system

Abstract

The system according to this embodiment aims to optimize energy utilization. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects energy data. The analysis unit analyzes the energy data collected by the collection unit. The provision unit provides an energy utilization optimization plan based on the results analyzed by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to optimize energy utilization.

[0005] The system according to the embodiment aims to optimize energy utilization.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects energy data. The analysis unit analyzes the energy data collected by the collection unit. The provision unit provides an optimization plan for energy utilization based on the result analyzed by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can optimize energy utilization. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An energy management system according to an embodiment of the present invention is a system that optimizes energy use in homes and businesses by analyzing energy data using generative AI. This energy management system collects energy data, and the generative AI analyzes that data to provide an energy use optimization plan. This plan includes promoting the use of renewable energy and reducing energy costs. This enables sustainable energy management and cost reduction. For example, the energy management system collects detailed energy consumption data from homes and businesses using sensors and smart meters. For example, it collects data such as electricity consumption, gas usage, and renewable energy generation. This allows for an overall understanding of energy consumption. Next, the energy management system uses generative AI to analyze the collected energy data. The generative AI analyzes energy consumption patterns and generates an energy use optimization plan. For example, it can identify peak electricity consumption times and propose a plan that prioritizes the use of renewable energy during those times. This improves energy efficiency and reduces costs. Furthermore, the energy management system provides an energy use optimization plan based on the results of the analysis by the generative AI. This plan includes promoting the use of renewable energy and reducing energy costs. For example, plans can be proposed to promote the use of solar and wind power generation, thereby reducing energy costs. This enables sustainable energy management and cost reduction. For instance, households can reduce energy costs by using renewable energy during peak electricity consumption hours. Businesses can reduce costs by analyzing energy consumption patterns and improving energy efficiency. Furthermore, this system provides solutions to challenges such as rising energy costs and the need for renewable energy. Rising energy costs are a global concern, causing economic and social problems. Renewable energy is attracting attention as a sustainable energy source, and can promote long-term energy stability while improving environmental issues.This system enables reduced energy costs and promotes the use of renewable energy, resulting in sustainable energy management. Thus, the energy management system can achieve sustainable energy management and cost reduction.

[0029] The energy management system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects energy data. For example, the collection unit collects energy consumption data from households and businesses using sensors or smart meters. For example, the collection unit collects data such as electricity consumption, gas usage, and renewable energy generation. For example, the collection unit can collect energy data in real time using smart meters. For example, the collection unit can also collect environmental data that affects energy consumption using temperature sensors. The analysis unit analyzes the energy data collected by the collection unit. For example, the analysis unit analyzes energy consumption patterns using generative AI. For example, the analysis unit identifies peak time periods for electricity consumption and generates a plan that prioritizes the use of renewable energy during those times. For example, the analysis unit can also analyze seasonal fluctuations in energy consumption and generate optimal energy use plans for each season. For example, the analysis unit can build a predictive model for energy consumption using machine learning algorithms and predict future energy consumption. The provision unit provides an optimized energy use plan based on the results analyzed by the analysis unit. The service provider offers, for example, optimization plans that include promoting the use of renewable energy and reducing energy costs. The service provider offers, for example, plans that promote the use of solar and wind power generation. The service provider can also offer, for example, plans that include improving energy efficiency and reducing consumption. The service provider can also offer, for example, plans that prioritize the use of renewable energy during peak energy consumption hours. As a result, the energy management system according to the embodiment can optimize energy use by collecting and analyzing energy data and providing optimization plans.

[0030] The data collection unit collects energy data. For example, it collects energy consumption data from homes and businesses using sensors and smart meters. Specifically, it obtains detailed electricity consumption data through smart plugs attached to each electrical appliance in the home and energy monitoring devices installed in each department of a company. This allows for accurate identification of which devices consume how much energy at what time of day. Furthermore, it also collects gas usage data in real time using sensors connected to gas meters. For renewable energy generation, it obtains data from power generation measurement devices installed in solar and wind power generation systems. This allows the data collection unit to centrally collect a wide range of energy data, including electricity consumption, gas usage, and renewable energy generation. The data collection unit can also collect environmental data that affects energy consumption using temperature sensors. For example, it collects data such as indoor and outdoor temperature, humidity, and solar radiation, and uses this environmental data as basic data to analyze the impact of this environmental data on energy consumption. This allows the data collection unit to grasp the detailed situation of energy consumption and provide accurate data to the analysis and provision units. Furthermore, the data collection unit can transmit this data to a cloud server, allowing for real-time data storage and management. This enables the data collection unit to efficiently and accurately collect energy data, improving the overall system performance.

