system

Abstract

The system according to this embodiment aims to procure optimal decarbonized electricity without requiring specialized knowledge. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and an execution unit. The reception unit inputs the requirements for power procurement. The generation unit analyzes the requirements input by the reception unit and generates an optimal decarbonized power procurement plan. The execution unit executes power procurement based on the plan generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the process of procuring decarbonized power at an optimal price is complicated and difficult to execute without specialized knowledge.

[0005] The system according to the embodiment aims to procure optimal decarbonized power without specialized knowledge.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a generation unit, and an execution unit. The reception unit inputs requirements for power procurement. The generation unit analyzes the requirements input by the reception unit and generates an optimal decarbonized power procurement plan. The execution unit executes power procurement based on the plan generated by the generation unit.

Effects of the Invention

[0007] The system according to this embodiment allows for the procurement of optimal decarbonized electricity even without specialized knowledge. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The power procurement system according to an embodiment of the present invention is a system that utilizes a generating AI to enable businesses and households to procure optimal decarbonized electricity without specialized knowledge. This power procurement system works by having the user input power procurement requirements, which the generating AI then analyzes to generate an optimal decarbonized electricity procurement plan. Based on the generated plan, the user can then execute the optimal power procurement. This mechanism enables businesses and households to execute optimal power procurement without specialized knowledge, reducing the burden of electricity costs and contributing to the realization of a decarbonized society. For example, the user inputs power procurement requirements. At this time, the user inputs the required amount of electricity, budget, and desired power source type (renewable energy, etc.). For example, the user inputs a requirement such as "I want to procure 1000kWh of electricity per year within budget." This information is input to the generating AI. Next, the generating AI analyzes the input requirements and generates an optimal decarbonized electricity procurement plan. The generating AI calculates the optimal procurement plan based on electricity market data and the supply status of renewable energy. For example, it generates an optimal procurement plan considering periods when renewable energy supply is high or prices are low. Based on the generated plan, the user can then execute the optimal power procurement. For example, a contract is signed to procure renewable energy according to a plan proposed by a generating AI. In this way, users can perform optimal power procurement without specialized knowledge. This system can reduce the burden of electricity costs for businesses and households. For example, by having the generating AI propose an optimal procurement plan, a reduction in electricity costs can be expected. Furthermore, by promoting the use of renewable energy, it can contribute to the realization of a decarbonized society. In addition, the generating AI also handles complex operations such as supply and demand forecasting, price forecasting, and supply and demand planning. This allows companies to optimize power procurement without increasing costs such as personnel expenses. For example, by having the generating AI perform supply and demand forecasting and propose the optimal procurement timing, power procurement costs can be reduced. In this way, a power procurement system utilizing generating AI can provide a mechanism that allows businesses and households to procure optimal decarbonized electricity without specialized knowledge, reduce the burden of electricity costs, and contribute to the realization of a decarbonized society.

[0029] The power procurement system according to this embodiment comprises a reception unit, a generation unit, and an execution unit. The reception unit receives input from the user regarding power procurement requirements. When the user inputs power procurement requirements, they can, for example, input the required amount of electricity, budget, and desired power source type (such as renewable energy). For example, the reception unit can receive input such as, "I want to procure 1000 kWh of electricity per year within budget." The generation unit uses a generation AI to analyze the requirements input by the reception unit and generate an optimal decarbonized power procurement plan. The generation unit calculates the optimal procurement plan based, for example, on electricity market data and the supply status of renewable energy. For example, the generation unit generates an optimal procurement plan considering periods when renewable energy supply is high and prices are low. The generation unit also handles complex operations such as supply and demand forecasting, price forecasting, and supply and demand plan formulation using a generation AI. For example, the generation unit can perform supply and demand forecasting and propose the optimal procurement timing. The execution unit executes power procurement based on the plan generated by the generation unit. The execution unit can, for example, enter into a contract to procure renewable energy according to a plan proposed by the generation AI. The execution unit can also execute electricity procurement based on the optimal procurement timing proposed by the generation AI. This allows the electricity procurement system according to the embodiment to enable businesses and households to procure optimal decarbonized electricity without specialized knowledge. Some or all of the above-described processes in the generation unit may be performed using the generation AI or not. For example, the generation unit can generate an optimal procurement plan using a generation AI model that takes electricity market data and renewable energy supply status as input and outputs an optimal procurement plan. Some or all of the above-described processes in the execution unit may be performed using the generation AI or not. For example, the execution unit can execute electricity procurement using a generation AI model that takes the plan generated by the generation unit as input and executes electricity procurement.

[0030] The reception desk receives user input for power procurement requirements. When users input their requirements, they can, for example, specify the required amount of electricity, budget, and preferred power source (e.g., renewable energy). Specifically, through a dedicated interface, users can input detailed information such as annual electricity consumption, budget limits, and preferred power source types (e.g., solar, wind, hydro). The reception desk receives this information and stores it in a database. Furthermore, the reception desk can provide initial feedback based on the user's input. For example, it can verify whether the user's budget and required electricity amount are realistic and suggest adjustments as needed. The reception desk can also refer to data previously entered by the user, considering past electricity consumption patterns and budget history to support more accurate requirement input. This allows the reception desk to enable users to easily and accurately input power procurement requirements, improving the overall efficiency of the system.

[0031] The generation unit uses a generation AI to analyze the requirements entered by the reception unit and generate an optimal decarbonized electricity procurement plan. For example, the generation unit calculates the optimal procurement plan based on electricity market data and renewable energy supply status. Specifically, the generation AI receives various data as input, such as electricity market price trends, renewable energy supply forecasts, and supply-demand balance, and analyzes this data to generate an optimal procurement plan. The generation AI uses machine learning algorithms to learn patterns from past data and makes future supply-demand and price forecasts. For example, the generation AI identifies periods when renewable energy supply is high or prices are low, and proposes the optimal procurement timing based on this information. The generation AI also simulates multiple scenarios based on the user's requirements and selects the most cost-effective plan. Furthermore, the generation unit presents the plan proposed by the generation AI to the user and supports the user's review and approval process. This allows the generation unit to enable users to procure optimal decarbonized electricity without specialized knowledge, thereby promoting sustainable energy use.

[0032] The execution unit carries out power procurement based on the plan generated by the generation unit. Specifically, the execution unit can enter into contracts to procure renewable energy according to the plan proposed by the generation AI. For example, the execution unit can automate contract procedures with power suppliers to procure power quickly and efficiently. The execution unit can also carry out power procurement based on the optimal procurement timing proposed by the generation AI. For example, it can conclude power procurement contracts to coincide with periods of high renewable energy supply or low prices. Furthermore, the execution unit can monitor the supply status of the procured power and make adjustments as needed. For example, if an unexpected supply shortage or price fluctuation occurs, the execution unit can respond quickly and secure alternative power sources. The execution unit also provides users with reports on procurement status and costs to ensure transparency. This allows the execution unit to enable users to entrust power procurement to it with confidence and improves the reliability of the entire system.

[0033] The generation unit can generate an optimal procurement plan based on electricity market data and renewable energy supply status. For example, the generation unit calculates an optimal procurement plan based on electricity market price data and supply volume data. For example, the generation unit generates an optimal procurement plan considering periods of high renewable energy supply and low prices. The generation unit can also generate an optimal procurement plan by analyzing electricity market data and renewable energy supply status using generation AI. For example, the generation unit can generate an optimal procurement plan using a generation AI model that takes electricity market price data and supply volume data as input and outputs an optimal procurement plan. This allows the generation unit to generate an optimal procurement plan that takes electricity market data and renewable energy supply status into consideration.

