system

Abstract

The system according to this embodiment aims to respond to the increasing demand for electricity and the spread of green energy, and to achieve efficient power management. [Solution] The system according to the embodiment comprises a power storage unit, a communication unit, a control unit, and a billing unit. The power storage unit stores electricity using a household battery. The communication unit acquires real-time market electricity price information using communication technology. The control unit charges the system when electricity demand is low and supplies power when demand is high, based on the information acquired by the communication unit. The billing unit introduces a demand-responsive billing plan.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to adjust the supply and demand with the increase in power demand and the spread of green energy, and efficient power management is required.

[0005] The system according to the embodiment aims to cope with the increase in power demand and the spread of green energy and realize efficient power management.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a power storage unit, a communication unit, a control unit, and a billing unit. The power storage unit stores electricity using a household battery. The communication unit acquires real-time market electricity price information using communication technology. The control unit charges the system when electricity demand is low and supplies power when demand is high, based on the information acquired by the communication unit. The billing unit implements a demand-responsive billing plan. [Effects of the Invention]

[0007] The system according to this embodiment can respond to the increase in electricity demand and the spread of green energy, and achieve efficient power management. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. [[ID=​​​​​​The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The power demand control system according to an embodiment of the present invention is a system that balances a significant increase in power demand in the next-generation society with the achievement of carbon neutrality. This system rapidly responds to changes in power demand and supply by combining home battery storage, communication technology, AI technology, and demand-response (DR) type pricing plans. For example, it stores power using home battery storage and obtains real-time market electricity price information using communication technology. Next, it provides a mechanism to reduce customers' electricity bills by using AI technology to charge when power demand is low and supply power when demand is high. Furthermore, it responds to fluctuations in power demand by introducing DR type pricing plans. In the future, it will utilize generative AI to automatically suggest home appliances and related products that reduce electricity bills tailored to the customer, using electricity usage information as input information through the "Grid One" management app. This will automate upselling and cross-selling, creating a mechanism to maximize business revenue. By utilizing data on products and home appliances purchased by customers, it will be possible to further refine demand forecasting, leading to optimized power procurement and the provision of lower electricity rates. This system aggregates and controls end-user electricity demand, creating a competitive advantage over other companies through AI, while providing flexibility in electricity supply and leading the realization of next-generation social infrastructure. As a result, the electricity demand control system can simultaneously address the significant increase in electricity demand in the next-generation society and achieve carbon neutrality.

[0029] The power demand control system according to this embodiment comprises a power storage unit, a communication unit, a control unit, and a billing unit. The power storage unit stores electricity using a household battery. For example, the power storage unit can store electricity during off-peak hours at night using a household battery. The power storage unit can also store electricity using renewable energy sources such as solar power generation, using a household battery. Furthermore, the power storage unit can also supply electricity during off-peak hours using a household battery. The communication unit acquires real-time market electricity price information using communication technology. For example, the communication unit can acquire market electricity price information via the internet. The communication unit can also acquire household electricity consumption data using a smart meter. Furthermore, the communication unit can acquire market electricity price information using wireless communication technology. The control unit charges when electricity demand is low and supplies power when demand is high, based on the information acquired by the communication unit. For example, the control unit can use AI technology to predict when electricity demand is low and charge at that time. Furthermore, the control unit can use AI technology to predict times of high electricity demand and supply power at those times. In addition, the control unit can use AI technology to automatically switch between charging and supplying power in response to fluctuations in electricity demand. The tariff unit introduces demand-responsive tariff plans. For example, the tariff unit can set lower rates during periods of low electricity demand and higher rates during periods of high electricity demand. The tariff unit can also adjust rates in real time in response to fluctuations in electricity demand. Furthermore, the tariff unit can propose the optimal tariff plan according to the customer's electricity usage. As a result, the electricity demand control system according to this embodiment can respond quickly to fluctuations in electricity demand and reduce the customer's electricity bill.

[0030] The system includes an analysis unit that uses a generation AI to analyze electricity usage. For example, the analysis unit can input power consumption data into the generation AI to analyze power consumption patterns. It can also input market electricity price information into the generation AI to predict electricity demand. Furthermore, the analysis unit can input household power consumption data into the generation AI to make suggestions for improving the efficiency of electricity use. As a result, the analysis unit improves the accuracy of its electricity demand predictions by analyzing electricity usage.

[0031] The company has a proposal department that makes proposals based on customer needs. For example, the proposal department can analyze a customer's electricity usage and propose the optimal way to use electricity. It can also analyze a customer's electricity consumption patterns and propose the optimal pricing plan. Furthermore, the proposal department can propose home appliances and related products to improve the efficiency of electricity use according to the customer's electricity usage. In this way, the proposal department can improve the efficiency of electricity use by making the most suitable proposals for customers.

[0032] The system includes a data department that utilizes data on products and home appliances purchased by customers. For example, the data department can analyze a customer's purchase history and make suggestions to improve the efficiency of their electricity usage. It can also analyze a customer's appliance usage and suggest the optimal way to use electricity. Furthermore, the data department can combine customer purchase history and appliance usage data to forecast electricity demand. As a result, the data department can further improve the accuracy of its electricity demand forecasts by utilizing customer data.

[0033] The analysis unit can analyze electricity usage using a generating AI. For example, the analysis unit can input power consumption data into the generating AI to analyze power consumption patterns. It can also input market electricity price information into the generating AI to predict electricity demand. Furthermore, the analysis unit can input household power consumption data into the generating AI to make suggestions for improving the efficiency of electricity use. As a result, the analysis unit can improve the accuracy of its electricity usage analysis by using the generating AI.

[0034] The proposal department can provide personalized proposals to customers using generative AI. For example, the proposal department can input a customer's electricity usage data into the generative AI and propose the optimal electricity usage method. It can also input a customer's electricity consumption pattern into the generative AI and propose the optimal pricing plan. Furthermore, the proposal department can input a customer's electricity usage data into the generative AI and propose home appliances and related products to improve electricity efficiency. In this way, the proposal department can provide optimal proposals to customers by using generative AI.

[0035] The Data Department can utilize data on products and home appliances purchased by customers. For example, the Data Department can analyze customers' purchase history and make suggestions to improve the efficiency of their electricity usage. It can also analyze customers' appliance usage and suggest optimal electricity usage methods. Furthermore, the Data Department can combine customer purchase history and appliance usage data to forecast electricity demand. This allows the Data Department to improve the accuracy of its electricity demand forecasts by utilizing customer data.

[0036] The energy storage unit can determine the appropriate timing for energy storage by referring to weather forecast data. For example, based on weather forecast data, the energy storage unit can prioritize energy storage on sunny days and refrain from storing energy on cloudy days. It can also refrain from storing energy on rainy days and store energy on sunny days. Furthermore, based on weather forecast data, the energy storage unit can prioritize energy storage on days when wind power generation is expected. In this way, the energy storage unit can determine the optimal timing for energy storage by referring to weather forecast data.

