System

Abstract

An object of a system according to an embodiment is to efficiently collect surplus energy and provide the surplus energy to an appropriate supply destination.SOLUTION: A system includes a collection unit, an analysis unit, an identification unit, and a provision unit. The collection unit collects surplus energy. The analysis unit analyzes the energy information collected by the collection unit. The specifying unit specifies an appropriate supply destination based on the information analyzed by the analyzing unit. The providing unit provides energy to the supply destination specified by the specifying unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately collect and distribute surplus energy efficiently, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently collect surplus energy and provide it to an appropriate supply destination. [Means for solving the problem]

[0006] A system according to an embodiment includes a collection unit, an analysis unit, an identification unit, and a provision unit. The collection unit collects surplus energy. The analysis unit analyzes the energy information collected by the collection unit. The identification unit identifies an appropriate supply destination based on the information analyzed by the analysis unit. The provision unit provides energy to the supply destination identified by the identification unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect surplus energy and provide it to an appropriate destination. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

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

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A virtual power plant system according to an embodiment of the present invention collects and analyzes surplus energy, identifies optimal supply destinations, and provides the energy. The virtual power plant system collects energy from businesses, regions, and individuals with surplus energy, analyzes it using a generation AI, identifies optimal supply destinations, and provides the energy. This mechanism expands the energy supply infrastructure and enables the provision of supply services without the need for power generation facilities. For example, the virtual power plant system collects energy from businesses, regions, and individuals with surplus energy. Examples of such energy include surplus energy from solar power generation and surplus energy from wind power generation. The virtual power plant system then analyzes the collected energy using a generation AI. The generation AI analyzes information such as the type and amount of collected energy and the available supply time periods to identify optimal supply destinations. For example, it identifies areas or customers with high energy demand and supplies energy to those areas or customers. The virtual power plant system then provides energy to the identified supply destinations. In this process, the system adjusts the energy supply method and schedule. Examples include supplying energy via the power grid and using batteries. This allows the virtual power plant system to expand the energy supply infrastructure and provide supply services that do not require power generation facilities. This allows the virtual power plant system to expand the energy supply infrastructure and provide supply services that do not require power generation facilities. For example, by supplying energy from rural areas with surplus energy to urban areas with high energy demand, efficient energy use can be achieved. In addition, since there is no need to install new power generation facilities, cost reductions can be expected.

[0029] The virtual power plant system according to the embodiment includes a collection unit, an analysis unit, an identification unit, and a provision unit. The collection unit collects surplus energy. Examples of surplus energy include, but are not limited to, surplus power generated by solar power generation, surplus power generated by wind power generation, and waste heat from factories. The collection unit collects, for example, the type and amount of energy and the time periods during which the energy can be supplied. The collection unit collects, for example, the type and amount of energy and the time periods during which the energy can be supplied. The analysis unit uses a generation AI to analyze the energy information collected by the collection unit. The analysis is performed, for example, based on a data analysis method and the accuracy of the analysis, but is not limited to, the example. The analysis unit analyzes, for example, the collected energy information and identifies an optimal supply destination. The identification unit uses the generation AI to identify an optimal supply destination based on the information analyzed by the analysis unit. The identification is performed, for example, based on areas with high energy demand or specific industries, but is not limited to, the example. The identification unit identifies, for example, areas or customers with high energy demand. The provision unit provides energy to the supply destination identified by the identification unit. The provision of energy includes, but is not limited to, a method of supplying energy through a power grid or a method of supplying energy using a battery, for example. The providing unit supplies energy through a power grid, for example. The providing unit can also supply energy using a battery. This enables the virtual power plant system according to the embodiment to efficiently collect, analyze, identify, and provide surplus energy.

[0030] The collection unit can collect the type or amount of energy and the time periods during which it can be supplied. The collection unit, for example, collects the type or amount of energy and the time periods during which it can be supplied. The type of energy includes, for example, electricity, thermal energy, chemical energy, etc. The amount of energy is measured in units such as kilowatt-hours (kWh) or joules (J). The time periods during which it can be supplied include, for example, peak time periods and off-peak time periods. By collecting detailed energy information, it becomes possible to identify the optimal supply destination. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the type or amount of energy and the time periods during which it can be supplied to AI, which then outputs the analysis results.

[0031] The analysis unit can analyze the collected energy information and identify an appropriate supply destination. The analysis unit, for example, analyzes the collected energy information and identifies an optimal supply destination. The analysis is performed, for example, based on a data analysis method and the accuracy of the analysis. Suitable supply destinations include, for example, areas with high energy demand and specific industries. This improves the accuracy of identifying the optimal supply destination by analyzing the collected energy information. Some or all of the above-described processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the collected energy information into a generation AI, which can identify the optimal supply destination.

[0032] The identification unit can identify areas or customers with high energy demand. The identification unit, for example, identifies areas or customers with high energy demand. Areas with high energy demand include, for example, urban areas and industrial areas. Customers include, for example, homes, businesses, and public facilities. By identifying areas and customers with high energy demand, efficient energy supply becomes possible. Some or all of the above-described processing in the identification unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the identification unit can input areas or customers with high energy demand into a generation AI, which can then identify the optimal supply destination.

[0033] The providing unit can supply energy through a power grid. The providing unit, for example, supplies energy through a power grid. Power grids include, for example, regional power grids and national power grids. As a result, supplying energy through a power grid enables a stable energy supply. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input a method for supplying energy through a power grid into AI, which can identify the optimal supply method.

[0034] The providing unit can supply energy using a battery. The providing unit supplies energy using, for example, a battery. Examples of batteries include lithium-ion batteries and lead-acid batteries. By using the battery, energy can be supplied even when the power grid is unavailable. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input a method of supplying energy using a battery into AI, which can identify the optimal supply method.

[0035] The collection unit can evaluate the quality and stability of energy at the time of collection and select an appropriate collection method. The collection unit, for example, evaluates the quality and stability of energy at the time of collection and selects the optimal collection method. Energy quality includes, for example, voltage stability and frequency stability. Energy stability includes, for example, continuity of supply and little fluctuation. For example, when the energy quality is high, the collection unit directly collects energy to efficiently obtain it. Furthermore, when the energy stability is low, the collection unit can temporarily store energy using a battery to ensure a stable supply. Furthermore, the collection unit can evaluate the quality and stability of energy in real time and dynamically select the optimal collection method. This enables efficient energy collection by evaluating the quality and stability of energy. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input data on the quality and stability of energy to a generation AI, which can select the optimal collection method.

[0036] The collection unit can optimize the collection method during collection by taking into account the geographical conditions of the energy source. For example, the collection unit optimizes the collection method during collection by taking into account the geographical conditions of the energy source. Geographical conditions include, for example, topography and climatic conditions. For example, if the energy source is in a mountainous area, the collection unit collects energy using a drone. Alternatively, if the energy source is in an urban area, the collection unit can collect energy through a power grid. The collection unit can also evaluate the geographical conditions of the energy source in real time and dynamically select an optimal collection method. This allows the optimal collection method to be selected by taking into account the geographical conditions of the energy source. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the geographical conditions of the energy source into a generation AI, which can select the optimal collection method.

[0037] The collection unit can analyze the past supply history of the energy supplier during collection to improve collection efficiency. For example, the collection unit analyzes the past supply history of the energy supplier during collection to improve collection efficiency. The past supply history includes, for example, fluctuations in supply amount and supply frequency. The collection unit, for example, predicts the optimal collection timing based on the past supply history of the supplier. The collection unit can also analyze the past supply history of the supplier and identify patterns for improving collection efficiency. The collection unit can also refer to the past supply history of the supplier to formulate a strategy for maximizing collection efficiency. In this way, collection efficiency is improved by analyzing the past supply history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the past supply history of the supplier into a generation AI, which can predict the optimal collection timing.