[0034] The generation unit can be equipped with a forecasting unit that performs supply and demand forecasting and price forecasting. The generation unit performs supply and demand forecasting and price forecasting using, for example, supply and demand forecasting algorithms and price forecasting models. For example, the generation unit can perform supply and demand forecasting and price forecasting based on electricity market data and renewable energy supply status. The generation unit can also perform supply and demand forecasting and price forecasting using a generation AI. For example, the generation unit can perform supply and demand forecasting and price forecasting using a generation AI model that takes electricity market data and renewable energy supply status as input and outputs supply and demand forecasting and price forecasting. As a result, the generation unit can generate more accurate procurement plans by performing supply and demand forecasting and price forecasting.

[0035] The generation unit may include a planning unit that formulates supply and demand plans. The generation unit can, for example, formulate supply and demand plans using a supply and demand planning algorithm. For example, the generation unit can formulate supply and demand plans based on electricity market data and renewable energy supply status. The generation unit can also formulate supply and demand plans using a generation AI. For example, the generation unit can formulate supply and demand plans using a generation AI model that takes electricity market data and renewable energy supply status as input and outputs supply and demand plans. As a result, the generation unit can improve the planning accuracy of electricity procurement by formulating supply and demand plans.

[0036] The execution unit can enter into contracts to procure renewable energy based on the generated plan. For example, the execution unit can enter into contracts to procure renewable energy according to the plan proposed by the generating AI. The execution unit can also use the generating AI to enter into contracts to procure renewable energy based on the generated plan. For example, the execution unit can use a generating AI model that takes a plan generated by the generating AI as input and outputs a contract to procure renewable energy to enter into a contract to procure renewable energy. In this way, the execution unit can achieve decarbonized electricity procurement by entering into contracts to procure renewable energy based on the generated plan.

[0037] The execution unit can perform power procurement based on the optimal procurement timing proposed by the generative AI. For example, the execution unit can perform power procurement based on the optimal procurement timing proposed by the generative AI. The execution unit can also use the generative AI to perform power procurement based on the optimal procurement timing proposed by the generative AI. For example, the execution unit can perform power procurement using a generative AI model that takes the optimal procurement timing proposed by the generative AI as input. As a result, the execution unit can reduce power procurement costs by performing power procurement based on the optimal procurement timing proposed by the generative AI.

[0038] The reception desk can analyze past power procurement history and suggest the most suitable input format to the user. For example, the reception desk can automatically display power procurement requirements that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest requirements to be used during specific time periods based on the user's past power procurement history. In this way, the reception desk can suggest the most suitable input format to the user by analyzing past power procurement history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input past power procurement history data into a generating AI and have the generating AI suggest the most suitable input format.

[0039] The reception unit can monitor the user's power usage patterns in real time and suggest the optimal input timing. For example, if the user makes an input during peak power usage, the reception unit can suggest the optimal timing. The reception unit can also suggest the optimal timing if the user makes an input during off-peak power usage times. The reception unit can also analyze the user's power usage patterns and notify the user of the optimal input timing in real time. This allows the reception unit to suggest the optimal input timing by monitoring the user's power usage patterns in real time. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's power usage pattern data into a generating AI and have the generating AI suggest the optimal input timing.

[0040] The reception unit can automatically propose region-specific power procurement requirements, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit will propose requirements considering the power supply situation in that region. If the user is on the move, the reception unit can also propose optimal power procurement requirements based on the user's current location. If the user is staying in a specific region for an extended period, the reception unit can also propose requirements based on the power supply situation in that region. In this way, the reception unit can propose region-specific power procurement requirements by taking into account the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI execute the proposal of region-specific power procurement requirements.

[0041] The reception desk can analyze a user's social media activity and automatically input relevant power procurement requirements. For example, the reception desk can automatically input power procurement requirements based on information shared by the user on social media. The reception desk can also suggest power procurement requirements based on information about accounts the user follows on social media. The reception desk can also automatically input power procurement requirements based on information about groups the user participates in on social media. In this way, the reception desk can automatically input relevant power procurement requirements by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI input the relevant power procurement requirements.

[0042] The generation unit can update electricity market data in real time and generate an optimal procurement plan. For example, the generation unit can reflect electricity market price fluctuations in real time and generate an optimal procurement plan. The generation unit can also reflect electricity market supply conditions in real time and generate an optimal procurement plan. The generation unit can also reflect electricity market demand forecasts in real time and generate an optimal procurement plan. In this way, the generation unit can generate an optimal procurement plan by updating electricity market data in real time. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input electricity market data into a generation AI and have the generation AI perform the generation of an optimal procurement plan.

[0043] The generation unit can analyze the renewable energy supply situation in detail and generate an optimal procurement plan for each season. For example, the generation unit can generate a procurement plan tailored to seasons with high renewable energy supply. The generation unit can also generate a procurement plan in preparation for seasons with low renewable energy supply. The generation unit can analyze the renewable energy supply situation seasonally and generate an optimal procurement plan. In this way, the generation unit can generate an optimal procurement plan for each season by analyzing the renewable energy supply situation in detail. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input renewable energy supply data into a generation AI and have the generation AI generate an optimal procurement plan for each season.

[0044] The generation unit can generate procurement plans by considering the user's past electricity usage data in addition to electricity market data. For example, the generation unit can generate an optimal procurement plan based on the user's past electricity usage data. The generation unit can also analyze the user's past electricity usage patterns and generate an optimal procurement plan. The generation unit can also generate the most efficient procurement plan by considering the user's past electricity usage data. In this way, the generation unit can generate a more optimal procurement plan by considering the user's past electricity usage data. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's past electricity usage data into a generation AI and have the generation AI perform the generation of a procurement plan.

[0045] The generation unit can generate procurement plans by considering the supply status of other energy sources in addition to the supply status of renewable energy. For example, the generation unit can generate procurement plans by considering the supply status of fossil fuels in addition to renewable energy. The generation unit can also generate procurement plans by considering the supply status of nuclear power in addition to renewable energy. The generation unit can also generate procurement plans by considering the supply status of hydroelectric power in addition to renewable energy. In this way, the generation unit can generate more comprehensive procurement plans by considering the supply status of other energy sources. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input supply status data of other energy sources into a generation AI and have the generation AI perform the generation of a procurement plan.

[0046] The execution unit can propose optimal contract terms by referring to the user's past contract history when executing power procurement. For example, the execution unit proposes optimal contract terms based on the contract terms the user has previously entered into. The execution unit can also propose the most advantageous contract terms from the user's past contract history. The execution unit can also analyze the user's past contract history and propose the most efficient contract terms. In this way, the execution unit can propose optimal contract terms by referring to the user's past contract history. Some or all of the above processing in the execution unit may be performed using AI or not. For example, the execution unit can input the user's past contract history data into a generating AI and have the generating AI execute the proposal of optimal contract terms.

[0047] The execution unit can monitor the user's current power usage in real time when procuring power and propose the optimal procurement timing. For example, the execution unit can monitor the user's current power usage in real time and propose the optimal procurement timing. The execution unit can also propose the optimal procurement timing when the user's power usage reaches its peak. The execution unit can also propose the optimal procurement timing when the user's power usage reaches a low-power period. In this way, the execution unit can propose the optimal procurement timing by monitoring the user's current power usage in real time. Some or all of the above processing in the execution unit may be performed using AI or not. For example, the execution unit can input the user's current power usage data into a generating AI and have the generating AI propose the optimal procurement timing.