[0037] The energy storage unit can analyze household power consumption patterns and select an appropriate energy storage method during storage. For example, it can analyze household power consumption patterns and store energy during off-peak hours at night. It can also analyze household power consumption patterns and refrain from storing energy during peak hours in the daytime. Furthermore, it can analyze household power consumption patterns and store energy on weekdays if weekend power consumption is high. In this way, the energy storage unit can select an efficient energy storage method by analyzing household power consumption patterns.

[0038] The energy storage unit can determine energy storage priorities by referring to regional electricity demand data during energy storage. For example, the energy storage unit can prioritize energy storage during periods of low demand based on regional electricity demand data. It can also refrain from energy storage during periods of high demand based on regional electricity demand data. Furthermore, the energy storage unit can store energy before demand surges occur based on regional electricity demand data. In this way, the energy storage unit can determine energy storage priorities by referring to regional electricity demand data.

[0039] The energy storage unit can select the optimal energy storage method by coordinating with other smart devices in the home during energy storage. For example, the energy storage unit can coordinate with smart devices in the home to store energy when devices with low power consumption are running. It can also coordinate with smart devices in the home to reduce energy storage when devices with high power consumption are running. Furthermore, the energy storage unit can coordinate with smart devices in the home to store energy according to the operating schedule of those devices. In this way, the energy storage unit can select the optimal energy storage method by coordinating with other smart devices in the home.

[0040] The Communications Department can analyze trends in the electricity market in real time during communication and obtain optimal information. For example, the Communications Department can analyze price fluctuations in the electricity market in real time and obtain information at the optimal time. Furthermore, the Communications Department can analyze the supply and demand balance of the electricity market in real time and obtain optimal information. In addition, the Communications Department can analyze trends in the electricity market in real time, predict future trends, and obtain information. As a result, the Communications Department can obtain optimal information by analyzing trends in the electricity market in real time.

[0041] The communications department can transmit household electricity consumption data in real time during communication and link it with market information. For example, the communications department can transmit household electricity consumption data in real time and link it with market information to suggest optimal electricity usage. Furthermore, the communications department can transmit household electricity consumption data in real time and link it with market information to optimize electricity rates. In addition, the communications department can transmit household electricity consumption data in real time and link it with market information to perform demand forecasting. As a result, by transmitting household electricity consumption data in real time and linking it with market information, the communications department can enable optimal electricity usage.

[0042] The communications department can determine communication priorities by considering the local power supply situation. For example, the communications department can prioritize communications during times when the power supply is stable, based on the local power supply situation. It can also refrain from communications during times when the power supply is unstable, based on the local power supply situation. Furthermore, the communications department can initiate communications before a surge in power supply occurs, based on the local power supply situation. In this way, the communications department can determine communication priorities by considering the local power supply situation.

[0043] The communications unit can share information with other home smart devices during communication. For example, the communications unit can share power consumption data with other smart devices to suggest optimal power usage. It can also share power rate information with other smart devices to suggest the best rate plan. Furthermore, the communications unit can share demand forecast data with other smart devices to optimize power supply. In this way, the communications unit can share information by collaborating with other home smart devices.

[0044] The control unit can analyze electricity market price fluctuations in real time during control operations to determine the optimal power supply timing. For example, the control unit can analyze electricity market price fluctuations in real time and supply power at the optimal timing. Furthermore, the control unit can analyze the supply and demand balance of the electricity market in real time to determine the optimal power supply timing. In addition, the control unit can analyze electricity market trends in real time, predict future trends, and determine the power supply timing. Thus, the control unit can determine the optimal power supply timing by analyzing electricity market price fluctuations in real time.

[0045] The control unit can analyze the household's power consumption patterns during control and select an efficient power supply method. For example, the control unit can analyze the household's power consumption patterns and supply power during off-peak hours at night. It can also analyze the household's power consumption patterns and reduce power supply during peak hours in the daytime. Furthermore, if power consumption is high on weekends, the control unit can supply power on weekdays. In this way, the control unit can select an efficient power supply method by analyzing the household's power consumption patterns.

[0046] The control unit can determine the priority of power supply by referring to regional power demand data during control. For example, the control unit can prioritize power supply during periods of low demand based on regional power demand data. It can also refrain from supplying power during periods of high demand based on regional power demand data. Furthermore, the control unit can supply power before demand surges occur based on regional power demand data. Thus, the control unit can determine the priority of power supply by referring to regional power demand data.

[0047] The control unit can select the optimal power supply method in conjunction with other smart devices in the home during control. For example, the control unit can work with smart devices in the home to supply power when devices with low power consumption are in operation. It can also work with smart devices in the home to reduce power supply when devices with high power consumption are in operation. Furthermore, the control unit can work with smart devices in the home to supply power according to the operating schedule of devices. In this way, the control unit can select the optimal power supply method by working with other smart devices in the home.

[0048] The pricing department can provide the optimal plan by analyzing electricity market price fluctuations in real time when setting up pricing plans. For example, the pricing department can analyze electricity market price fluctuations in real time and provide the optimal pricing plan. Furthermore, the pricing department can analyze the supply and demand balance of the electricity market in real time and provide the optimal pricing plan. In addition, the pricing department can analyze electricity market trends in real time, predict future trends, and provide the optimal pricing plan. Thus, by analyzing electricity market price fluctuations in real time, the pricing department can provide the optimal pricing plan.

[0049] The pricing department can analyze household electricity consumption patterns when setting up pricing plans and select the most efficient plan. For example, the pricing department can analyze household electricity consumption patterns and offer a plan with lower nighttime rates if nighttime electricity consumption is low. It can also analyze household electricity consumption patterns and offer a plan with lower daytime rates if daytime electricity consumption is high. Furthermore, it can analyze household electricity consumption patterns and offer a plan with lower weekend rates if weekend electricity consumption is high. In this way, the pricing department can select the most efficient pricing plan by analyzing household electricity consumption patterns.

[0050] The pricing department can customize pricing plans when setting them, taking into account the local power supply situation. For example, based on the local power supply situation, the pricing department can offer lower-priced pricing plans in areas with stable supply. Conversely, based on the local power supply situation, the pricing department can also offer higher-priced pricing plans in areas with unstable supply. Furthermore, based on the local power supply situation, the pricing department can customize pricing plans before a surge in supply occurs. In this way, the pricing department can provide the optimal pricing plan by taking into account the local power supply situation.

[0051] The pricing department can provide the optimal plan by collaborating with other home smart devices when setting up pricing plans. For example, the pricing department can collaborate with other smart devices to share electricity consumption data and provide the optimal pricing plan. It can also collaborate with other smart devices to share electricity billing information and provide the optimal pricing plan. Furthermore, the pricing department can collaborate with other smart devices to share demand forecast data and provide the optimal pricing plan. In this way, the pricing department can provide the optimal pricing plan by collaborating with other home smart devices.

[0052] The analysis unit can analyze trends in the electricity market in real time during analysis and provide optimal analysis results. For example, the analysis unit can analyze price fluctuations in the electricity market in real time and provide optimal analysis results. Furthermore, the analysis unit can analyze the supply and demand balance of the electricity market in real time and provide optimal analysis results. In addition, the analysis unit can analyze trends in the electricity market in real time, predict future trends, and provide optimal analysis results. Thus, the analysis unit can provide optimal analysis results by analyzing trends in the electricity market in real time.