[0038] The collection unit can evaluate the environmental impact of the energy supplier at the time of collection and select an environmentally friendly collection method. For example, the collection unit can evaluate the environmental impact of the energy supplier at the time of collection and select an environmentally friendly collection method. Environmental impact includes, for example, CO2 emissions and environmental load. For example, if the supplier provides environmentally friendly energy, the collection unit can preferentially collect that energy. The collection unit can also evaluate the environmental impact of the supplier and select an environmentally friendly collection method. The collection unit can also evaluate the environmental impact of the supplier in real time and dynamically select an optimal collection method. In this way, an environmentally friendly collection method can be selected by evaluating the environmental impact of the supplier. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input data on the environmental impact of the supplier into a generation AI, which can select the optimal collection method.

[0039] The collection unit can customize the collection method at the time of collection by taking into account the contract terms of the energy supplier. For example, the collection unit customizes the collection method at the time of collection by taking into account the contract terms of the energy supplier. The contract terms include, for example, an upper limit on the supply amount and a supply time period. The collection unit customizes the collection method, for example, based on the contract terms of the supplier. The collection unit can also select an optimal collection method by taking into account the contract terms of the supplier. The collection unit can also evaluate the contract terms of the supplier in real time and dynamically select an optimal collection method. In this way, the optimal collection method can be selected by taking into account the contract terms of the supplier. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without using AI. For example, the collection unit can input data on the contract terms of the supplier into a generation AI, which can select an optimal collection method.

[0040] The collection unit can evaluate the technical capabilities of the energy supplier at the time of collection and select the optimal collection means. For example, the collection unit evaluates the technical capabilities of the energy supplier at the time of collection and selects the optimal collection means. Technical capabilities include, for example, the performance of the supply equipment and the skills of the technical staff. For example, the collection unit evaluates the technical capabilities of the supplier and selects the optimal collection means. The collection unit can also evaluate the technical capabilities of the supplier in real time and dynamically select the optimal collection means. The collection unit can also customize the collection means based on the technical capabilities of the supplier. In this way, the optimal collection means can be selected by evaluating the technical capabilities of the supplier. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the technical capabilities of the supplier to a generation AI, which can select the optimal collection means.

[0041] The analysis unit can apply different analysis algorithms for each type of energy during analysis. The analysis unit, for example, applies different analysis algorithms for each type of energy during analysis. Energy types include, for example, electric power, thermal energy, and chemical energy. The analysis unit applies a specific analysis algorithm for energy generated by solar power, for example. The analysis unit can also apply a different analysis algorithm for energy generated by wind power. The analysis unit can also dynamically select an optimal analysis algorithm for each type of energy. This improves the accuracy of the analysis by applying the optimal analysis algorithm for each type of energy. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input different analysis algorithms for each type of energy into the generation AI, and the generation AI can apply the optimal analysis algorithm.

[0042] The analysis unit can improve the accuracy of the analysis by taking into account the characteristics of the energy supplier during the analysis. The analysis unit, for example, improves the accuracy of the analysis by taking into account the characteristics of the energy supplier during the analysis. The characteristics of the supplier include, for example, supply capacity and supply stability. The analysis unit, for example, improves the accuracy of the analysis by taking into account the characteristics of the supplier. The analysis unit can also evaluate the characteristics of the supplier in real time and dynamically improve the accuracy of the analysis. The analysis unit can also customize the analysis algorithm based on the characteristics of the supplier. In this way, the accuracy of the analysis is improved by taking into account the characteristics of the supplier. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the characteristics of the supplier into the generation AI, and the generation AI can apply an optimal analysis algorithm.

[0043] The analysis unit can optimize the analysis algorithm by referring to past data of the energy supplier during analysis. The analysis unit, for example, optimizes the analysis algorithm by referring to past data of the energy supplier during analysis. The past data includes, for example, fluctuations in supply amount and supply frequency. The analysis unit, for example, optimizes the analysis algorithm based on the past data of the supplier. The analysis unit can also dynamically optimize the analysis algorithm by referring to the past data of the supplier in real time. The analysis unit can also analyze the past data of the supplier and select an optimal analysis algorithm. This makes it possible to optimize the analysis algorithm by referring to the past data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the past data of the supplier into the generation AI, which can then apply the optimal analysis algorithm.

[0044] The analysis unit may perform the analysis while taking into account the geographical conditions of the energy supplier. For example, the analysis unit may perform the analysis while taking into account the geographical conditions of the energy supplier. Geographical conditions include, for example, topography and climatic conditions. For example, the analysis unit may apply a specific analysis algorithm when the supplier is in a mountainous area. The analysis unit may also apply a different analysis algorithm when the supplier is in an urban area. The analysis unit may also evaluate the geographical conditions of the supplier in real time and dynamically select an optimal analysis algorithm. This enables optimal analysis by taking into account the geographical conditions of the supplier. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit may input data on the geographical conditions of the supplier into the generation AI, which may then apply an optimal analysis algorithm.

[0045] The analysis unit can customize the analysis method during analysis by taking into account the contract terms of the energy supplier. For example, the analysis unit customizes the analysis method during analysis by taking into account the contract terms of the energy supplier. The contract terms include, for example, an upper limit on the supply amount and a supply time period. The analysis unit customizes the analysis method, for example, based on the contract terms of the supplier. The analysis unit can also select an optimal analysis method by taking into account the contract terms of the supplier. The analysis unit can also evaluate the contract terms of the supplier in real time and dynamically select an optimal analysis method. This makes it possible to select an optimal analysis method by taking into account the contract terms of the supplier. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input data on the contract terms of the supplier into the generation AI, which can select an optimal analysis method.

[0046] The analysis unit can evaluate the technical capabilities of the energy supplier during analysis and select the optimal analysis method. The analysis unit, for example, evaluates the technical capabilities of the energy supplier during analysis and selects the optimal analysis method. Technical capabilities include, for example, the performance of the supply equipment and the skills of the technical staff. The analysis unit, for example, evaluates the technical capabilities of the supplier and selects the optimal analysis method. The analysis unit can also evaluate the technical capabilities of the supplier in real time and dynamically select the optimal analysis method. The analysis unit can also customize the analysis method based on the technical capabilities of the supplier. In this way, the optimal analysis method can be selected by evaluating the technical capabilities of the supplier. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the technical capabilities of the supplier into the generation AI, which can select the optimal analysis method.

[0047] The identification unit can predict fluctuations in energy demand at the time of identification and identify an appropriate supply destination. The identification unit, for example, predicts fluctuations in energy demand at the time of identification and identifies an optimal supply destination. Energy demand fluctuations include, for example, seasonal fluctuations and demand by time of day. The identification unit, for example, predicts fluctuations in energy demand and identifies an optimal supply destination. The identification unit can also evaluate fluctuations in energy demand in real time and dynamically identify an optimal supply destination. The identification unit can also customize the supply destination based on fluctuations in energy demand. This improves the accuracy of identifying the optimal supply destination by predicting fluctuations in energy demand. Some or all of the above-described processing in the identification unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the identification unit can input data on fluctuations in energy demand into a generation AI, which can then identify the optimal supply destination.