[0048] The execution unit can propose optimal contract terms when procuring electricity, taking into account the user's geographical location. For example, if the user is in a specific region, the execution unit will propose contract terms considering the electricity supply situation in that region. If the user is on the move, the execution unit can also propose optimal contract terms based on the user's current location. If the user is staying in a specific region for an extended period, the execution unit can also propose contract terms based on the electricity supply situation in that region. In this way, the execution unit can propose optimal contract terms by taking into account the user's geographical location. Some or all of the above processing in the execution unit may be performed using AI or not. For example, the execution unit can input the user's geographical location data into a generating AI and have the generating AI propose optimal contract terms.

[0049] The execution unit can analyze the user's social media activity and propose relevant contract terms when executing power procurement. For example, the execution unit can propose contract terms based on information shared by the user on social media. The execution unit can also propose contract terms based on information about accounts followed by the user on social media. The execution unit can also propose contract terms based on information about groups the user participates in on social media. In this way, the execution unit can propose relevant contract terms by analyzing the user's social media activity. Some or all of the above processing in the execution unit may be performed using AI or not. For example, the execution unit can input the user's social media activity data into a generating AI and have the generating AI propose relevant contract terms.

[0050] The forecasting unit can analyze past supply and demand data in detail to improve the accuracy of future supply and demand forecasts. For example, the forecasting unit can analyze seasonal supply and demand patterns based on past supply and demand data to make future supply and demand forecasts. The forecasting unit can also make supply and demand forecasts based on past supply and demand data in accordance with specific events or situations. The forecasting unit can also analyze past supply and demand data in detail to improve the accuracy of supply and demand forecasts. In this way, the forecasting unit can improve the accuracy of future supply and demand forecasts by analyzing past supply and demand data in detail. Some or all of the above processing in the forecasting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the forecasting unit can input past supply and demand data into a generation AI and have the generation AI perform the task of improving the accuracy of supply and demand forecasts.

[0051] The forecasting unit can monitor trends in the electricity market in real time and update supply and demand forecasts. For example, the forecasting unit can monitor price fluctuations in the electricity market in real time and update supply and demand forecasts. The forecasting unit can also monitor the supply situation in the electricity market in real time and update supply and demand forecasts. The forecasting unit can also monitor demand forecasts in the electricity market in real time and update supply and demand forecasts. In this way, the forecasting unit can update supply and demand forecasts by monitoring trends in the electricity market in real time. Some or all of the above processing in the forecasting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the forecasting unit can input electricity market trend data into a generation AI and have the generation AI perform the update of supply and demand forecasts.

[0052] The forecasting unit can perform supply and demand forecasts by considering weather data in addition to electricity market data. For example, the forecasting unit can predict fluctuations in electricity demand and perform supply and demand forecasts based on weather data. The forecasting unit can also predict the supply status of renewable energy and perform supply and demand forecasts based on weather data. The forecasting unit can also improve the accuracy of supply and demand forecasts by analyzing weather data in detail. In this way, the forecasting unit can improve the accuracy of supply and demand forecasts by considering weather data. Some or all of the above processing in the forecasting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the forecasting unit can input weather data into a generation AI and have the generation AI perform the supply and demand forecast.

[0053] The forecasting unit can simultaneously perform supply and demand forecasts as well as price forecasts, and propose the optimal procurement timing. For example, the forecasting unit can simultaneously perform supply and demand forecasts and price forecasts and propose the optimal procurement timing. The forecasting unit can also propose the most cost-effective procurement timing based on supply and demand forecasts and price forecasts. The forecasting unit can also analyze supply and demand forecasts and price forecasts in detail and propose the optimal procurement timing. In this way, the forecasting unit can propose the optimal procurement timing by simultaneously performing supply and demand forecasts and price forecasts. Some or all of the above processing in the forecasting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the forecasting unit can input supply and demand forecast and price forecast data into a generation AI and have the generation AI propose the optimal procurement timing.

[0054] The planning department can analyze past supply and demand planning data in detail to improve the accuracy of future supply and demand plans. For example, the planning department can analyze seasonal supply and demand patterns based on past supply and demand planning data and create future supply and demand plans. The planning department can also create supply and demand plans tailored to specific events or situations based on past supply and demand planning data. The planning department can also analyze past supply and demand planning data in detail to improve the accuracy of supply and demand plans. In this way, the planning department can improve the accuracy of future supply and demand plans by analyzing past supply and demand planning data in detail. Some or all of the above processes in the planning department may be performed using or without a generation AI. For example, the planning department can input past supply and demand planning data into a generation AI and have the generation AI perform the task of improving the accuracy of supply and demand plans.

[0055] The planning department can monitor trends in the electricity market in real time and update the supply and demand plan. For example, the planning department can monitor price fluctuations in the electricity market in real time and update the supply and demand plan. The planning department can also monitor the supply situation in the electricity market in real time and update the supply and demand plan. The planning department can also monitor demand forecasts in the electricity market in real time and update the supply and demand plan. In this way, the planning department can update the supply and demand plan by monitoring trends in the electricity market in real time. Some or all of the above processes in the planning department may be performed using a generation AI, or they may not be performed using a generation AI. For example, the planning department can input electricity market trend data into a generation AI and have the generation AI perform the update of the supply and demand plan.

[0056] The planning department can formulate supply and demand plans by considering the user's past electricity usage data in addition to electricity market data. For example, the planning department can formulate an optimal supply and demand plan based on the user's past electricity usage data. The planning department can also formulate an optimal supply and demand plan by analyzing the user's past electricity usage patterns. The planning department can also formulate the most efficient supply and demand plan by considering the user's past electricity usage data. In this way, the planning department can formulate a more optimal supply and demand plan by considering the user's past electricity usage data. Some or all of the above processes in the planning department may be performed using or without a generation AI. For example, the planning department can input the user's past electricity usage data into a generation AI and have the generation AI formulate a supply and demand plan.

[0057] The planning department can formulate both supply and demand plans and price plans simultaneously, and propose the optimal procurement timing. For example, the planning department can formulate supply and demand plans and price plans simultaneously and propose the optimal procurement timing. The planning department can also propose the most cost-effective procurement timing based on the supply and demand plans and price plans. The planning department can also analyze the supply and demand plans and price plans in detail and propose the optimal procurement timing. In this way, the planning department can propose the optimal procurement timing by formulating supply and demand plans and price plans simultaneously. Some or all of the above processes in the planning department may be performed using or without a generation AI. For example, the planning department can input supply and demand plan and price plan data into a generation AI and have the generation AI propose the optimal procurement timing.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The generation unit can generate procurement plans by considering not only electricity market data but also the user's past electricity usage data. For example, it can generate the optimal procurement plan based on the user's past electricity usage data. It can also analyze the user's past electricity usage patterns and generate the optimal procurement plan. It can also generate the most efficient procurement plan by considering the user's past electricity usage data. In this way, the generation unit can generate more optimal procurement plans by considering the user's past electricity usage data.

[0060] The reception desk can analyze past power procurement history and suggest the most suitable input format for the user. For example, it can automatically display power procurement requirements that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). It can even predict and suggest requirements to be used during specific time periods based on the user's past power procurement history. In this way, the reception desk can suggest the most suitable input format for the user by analyzing past power procurement history.

[0061] The generation unit can generate procurement plans that consider the supply status of other energy sources in addition to the supply status of renewable energy. For example, it can generate procurement plans that consider the supply status of fossil fuels in addition to renewable energy. It can also generate procurement plans that consider the supply status of nuclear power in addition to renewable energy. It can also generate procurement plans that consider the supply status of hydroelectric power in addition to renewable energy. In this way, the generation unit can generate more comprehensive procurement plans by considering the supply status of other energy sources as well.