[0053] The analysis unit can select an efficient analysis method by thoroughly analyzing household electricity consumption data during the analysis process. For example, the analysis unit can analyze household electricity consumption data in detail and provide analysis results that lower nighttime rates if nighttime electricity consumption is low. It can also analyze household electricity consumption data in detail and provide analysis results that lower daytime rates if daytime electricity consumption is high. Furthermore, it can analyze household electricity consumption data in detail and provide analysis results that lower weekend rates if weekend electricity consumption is high. In this way, the analysis unit can select an efficient analysis method by thoroughly analyzing household electricity consumption data.

[0054] The analysis unit can determine the priority of analysis by considering the regional power supply situation. For example, the analysis unit can prioritize analyzing data from areas with stable power supply based on the regional power supply situation. It can also prioritize analyzing data from areas with unstable power supply based on the regional power supply situation. Furthermore, the analysis unit can analyze data before a sudden surge in supply occurs, based on the regional power supply situation. In this way, the analysis unit can determine the priority of analysis by considering the regional power supply situation.

[0055] The analysis unit can collaborate with other home smart devices during analysis to share analysis results. For example, the analysis unit can collaborate with other smart devices to share power consumption data and provide optimal analysis results. It can also collaborate with other smart devices to share electricity bill information and provide optimal analysis results. Furthermore, the analysis unit can collaborate with other smart devices to share demand forecast data and provide optimal analysis results. In this way, the analysis unit can provide optimal analysis results by collaborating with other home smart devices.

[0056] The proposal department can provide optimal proposals by analyzing electricity market trends in real time. For example, the proposal department can analyze electricity market price fluctuations in real time and provide optimal proposals. Furthermore, the proposal department can analyze the supply and demand balance of the electricity market in real time and provide optimal proposals. In addition, the proposal department can analyze electricity market trends in real time, predict future trends, and provide optimal proposals. Thus, by analyzing electricity market trends in real time, the proposal department can provide optimal proposals.

[0057] The proposal department can analyze household electricity consumption patterns and select the most efficient proposal method when making a proposal. For example, if the proposal department analyzes household electricity consumption patterns and finds that nighttime electricity consumption is low, it can offer a proposal to lower nighttime rates. Similarly, if daytime electricity consumption is high, it can offer a proposal to lower daytime rates. Furthermore, if weekend electricity consumption is high, it can offer a proposal to lower weekend rates. In this way, the proposal department can select the most efficient proposal method by analyzing household electricity consumption patterns.

[0058] The proposal department can customize its proposals when making them, taking into account the local power supply situation. For example, based on the local power supply situation, the proposal department can offer low-cost proposals to areas with stable supply. Conversely, based on the local power supply situation, the proposal department can also offer high-cost proposals to areas with unstable supply. Furthermore, based on the local power supply situation, the proposal department can customize proposals before a surge in supply occurs. In this way, the proposal department can provide the most optimal proposal by taking into account the local power supply situation.

[0059] The proposal department can provide optimal proposals by collaborating with other home smart devices during the proposal process. For example, the proposal department can collaborate with other smart devices, share power consumption data, and provide optimal proposals. It can also collaborate with other smart devices, share power rate information, and provide optimal proposals. Furthermore, the proposal department can collaborate with other smart devices, share demand forecast data, and provide optimal proposals. In this way, the proposal department can provide optimal proposals by collaborating with other home smart devices.

[0060] The data department can collect optimal data by analyzing electricity market trends in real time during data collection. For example, the data department can analyze electricity market price fluctuations in real time and collect optimal data. It can also analyze the supply and demand balance of the electricity market in real time and collect optimal data. Furthermore, the data department can analyze electricity market trends in real time, predict future trends, and collect optimal data. In this way, the data department can collect optimal data by analyzing electricity market trends in real time.

[0061] The data department can select an efficient data collection method by collecting detailed household electricity consumption data during data collection. For example, the data department can collect detailed household electricity consumption data and collect data to lower nighttime rates if nighttime electricity consumption is low. It can also collect detailed household electricity consumption data and collect data to lower daytime rates if daytime electricity consumption is high. Furthermore, the data department can collect detailed household electricity consumption data and collect data to lower weekend rates if weekend electricity consumption is high. In this way, the data department can select an efficient data collection method by collecting detailed household electricity consumption data.

[0062] The data unit can prioritize data collection by considering the local power supply situation. For example, the data unit can prioritize collecting data from areas with stable power supply based on the local power supply situation. It can also prioritize collecting data from areas with unstable power supply based on the local power supply situation. Furthermore, the data unit can collect data before a surge in supply occurs, based on the local power supply situation. In this way, the data unit can prioritize data by considering the local power supply situation.

[0063] The data unit can collaborate with other home smart devices to share data during data collection. For example, the data unit can collaborate with other smart devices to share power consumption data for optimal data collection. It can also collaborate with other smart devices to share power billing information for optimal data collection. Furthermore, the data unit can collaborate with other smart devices to share demand forecast data for optimal data collection. This enables the data unit to achieve optimal data collection by collaborating with other home smart devices.

[0064] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0065] The control unit can analyze electricity market price fluctuations in real time during control to determine the optimal power supply timing. For example, the control unit can analyze electricity market price fluctuations in real time and supply power at the optimal timing. Furthermore, the control unit can analyze the supply and demand balance of the electricity market in real time to determine the optimal power supply timing. In addition, the control unit can analyze electricity market trends in real time, predict future trends, and determine the power supply timing. Thus, the control unit can determine the optimal power supply timing by analyzing electricity market price fluctuations in real time.

[0066] The communications department can transmit household electricity consumption data in real time during communication and link it with market information. For example, the communications department can transmit household electricity consumption data in real time and link it with market information to suggest optimal electricity usage. Furthermore, the communications department can transmit household electricity consumption data in real time and link it with market information to optimize electricity rates. In addition, the communications department can transmit household electricity consumption data in real time and link it with market information to perform demand forecasting. As a result, by transmitting household electricity consumption data in real time and linking it with market information, the communications department can enable optimal electricity usage.

[0067] The pricing department can provide the optimal plan by analyzing electricity market price fluctuations in real time when setting pricing plans. For example, the pricing department can analyze electricity market price fluctuations in real time and provide the optimal pricing plan. Furthermore, the pricing department can analyze the supply and demand balance of the electricity market in real time and provide the optimal pricing plan. In addition, the pricing department can analyze electricity market trends in real time, predict future trends, and provide the optimal pricing plan. Thus, the pricing department can provide the optimal pricing plan by analyzing electricity market price fluctuations in real time.

[0068] The analysis unit can analyze trends in the electricity market in real time during analysis and provide optimal analysis results. For example, the analysis unit can analyze price fluctuations in the electricity market in real time and provide optimal analysis results. Furthermore, the analysis unit can analyze the supply and demand balance of the electricity market in real time and provide optimal analysis results. In addition, the analysis unit can analyze trends in the electricity market in real time, predict future trends, and provide optimal analysis results. Thus, the analysis unit can provide optimal analysis results by analyzing trends in the electricity market in real time.