[0048] The identification unit can improve the accuracy of identification by taking into account the geographical conditions of the supply destination during identification. For example, the identification unit improves the accuracy of identification by taking into account the geographical conditions of the supply destination during identification. Geographical conditions include, for example, topography and climatic conditions. For example, the identification unit improves the accuracy of identification by taking into account the geographical conditions of the supply destination. The identification unit can also evaluate the geographical conditions of the supply destination in real time and dynamically improve the accuracy of identification. The identification unit can also customize the identification algorithm based on the geographical conditions of the supply destination. In this way, the accuracy of identification is improved by taking into account the geographical conditions of the supply destination. Some or all of the above-described processing in the identification unit may be performed using, or without, a generation AI. For example, the identification unit can input data on the geographical conditions of the supply destination into the generation AI, which can then apply an optimal identification algorithm.

[0049] The identification unit can optimize the identification algorithm by referring to the past energy consumption data of the supply destination at the time of identification. For example, the identification unit optimizes the identification algorithm by referring to the past energy consumption data of the supply destination at the time of identification. The past energy consumption data includes, for example, fluctuations in consumption amount and frequency of consumption. The identification unit optimizes the identification algorithm based on, for example, the past energy consumption data of the supply destination. The identification unit can also dynamically optimize the identification algorithm by referring to the past energy consumption data of the supply destination in real time. The identification unit can also analyze the past energy consumption data of the supply destination and select an optimal identification algorithm. This makes it possible to optimize the identification algorithm by referring to the past energy consumption data. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the identification unit can input the past energy consumption data of the supply destination into the generation AI, and the generation AI can apply the optimal identification algorithm.

[0050] The identification unit can evaluate the environmental impact of the supply destination at the time of identification and identify an environmentally friendly supply destination. For example, the identification unit evaluates the environmental impact of the supply destination at the time of identification and identify an environmentally friendly supply destination. The environmental impact includes, for example, CO2 emissions and environmental load. For example, the identification unit can evaluate the environmental impact of the supply destination and identify an environmentally friendly supply destination. The identification unit can also evaluate the environmental impact of the supply destination in real time and dynamically identify an optimal supply destination. The identification unit can also customize the supply destination based on the environmental impact of the supply destination. In this way, an environmentally friendly supply destination can be identified by evaluating the environmental impact of the supply destination. Some or all of the above-mentioned processing in the identification unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the identification unit can input data on the environmental impact of the supply destination into the generation AI, and the generation AI can identify the optimal supply destination.

[0051] The identification unit can customize the identification method during identification, taking into account the contractual conditions of the supply destination. For example, the identification unit customizes the identification method during identification, taking into account the contractual conditions of the supply destination. The contractual conditions include, for example, an upper limit on the supply volume and a supply time period. The identification unit customizes the identification method, for example, based on the contractual conditions of the supply destination. The identification unit can also select an optimal identification method by taking into account the contractual conditions of the supply destination. The identification unit can also evaluate the contractual conditions of the supply destination in real time and dynamically select an optimal identification method. This makes it possible to select an optimal identification method by taking into account the contractual conditions of the supply destination. Some or all of the above-described processing in the identification unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the identification unit can input data on the contractual conditions of the supply destination into the generation AI, which can select the optimal identification method.

[0052] The identification unit can evaluate the technical capabilities of the supply destination at the time of identification and identify the optimal supply destination. The identification unit, for example, evaluates the technical capabilities of the supply destination at the time of identification and identifies the optimal supply destination. Technical capabilities include, for example, the performance of the supply equipment and the skills of the technical staff. The identification unit, for example, evaluates the technical capabilities of the supply destination and identifies the optimal supply destination. The identification unit can also evaluate the technical capabilities of the supply destination in real time and dynamically identify the optimal supply destination. The identification unit can also customize the supply destination based on the technical capabilities of the supply destination. In this way, the optimal supply destination can be identified by evaluating the technical capabilities of the supply destination. Some or all of the above-mentioned processing in the identification unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the identification unit can input data on the technical capabilities of the supply destination into the generation AI, which can identify the optimal supply destination.

[0053] The providing unit can apply an algorithm for streamlining the energy supply schedule when providing the energy supply schedule. The providing unit, for example, applies an algorithm for optimizing the energy supply schedule when providing the energy supply schedule. The supply schedule includes, for example, adjustment of supply timing and supply amount. The providing unit, for example, applies an algorithm for optimizing the energy supply schedule. The providing unit can also evaluate the energy supply schedule in real time and dynamically adjust the optimal schedule. The providing unit can also customize the supply method based on the energy supply schedule. This enables efficient energy supply by optimizing the supply schedule. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input supply schedule data to a generating AI, which can then apply the optimal supply schedule.

[0054] The providing unit can predict fluctuations in the energy demand of the supply destination and adjust the supply method when providing the data. The providing unit, for example, predicts fluctuations in the energy demand of the supply destination and adjusts the supply method when providing the data. Fluctuations in energy demand include, for example, seasonal fluctuations and demand by time of day. The providing unit, for example, predicts fluctuations in the energy demand of the supply destination and adjusts the supply method. The providing unit can also evaluate fluctuations in the energy demand of the supply destination in real time and dynamically adjust the optimal supply method. The providing unit can also customize the supply method based on fluctuations in the energy demand of the supply destination. In this way, the optimal supply method can be selected by predicting fluctuations in energy demand. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input data on fluctuations in the energy demand of the supply destination to a generating AI, which can then apply the optimal supply method.

[0055] The providing unit can optimize the supply method by referring to the past energy consumption data of the supply destination when providing the data. The providing unit, for example, optimizes the supply method by referring to the past energy consumption data of the supply destination when providing the data. The past energy consumption data includes, for example, fluctuations in consumption amount and frequency of consumption. The providing unit, for example, optimizes the supply method based on the past energy consumption data of the supply destination. The providing unit can also dynamically optimize the supply method by referring to the past energy consumption data of the supply destination in real time. The providing unit can also analyze the past energy consumption data of the supply destination and select the optimal supply method. This makes it possible to optimize the supply method by referring to the past energy consumption data. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the past energy consumption data of the supply destination to the generation AI, which can then apply the optimal supply method.

[0056] The providing unit can customize the supply method at the time of provision, taking into account the geographical conditions of the supply destination. For example, the providing unit customizes the supply method at the time of provision, taking into account the geographical conditions of the supply destination. Geographical conditions include, for example, topography, climatic conditions, and the like. For example, the providing unit applies a specific supply method when the supply destination is in a mountainous area. The providing unit can also apply a different supply method when the supply destination is in an urban area. The providing unit can also evaluate the geographical conditions of the supply destination in real time and dynamically select the optimal supply method. This makes it possible to select the optimal supply method by taking into account the geographical conditions of the supply destination. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input data on the geographical conditions of the supply destination into the generating AI, and the generating AI can apply the optimal supply method.

[0057] The providing unit can adjust the supply method at the time of provision, taking into account the contract conditions of the supply destination. For example, the providing unit adjusts the supply method at the time of provision, taking into account the contract conditions of the supply destination. The contract conditions include, for example, an upper limit on the supply amount and a supply time period. The providing unit adjusts the supply method, for example, based on the contract conditions of the supply destination. The providing unit can also select the optimal supply method by taking into account the contract conditions of the supply destination. The providing unit can also evaluate the contract conditions of the supply destination in real time and dynamically select the optimal supply method. In this way, the optimal supply method can be selected by taking into account the contract conditions of the supply destination. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input data on the contract conditions of the supply destination into a generating AI, which can then apply the optimal supply method.