[0062] The execution unit can propose optimal contract terms by referring to the user's past contract history when executing power procurement. For example, it can propose optimal contract terms based on the contract terms the user has previously entered into. It can also propose the most advantageous contract terms from the user's past contract history. It can also analyze the user's past contract history and propose the most efficient contract terms. In this way, the execution unit can propose optimal contract terms by referring to the user's past contract history.

[0063] The forecasting unit can perform supply and demand forecasts by considering weather data in addition to electricity market data. For example, it can predict fluctuations in electricity demand and perform supply and demand forecasts based on weather data. It can also predict the supply status of renewable energy and perform supply and demand forecasts based on weather data. It can also improve the accuracy of supply and demand forecasts by analyzing weather data in detail. In this way, the forecasting unit can improve the accuracy of supply and demand forecasts by considering weather data.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The reception desk receives the user's electricity procurement requirements. Users can enter the required amount of electricity, budget, and preferred power source (such as renewable energy). For example, they can enter requirements such as "I would like to procure 1000 kWh of electricity per year within my budget." Step 2: The generation unit analyzes the requirements entered by the reception unit and generates an optimal decarbonization electricity procurement plan. The generation unit calculates the optimal procurement plan based on electricity market data and renewable energy supply status. For example, it generates an optimal procurement plan by considering periods when renewable energy supply is high or prices are low. The generation unit is also responsible for complex operations such as supply and demand forecasting, price forecasting, and supply and demand planning. Step 3: The execution unit performs power procurement based on the plan generated by the generation unit. The execution unit can enter into contracts to procure renewable energy according to the plan proposed by the generation AI. It can also perform power procurement based on the optimal procurement timing proposed by the generation AI.

[0066] (Example of form 2) The power procurement system according to an embodiment of the present invention is a system that utilizes a generating AI to enable businesses and households to procure optimal decarbonized electricity without specialized knowledge. This power procurement system works by having the user input power procurement requirements, which the generating AI then analyzes to generate an optimal decarbonized electricity procurement plan. Based on the generated plan, the user can then execute the optimal power procurement. This mechanism enables businesses and households to execute optimal power procurement without specialized knowledge, reducing the burden of electricity costs and contributing to the realization of a decarbonized society. For example, the user inputs power procurement requirements. At this time, the user inputs the required amount of electricity, budget, and desired power source type (renewable energy, etc.). For example, the user inputs a requirement such as "I want to procure 1000kWh of electricity per year within budget." This information is input to the generating AI. Next, the generating AI analyzes the input requirements and generates an optimal decarbonized electricity procurement plan. The generating AI calculates the optimal procurement plan based on electricity market data and the supply status of renewable energy. For example, it generates an optimal procurement plan considering periods when renewable energy supply is high or prices are low. Based on the generated plan, the user can then execute the optimal power procurement. For example, a contract is signed to procure renewable energy according to a plan proposed by a generating AI. In this way, users can perform optimal power procurement without specialized knowledge. This system can reduce the burden of electricity costs for businesses and households. For example, by having the generating AI propose an optimal procurement plan, a reduction in electricity costs can be expected. Furthermore, by promoting the use of renewable energy, it can contribute to the realization of a decarbonized society. In addition, the generating AI also handles complex operations such as supply and demand forecasting, price forecasting, and supply and demand planning. This allows companies to optimize power procurement without increasing costs such as personnel expenses. For example, by having the generating AI perform supply and demand forecasting and propose the optimal procurement timing, power procurement costs can be reduced. In this way, a power procurement system utilizing generating AI can provide a mechanism that allows businesses and households to procure optimal decarbonized electricity without specialized knowledge, reduce the burden of electricity costs, and contribute to the realization of a decarbonized society.

[0067] The power procurement system according to this embodiment comprises a reception unit, a generation unit, and an execution unit. The reception unit receives input from the user regarding power procurement requirements. When the user inputs power procurement requirements, they can, for example, input the required amount of electricity, budget, and desired power source type (such as renewable energy). For example, the reception unit can receive input such as, "I want to procure 1000 kWh of electricity per year within budget." The generation unit uses a generation AI to analyze the requirements input by the reception unit and generate an optimal decarbonized power procurement plan. The generation unit calculates the optimal procurement plan based, for example, on electricity market data and the supply status of renewable energy. For example, the generation unit generates an optimal procurement plan considering periods when renewable energy supply is high and prices are low. The generation unit also handles complex operations such as supply and demand forecasting, price forecasting, and supply and demand plan formulation using a generation AI. For example, the generation unit can perform supply and demand forecasting and propose the optimal procurement timing. The execution unit executes power procurement based on the plan generated by the generation unit. The execution unit can, for example, enter into a contract to procure renewable energy according to a plan proposed by the generation AI. The execution unit can also execute electricity procurement based on the optimal procurement timing proposed by the generation AI. This allows the electricity procurement system according to the embodiment to enable businesses and households to procure optimal decarbonized electricity without specialized knowledge. Some or all of the above-described processes in the generation unit may be performed using the generation AI or not. For example, the generation unit can generate an optimal procurement plan using a generation AI model that takes electricity market data and renewable energy supply status as input and outputs an optimal procurement plan. Some or all of the above-described processes in the execution unit may be performed using the generation AI or not. For example, the execution unit can execute electricity procurement using a generation AI model that takes the plan generated by the generation unit as input and executes electricity procurement.

[0068] The reception desk receives user input for power procurement requirements. When users input their requirements, they can, for example, specify the required amount of electricity, budget, and preferred power source (e.g., renewable energy). Specifically, through a dedicated interface, users can input detailed information such as annual electricity consumption, budget limits, and preferred power source types (e.g., solar, wind, hydro). The reception desk receives this information and stores it in a database. Furthermore, the reception desk can provide initial feedback based on the user's input. For example, it can verify whether the user's budget and required electricity amount are realistic and suggest adjustments as needed. The reception desk can also refer to data previously entered by the user, considering past electricity consumption patterns and budget history to support more accurate requirement input. This allows the reception desk to enable users to easily and accurately input power procurement requirements, improving the overall efficiency of the system.

[0069] The generation unit uses a generation AI to analyze the requirements entered by the reception unit and generate an optimal decarbonized electricity procurement plan. For example, the generation unit calculates the optimal procurement plan based on electricity market data and renewable energy supply status. Specifically, the generation AI receives various data as input, such as electricity market price trends, renewable energy supply forecasts, and supply-demand balance, and analyzes this data to generate an optimal procurement plan. The generation AI uses machine learning algorithms to learn patterns from past data and makes future supply-demand and price forecasts. For example, the generation AI identifies periods when renewable energy supply is high or prices are low, and proposes the optimal procurement timing based on this information. The generation AI also simulates multiple scenarios based on the user's requirements and selects the most cost-effective plan. Furthermore, the generation unit presents the plan proposed by the generation AI to the user and supports the user's review and approval process. This allows the generation unit to enable users to procure optimal decarbonized electricity without specialized knowledge, thereby promoting sustainable energy use.

[0070] The execution unit carries out power procurement based on the plan generated by the generation unit. Specifically, the execution unit can enter into contracts to procure renewable energy according to the plan proposed by the generation AI. For example, the execution unit can automate contract procedures with power suppliers to procure power quickly and efficiently. The execution unit can also carry out power procurement based on the optimal procurement timing proposed by the generation AI. For example, it can conclude power procurement contracts to coincide with periods of high renewable energy supply or low prices. Furthermore, the execution unit can monitor the supply status of the procured power and make adjustments as needed. For example, if an unexpected supply shortage or price fluctuation occurs, the execution unit can respond quickly and secure alternative power sources. The execution unit also provides users with reports on procurement status and costs to ensure transparency. This allows the execution unit to enable users to entrust power procurement to it with confidence and improves the reliability of the entire system.