[0069] The data unit can collaborate with other home smart devices to share data during data collection. For example, the data unit can collaborate with other smart devices to share power consumption data and perform optimal data collection. It can also collaborate with other smart devices to share power billing information and perform optimal data collection. Furthermore, the data unit can collaborate with other smart devices to share demand forecast data and perform optimal data collection. This enables the data unit to perform optimal data collection by collaborating with other home smart devices.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The energy storage unit stores electricity using a household battery. For example, electricity can be stored during off-peak hours at night when electricity demand is low, or electricity can be stored using renewable energy sources such as solar power. It can also be used to supply electricity during peak hours when electricity demand is high. Step 2: The communications department uses communication technology to obtain real-time market electricity price information. For example, it can obtain market electricity price information via the internet or obtain household electricity consumption data using smart meters. Furthermore, it can also obtain market electricity price information using wireless communication technology. Step 3: The control unit charges the device when power demand is low and supplies power when demand is high, based on the information acquired by the communication unit. For example, it can use AI technology to predict when power demand is low and charge the device at that time. It can also predict when power demand is high and supply power at that time. Furthermore, it can automatically switch between charging and supplying power in response to fluctuations in power demand. Step 4: The pricing department implements demand-responsive pricing plans. For example, lower rates during periods of low electricity demand and higher rates during periods of high demand. It is also possible to adjust rates in real time according to fluctuations in electricity demand. Furthermore, it is possible to propose the optimal pricing plan based on the customer's electricity usage.

[0072] (Example of form 2) The power demand control system according to an embodiment of the present invention is a system that balances a significant increase in power demand in the next-generation society with the achievement of carbon neutrality. This system rapidly responds to changes in power demand and supply by combining home battery storage, communication technology, AI technology, and demand-response (DR) type pricing plans. For example, it stores power using home battery storage and obtains real-time market electricity price information using communication technology. Next, it provides a mechanism to reduce customers' electricity bills by using AI technology to charge when power demand is low and supply power when demand is high. Furthermore, it responds to fluctuations in power demand by introducing DR type pricing plans. In the future, it will utilize generative AI to automatically suggest home appliances and related products that reduce electricity bills tailored to the customer, using electricity usage information as input information through the "Grid One" management app. This will automate upselling and cross-selling, creating a mechanism to maximize business revenue. By utilizing data on products and home appliances purchased by customers, it will be possible to further refine demand forecasting, leading to optimized power procurement and the provision of lower electricity rates. This system aggregates and controls end-user electricity demand, creating a competitive advantage over other companies through AI, while providing flexibility in electricity supply and leading the realization of next-generation social infrastructure. As a result, the electricity demand control system can simultaneously address the significant increase in electricity demand in the next-generation society and achieve carbon neutrality.

[0073] The power demand control system according to this embodiment comprises a power storage unit, a communication unit, a control unit, and a billing unit. The power storage unit stores electricity using a household battery. For example, the power storage unit can store electricity during off-peak hours at night using a household battery. The power storage unit can also store electricity using renewable energy sources such as solar power generation, using a household battery. Furthermore, the power storage unit can also supply electricity during off-peak hours using a household battery. The communication unit acquires real-time market electricity price information using communication technology. For example, the communication unit can acquire market electricity price information via the internet. The communication unit can also acquire household electricity consumption data using a smart meter. Furthermore, the communication unit can acquire market electricity price information using wireless communication technology. The control unit charges when electricity demand is low and supplies power when demand is high, based on the information acquired by the communication unit. For example, the control unit can use AI technology to predict when electricity demand is low and charge at that time. Furthermore, the control unit can use AI technology to predict times of high electricity demand and supply power at those times. In addition, the control unit can use AI technology to automatically switch between charging and supplying power in response to fluctuations in electricity demand. The tariff unit introduces demand-responsive tariff plans. For example, the tariff unit can set lower rates during periods of low electricity demand and higher rates during periods of high electricity demand. The tariff unit can also adjust rates in real time in response to fluctuations in electricity demand. Furthermore, the tariff unit can propose the optimal tariff plan according to the customer's electricity usage. As a result, the electricity demand control system according to this embodiment can respond quickly to fluctuations in electricity demand and reduce the customer's electricity bill.

[0074] The system includes an analysis unit that uses a generation AI to analyze electricity usage. For example, the analysis unit can input power consumption data into the generation AI to analyze power consumption patterns. It can also input market electricity price information into the generation AI to predict electricity demand. Furthermore, the analysis unit can input household power consumption data into the generation AI to make suggestions for improving the efficiency of electricity use. As a result, the analysis unit improves the accuracy of its electricity demand predictions by analyzing electricity usage.

[0075] The company has a proposal department that makes proposals based on customer needs. For example, the proposal department can analyze a customer's electricity usage and propose the optimal way to use electricity. It can also analyze a customer's electricity consumption patterns and propose the optimal pricing plan. Furthermore, the proposal department can propose home appliances and related products to improve the efficiency of electricity use according to the customer's electricity usage. In this way, the proposal department can improve the efficiency of electricity use by making the most suitable proposals for customers.

[0076] The system includes a data department that utilizes data on products and home appliances purchased by customers. For example, the data department can analyze a customer's purchase history and make suggestions to improve the efficiency of their electricity usage. It can also analyze a customer's appliance usage and suggest the optimal way to use electricity. Furthermore, the data department can combine customer purchase history and appliance usage data to forecast electricity demand. As a result, the data department can further improve the accuracy of its electricity demand forecasts by utilizing customer data.

[0077] The analysis unit can analyze electricity usage using a generating AI. For example, the analysis unit can input power consumption data into the generating AI to analyze power consumption patterns. It can also input market electricity price information into the generating AI to predict electricity demand. Furthermore, the analysis unit can input household power consumption data into the generating AI to make suggestions for improving the efficiency of electricity use. As a result, the analysis unit can improve the accuracy of its electricity usage analysis by using the generating AI.

[0078] The proposal department can provide personalized proposals to customers using generative AI. For example, the proposal department can input a customer's electricity usage data into the generative AI and propose the optimal electricity usage method. It can also input a customer's electricity consumption pattern into the generative AI and propose the optimal pricing plan. Furthermore, the proposal department can input a customer's electricity usage data into the generative AI and propose home appliances and related products to improve electricity efficiency. In this way, the proposal department can provide optimal proposals to customers by using generative AI.

[0079] The Data Department can utilize data on products and home appliances purchased by customers. For example, the Data Department can analyze customers' purchase history and make suggestions to improve the efficiency of their electricity usage. It can also analyze customers' appliance usage and suggest optimal electricity usage methods. Furthermore, the Data Department can combine customer purchase history and appliance usage data to forecast electricity demand. This allows the Data Department to improve the accuracy of its electricity demand forecasts by utilizing customer data.

