user is feeling stressed, the generation AI can propose a power usage plan to create a relaxing environment, such as adjusting the lighting or playing music. It can also propose a power usage plan that will allow the user to be most comfortable based on the user's emotional state. This makes it possible to propose a power usage plan that takes the user's emotional state into account.

[0093] The power procurement system can further estimate the user's emotional state and propose a power usage plan based on the emotion. For example, to propose a plan that gives the user a sense of security, a new plan can be created based on power usage patterns that have previously given the user a sense of security. The system can also propose a power usage plan that allows the user to feel most relaxed based on the user's emotional state. This makes it possible to propose a power usage plan that takes the user's emotional state into consideration.

[0094] The power procurement system can also estimate the user's emotional state and propose a power usage plan based on the user's emotions. For example, to propose a plan that will satisfy the user, a new plan can be created based on power usage patterns that have previously shown satisfaction. The system can also propose a power usage plan that will make the user most comfortable based on the user's emotional state. This makes it possible to propose a power usage plan that takes the user's emotional state into consideration.

[0095] The power procurement system can also estimate the user's emotional state and propose a power usage plan based on that emotion. For example, if the user is feeling stressed, the generation AI can propose a power usage plan to create a relaxing environment, such as adjusting the lighting or playing music. It can also propose a power usage plan that will allow the user to be most comfortable based on the user's emotional state. This makes it possible to propose a power usage plan that takes the user's emotional state into account.

[0096] The power procurement system can further estimate the user's emotional state and propose a power usage plan based on the emotion. For example, to propose a plan that gives the user a sense of security, a new plan can be created based on power usage patterns that have previously given the user a sense of security. The system can also propose a power usage plan that allows the user to feel most relaxed based on the user's emotional state. This makes it possible to propose a power usage plan that takes the user's emotional state into consideration.

[0097] The power procurement system can also estimate the user's emotional state and propose a power usage plan based on the user's emotions. For example, to propose a plan that will satisfy the user, a new plan can be created based on power usage patterns that have previously shown satisfaction. The system can also propose a power usage plan that will make the user most comfortable based on the user's emotional state. This makes it possible to propose a power usage plan that takes the user's emotional state into consideration.

[0098] The power procurement system can also estimate the user's emotional state and propose a power usage plan based on that emotion. For example, if the user is feeling stressed, the generation AI can propose a power usage plan to create a relaxing environment, such as adjusting the lighting or playing music. It can also propose a power usage plan that will allow the user to be most comfortable based on the user's emotional state. This makes it possible to propose a power usage plan that takes the user's emotional state into account.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The planning unit formulates a power procurement plan based on input from the user. For example, if a user inputs, "I would like to formulate a power procurement plan for next year," the generation AI analyzes past power usage data and market trends and proposes an optimal procurement plan. The planning unit can also formulate a plan based on prompts containing user instructions. Step 2: The supply and demand forecasting unit forecasts electricity supply and demand based on the plan formulated by the planning unit. For example, if a user inputs, "I want you to predict electricity demand for the next three months," the generation AI will analyze past usage data, seasonal fluctuations, economic indicators, etc., and make a supply and demand forecast. The supply and demand forecasting unit can also make supply and demand forecasts based on prompts containing user instructions. Step 3: The plan selection unit selects the optimal power procurement plan based on the supply and demand predicted by the supply and demand forecasting unit. For example, if a user inputs "I want to keep costs down," the generation AI will analyze market electricity prices and the renewable energy supply situation and propose the optimal plan. The plan selection unit can also select a plan based on prompts containing user instructions.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0109] 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's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0111] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0112] 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.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0114] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0115] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0124] 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's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] 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.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0129] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] 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, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0139] 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 image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] 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.

[0142] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] 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.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0145] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice 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 voice data.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0150] The emotion identification model 59 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 FIG. 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.

[0151] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the 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 behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0152] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0153] 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 you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0154] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0156] 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 located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0157] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0158] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, 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.

[0159] 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.

[0160] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0161] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0162] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0163] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0165] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0166] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0167] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a planning unit that formulates a power procurement plan based on input from a user; a supply and demand prediction unit that predicts power supply and demand based on the plan formulated by the plan formulation unit; a plan selection unit that selects an optimal power procurement plan based on the supply and demand predicted by the supply and demand prediction unit. A system characterized by:

2. The planning unit Analyze users' past power usage patterns, predict future usage patterns, and reflect them in planning.

2. The system of claim 1.

3. The supply and demand prediction unit Analyze past weather data and forecast supply and demand based on weather conditions 2. The system of claim 1.

4. The plan selection unit Analyzes the user's past electricity usage data and proposes the optimal plan 2. The system of claim 1.

5. The system comprises: Analyzing information on renewable energy suppliers and proposing the most suitable supplier 2. The system of claim 1.

6. The planning unit Using emotion estimation capabilities, the system takes into account the user's emotional state and proposes a plan to reduce stress.

2. The system of claim 1.

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