[0071] The generation unit can generate an optimal procurement plan based on electricity market data and renewable energy supply status. For example, the generation unit calculates an optimal procurement plan based on electricity market price data and supply volume data. For example, the generation unit generates an optimal procurement plan considering periods of high renewable energy supply and low prices. The generation unit can also generate an optimal procurement plan by analyzing electricity market data and renewable energy supply status using generation AI. For example, the generation unit can generate an optimal procurement plan using a generation AI model that takes electricity market price data and supply volume data as input and outputs an optimal procurement plan. This allows the generation unit to generate an optimal procurement plan that takes electricity market data and renewable energy supply status into consideration.

[0072] The generation unit can be equipped with a forecasting unit that performs supply and demand forecasting and price forecasting. The generation unit performs supply and demand forecasting and price forecasting using, for example, supply and demand forecasting algorithms and price forecasting models. For example, the generation unit can perform supply and demand forecasting and price forecasting based on electricity market data and renewable energy supply status. The generation unit can also perform supply and demand forecasting and price forecasting using a generation AI. For example, the generation unit can perform supply and demand forecasting and price forecasting using a generation AI model that takes electricity market data and renewable energy supply status as input and outputs supply and demand forecasting and price forecasting. As a result, the generation unit can generate more accurate procurement plans by performing supply and demand forecasting and price forecasting.

[0073] The generation unit may include a planning unit that formulates supply and demand plans. The generation unit can, for example, formulate supply and demand plans using a supply and demand planning algorithm. For example, the generation unit can formulate supply and demand plans based on electricity market data and renewable energy supply status. The generation unit can also formulate supply and demand plans using a generation AI. For example, the generation unit can formulate supply and demand plans using a generation AI model that takes electricity market data and renewable energy supply status as input and outputs supply and demand plans. As a result, the generation unit can improve the planning accuracy of electricity procurement by formulating supply and demand plans.

[0074] The execution unit can enter into contracts to procure renewable energy based on the generated plan. For example, the execution unit can enter into contracts to procure renewable energy according to the plan proposed by the generating AI. The execution unit can also use the generating AI to enter into contracts to procure renewable energy based on the generated plan. For example, the execution unit can use a generating AI model that takes a plan generated by the generating AI as input and outputs a contract to procure renewable energy to enter into a contract to procure renewable energy. In this way, the execution unit can achieve decarbonized electricity procurement by entering into contracts to procure renewable energy based on the generated plan.

[0075] The execution unit can perform power procurement based on the optimal procurement timing proposed by the generative AI. For example, the execution unit can perform power procurement based on the optimal procurement timing proposed by the generative AI. The execution unit can also use the generative AI to perform power procurement based on the optimal procurement timing proposed by the generative AI. For example, the execution unit can perform power procurement using a generative AI model that takes the optimal procurement timing proposed by the generative AI as input. As a result, the execution unit can reduce power procurement costs by performing power procurement based on the optimal procurement timing proposed by the generative AI.

[0076] The reception desk can estimate the user's emotions and customize the input interface for power procurement requirements based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of power procurement requirements. In this way, the reception desk can improve user convenience by providing an input interface that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0077] The reception desk can analyze past power procurement history and suggest the most suitable input format to the user. For example, the reception desk can automatically display power procurement requirements that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest requirements to be used during specific time periods based on the user's past power procurement history. In this way, the reception desk can suggest the most suitable input format to the user by analyzing past power procurement history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input past power procurement history data into a generating AI and have the generating AI suggest the most suitable input format.

[0078] The reception unit can monitor the user's power usage patterns in real time and suggest the optimal input timing. For example, if the user makes an input during peak power usage, the reception unit can suggest the optimal timing. The reception unit can also suggest the optimal timing if the user makes an input during off-peak power usage times. The reception unit can also analyze the user's power usage patterns and notify the user of the optimal input timing in real time. This allows the reception unit to suggest the optimal input timing by monitoring the user's power usage patterns in real time. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's power usage pattern data into a generating AI and have the generating AI suggest the optimal input timing.

[0079] The reception desk can estimate the user's emotions and automatically prioritize input requirements based on the estimated emotions. For example, if the user is stressed, the reception desk can prioritize inputting important requirements. If the user is relaxed, the reception desk can also prioritize inputting detailed requirements. If the user is in a hurry, the reception desk can also prioritize inputting only the most important requirements. This allows the reception desk to improve user convenience by prioritizing input requirements based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0080] The reception unit can automatically propose region-specific power procurement requirements, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit will propose requirements considering the power supply situation in that region. If the user is on the move, the reception unit can also propose optimal power procurement requirements based on the user's current location. If the user is staying in a specific region for an extended period, the reception unit can also propose requirements based on the power supply situation in that region. In this way, the reception unit can propose region-specific power procurement requirements by taking into account the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI execute the proposal of region-specific power procurement requirements.

[0081] The reception desk can analyze a user's social media activity and automatically input relevant power procurement requirements. For example, the reception desk can automatically input power procurement requirements based on information shared by the user on social media. The reception desk can also suggest power procurement requirements based on information about accounts the user follows on social media. The reception desk can also automatically input power procurement requirements based on information about groups the user participates in on social media. In this way, the reception desk can automatically input relevant power procurement requirements by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI input the relevant power procurement requirements.

[0082] The generation unit can estimate the user's emotions and adjust the presentation of the procurement plan based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a procurement plan with detailed explanations. If the user is in a hurry, the generation unit can also generate a concise and to-the-point procurement plan. If the user is excited, the generation unit can also generate a procurement plan with visually appealing effects. In this way, the generation unit can deepen the user's understanding by adjusting the presentation of the procurement plan based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the presentation of the procurement plan.

[0083] The generation unit can update electricity market data in real time and generate an optimal procurement plan. For example, the generation unit can reflect electricity market price fluctuations in real time and generate an optimal procurement plan. The generation unit can also reflect electricity market supply conditions in real time and generate an optimal procurement plan. The generation unit can also reflect electricity market demand forecasts in real time and generate an optimal procurement plan. In this way, the generation unit can generate an optimal procurement plan by updating electricity market data in real time. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input electricity market data into a generation AI and have the generation AI perform the generation of an optimal procurement plan.

[0084] The generation unit can analyze the renewable energy supply situation in detail and generate an optimal procurement plan for each season. For example, the generation unit can generate a procurement plan tailored to seasons with high renewable energy supply. The generation unit can also generate a procurement plan in preparation for seasons with low renewable energy supply. The generation unit can analyze the renewable energy supply situation seasonally and generate an optimal procurement plan. In this way, the generation unit can generate an optimal procurement plan for each season by analyzing the renewable energy supply situation in detail. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input renewable energy supply data into a generation AI and have the generation AI generate an optimal procurement plan for each season.

[0085] The generation unit can estimate the user's emotions and prioritize procurement plans based on those emotions. For example, if the user is stressed, the generation unit will prioritize displaying important procurement plans. If the user is relaxed, the generation unit may also prioritize displaying detailed procurement plans. If the user is in a hurry, the generation unit may also display only the most important procurement plans. In this way, the generation unit can improve user convenience by prioritizing procurement plans based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into a generation AI and have the generation AI prioritize procurement plans.

[0086] The generation unit can generate procurement plans by considering the user's past electricity usage data in addition to electricity market data. For example, the generation unit can generate an optimal procurement plan based on the user's past electricity usage data. The generation unit can also analyze the user's past electricity usage patterns and generate an optimal procurement plan. The generation unit can also generate the most efficient procurement plan by considering the user's past electricity usage data. In this way, the generation unit can generate a more optimal procurement plan by considering the user's past electricity usage data. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's past electricity usage data into a generation AI and have the generation AI perform the generation of a procurement plan.