[0080] The energy storage unit can estimate the user's emotions and adjust the timing of energy storage based on those emotions. For example, if the user is stressed, the energy storage unit can set the energy storage timing to nighttime to prioritize daytime power use. If the user is relaxed, the energy storage unit can set the energy storage timing to daytime to prioritize nighttime power use. Furthermore, if the user is in a hurry, the energy storage unit can shorten the energy storage timing to supply power quickly. In this way, the energy storage unit can use power more efficiently by adjusting the energy storage timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The energy storage unit can determine the appropriate timing for energy storage by referring to weather forecast data. For example, based on weather forecast data, the energy storage unit can prioritize energy storage on sunny days and refrain from storing energy on cloudy days. It can also refrain from storing energy on rainy days and store energy on sunny days. Furthermore, based on weather forecast data, the energy storage unit can prioritize energy storage on days when wind power generation is expected. In this way, the energy storage unit can determine the optimal timing for energy storage by referring to weather forecast data.

[0082] The energy storage unit can analyze household power consumption patterns and select an appropriate energy storage method during storage. For example, it can analyze household power consumption patterns and store energy during off-peak hours at night. It can also analyze household power consumption patterns and refrain from storing energy during peak hours in the daytime. Furthermore, it can analyze household power consumption patterns and store energy on weekdays if weekend power consumption is high. In this way, the energy storage unit can select an efficient energy storage method by analyzing household power consumption patterns.

[0083] The energy storage unit can estimate the user's emotions and adjust the amount of stored energy based on those emotions. For example, if the user is stressed, the unit can reduce the amount of stored energy and prioritize power use. If the user is relaxed, the unit can increase the amount of stored energy to prepare for future power use. Furthermore, if the user is in a hurry, the unit can minimize the amount of stored energy to provide power quickly. This allows for more efficient power use by adjusting the amount of stored energy according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The energy storage unit can determine energy storage priorities by referring to regional electricity demand data during energy storage. For example, the energy storage unit can prioritize energy storage during periods of low demand based on regional electricity demand data. It can also refrain from energy storage during periods of high demand based on regional electricity demand data. Furthermore, the energy storage unit can store energy before demand surges occur based on regional electricity demand data. In this way, the energy storage unit can determine energy storage priorities by referring to regional electricity demand data.

[0085] The energy storage unit can select the optimal energy storage method by coordinating with other smart devices in the home during energy storage. For example, the energy storage unit can coordinate with smart devices in the home to store energy when devices with low power consumption are running. It can also coordinate with smart devices in the home to reduce energy storage when devices with high power consumption are running. Furthermore, the energy storage unit can coordinate with smart devices in the home to store energy according to the operating schedule of those devices. In this way, the energy storage unit can select the optimal energy storage method by coordinating with other smart devices in the home.

[0086] The communication unit can estimate the user's emotions and adjust the communication frequency based on the estimated emotions. For example, if the user is stressed, the communication unit can reduce the communication frequency and send only important information. If the user is relaxed, the communication unit can increase the communication frequency and provide more detailed information. Furthermore, if the user is in a hurry, the communication unit can minimize the communication frequency and quickly send the necessary information. In this way, the communication unit can provide more appropriate information by adjusting the communication frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The Communications Department can analyze trends in the electricity market in real time during communication and obtain optimal information. For example, the Communications Department can analyze price fluctuations in the electricity market in real time and obtain information at the optimal time. Furthermore, the Communications Department can analyze the supply and demand balance of the electricity market in real time and obtain optimal information. In addition, the Communications Department can analyze trends in the electricity market in real time, predict future trends, and obtain information. As a result, the Communications Department can obtain optimal information by analyzing trends in the electricity market in real time.

[0088] The communications department can transmit household electricity consumption data in real time during communication and link it with market information. For example, the communications department can transmit household electricity consumption data in real time and link it with market information to suggest optimal electricity usage. Furthermore, the communications department can transmit household electricity consumption data in real time and link it with market information to optimize electricity rates. In addition, the communications department can transmit household electricity consumption data in real time and link it with market information to perform demand forecasting. As a result, by transmitting household electricity consumption data in real time and linking it with market information, the communications department can enable optimal electricity usage.

[0089] The communication unit can estimate the user's emotions and customize the content of the communication based on those emotions. For example, if the user is stressed, the communication unit can send only concise and important information. If the user is relaxed, the communication unit can also provide communication content that includes detailed information. Furthermore, if the user is in a hurry, the communication unit can quickly send the necessary information. In this way, the communication unit can provide more appropriate information by customizing the content of the communication according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The communications department can determine communication priorities by considering the local power supply situation. For example, the communications department can prioritize communications during times when the power supply is stable, based on the local power supply situation. It can also refrain from communications during times when the power supply is unstable, based on the local power supply situation. Furthermore, the communications department can initiate communications before a surge in power supply occurs, based on the local power supply situation. In this way, the communications department can determine communication priorities by considering the local power supply situation.

[0091] The communications unit can share information with other home smart devices during communication. For example, the communications unit can share power consumption data with other smart devices to suggest optimal power usage. It can also share power rate information with other smart devices to suggest the best rate plan. Furthermore, the communications unit can share demand forecast data with other smart devices to optimize power supply. In this way, the communications unit can share information by collaborating with other home smart devices.

[0092] The control unit can estimate the user's emotions and adjust the timing of power supply based on the estimated emotions. For example, if the user is stressed, the control unit can set the power supply timing to nighttime, prioritizing daytime power use. Similarly, if the user is relaxed, the control unit can set the power supply timing to daytime, prioritizing nighttime power use. Furthermore, if the user is in a hurry, the control unit can shorten the power supply timing to provide power quickly. This allows the control unit to use power more efficiently by adjusting the power supply timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The control unit can analyze electricity market price fluctuations in real time during control operations to determine the optimal power supply timing. For example, the control unit can analyze electricity market price fluctuations in real time and supply power at the optimal timing. Furthermore, the control unit can analyze the supply and demand balance of the electricity market in real time to determine the optimal power supply timing. In addition, the control unit can analyze electricity market trends in real time, predict future trends, and determine the power supply timing. Thus, the control unit can determine the optimal power supply timing by analyzing electricity market price fluctuations in real time.

[0094] The control unit can analyze the household's power consumption patterns during control and select an efficient power supply method. For example, the control unit can analyze the household's power consumption patterns and supply power during off-peak hours at night. It can also analyze the household's power consumption patterns and reduce power supply during peak hours in the daytime. Furthermore, if power consumption is high on weekends, the control unit can supply power on weekdays. In this way, the control unit can select an efficient power supply method by analyzing the household's power consumption patterns.

[0095] The control unit can estimate the user's emotions and adjust the power supply based on the estimated emotions. For example, if the user is stressed, the control unit can reduce the power supply and prioritize power use. If the user is relaxed, the control unit can increase the power supply to prepare for future power use. Furthermore, if the user is in a hurry, the control unit can minimize the power supply and provide power quickly. In this way, the control unit enables more efficient power use by adjusting the power supply according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The control unit can determine the priority of power supply by referring to regional power demand data during control. For example, the control unit can prioritize power supply during periods of low demand based on regional power demand data. It can also refrain from supplying power during periods of high demand based on regional power demand data. Furthermore, the control unit can supply power before demand surges occur based on regional power demand data. Thus, the control unit can determine the priority of power supply by referring to regional power demand data.