[0058] The provision unit can evaluate the technical capabilities of the supply destination at the time of provision and select the optimal supply means. For example, the provision unit evaluates the technical capabilities of the supply destination at the time of provision and selects the optimal supply means. Technical capabilities include, for example, the performance of the supply equipment and the skills of the technical staff. For example, the provision unit evaluates the technical capabilities of the supply destination and selects the optimal supply means. The provision unit can also evaluate the technical capabilities of the supply destination in real time and dynamically select the optimal supply means. The provision unit can also customize the supply means based on the technical capabilities of the supply destination. In this way, the optimal supply means can be selected by evaluating the technical capabilities of the supply destination. Some or all of the above-mentioned processing in the provision unit may be performed, for example, using AI or without AI. For example, the provision unit can input data on the technical capabilities of the supply destination into a generation AI, which can then apply the optimal supply means.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] When collecting energy, the collection unit can learn the energy generation pattern of the supplier and predict the optimal collection timing. For example, in the case of a solar power supplier, the optimal collection timing can be predicted based on sunlight hours and weather data. In the case of a wind power supplier, the collection timing can also be adjusted based on wind speed and direction data. Furthermore, when using waste heat from a factory, the collection timing can be optimized taking into account the factory's operating schedule. This allows the collection unit to learn the energy generation pattern of the supplier and enable efficient energy collection.

[0061] When analyzing energy, the analysis unit can learn the energy generation patterns of the supplier and apply the optimal analysis algorithm. For example, in the case of a solar power supplier, the analysis algorithm can be adjusted based on sunlight hours and weather data. In the case of a wind power supplier, the analysis algorithm can also be optimized based on wind speed and direction data. Furthermore, when using waste heat from a factory, the analysis algorithm can be customized taking into account the factory's operating schedule. This allows the analysis unit to learn the energy generation patterns of the supplier and improve the accuracy of the analysis.

[0062] The identification unit can analyze past energy consumption data and build a demand forecasting model to predict fluctuations in energy demand. For example, it can analyze seasonal energy consumption patterns and predict peak demand periods. It can also predict demand fluctuations during the day and night based on energy consumption data by time of day. Furthermore, it can analyze energy consumption data for specific industries or regions and customize the demand forecasting model. This allows the identification unit to predict fluctuations in energy demand using past data and identify optimal supply destinations.

[0063] When supplying energy, the provider can learn the energy consumption patterns of the destination and optimize the supply schedule. For example, it can analyze the energy consumption patterns of households and supply energy during peak hours. It can also set a supply schedule based on business hours based on a company's energy consumption data. Furthermore, it can take into account the energy consumption patterns of public facilities and adjust the supply schedule to suit events and special occasions. This allows the provider to learn the energy consumption patterns of the destination and enable efficient energy supply.

[0064] When collecting energy, the collection unit can learn the energy generation pattern of the supplier and predict the optimal collection timing. For example, in the case of a solar power supplier, the optimal collection timing can be predicted based on sunlight hours and weather data. In the case of a wind power supplier, the collection timing can also be adjusted based on wind speed and direction data. Furthermore, when using waste heat from a factory, the collection timing can be optimized taking into account the factory's operating schedule. This allows the collection unit to learn the energy generation pattern of the supplier and enable efficient energy collection.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection unit collects surplus energy. Examples of surplus energy include surplus power generated by solar power generation, surplus power generated by wind power generation, and waste heat from factories. The collection unit collects information on the type and amount of energy and the time periods during which it can be supplied. Step 2: The analysis unit uses the generation AI to analyze the energy information collected by the collection unit. The analysis is performed based on the data analysis method and accuracy of the analysis. The analysis unit analyzes the collected energy information and identifies the optimal supply destination. Step 3: The Identification Unit uses the Generative AI to identify optimal supply destinations based on the information analyzed by the Analysis Unit. Identification is based on areas with high energy demand, specific industries, etc. The Identification Unit identifies areas and customers with high energy demand. Step 4: The providing unit provides energy to the supply destination identified by the identifying unit. The providing includes a method of supplying energy through a power grid or a method of supplying energy using a battery. The providing unit can also supply energy through a power grid or using a battery.

[0067] (Example 2) A virtual power plant system according to an embodiment of the present invention collects and analyzes surplus energy, identifies optimal supply destinations, and provides the energy. The virtual power plant system collects energy from businesses, regions, and individuals with surplus energy, analyzes it using a generation AI, identifies optimal supply destinations, and provides the energy. This mechanism expands the energy supply infrastructure and enables the provision of supply services without the need for power generation facilities. For example, the virtual power plant system collects energy from businesses, regions, and individuals with surplus energy. Examples of such energy include surplus energy from solar power generation and surplus energy from wind power generation. The virtual power plant system then analyzes the collected energy using a generation AI. The generation AI analyzes information such as the type and amount of collected energy and the available supply time periods to identify optimal supply destinations. For example, it identifies areas or customers with high energy demand and supplies energy to those areas or customers. The virtual power plant system then provides energy to the identified supply destinations. In this process, the system adjusts the energy supply method and schedule. Examples include supplying energy via the power grid and using batteries. This allows the virtual power plant system to expand the energy supply infrastructure and provide supply services that do not require power generation facilities. This allows the virtual power plant system to expand the energy supply infrastructure and provide supply services that do not require power generation facilities. For example, by supplying energy from rural areas with surplus energy to urban areas with high energy demand, efficient energy use can be achieved. In addition, since there is no need to install new power generation facilities, cost reductions can be expected.

[0068] The virtual power plant system according to the embodiment includes a collection unit, an analysis unit, an identification unit, and a provision unit. The collection unit collects surplus energy. Examples of surplus energy include, but are not limited to, surplus power generated by solar power generation, surplus power generated by wind power generation, and waste heat from factories. The collection unit collects, for example, the type and amount of energy and the time periods during which the energy can be supplied. The collection unit collects, for example, the type and amount of energy and the time periods during which the energy can be supplied. The analysis unit uses a generation AI to analyze the energy information collected by the collection unit. The analysis is performed, for example, based on a data analysis method and the accuracy of the analysis, but is not limited to, the example. The analysis unit analyzes, for example, the collected energy information and identifies an optimal supply destination. The identification unit uses the generation AI to identify an optimal supply destination based on the information analyzed by the analysis unit. The identification is performed, for example, based on areas with high energy demand or specific industries, but is not limited to, the example. The identification unit identifies, for example, areas or customers with high energy demand. The provision unit provides energy to the supply destination identified by the identification unit. The provision of energy includes, but is not limited to, a method of supplying energy through a power grid or a method of supplying energy using a battery, for example. The providing unit supplies energy through a power grid, for example. The providing unit can also supply energy using a battery. This enables the virtual power plant system according to the embodiment to efficiently collect, analyze, identify, and provide surplus energy.

[0069] The collection unit can collect the type or amount of energy and the time periods during which it can be supplied. The collection unit, for example, collects the type or amount of energy and the time periods during which it can be supplied. The type of energy includes, for example, electricity, thermal energy, chemical energy, etc. The amount of energy is measured in units such as kilowatt-hours (kWh) or joules (J). The time periods during which it can be supplied include, for example, peak time periods and off-peak time periods. By collecting detailed energy information, it becomes possible to identify the optimal supply destination. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the type or amount of energy and the time periods during which it can be supplied to AI, which then outputs the analysis results.

[0070] The analysis unit can analyze the collected energy information and identify an appropriate supply destination. The analysis unit, for example, analyzes the collected energy information and identifies an optimal supply destination. The analysis is performed, for example, based on a data analysis method and the accuracy of the analysis. Suitable supply destinations include, for example, areas with high energy demand and specific industries. This improves the accuracy of identifying the optimal supply destination by analyzing the collected energy information. Some or all of the above-described processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the collected energy information into a generation AI, which can identify the optimal supply destination.

[0071] The identification unit can identify areas or customers with high energy demand. The identification unit, for example, identifies areas or customers with high energy demand. Areas with high energy demand include, for example, urban areas and industrial areas. Customers include, for example, homes, businesses, and public facilities. By identifying areas and customers with high energy demand, efficient energy supply becomes possible. Some or all of the above-described processing in the identification unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the identification unit can input areas or customers with high energy demand into a generation AI, which can then identify the optimal supply destination.