[0087] The generation unit can generate procurement plans by considering the supply status of other energy sources in addition to the supply status of renewable energy. For example, the generation unit can generate procurement plans by considering the supply status of fossil fuels in addition to renewable energy. The generation unit can also generate procurement plans by considering the supply status of nuclear power in addition to renewable energy. The generation unit can also generate procurement plans by considering the supply status of hydroelectric power in addition to renewable energy. In this way, the generation unit can generate more comprehensive procurement plans by considering the supply status of other energy sources. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input supply status data of other energy sources into a generation AI and have the generation AI perform the generation of a procurement plan.

[0088] The execution unit can estimate the user's emotions and adjust the power procurement process based on the estimated emotions. For example, if the user is relaxed, the execution unit may provide a process that includes detailed explanations. If the user is in a hurry, the execution unit may also provide a concise and quick process. If the user is excited, the execution unit may also provide a process that includes visually appealing effects. This allows the execution unit to improve user convenience by adjusting the power procurement process based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the execution unit may be performed using AI or not. For example, the execution unit can input user emotion data into a generative AI and have the generative AI adjust the power procurement process.

[0089] The execution unit can propose optimal contract terms by referring to the user's past contract history when executing power procurement. For example, the execution unit proposes optimal contract terms based on the contract terms the user has previously entered into. The execution unit can also propose the most advantageous contract terms from the user's past contract history. The execution unit can also analyze the user's past contract history and propose the most efficient contract terms. In this way, the execution unit can propose optimal contract terms by referring to the user's past contract history. Some or all of the above processing in the execution unit may be performed using AI or not. For example, the execution unit can input the user's past contract history data into a generating AI and have the generating AI execute the proposal of optimal contract terms.

[0090] The execution unit can monitor the user's current power usage in real time when procuring power and propose the optimal procurement timing. For example, the execution unit can monitor the user's current power usage in real time and propose the optimal procurement timing. The execution unit can also propose the optimal procurement timing when the user's power usage reaches its peak. The execution unit can also propose the optimal procurement timing when the user's power usage reaches a low-power period. In this way, the execution unit can propose the optimal procurement timing by monitoring the user's current power usage in real time. Some or all of the above processing in the execution unit may be performed using AI or not. For example, the execution unit can input the user's current power usage data into a generating AI and have the generating AI propose the optimal procurement timing.

[0091] The execution unit can estimate the user's emotions and prioritize power procurement based on the estimated emotions. For example, if the user is stressed, the execution unit will prioritize important power procurement. If the user is relaxed, the execution unit may also prioritize detailed power procurement. If the user is in a hurry, the execution unit may only prioritize the most important power procurement. In this way, the execution unit can improve user convenience by prioritizing power procurement based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the execution unit may be performed using AI or not. For example, the execution unit can input user emotion data into a generative AI and have the generative AI prioritize power procurement.

[0092] The execution unit can propose optimal contract terms when procuring electricity, taking into account the user's geographical location. For example, if the user is in a specific region, the execution unit will propose contract terms considering the electricity supply situation in that region. If the user is on the move, the execution unit can also propose optimal contract terms based on the user's current location. If the user is staying in a specific region for an extended period, the execution unit can also propose contract terms based on the electricity supply situation in that region. In this way, the execution unit can propose optimal contract terms by taking into account the user's geographical location. Some or all of the above processing in the execution unit may be performed using AI or not. For example, the execution unit can input the user's geographical location data into a generating AI and have the generating AI propose optimal contract terms.

[0093] The execution unit can analyze the user's social media activity and propose relevant contract terms when executing power procurement. For example, the execution unit can propose contract terms based on information shared by the user on social media. The execution unit can also propose contract terms based on information about accounts followed by the user on social media. The execution unit can also propose contract terms based on information about groups the user participates in on social media. In this way, the execution unit can propose relevant contract terms by analyzing the user's social media activity. Some or all of the above processing in the execution unit may be performed using AI or not. For example, the execution unit can input the user's social media activity data into a generating AI and have the generating AI propose relevant contract terms.

[0094] The forecasting unit can estimate the user's emotions and adjust the display method of the supply and demand forecast based on the estimated user emotions. For example, if the user is stressed, the forecasting unit can provide a simple and highly visible display method. If the user is relaxed, the forecasting unit can also provide a display method that includes detailed information. If the user is in a hurry, the forecasting unit can also provide a display method that gets straight to the point. In this way, the forecasting unit can deepen the user's understanding by adjusting the display method of the supply and demand forecast based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the forecasting unit may be performed using AI or not. For example, the forecasting unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method of the supply and demand forecast.

[0095] The forecasting unit can analyze past supply and demand data in detail to improve the accuracy of future supply and demand forecasts. For example, the forecasting unit can analyze seasonal supply and demand patterns based on past supply and demand data to make future supply and demand forecasts. The forecasting unit can also make supply and demand forecasts based on past supply and demand data in accordance with specific events or situations. The forecasting unit can also analyze past supply and demand data in detail to improve the accuracy of supply and demand forecasts. In this way, the forecasting unit can improve the accuracy of future supply and demand forecasts by analyzing past supply and demand data in detail. Some or all of the above processing in the forecasting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the forecasting unit can input past supply and demand data into a generation AI and have the generation AI perform the task of improving the accuracy of supply and demand forecasts.

[0096] The forecasting unit can monitor trends in the electricity market in real time and update supply and demand forecasts. For example, the forecasting unit can monitor price fluctuations in the electricity market in real time and update supply and demand forecasts. The forecasting unit can also monitor the supply situation in the electricity market in real time and update supply and demand forecasts. The forecasting unit can also monitor demand forecasts in the electricity market in real time and update supply and demand forecasts. In this way, the forecasting unit can update supply and demand forecasts by monitoring trends in the electricity market in real time. Some or all of the above processing in the forecasting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the forecasting unit can input electricity market trend data into a generation AI and have the generation AI perform the update of supply and demand forecasts.

[0097] The prediction unit can estimate the user's emotions and prioritize supply and demand forecasts based on the estimated emotions. For example, if the user is stressed, the prediction unit will prioritize displaying important supply and demand forecasts. If the user is relaxed, the prediction unit may also prioritize displaying detailed supply and demand forecasts. If the user is in a hurry, the prediction unit may also display only the most important supply and demand forecasts. In this way, the prediction unit can improve user convenience by prioritizing supply and demand forecasts based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI or not. For example, the prediction unit can input user emotion data into a generative AI and have the generative AI perform the task of prioritizing supply and demand forecasts.

[0098] The forecasting unit can perform supply and demand forecasts by considering weather data in addition to electricity market data. For example, the forecasting unit can predict fluctuations in electricity demand and perform supply and demand forecasts based on weather data. The forecasting unit can also predict the supply status of renewable energy and perform supply and demand forecasts based on weather data. The forecasting unit can also improve the accuracy of supply and demand forecasts by analyzing weather data in detail. In this way, the forecasting unit can improve the accuracy of supply and demand forecasts by considering weather data. Some or all of the above processing in the forecasting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the forecasting unit can input weather data into a generation AI and have the generation AI perform the supply and demand forecast.