[0097] The control unit can select the optimal power supply method in conjunction with other smart devices in the home during control. For example, the control unit can work with smart devices in the home to supply power when devices with low power consumption are in operation. It can also work with smart devices in the home to reduce power supply when devices with high power consumption are in operation. Furthermore, the control unit can work with smart devices in the home to supply power according to the operating schedule of devices. In this way, the control unit can select the optimal power supply method by working with other smart devices in the home.

[0098] The pricing department can estimate the user's emotions and customize pricing plans based on those emotions. For example, if the user is stressed, the pricing department can offer a simple and easy-to-understand pricing plan. If the user is relaxed, the pricing department can offer a pricing plan with more detailed options. Furthermore, if the user is in a hurry, the pricing department can offer a pricing plan that can be quickly selected. In this way, the pricing department can provide more appropriate pricing plans by customizing them according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The pricing department can provide the optimal plan by analyzing electricity market price fluctuations in real time when setting up pricing plans. For example, the pricing department can analyze electricity market price fluctuations in real time and provide the optimal pricing plan. Furthermore, the pricing department can analyze the supply and demand balance of the electricity market in real time and provide the optimal pricing plan. In addition, the pricing department can analyze electricity market trends in real time, predict future trends, and provide the optimal pricing plan. Thus, by analyzing electricity market price fluctuations in real time, the pricing department can provide the optimal pricing plan.

[0100] The pricing department can analyze household electricity consumption patterns when setting up pricing plans and select the most efficient plan. For example, the pricing department can analyze household electricity consumption patterns and offer a plan with lower nighttime rates if nighttime electricity consumption is low. It can also analyze household electricity consumption patterns and offer a plan with lower daytime rates if daytime electricity consumption is high. Furthermore, it can analyze household electricity consumption patterns and offer a plan with lower weekend rates if weekend electricity consumption is high. In this way, the pricing department can select the most efficient pricing plan by analyzing household electricity consumption patterns.

[0101] The pricing department can estimate the user's emotions and prioritize pricing plans based on those emotions. For example, if the user is stressed, the pricing department can prioritize a simple and easy-to-understand pricing plan. If the user is relaxed, the pricing department can prioritize a pricing plan with more detailed options. Furthermore, if the user is in a hurry, the pricing department can prioritize a pricing plan that can be selected quickly. This allows the pricing department to provide more appropriate pricing plans by prioritizing them according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The pricing department can customize pricing plans when setting them, taking into account the local power supply situation. For example, based on the local power supply situation, the pricing department can offer lower-priced pricing plans in areas with stable supply. Conversely, based on the local power supply situation, the pricing department can also offer higher-priced pricing plans in areas with unstable supply. Furthermore, based on the local power supply situation, the pricing department can customize pricing plans before a surge in supply occurs. In this way, the pricing department can provide the optimal pricing plan by taking into account the local power supply situation.

[0103] The pricing department can provide the optimal plan by collaborating with other home smart devices when setting up pricing plans. For example, the pricing department can collaborate with other smart devices to share electricity consumption data and provide the optimal pricing plan. It can also collaborate with other smart devices to share electricity billing information and provide the optimal pricing plan. Furthermore, the pricing department can collaborate with other smart devices to share demand forecast data and provide the optimal pricing plan. In this way, the pricing department can provide the optimal pricing plan by collaborating with other home smart devices.

[0104] The analysis unit can estimate the user's emotions and adjust the frequency of analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can reduce the frequency of analysis and provide only essential information. Conversely, if the user is relaxed, the analysis unit can increase the frequency of analysis and provide more detailed information. Furthermore, if the user is in a hurry, the analysis unit can minimize the frequency of analysis and quickly provide the necessary information. In this way, the analysis unit can provide more appropriate information by adjusting the frequency of analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The analysis unit can analyze trends in the electricity market in real time during analysis and provide optimal analysis results. For example, the analysis unit can analyze price fluctuations in the electricity market in real time and provide optimal analysis results. Furthermore, the analysis unit can analyze the supply and demand balance of the electricity market in real time and provide optimal analysis results. In addition, the analysis unit can analyze trends in the electricity market in real time, predict future trends, and provide optimal analysis results. Thus, the analysis unit can provide optimal analysis results by analyzing trends in the electricity market in real time.

[0106] The analysis unit can select an efficient analysis method by thoroughly analyzing household electricity consumption data during the analysis process. For example, the analysis unit can analyze household electricity consumption data in detail and provide analysis results that lower nighttime rates if nighttime electricity consumption is low. It can also analyze household electricity consumption data in detail and provide analysis results that lower daytime rates if daytime electricity consumption is high. Furthermore, it can analyze household electricity consumption data in detail and provide analysis results that lower weekend rates if weekend electricity consumption is high. In this way, the analysis unit can select an efficient analysis method by thoroughly analyzing household electricity consumption data.

[0107] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a concise and easy-to-read display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, the analysis unit can provide more appropriate information by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The analysis unit can determine the priority of analysis by considering the regional power supply situation. For example, the analysis unit can prioritize analyzing data from areas with stable power supply based on the regional power supply situation. It can also prioritize analyzing data from areas with unstable power supply based on the regional power supply situation. Furthermore, the analysis unit can analyze data before a sudden surge in supply occurs, based on the regional power supply situation. In this way, the analysis unit can determine the priority of analysis by considering the regional power supply situation.

[0109] The analysis unit can collaborate with other home smart devices during analysis to share analysis results. For example, the analysis unit can collaborate with other smart devices to share power consumption data and provide optimal analysis results. It can also collaborate with other smart devices to share electricity bill information and provide optimal analysis results. Furthermore, the analysis unit can collaborate with other smart devices to share demand forecast data and provide optimal analysis results. In this way, the analysis unit can provide optimal analysis results by collaborating with other home smart devices.

[0110] The suggestion function can estimate the user's emotions and customize the suggestions based on those emotions. For example, if the user is stressed, the suggestion function can provide concise and important suggestions. If the user is relaxed, it can provide suggestions with more detailed information. Furthermore, if the user is in a hurry, it can quickly provide the necessary suggestions. This allows the suggestion function to provide more appropriate suggestions by customizing them according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0111] The proposal department can provide optimal proposals by analyzing electricity market trends in real time. For example, the proposal department can analyze electricity market price fluctuations in real time and provide optimal proposals. Furthermore, the proposal department can analyze the supply and demand balance of the electricity market in real time and provide optimal proposals. In addition, the proposal department can analyze electricity market trends in real time, predict future trends, and provide optimal proposals. Thus, by analyzing electricity market trends in real time, the proposal department can provide optimal proposals.

[0112] The proposal department can analyze household electricity consumption patterns and select the most efficient proposal method when making a proposal. For example, if the proposal department analyzes household electricity consumption patterns and finds that nighttime electricity consumption is low, it can offer a proposal to lower nighttime rates. Similarly, if daytime electricity consumption is high, it can offer a proposal to lower daytime rates. Furthermore, if weekend electricity consumption is high, it can offer a proposal to lower weekend rates. In this way, the proposal department can select the most efficient proposal method by analyzing household electricity consumption patterns.