[0072] The providing unit can supply energy through a power grid. The providing unit, for example, supplies energy through a power grid. Power grids include, for example, regional power grids and national power grids. As a result, supplying energy through a power grid enables a stable energy supply. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input a method for supplying energy through a power grid into AI, which can identify the optimal supply method.

[0073] The providing unit can supply energy using a battery. The providing unit supplies energy using, for example, a battery. Examples of batteries include lithium-ion batteries and lead-acid batteries. By using the battery, energy can be supplied even when the power grid is unavailable. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input a method of supplying energy using a battery into AI, which can identify the optimal supply method.

[0074] The collection unit can estimate the user's emotion and adjust the timing of energy collection based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and adjusts the timing of energy collection based on the estimated user's emotion. User emotions include, for example, stress, relaxation, and hurry. For example, when the user is feeling stressed, the collection unit delays the timing of energy collection to reduce the burden on the user. Furthermore, when the user is relaxed, the collection unit can advance the timing of energy collection to efficiently collect energy. Furthermore, when the user is hurrying, the collection unit can optimize the timing of energy collection to quickly collect energy. In this way, the burden on the user is reduced by adjusting the timing of energy collection according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotional data into the generation AI, which can then adjust the timing of energy collection.

[0075] The collection unit can evaluate the quality and stability of energy at the time of collection and select an appropriate collection method. The collection unit, for example, evaluates the quality and stability of energy at the time of collection and selects the optimal collection method. Energy quality includes, for example, voltage stability and frequency stability. Energy stability includes, for example, continuity of supply and little fluctuation. For example, when the energy quality is high, the collection unit directly collects energy to efficiently obtain it. Furthermore, when the energy stability is low, the collection unit can temporarily store energy using a battery to ensure a stable supply. Furthermore, the collection unit can evaluate the quality and stability of energy in real time and dynamically select the optimal collection method. This enables efficient energy collection by evaluating the quality and stability of energy. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input data on the quality and stability of energy to a generation AI, which can select the optimal collection method.

[0076] The collection unit can optimize the collection method during collection by taking into account the geographical conditions of the energy source. For example, the collection unit optimizes the collection method during collection by taking into account the geographical conditions of the energy source. Geographical conditions include, for example, topography and climatic conditions. For example, if the energy source is in a mountainous area, the collection unit collects energy using a drone. Alternatively, if the energy source is in an urban area, the collection unit can collect energy through a power grid. The collection unit can also evaluate the geographical conditions of the energy source in real time and dynamically select an optimal collection method. This allows the optimal collection method to be selected by taking into account the geographical conditions of the energy source. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the geographical conditions of the energy source into a generation AI, which can select the optimal collection method.

[0077] The collection unit can analyze the past supply history of the energy supplier during collection to improve collection efficiency. For example, the collection unit analyzes the past supply history of the energy supplier during collection to improve collection efficiency. The past supply history includes, for example, fluctuations in supply amount and supply frequency. The collection unit, for example, predicts the optimal collection timing based on the past supply history of the supplier. The collection unit can also analyze the past supply history of the supplier and identify patterns for improving collection efficiency. The collection unit can also refer to the past supply history of the supplier to formulate a strategy for maximizing collection efficiency. In this way, collection efficiency is improved by analyzing the past supply history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the past supply history of the supplier into a generation AI, which can predict the optimal collection timing.

[0078] The collection unit can estimate the user's emotions and determine the priority of the energy to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of the energy to be collected based on the estimated user emotions. User emotions include, for example, stress, relaxation, and hurry. For example, when the user is feeling stressed, the collection unit sets the priority of the energy to be collected low to reduce the burden on the user. Furthermore, when the user is relaxed, the collection unit can set the priority of the energy to be collected high to efficiently collect energy. Furthermore, when the user is in a hurry, the collection unit can optimize the priority of the energy to be collected and collect energy quickly. This enables efficient energy collection by determining the priority of energy according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotional data into the generation AI, which can then determine energy priorities.

[0079] The collection unit can evaluate the environmental impact of the energy supplier at the time of collection and select an environmentally friendly collection method. For example, the collection unit can evaluate the environmental impact of the energy supplier at the time of collection and select an environmentally friendly collection method. Environmental impact includes, for example, CO2 emissions and environmental load. For example, if the supplier provides environmentally friendly energy, the collection unit can preferentially collect that energy. The collection unit can also evaluate the environmental impact of the supplier and select an environmentally friendly collection method. The collection unit can also evaluate the environmental impact of the supplier in real time and dynamically select an optimal collection method. In this way, an environmentally friendly collection method can be selected by evaluating the environmental impact of the supplier. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input data on the environmental impact of the supplier into a generation AI, which can select the optimal collection method.

[0080] The collection unit can customize the collection method at the time of collection by taking into account the contract terms of the energy supplier. For example, the collection unit customizes the collection method at the time of collection by taking into account the contract terms of the energy supplier. The contract terms include, for example, an upper limit on the supply amount and a supply time period. The collection unit customizes the collection method, for example, based on the contract terms of the supplier. The collection unit can also select an optimal collection method by taking into account the contract terms of the supplier. The collection unit can also evaluate the contract terms of the supplier in real time and dynamically select an optimal collection method. In this way, the optimal collection method can be selected by taking into account the contract terms of the supplier. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without using AI. For example, the collection unit can input data on the contract terms of the supplier into a generation AI, which can select an optimal collection method.

[0081] The collection unit can evaluate the technical capabilities of the energy supplier at the time of collection and select the optimal collection means. For example, the collection unit evaluates the technical capabilities of the energy supplier at the time of collection and selects the optimal collection means. Technical capabilities include, for example, the performance of the supply equipment and the skills of the technical staff. For example, the collection unit evaluates the technical capabilities of the supplier and selects the optimal collection means. The collection unit can also evaluate the technical capabilities of the supplier in real time and dynamically select the optimal collection means. The collection unit can also customize the collection means based on the technical capabilities of the supplier. In this way, the optimal collection means can be selected by evaluating the technical capabilities of the supplier. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the technical capabilities of the supplier to a generation AI, which can select the optimal collection means.

[0082] The analysis unit can estimate the user's emotion and adjust the display method of the analysis results based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the display method of the analysis results based on the estimated user's emotion. User emotions include, for example, tension, relaxation, and hurry. For example, if the user is tension, the analysis unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is hurrying, the analysis unit can provide a display method that focuses on the main points. This allows the display method of the analysis results to be adjusted according to the user's emotion, enabling a display that is easy for the user to view. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's emotional data into the generation AI, which can then adjust how the analysis results are displayed.

[0083] The analysis unit can apply different analysis algorithms for each type of energy during analysis. The analysis unit, for example, applies different analysis algorithms for each type of energy during analysis. Energy types include, for example, electric power, thermal energy, and chemical energy. The analysis unit applies a specific analysis algorithm for energy generated by solar power, for example. The analysis unit can also apply a different analysis algorithm for energy generated by wind power. The analysis unit can also dynamically select an optimal analysis algorithm for each type of energy. This improves the accuracy of the analysis by applying the optimal analysis algorithm for each type of energy. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input different analysis algorithms for each type of energy into the generation AI, and the generation AI can apply the optimal analysis algorithm.