[0099] The forecasting unit can simultaneously perform supply and demand forecasts as well as price forecasts, and propose the optimal procurement timing. For example, the forecasting unit can simultaneously perform supply and demand forecasts and price forecasts and propose the optimal procurement timing. The forecasting unit can also propose the most cost-effective procurement timing based on supply and demand forecasts and price forecasts. The forecasting unit can also analyze supply and demand forecasts and price forecasts in detail and propose the optimal procurement timing. In this way, the forecasting unit can propose the optimal procurement timing by simultaneously performing supply and demand forecasts and price forecasts. Some or all of the above processing in the forecasting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the forecasting unit can input supply and demand forecast and price forecast data into a generation AI and have the generation AI propose the optimal procurement timing.

[0100] The planning unit can estimate the user's emotions and adjust the display method of the supply and demand plan based on the estimated user emotions. For example, if the user is stressed, the planning unit can provide a simple and highly visible display method. If the user is relaxed, the planning unit can also provide a display method that includes detailed information. If the user is in a hurry, the planning unit can provide a concise display method. In this way, the planning unit can deepen the user's understanding by adjusting the display method of the supply and demand plan based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the planning unit may be performed using AI or not. For example, the planning unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the supply and demand plan.

[0101] The planning department can analyze past supply and demand planning data in detail to improve the accuracy of future supply and demand plans. For example, the planning department can analyze seasonal supply and demand patterns based on past supply and demand planning data and create future supply and demand plans. The planning department can also create supply and demand plans tailored to specific events or situations based on past supply and demand planning data. The planning department can also analyze past supply and demand planning data in detail to improve the accuracy of supply and demand plans. In this way, the planning department can improve the accuracy of future supply and demand plans by analyzing past supply and demand planning data in detail. Some or all of the above processes in the planning department may be performed using or without a generation AI. For example, the planning department can input past supply and demand planning data into a generation AI and have the generation AI perform the task of improving the accuracy of supply and demand plans.

[0102] The planning department can monitor trends in the electricity market in real time and update the supply and demand plan. For example, the planning department can monitor price fluctuations in the electricity market in real time and update the supply and demand plan. The planning department can also monitor the supply situation in the electricity market in real time and update the supply and demand plan. The planning department can also monitor demand forecasts in the electricity market in real time and update the supply and demand plan. In this way, the planning department can update the supply and demand plan by monitoring trends in the electricity market in real time. Some or all of the above processes in the planning department may be performed using a generation AI, or they may not be performed using a generation AI. For example, the planning department can input electricity market trend data into a generation AI and have the generation AI perform the update of the supply and demand plan.

[0103] The planning unit can estimate the user's emotions and prioritize supply and demand plans based on those emotions. For example, if the user is stressed, the planning unit will prioritize displaying important supply and demand plans. If the user is relaxed, the planning unit may also prioritize displaying detailed supply and demand plans. If the user is in a hurry, the planning unit may also display only the most important supply and demand plans. This allows the planning unit to improve user convenience by prioritizing supply and demand plans based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the planning unit may be performed using AI or not. For example, the planning unit can input user emotion data into a generative AI and have the generative AI prioritize supply and demand plans.

[0104] The planning department can formulate supply and demand plans by considering the user's past electricity usage data in addition to electricity market data. For example, the planning department can formulate an optimal supply and demand plan based on the user's past electricity usage data. The planning department can also formulate an optimal supply and demand plan by analyzing the user's past electricity usage patterns. The planning department can also formulate the most efficient supply and demand plan by considering the user's past electricity usage data. In this way, the planning department can formulate a more optimal supply and demand plan by considering the user's past electricity usage data. Some or all of the above processes in the planning department may be performed using or without a generation AI. For example, the planning department can input the user's past electricity usage data into a generation AI and have the generation AI formulate a supply and demand plan.

[0105] The planning department can formulate both supply and demand plans and price plans simultaneously, and propose the optimal procurement timing. For example, the planning department can formulate supply and demand plans and price plans simultaneously and propose the optimal procurement timing. The planning department can also propose the most cost-effective procurement timing based on the supply and demand plans and price plans. The planning department can also analyze the supply and demand plans and price plans in detail and propose the optimal procurement timing. In this way, the planning department can propose the optimal procurement timing by formulating supply and demand plans and price plans simultaneously. Some or all of the above processes in the planning department may be performed using or without a generation AI. For example, the planning department can input supply and demand plan and price plan data into a generation AI and have the generation AI propose the optimal procurement timing.

[0106] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0107] The reception desk can estimate the user's emotions and customize the input interface for power procurement requirements based on those emotions. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. If the user is in a hurry, it can prioritize voice input to allow for quick input of power procurement requirements. In this way, the reception desk can improve user convenience by providing an input interface that responds to the user's emotions.

[0108] The generation unit can generate procurement plans by considering not only electricity market data but also the user's past electricity usage data. For example, it can generate the optimal procurement plan based on the user's past electricity usage data. It can also analyze the user's past electricity usage patterns and generate the optimal procurement plan. It can also generate the most efficient procurement plan by considering the user's past electricity usage data. In this way, the generation unit can generate more optimal procurement plans by considering the user's past electricity usage data.

[0109] The execution unit can estimate the user's emotions and adjust the power procurement process based on those emotions. For example, if the user is relaxed, it can provide an execution method that includes detailed explanations. If the user is in a hurry, it can provide a concise and quick execution method. If the user is excited, it can provide an execution method with visually appealing effects. In this way, the execution unit can improve user convenience by adjusting the power procurement process based on the user's emotions.

[0110] The reception desk can analyze past power procurement history and suggest the most suitable input format for the user. For example, it can automatically display power procurement requirements that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). It can even predict and suggest requirements to be used during specific time periods based on the user's past power procurement history. In this way, the reception desk can suggest the most suitable input format for the user by analyzing past power procurement history.

[0111] The generation unit can generate procurement plans that consider the supply status of other energy sources in addition to the supply status of renewable energy. For example, it can generate procurement plans that consider the supply status of fossil fuels in addition to renewable energy. It can also generate procurement plans that consider the supply status of nuclear power in addition to renewable energy. It can also generate procurement plans that consider the supply status of hydroelectric power in addition to renewable energy. In this way, the generation unit can generate more comprehensive procurement plans by considering the supply status of other energy sources as well.

[0112] The reception desk can estimate the user's emotions and automatically prioritize input requirements based on those emotions. For example, if the user is stressed, it can be set to prioritize important requirements. If the user is relaxed, it can be set to prioritize detailed requirements. If the user is in a hurry, it can be set to prioritize only the most important requirements. In this way, the reception desk can improve user convenience by prioritizing input requirements based on the user's emotions.

[0113] The execution unit can propose optimal contract terms by referring to the user's past contract history when executing power procurement. For example, it can propose optimal contract terms based on the contract terms the user has previously entered into. It can also propose the most advantageous contract terms from the user's past contract history. It can also analyze the user's past contract history and propose the most efficient contract terms. In this way, the execution unit can propose optimal contract terms by referring to the user's past contract history.

[0114] The generation unit can estimate the user's emotions and adjust how the procurement plan is presented based on those emotions. For example, if the user is relaxed, it can generate a procurement plan with detailed explanations. If the user is in a hurry, it can generate a concise and to-the-point procurement plan. If the user is excited, it can generate a procurement plan with visually appealing effects. In this way, the generation unit can deepen the user's understanding by adjusting how the procurement plan is presented based on the user's emotions.

[0115] The forecasting unit can perform supply and demand forecasts by considering weather data in addition to electricity market data. For example, it can predict fluctuations in electricity demand and perform supply and demand forecasts based on weather data. It can also predict the supply status of renewable energy and perform supply and demand forecasts based on weather data. It can also improve the accuracy of supply and demand forecasts by analyzing weather data in detail. In this way, the forecasting unit can improve the accuracy of supply and demand forecasts by considering weather data.