[0113] The suggestion function can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion function can prioritize concise and important suggestions. If the user is relaxed, it can prioritize suggestions containing more detailed information. Furthermore, if the user is in a hurry, it can prioritize suggestions that are needed quickly. This allows the suggestion function to provide more appropriate suggestions by prioritizing suggestions according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0114] The proposal department can customize its proposals when making them, taking into account the local power supply situation. For example, based on the local power supply situation, the proposal department can offer low-cost proposals to areas with stable supply. Conversely, based on the local power supply situation, the proposal department can also offer high-cost proposals to areas with unstable supply. Furthermore, based on the local power supply situation, the proposal department can customize proposals before a surge in supply occurs. In this way, the proposal department can provide the most optimal proposal by taking into account the local power supply situation.

[0115] The proposal department can provide optimal proposals by collaborating with other home smart devices during the proposal process. For example, the proposal department can collaborate with other smart devices, share power consumption data, and provide optimal proposals. It can also collaborate with other smart devices, share power rate information, and provide optimal proposals. Furthermore, the proposal department can collaborate with other smart devices, share demand forecast data, and provide optimal proposals. In this way, the proposal department can provide optimal proposals by collaborating with other home smart devices.

[0116] The data unit can estimate the user's emotions and adjust the data collection frequency based on the estimated emotions. For example, if the user is stressed, the data unit can reduce the data collection frequency and collect only important data. Conversely, if the user is relaxed, the data unit can increase the data collection frequency and collect more detailed data. Furthermore, if the user is in a hurry, the data unit can minimize the data collection frequency and quickly collect the necessary data. This allows the data unit to collect more appropriate data by adjusting the data collection frequency according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0117] The data department can collect optimal data by analyzing electricity market trends in real time during data collection. For example, the data department can analyze electricity market price fluctuations in real time and collect optimal data. It can also analyze the supply and demand balance of the electricity market in real time and collect optimal data. Furthermore, the data department can analyze electricity market trends in real time, predict future trends, and collect optimal data. In this way, the data department can collect optimal data by analyzing electricity market trends in real time.

[0118] The data department can select an efficient data collection method by collecting detailed household electricity consumption data during data collection. For example, the data department can collect detailed household electricity consumption data and collect data to lower nighttime rates if nighttime electricity consumption is low. It can also collect detailed household electricity consumption data and collect data to lower daytime rates if daytime electricity consumption is high. Furthermore, the data department can collect detailed household electricity consumption data and collect data to lower weekend rates if weekend electricity consumption is high. In this way, the data department can select an efficient data collection method by collecting detailed household electricity consumption data.

[0119] The data unit can estimate the user's emotions and adjust how the data is displayed based on those emotions. For example, if the user is stressed, the data unit can provide a concise and easy-to-read display. If the user is relaxed, the data unit can provide a display that includes detailed information. Furthermore, if the user is in a hurry, the data unit can provide a concise display. In this way, the data unit can provide more appropriate information by adjusting how the data is displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0120] The data unit can prioritize data collection by considering the local power supply situation. For example, the data unit can prioritize collecting data from areas with stable power supply based on the local power supply situation. It can also prioritize collecting data from areas with unstable power supply based on the local power supply situation. Furthermore, the data unit can collect data before a surge in supply occurs, based on the local power supply situation. In this way, the data unit can prioritize data by considering the local power supply situation.

[0121] The data unit can collaborate with other home smart devices to share data during data collection. For example, the data unit can collaborate with other smart devices to share power consumption data for optimal data collection. It can also collaborate with other smart devices to share power billing information for optimal data collection. Furthermore, the data unit can collaborate with other smart devices to share demand forecast data for optimal data collection. This enables the data unit to achieve optimal data collection by collaborating with other home smart devices. === Hard Collateral 1-1 === Each of the multiple elements described above, including the energy storage unit, communication unit, control unit, billing unit, analysis unit, proposal unit, and data unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the energy storage unit can store electricity using the home battery of the smart device 14. The communication unit can acquire market electricity price information using the communication I / F 44 of the smart device 14. The control unit can automatically switch between charging and supplying power in response to fluctuations in electricity demand using the processor 46 of the smart device 14. The billing unit can introduce demand-responsive billing plans using the specific processing unit 290 of the data processing unit 12. The analysis unit can analyze electricity usage using generated AI using the specific processing unit 290 of the data processing unit 12. The proposal unit can make proposals based on customer needs using the specific processing unit 290 of the data processing unit 12. The data unit can utilize data on products and home appliances purchased by customers using the database 24 of the data processing unit 12. === Hard Collateral 1-2 === Each of the multiple elements described above, including the energy storage unit, communication unit, control unit, billing unit, analysis unit, proposal unit, and data unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the energy storage unit can store power using the home battery of the smart glasses 214. The communication unit can acquire market electricity price information using the communication I / F 44 of the smart glasses 214. The control unit can automatically switch between charging and supplying power in response to fluctuations in electricity demand using the processor 46 of the smart glasses 214. The billing unit can introduce demand-responsive billing plans using the specific processing unit 290 of the data processing unit 12. The analysis unit can analyze electricity usage using generated AI using the specific processing unit 290 of the data processing unit 12. The proposal unit can make proposals based on customer needs using the specific processing unit 290 of the data processing unit 12. The data unit can utilize data on products and home appliances purchased by customers using the database 24 of the data processing unit 12. === Hard Collateral 1-3 === Each of the multiple elements described above, including the energy storage unit, communication unit, control unit, billing unit, analysis unit, proposal unit, and data unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the energy storage unit can store power using the home battery of the headset terminal 314. The communication unit can acquire market electricity price information using the communication I / F 44 of the headset terminal 314. The control unit can automatically switch between charging and power supply in response to fluctuations in power demand using the processor 46 of the headset terminal 314. The billing unit can introduce demand-responsive billing plans using the specific processing unit 290 of the data processing unit 12. The analysis unit can analyze electricity usage using generated AI using the specific processing unit 290 of the data processing unit 12. The proposal unit can make proposals based on customer needs using the specific processing unit 290 of the data processing unit 12. The data unit can utilize data on products and home appliances purchased by customers using the database 24 of the data processing unit 12. === Hard Collateral 1-4 === Each of the multiple elements described above, including the energy storage unit, communication unit, control unit, billing unit, analysis unit, proposal unit, and data unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the energy storage unit can store electricity using the robot 414's home battery. The communication unit can acquire market electricity price information using the robot 414's communication I / F 44. The control unit can automatically switch between charging and supplying power in response to fluctuations in electricity demand using the robot 414's processor 46. The billing unit can introduce demand-responsive billing plans using the specific processing unit 290 of the data processing unit 12. The analysis unit can analyze electricity usage using generated AI using the specific processing unit 290 of the data processing unit 12. The proposal unit can make proposals based on customer needs using the specific processing unit 290 of the data processing unit 12. The data unit can utilize data on products and home appliances purchased by customers using the database 24 of the data processing unit 12.

[0122] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0123] The control unit can estimate the user's emotions and determine power usage priorities based on those emotions. For example, if the user is stressed, the control unit can prioritize power usage for lighting and air conditioning to provide a relaxing environment. If the user is relaxed, it can also prioritize power usage for entertainment devices. Furthermore, if the user is in a hurry, it can prioritize power usage for essential appliances to quickly supply the necessary power. In this way, the control unit can provide a more comfortable living environment by determining power usage priorities according to the user's emotions.