[0084] The analysis unit can improve the accuracy of the analysis by taking into account the characteristics of the energy supplier during the analysis. The analysis unit, for example, improves the accuracy of the analysis by taking into account the characteristics of the energy supplier during the analysis. The characteristics of the supplier include, for example, supply capacity and supply stability. The analysis unit, for example, improves the accuracy of the analysis by taking into account the characteristics of the supplier. The analysis unit can also evaluate the characteristics of the supplier in real time and dynamically improve the accuracy of the analysis. The analysis unit can also customize the analysis algorithm based on the characteristics of the supplier. In this way, the accuracy of the analysis is improved by taking into account the characteristics of the supplier. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the characteristics of the supplier into the generation AI, and the generation AI can apply an optimal analysis algorithm.

[0085] The analysis unit can optimize the analysis algorithm by referring to past data of the energy supplier during analysis. The analysis unit, for example, optimizes the analysis algorithm by referring to past data of the energy supplier during analysis. The past data includes, for example, fluctuations in supply amount and supply frequency. The analysis unit, for example, optimizes the analysis algorithm based on the past data of the supplier. The analysis unit can also dynamically optimize the analysis algorithm by referring to the past data of the supplier in real time. The analysis unit can also analyze the past data of the supplier and select an optimal analysis algorithm. This makes it possible to optimize the analysis algorithm by referring to the past data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the past data of the supplier into the generation AI, which can then apply the optimal analysis algorithm.

[0086] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. User emotions include, for example, tension, relaxation, and hurry. For example, when the user is tensioned, the analysis unit can prioritize displaying important analysis results. Furthermore, when the user is relaxed, the analysis unit can prioritize displaying detailed analysis results. Furthermore, when the user is hurrying, the analysis unit can prioritize displaying analysis results that highlight the main points. In this way, by prioritizing the analysis results according to the user's emotions, important information can be displayed preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's emotional data into the generation AI, which can then determine the priority of the analysis results.

[0087] The analysis unit may perform the analysis while taking into account the geographical conditions of the energy supplier. For example, the analysis unit may perform the analysis while taking into account the geographical conditions of the energy supplier. Geographical conditions include, for example, topography and climatic conditions. For example, the analysis unit may apply a specific analysis algorithm when the supplier is in a mountainous area. The analysis unit may also apply a different analysis algorithm when the supplier is in an urban area. The analysis unit may also evaluate the geographical conditions of the supplier in real time and dynamically select an optimal analysis algorithm. This enables optimal analysis by taking into account the geographical conditions of the supplier. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit may input data on the geographical conditions of the supplier into the generation AI, which may then apply an optimal analysis algorithm.

[0088] The analysis unit can customize the analysis method during analysis by taking into account the contract terms of the energy supplier. For example, the analysis unit customizes the analysis method during analysis by taking into account the contract terms of the energy supplier. The contract terms include, for example, an upper limit on the supply amount and a supply time period. The analysis unit customizes the analysis method, for example, based on the contract terms of the supplier. The analysis unit can also select an optimal analysis method by taking into account the contract terms of the supplier. The analysis unit can also evaluate the contract terms of the supplier in real time and dynamically select an optimal analysis method. This makes it possible to select an optimal analysis method by taking into account the contract terms of the supplier. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input data on the contract terms of the supplier into the generation AI, which can select an optimal analysis method.

[0089] The analysis unit can evaluate the technical capabilities of the energy supplier during analysis and select the optimal analysis method. The analysis unit, for example, evaluates the technical capabilities of the energy supplier during analysis and selects the optimal analysis method. Technical capabilities include, for example, the performance of the supply equipment and the skills of the technical staff. The analysis unit, for example, evaluates the technical capabilities of the supplier and selects the optimal analysis method. The analysis unit can also evaluate the technical capabilities of the supplier in real time and dynamically select the optimal analysis method. The analysis unit can also customize the analysis method based on the technical capabilities of the supplier. In this way, the optimal analysis method can be selected by evaluating the technical capabilities of the supplier. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the technical capabilities of the supplier into the generation AI, which can select the optimal analysis method.

[0090] The identification unit can estimate the user's emotion and adjust the method for identifying a supply destination based on the estimated user emotion. The identification unit, for example, estimates the user's emotion and adjusts the method for identifying a supply destination based on the estimated user emotion. User emotions include, for example, tension, relaxation, and hurry. For example, if the user is tension, the identification unit provides a simple and highly visible identification method. Furthermore, if the user is relaxed, the identification unit can provide a method for identifying that includes detailed information. Furthermore, if the user is hurrying, the identification unit can provide a method for identifying that focuses on the main points. This allows the method for identifying a supply destination to be adjusted according to the user's emotion, making it easy for the user to see. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the identification unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the identification unit can input user emotion data to the generation AI, and the generation AI can adjust the method for identifying the supply destination.

[0091] The identification unit can predict fluctuations in energy demand at the time of identification and identify an appropriate supply destination. The identification unit, for example, predicts fluctuations in energy demand at the time of identification and identifies an optimal supply destination. Energy demand fluctuations include, for example, seasonal fluctuations and demand by time of day. The identification unit, for example, predicts fluctuations in energy demand and identifies an optimal supply destination. The identification unit can also evaluate fluctuations in energy demand in real time and dynamically identify an optimal supply destination. The identification unit can also customize the supply destination based on fluctuations in energy demand. This improves the accuracy of identifying the optimal supply destination by predicting fluctuations in energy demand. Some or all of the above-described processing in the identification unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the identification unit can input data on fluctuations in energy demand into a generation AI, which can then identify the optimal supply destination.

[0092] The identification unit can improve the accuracy of identification by taking into account the geographical conditions of the supply destination during identification. For example, the identification unit improves the accuracy of identification by taking into account the geographical conditions of the supply destination during identification. Geographical conditions include, for example, topography and climatic conditions. For example, the identification unit improves the accuracy of identification by taking into account the geographical conditions of the supply destination. The identification unit can also evaluate the geographical conditions of the supply destination in real time and dynamically improve the accuracy of identification. The identification unit can also customize the identification algorithm based on the geographical conditions of the supply destination. In this way, the accuracy of identification is improved by taking into account the geographical conditions of the supply destination. Some or all of the above-described processing in the identification unit may be performed using, or without, a generation AI. For example, the identification unit can input data on the geographical conditions of the supply destination into the generation AI, which can then apply an optimal identification algorithm.

[0093] The identification unit can optimize the identification algorithm by referring to the past energy consumption data of the supply destination at the time of identification. For example, the identification unit optimizes the identification algorithm by referring to the past energy consumption data of the supply destination at the time of identification. The past energy consumption data includes, for example, fluctuations in consumption amount and frequency of consumption. The identification unit optimizes the identification algorithm based on, for example, the past energy consumption data of the supply destination. The identification unit can also dynamically optimize the identification algorithm by referring to the past energy consumption data of the supply destination in real time. The identification unit can also analyze the past energy consumption data of the supply destination and select an optimal identification algorithm. This makes it possible to optimize the identification algorithm by referring to the past energy consumption data. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the identification unit can input the past energy consumption data of the supply destination into the generation AI, and the generation AI can apply the optimal identification algorithm.

[0094] The identification unit can estimate the user's emotion and determine the priority of the supply destinations based on the estimated user's emotion. The identification unit, for example, estimates the user's emotion and determines the priority of the supply destinations based on the estimated user's emotion. User emotions include, for example, tension, relaxation, and hurry. For example, when the user is tensioned, the identification unit can prioritize identifying important supply destinations. Furthermore, when the user is relaxed, the identification unit can prioritize identifying detailed supply destinations. Furthermore, when the user is hurrying, the identification unit can prioritize identifying supply destinations that focus on the main points. In this way, by determining the priority of the supply destinations according to the user's emotion, important supply destinations can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the identification unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the identification unit can input the user's emotion data into the generation AI, and the generation AI can determine the priority of supply destinations.