[0116] The planning department can estimate the user's emotions and adjust the way the supply and demand plan is displayed based on those emotions. For example, if the user is stressed, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that gets straight to the point can be provided. In this way, the planning department can deepen the user's understanding by adjusting the way the supply and demand plan is displayed based on the user's emotions.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The reception desk receives the user's electricity procurement requirements. Users can enter the required amount of electricity, budget, and preferred power source (such as renewable energy). For example, they can enter requirements such as "I would like to procure 1000 kWh of electricity per year within my budget." Step 2: The generation unit analyzes the requirements entered by the reception unit and generates an optimal decarbonization electricity procurement plan. The generation unit calculates the optimal procurement plan based on electricity market data and renewable energy supply status. For example, it generates an optimal procurement plan by considering periods when renewable energy supply is high or prices are low. The generation unit is also responsible for complex operations such as supply and demand forecasting, price forecasting, and supply and demand planning. Step 3: The execution unit performs power procurement based on the plan generated by the generation unit. The execution unit can enter into contracts to procure renewable energy according to the plan proposed by the generation AI. It can also perform power procurement based on the optimal procurement timing proposed by the generation AI.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0121] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0122] Each of the multiple elements described above, including the reception unit, generation unit, and execution unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, where the user inputs the requirements for power procurement. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, where an optimal decarbonized power procurement plan is generated using generation AI. The execution unit is implemented, for example, by the control unit 46A of the smart device 14, where power procurement is carried out based on the generated plan. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0123] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0124] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements described above, including the reception unit, generation unit, and execution unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, where the user inputs the requirements for power procurement. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, where an optimal decarbonized power procurement plan is generated using generated AI. The execution unit is implemented, for example, by the control unit 46A of the smart glasses 214, where power procurement is carried out based on the generated plan. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0139] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0140] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0147] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements, including the reception unit, generation unit, and execution unit described above, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, where the user inputs the requirements for power procurement. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where an optimal decarbonized power procurement plan is generated using a generated AI. The execution unit is implemented by the control unit 46A of the headset terminal 314, where power procurement is carried out based on the generated plan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0155] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0156] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0159] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0161] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0162] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0163] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0164] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0165] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0166] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0167] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0169] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0170] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0171] Each of the multiple elements described above, including the reception unit, generation unit, and execution unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, where the user inputs the requirements for power procurement. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which generates an optimal decarbonized power procurement plan using generated AI. The execution unit is implemented by, for example, the control unit 46A of the robot 414, which executes power procurement based on the generated plan. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0172] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0174] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0175] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0176] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0180] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0181] 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.

[0182] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0183] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0184] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0185] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0187] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0188] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0189] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0190] (Note 1) A reception desk where the requirements for power procurement are entered, A generation unit analyzes the requirements entered by the reception unit and generates an optimal decarbonization power procurement plan, The system includes an execution unit that performs power procurement based on the plan generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is We generate an optimal procurement plan based on electricity market data and renewable energy supply status. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is It is equipped with a forecasting unit that performs supply and demand forecasting and price forecasting. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is It has a planning department that formulates supply and demand plans. The system described in Appendix 1, characterized by the features described herein. (Note 5) The execution unit is, A contract is signed to procure renewable energy based on the generated plan. The system described in Appendix 1, characterized by the features described herein. (Note 6) The execution unit is, Power procurement is performed based on the optimal procurement timing proposed by the generation AI. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates user sentiment and customizes the input interface for power procurement requirements based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We analyze past power procurement history and propose the optimal input format for the user. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It monitors the user's power usage patterns in real time and suggests the optimal input timing. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and automatically prioritizes input requirements based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is The system automatically suggests region-specific power procurement requirements, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is Analyze users' social media activity and automatically enter relevant power procurement requirements. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates user sentiment and adjusts how procurement plans are presented based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is We update electricity market data in real time and generate the optimal procurement plan. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is We analyze the renewable energy supply situation in detail and generate optimal procurement plans for each season. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is Estimate user sentiment and prioritize procurement plans based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is In addition to electricity market data, the system generates procurement plans by considering the user's past electricity usage data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is In addition to the supply status of renewable energy, procurement plans are generated by considering the supply status of other energy sources. The system described in Appendix 1, characterized by the features described herein. (Note 19) The execution unit is, It estimates user sentiment and adjusts how power procurement is performed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The execution unit is, When procuring electricity, we refer to the user's past contract history to propose the most suitable contract terms. The system described in Appendix 1, characterized by the features described herein. (Note 21) The execution unit is, When procuring electricity, the system monitors the user's current electricity usage in real time and proposes the optimal procurement timing. The system described in Appendix 1, characterized by the features described herein. (Note 22) The execution unit is, The system estimates user sentiment and prioritizes power procurement based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The execution unit is, When procuring electricity, we propose optimal contract terms that take into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The execution unit is, When procuring electricity, we analyze users' social media activity and propose relevant contract terms. The system described in Appendix 1, characterized by the features described herein. (Note 25) The prediction unit, The system estimates user sentiment and adjusts the display method of supply and demand forecasts based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 26) The prediction unit, By analyzing historical supply and demand data in detail, we can improve the accuracy of future supply and demand forecasts. The system described in Appendix 3, characterized by the features described herein. (Note 27) The prediction unit, We monitor electricity market trends in real time and update supply and demand forecasts. The system described in Appendix 3, characterized by the features described herein. (Note 28) The prediction unit, The system estimates user sentiment and prioritizes supply and demand forecasts based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 29) The prediction unit, In addition to electricity market data, weather data is also taken into consideration when forecasting supply and demand. The system described in Appendix 3, characterized by the features described herein. (Note 30) The prediction unit, In addition to supply and demand forecasts, we also forecast prices and propose the optimal procurement timing. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned planning department, The system estimates user sentiment and adjusts the display method of supply and demand plans based on the estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 32) The aforementioned planning department, By analyzing past supply and demand planning data in detail, we can improve the accuracy of future supply and demand planning. The system described in Appendix 4, characterized by the features described herein. (Note 33) The aforementioned planning department, We monitor electricity market trends in real time and update supply and demand plans accordingly. The system described in Appendix 4, characterized by the features described herein. (Note 34) The aforementioned planning department, The system estimates user sentiment and sets supply and demand priorities based on the estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 35) The aforementioned planning department, In addition to electricity market data, supply and demand plans are formulated by considering users' past electricity usage data. The system described in Appendix 4, characterized by the features described herein. (Note 36) The aforementioned planning department, In addition to supply and demand planning, we also develop pricing plans and propose the optimal procurement timing. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk where the requirements for power procurement are entered, A generation unit analyzes the requirements entered by the reception unit and generates an optimal decarbonization power procurement plan, The system includes an execution unit that performs power procurement based on the plan generated by the generation unit. A system characterized by the following features.

2. The generating unit is We generate an optimal procurement plan based on electricity market data and renewable energy supply status. The system according to feature 1.

3. The generating unit is It is equipped with a forecasting unit that performs supply and demand forecasting and price forecasting. The system according to feature 1.

4. The generating unit is It has a planning department that formulates supply and demand plans. The system according to feature 1.

5. The execution unit is, A contract is signed to procure renewable energy based on the generated plan. The system according to feature 1.

6. The execution unit is, Power procurement will be carried out based on the optimal procurement timing proposed by the generating AI. The system according to feature 1.

7. The aforementioned reception unit is It estimates user sentiment and customizes the input interface for power procurement requirements based on the estimated user sentiment. The system according to feature 1.

8. The aforementioned reception unit is We analyze past power procurement history and propose the optimal input format for the user. The system according to feature 1.

Images