[0124] The communications unit can estimate the user's emotions and customize the content of the communication based on those emotions. For example, if the user is stressed, the communications unit can send only concise and important information. If the user is relaxed, it can provide communication content that includes detailed information. Furthermore, if the user is in a hurry, it can quickly send the necessary information. In this way, the communications unit can provide more appropriate information by customizing the content of the communication according to the user's emotions.

[0125] The pricing department can estimate the user's emotions and customize pricing plans based on those emotions. For example, if the user is stressed, the pricing department can offer a simple and easy-to-understand pricing plan. If the user is relaxed, it can offer a pricing plan with more detailed options. Furthermore, if the user is in a hurry, it can offer a pricing plan that allows for quick selection. In this way, the pricing department can provide more appropriate pricing plans by customizing them according to the user's emotions.

[0126] The suggestion function can estimate the user's emotions and customize the suggestions based on those emotions. For example, if the user is stressed, the suggestion function can provide concise and important suggestions. If the user is relaxed, it can provide suggestions that include more detailed information. Furthermore, if the user is in a hurry, it can quickly provide the necessary suggestions. In this way, the suggestion function can provide more appropriate suggestions by customizing them according to the user's emotions.

[0127] The data unit can estimate the user's emotions and adjust the data collection frequency based on that estimation. For example, if the user is stressed, the data unit can reduce the data collection frequency and collect only essential data. Conversely, if the user is relaxed, it can increase the data collection frequency to collect more detailed data. Furthermore, if the user is in a hurry, it can minimize the data collection frequency to quickly collect the necessary data. In this way, the data unit can collect more appropriate data by adjusting the data collection frequency according to the user's emotions.

[0128] The control unit can analyze electricity market price fluctuations in real time during control to determine the optimal power supply timing. For example, the control unit can analyze electricity market price fluctuations in real time and supply power at the optimal timing. Furthermore, the control unit can analyze the supply and demand balance of the electricity market in real time to determine the optimal power supply timing. In addition, the control unit can analyze electricity market trends in real time, predict future trends, and determine the power supply timing. Thus, the control unit can determine the optimal power supply timing by analyzing electricity market price fluctuations in real time.

[0129] The communications department can transmit household electricity consumption data in real time during communication and link it with market information. For example, the communications department can transmit household electricity consumption data in real time and link it with market information to suggest optimal electricity usage. Furthermore, the communications department can transmit household electricity consumption data in real time and link it with market information to optimize electricity rates. In addition, the communications department can transmit household electricity consumption data in real time and link it with market information to perform demand forecasting. As a result, by transmitting household electricity consumption data in real time and linking it with market information, the communications department can enable optimal electricity usage.

[0130] The pricing department can provide the optimal plan by analyzing electricity market price fluctuations in real time when setting pricing plans. For example, the pricing department can analyze electricity market price fluctuations in real time and provide the optimal pricing plan. Furthermore, the pricing department can analyze the supply and demand balance of the electricity market in real time and provide the optimal pricing plan. In addition, the pricing department can analyze electricity market trends in real time, predict future trends, and provide the optimal pricing plan. Thus, the pricing department can provide the optimal pricing plan by analyzing electricity market price fluctuations in real time.

[0131] The analysis unit can analyze trends in the electricity market in real time during analysis and provide optimal analysis results. For example, the analysis unit can analyze price fluctuations in the electricity market in real time and provide optimal analysis results. Furthermore, the analysis unit can analyze the supply and demand balance of the electricity market in real time and provide optimal analysis results. In addition, the analysis unit can analyze trends in the electricity market in real time, predict future trends, and provide optimal analysis results. Thus, the analysis unit can provide optimal analysis results by analyzing trends in the electricity market in real time.

[0132] The data unit can collaborate with other home smart devices to share data during data collection. For example, the data unit can collaborate with other smart devices to share power consumption data and perform optimal data collection. It can also collaborate with other smart devices to share power billing information and perform optimal data collection. Furthermore, the data unit can collaborate with other smart devices to share demand forecast data and perform optimal data collection. This enables the data unit to perform optimal data collection by collaborating with other home smart devices.

[0133] The following briefly describes the processing flow for example form 2.

[0134] Step 1: The energy storage unit stores electricity using a household battery. For example, electricity can be stored during off-peak hours at night when electricity demand is low, or electricity can be stored using renewable energy sources such as solar power. It can also be used to supply electricity during peak hours when electricity demand is high. Step 2: The communications department uses communication technology to obtain real-time market electricity price information. For example, it can obtain market electricity price information via the internet or obtain household electricity consumption data using smart meters. Furthermore, it can also obtain market electricity price information using wireless communication technology. Step 3: The control unit charges the device when power demand is low and supplies power when demand is high, based on the information acquired by the communication unit. For example, it can use AI technology to predict when power demand is low and charge the device at that time. It can also predict when power demand is high and supply power at that time. Furthermore, it can automatically switch between charging and supplying power in response to fluctuations in power demand. Step 4: The pricing department implements demand-responsive pricing plans. For example, lower rates during periods of low electricity demand and higher rates during periods of high demand. It is also possible to adjust rates in real time according to fluctuations in electricity demand. Furthermore, it is possible to propose the optimal pricing plan based on the customer's electricity usage.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0137] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0139] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0140] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0142] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

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

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

[0146] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0149] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0151] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0153] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0155] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0156] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0158] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

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

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

[0162] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0163] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0164] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0165] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0166] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0167] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0168] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0169] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0170] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0171] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0172] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0173] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0174] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0175] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0176] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0177] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0178] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0179] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0180] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0181] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0182] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0183] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0184] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0185] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0186] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0187] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0188] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0189] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0190] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0191] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0192] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0193] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0194] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0195] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0196] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0197] 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.

[0198] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0199] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0200] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0201] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0202] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0203] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0204] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0205] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0206] [Explanation of Symbols]

[0207] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A power storage unit that stores electricity using a home battery, The communications department uses communication technology to acquire market electricity price information in real time, A control unit that charges when power demand is low and supplies power when demand is high, based on information acquired by the aforementioned communication unit, A pricing department that implements demand-responsive pricing plans, A system characterized by the following features.

2. It includes an analysis unit that uses generated AI to analyze electricity usage. The system according to feature 1.

3. We have a proposal department that makes proposals based on customer needs. The system according to feature 1.

4. It has a data department that utilizes data on products and home appliances purchased by customers. The system according to feature 1.

5. The aforementioned analysis unit, The AI ​​generates data to analyze electricity usage. The system according to feature 2.

6. The aforementioned proposal section is, Generating AI to provide personalized suggestions to customers The system according to claim 3.

7. The aforementioned data unit is Utilizing data on products and home appliances purchased by customers. The system according to feature 4.

8. The aforementioned energy storage unit is It estimates the user's emotions and adjusts the timing of energy storage based on those estimated emotions. The system according to feature 1.

9. The aforementioned energy storage unit is During energy storage, weather forecast data is referenced to determine the appropriate timing for storage. The system according to feature 1.

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