[0095] The identification unit can evaluate the environmental impact of the supply destination at the time of identification and identify an environmentally friendly supply destination. For example, the identification unit evaluates the environmental impact of the supply destination at the time of identification and identify an environmentally friendly supply destination. The environmental impact includes, for example, CO2 emissions and environmental load. For example, the identification unit can evaluate the environmental impact of the supply destination and identify an environmentally friendly supply destination. The identification unit can also evaluate the environmental impact of the supply destination in real time and dynamically identify an optimal supply destination. The identification unit can also customize the supply destination based on the environmental impact of the supply destination. In this way, an environmentally friendly supply destination can be identified by evaluating the environmental impact of the supply destination. Some or all of the above-mentioned processing in the identification unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the identification unit can input data on the environmental impact of the supply destination into the generation AI, and the generation AI can identify the optimal supply destination.

[0096] The identification unit can customize the identification method during identification, taking into account the contractual conditions of the supply destination. For example, the identification unit customizes the identification method during identification, taking into account the contractual conditions of the supply destination. The contractual conditions include, for example, an upper limit on the supply volume and a supply time period. The identification unit customizes the identification method, for example, based on the contractual conditions of the supply destination. The identification unit can also select an optimal identification method by taking into account the contractual conditions of the supply destination. The identification unit can also evaluate the contractual conditions of the supply destination in real time and dynamically select an optimal identification method. This makes it possible to select an optimal identification method by taking into account the contractual conditions of the supply destination. Some or all of the above-described processing in the identification unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the identification unit can input data on the contractual conditions of the supply destination into the generation AI, which can select the optimal identification method.

[0097] The identification unit can evaluate the technical capabilities of the supply destination at the time of identification and identify the optimal supply destination. The identification unit, for example, evaluates the technical capabilities of the supply destination at the time of identification and identifies the optimal supply destination. Technical capabilities include, for example, the performance of the supply equipment and the skills of the technical staff. The identification unit, for example, evaluates the technical capabilities of the supply destination and identifies the optimal supply destination. The identification unit can also evaluate the technical capabilities of the supply destination in real time and dynamically identify the optimal supply destination. The identification unit can also customize the supply destination based on the technical capabilities of the supply destination. In this way, the optimal supply destination can be identified by evaluating the technical capabilities of the supply destination. Some or all of the above-mentioned processing in the identification unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the identification unit can input data on the technical capabilities of the supply destination into the generation AI, which can identify the optimal supply destination.

[0098] The providing unit can estimate the user's emotions and adjust the energy supply method based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and adjusts the energy supply method based on the estimated user emotions. User emotions include, for example, tension, relaxation, and hurry. For example, when the user is tensioned, the providing unit provides a simple and highly visible energy supply method. Furthermore, when the user is relaxed, the providing unit can provide a energy supply method that includes detailed information. Furthermore, when the user is hurrying, the providing unit can provide a energy supply method that focuses on the main points. This allows the energy supply method to be adjusted according to the user's emotions, thereby enabling energy supply that is easy for the user to see. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotional data into the generating AI, which can then adjust the method of energy supply.

[0099] The providing unit can apply an algorithm for streamlining the energy supply schedule when providing the energy supply schedule. The providing unit, for example, applies an algorithm for optimizing the energy supply schedule when providing the energy supply schedule. The supply schedule includes, for example, adjustment of supply timing and supply amount. The providing unit, for example, applies an algorithm for optimizing the energy supply schedule. The providing unit can also evaluate the energy supply schedule in real time and dynamically adjust the optimal schedule. The providing unit can also customize the supply method based on the energy supply schedule. This enables efficient energy supply by optimizing the supply schedule. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input supply schedule data to a generating AI, which can then apply the optimal supply schedule.

[0100] The providing unit can predict fluctuations in the energy demand of the supply destination and adjust the supply method when providing the data. The providing unit, for example, predicts fluctuations in the energy demand of the supply destination and adjusts the supply method when providing the data. Fluctuations in energy demand include, for example, seasonal fluctuations and demand by time of day. The providing unit, for example, predicts fluctuations in the energy demand of the supply destination and adjusts the supply method. The providing unit can also evaluate fluctuations in the energy demand of the supply destination in real time and dynamically adjust the optimal supply method. The providing unit can also customize the supply method based on fluctuations in the energy demand of the supply destination. In this way, the optimal supply method can be selected by predicting fluctuations in energy demand. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input data on fluctuations in the energy demand of the supply destination to a generating AI, which can then apply the optimal supply method.

[0101] The providing unit can optimize the supply method by referring to the past energy consumption data of the supply destination when providing the data. The providing unit, for example, optimizes the supply method by referring to the past energy consumption data of the supply destination when providing the data. The past energy consumption data includes, for example, fluctuations in consumption amount and frequency of consumption. The providing unit, for example, optimizes the supply method based on the past energy consumption data of the supply destination. The providing unit can also dynamically optimize the supply method by referring to the past energy consumption data of the supply destination in real time. The providing unit can also analyze the past energy consumption data of the supply destination and select the optimal supply method. This makes it possible to optimize the supply method by referring to the past energy consumption data. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the past energy consumption data of the supply destination to the generation AI, which can then apply the optimal supply method.

[0102] The providing unit can estimate the user's emotions and determine the priority of energy supply based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and determines the priority of energy supply based on the estimated user emotions. User emotions include, for example, tension, relaxation, and hurry. For example, when the user is tensioned, the providing unit prioritizes supply to important energy supply destinations. Furthermore, when the user is relaxed, the providing unit can prioritize supply to detailed energy supply destinations. Furthermore, when the user is hurrying, the providing unit can prioritize supply to energy supply destinations that focus on the essential points. In this way, by determining the priority of energy supply according to the user's emotions, important energy supply destinations can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotional data into the generating AI, and the generating AI can determine the priority of energy supply.

[0103] The providing unit can customize the supply method at the time of provision, taking into account the geographical conditions of the supply destination. For example, the providing unit customizes the supply method at the time of provision, taking into account the geographical conditions of the supply destination. Geographical conditions include, for example, topography, climatic conditions, and the like. For example, the providing unit applies a specific supply method when the supply destination is in a mountainous area. The providing unit can also apply a different supply method when the supply destination is in an urban area. The providing unit can also evaluate the geographical conditions of the supply destination in real time and dynamically select the optimal supply method. This makes it possible to select the optimal supply method by taking into account the geographical conditions of the supply destination. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input data on the geographical conditions of the supply destination into the generating AI, and the generating AI can apply the optimal supply method.

[0104] The providing unit can adjust the supply method at the time of provision, taking into account the contract conditions of the supply destination. For example, the providing unit adjusts the supply method at the time of provision, taking into account the contract conditions of the supply destination. The contract conditions include, for example, an upper limit on the supply amount and a supply time period. The providing unit adjusts the supply method, for example, based on the contract conditions of the supply destination. The providing unit can also select the optimal supply method by taking into account the contract conditions of the supply destination. The providing unit can also evaluate the contract conditions of the supply destination in real time and dynamically select the optimal supply method. In this way, the optimal supply method can be selected by taking into account the contract conditions of the supply destination. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input data on the contract conditions of the supply destination into a generating AI, which can then apply the optimal supply method.

[0105] The provision unit can evaluate the technical capabilities of the supply destination at the time of provision and select the optimal supply means. For example, the provision unit evaluates the technical capabilities of the supply destination at the time of provision and selects the optimal supply means. Technical capabilities include, for example, the performance of the supply equipment and the skills of the technical staff. For example, the provision unit evaluates the technical capabilities of the supply destination and selects the optimal supply means. The provision unit can also evaluate the technical capabilities of the supply destination in real time and dynamically select the optimal supply means. The provision unit can also customize the supply means based on the technical capabilities of the supply destination. In this way, the optimal supply means can be selected by evaluating the technical capabilities of the supply destination. Some or all of the above-mentioned processing in the provision unit may be performed, for example, using AI or without AI. For example, the provision unit can input data on the technical capabilities of the supply destination into a generation AI, which can then apply the optimal supply means. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, identification unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the type and amount of surplus energy using the camera 42 or sensor of the smart device 14 and manages the collected data using the control unit 46A. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the collected energy information using a generation AI. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies the optimal supply destination based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and supplies energy via a power grid or a battery. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, identification unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the type and amount of surplus energy using the camera 42 or sensor of the smart glasses 214, and manages the collected data using the control unit 46A. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the collected energy information using a generation AI. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the optimal supply destination based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and supplies energy via a power grid or a battery. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, identification unit, and provision unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects the type and amount of surplus energy using the camera 42 or sensors of the headset-type terminal 314, and manages the collected data using the control unit 46A. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the collected energy information using a generation AI. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the optimal supply destination based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and supplies energy via a power grid or a battery. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, identification unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the type and amount of surplus energy using the camera 42 or sensors of the robot 414, and manages the collected data by the control unit 46A. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the collected energy information using a generation AI. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the optimal supply destination based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the robot 414, and supplies energy via a power grid or a battery.

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

[0107] When collecting energy, the collection unit can learn the energy generation pattern of the supplier and predict the optimal collection timing. For example, in the case of a solar power supplier, the optimal collection timing can be predicted based on sunlight hours and weather data. In the case of a wind power supplier, the collection timing can also be adjusted based on wind speed and direction data. Furthermore, when using waste heat from a factory, the collection timing can be optimized taking into account the factory's operating schedule. This allows the collection unit to learn the energy generation pattern of the supplier and enable efficient energy collection.

[0108] When analyzing energy, the analysis unit can learn the energy generation patterns of the supplier and apply the optimal analysis algorithm. For example, in the case of a solar power supplier, the analysis algorithm can be adjusted based on sunlight hours and weather data. In the case of a wind power supplier, the analysis algorithm can also be optimized based on wind speed and direction data. Furthermore, when using waste heat from a factory, the analysis algorithm can be customized taking into account the factory's operating schedule. This allows the analysis unit to learn the energy generation patterns of the supplier and improve the accuracy of the analysis.

[0109] The identification unit can analyze past energy consumption data and build a demand forecasting model to predict fluctuations in energy demand. For example, it can analyze seasonal energy consumption patterns and predict peak demand periods. It can also predict demand fluctuations during the day and night based on energy consumption data by time of day. Furthermore, it can analyze energy consumption data for specific industries or regions and customize the demand forecasting model. This allows the identification unit to predict fluctuations in energy demand using past data and identify optimal supply destinations.

[0110] When supplying energy, the provider can learn the energy consumption patterns of the destination and optimize the supply schedule. For example, it can analyze the energy consumption patterns of households and supply energy during peak hours. It can also set a supply schedule based on business hours based on a company's energy consumption data. Furthermore, it can take into account the energy consumption patterns of public facilities and adjust the supply schedule to suit events and special occasions. This allows the provider to learn the energy consumption patterns of the destination and enable efficient energy supply.

[0111] When collecting energy, the collection unit can learn the energy generation pattern of the supplier and predict the optimal collection timing. For example, in the case of a solar power supplier, the optimal collection timing can be predicted based on sunlight hours and weather data. In the case of a wind power supplier, the collection timing can also be adjusted based on wind speed and direction data. Furthermore, when using waste heat from a factory, the collection timing can be optimized taking into account the factory's operating schedule. This allows the collection unit to learn the energy generation pattern of the supplier and enable efficient energy collection.

[0112] The collection unit can estimate the user's emotions and adjust the timing of energy collection based on the estimated user emotions. For example, if the user is feeling stressed, the timing of energy collection can be delayed to reduce the burden on the user. Also, if the user is relaxed, the timing of energy collection can be advanced to allow efficient collection. Furthermore, if the user is in a hurry, the timing of energy collection can be optimized to allow rapid collection. In this way, the burden on the user can be reduced by adjusting the timing of energy collection according to the user's emotions.

[0113] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the display method of the analysis results according to the user's emotions, it is possible to provide a display that is easy for the user to see.

[0114] The identification unit can estimate the user's emotions and adjust the method for identifying the supply destination based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible identification method can be provided. If the user is relaxed, a method for identifying that includes detailed information can be provided. Furthermore, if the user is in a hurry, a method for identifying that focuses on the main points can be provided. In this way, by adjusting the method for identifying the supply destination according to the user's emotions, it is possible to identify the supply destination in an easy-to-see manner for the user.

[0115] The providing unit can estimate the user's emotions and adjust the energy supply method based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible energy supply method can be provided. If the user is relaxed, a energy supply method including detailed information can be provided. Furthermore, if the user is in a hurry, a energy supply method that focuses on the main points can be provided. In this way, by adjusting the energy supply method according to the user's emotions, it is possible to provide an energy supply that is easy for the user to see.

[0116] The providing unit can estimate the user's emotions and determine the priority of energy supply based on the estimated user's emotions. For example, if the user is nervous, important supply destinations can be given priority. Also, if the user is relaxed, detailed supply destinations can be given priority. Furthermore, if the user is in a hurry, supply destinations that focus on the essentials can be given priority. In this way, by determining the priority of energy supply according to the user's emotions, important supply destinations can be given priority.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The collection unit collects surplus energy. Examples of surplus energy include surplus power generated by solar power generation, surplus power generated by wind power generation, and waste heat from factories. The collection unit collects information on the type and amount of energy and the time periods during which it can be supplied. Step 2: The analysis unit uses the generation AI to analyze the energy information collected by the collection unit. The analysis is performed based on the data analysis method and accuracy of the analysis. The analysis unit analyzes the collected energy information and identifies the optimal supply destination. Step 3: The Identification Unit uses the Generative AI to identify optimal supply destinations based on the information analyzed by the Analysis Unit. Identification is based on areas with high energy demand, specific industries, etc. The Identification Unit identifies areas and customers with high energy demand. Step 4: The providing unit provides energy to the supply destination identified by the identifying unit. The providing includes a method of supplying energy through a power grid or a method of supplying energy using a battery. The providing unit can also supply energy through a power grid or using a battery.

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

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

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

[0124] 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 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

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

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

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

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

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

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

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

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

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

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

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

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

[0156] 7, a 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 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

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

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

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

[0163] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

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

[0173] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

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

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

[0179] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

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

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

[0183] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

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

[0185] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0190] [Explanation of symbols]

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

Claims

1. a collecting unit that collects surplus energy; an analysis unit that analyzes the energy information collected by the collection unit; an identification unit that identifies an appropriate supply destination based on the information analyzed by the analysis unit; a providing unit that provides energy to the supply destination identified by the identifying unit; Equipped with A system characterized by:

2. The collecting unit Collect information on the type or amount of energy and the time periods when it is available 2. The system of claim 1.

3. The analysis unit Analyzing collected energy information and identifying appropriate supply sources 2. The system of claim 1.

4. The identification unit Identify areas or customers with high energy demand 2. The system of claim 1.

5. The providing unit Supplying energy through the power grid 2. The system of claim 1.

6. The providing unit Supplying energy using batteries 2. The system of claim 1.

7. The collecting unit Estimate the user's emotions and adjust the timing of energy collection based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit At the time of collection, the quality and stability of energy is evaluated and an appropriate collection method is selected.

2. The system of claim 1.

Images