[0031] The analysis unit analyzes the energy data collected by the collection unit. For example, the analysis unit uses generative AI to analyze energy consumption patterns. Specifically, based on the collected energy data, it extracts the characteristics of each household's and company's energy consumption and classifies the consumption patterns. The generative AI learns from past data and can detect energy consumption trends and anomalies. For example, it can identify peak electricity consumption times and generate a plan that prioritizes the use of renewable energy during those times. This can shift the peak of electricity consumption, leading to reduced energy costs and stabilization of the power supply. Furthermore, the analysis unit can analyze seasonal fluctuations in energy consumption and generate optimal energy use plans for each season. For example, it can propose efficient energy use methods tailored to periods of increased energy consumption, such as summer and winter. The analysis unit can also use machine learning algorithms to build predictive models for energy consumption and forecast future energy consumption. This can be useful for energy supply planning and demand forecasting. For example, based on past data, it can predict increases and decreases in energy consumption during specific time periods or seasons and formulate appropriate energy supply plans. This allows the analysis unit to optimize and improve the efficiency of energy consumption, thereby enhancing the overall system performance.

[0032] The service provider offers energy utilization optimization plans based on the results analyzed by the analysis department. Specifically, it provides optimization plans that include promoting the use of renewable energy and reducing energy costs. For example, it offers plans that promote the use of solar and wind power. This maximizes the use of renewable energy and reduces the use of fossil fuels. The service provider can also offer plans that include improving energy efficiency and reducing consumption. For example, it can offer plans that prioritize the use of renewable energy during peak energy consumption hours. This shifts the peak of electricity consumption, reducing energy costs and stabilizing the power supply. Furthermore, the service provider has an interface for providing energy utilization optimization plans to users. For example, it provides users with information on their energy consumption status and optimization plans through a smartphone app or web portal. This allows users to understand their energy consumption status in real time and select the optimal energy utilization method. The service provider can collect feedback from users and continuously improve the accuracy and effectiveness of the optimization plans. For example, it can customize the optimization plan according to the user's energy consumption patterns and environmental conditions, proposing more effective energy utilization methods. This allows the service provider to offer users the optimal energy utilization method and achieve energy utilization optimization.

[0033] The data collection unit can collect energy consumption data from households and businesses using sensors and smart meters. For example, the data collection unit can collect electricity consumption data from households using smart meters. For example, the data collection unit can also collect gas usage data from businesses using sensors. For example, the data collection unit can also collect renewable energy generation data using smart meters. This allows for the optimization of energy use by collecting detailed energy consumption data from households and businesses. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from smart meters into a generating AI and have the generating AI perform data analysis.

[0034] The analysis unit can analyze energy data using algorithms that analyze energy consumption patterns. For example, the analysis unit can analyze energy consumption patterns using machine learning algorithms. The analysis unit can also analyze energy consumption patterns using statistical analysis algorithms. The analysis unit can also analyze energy consumption patterns using generative AI. By analyzing energy consumption patterns, it becomes possible to optimize energy use. Some or all of the above-described processes in the analysis unit may be performed using generative AI, for example, or without using generative AI. For example, the analysis unit can input energy data into a generative AI and have the generative AI perform the analysis of energy consumption patterns.

[0035] The service provider can provide optimization plans that include promoting the use of renewable energy and reducing energy costs. For example, the service provider can provide a plan that promotes the use of solar power. For example, the service provider can also provide a plan that promotes the use of wind power. For example, the service provider can also provide a plan that includes improving energy efficiency and reducing consumption. This enables sustainable energy management by providing optimization plans that include promoting the use of renewable energy and reducing energy costs. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can use a generative AI to generate optimization plans that include promoting the use of renewable energy and reducing energy costs.

[0036] The service provider can provide plans that promote the use of solar power and wind power. For example, the service provider can provide a plan that promotes the use of solar power. The service provider can also provide a plan that promotes the use of wind power. The service provider can also provide a plan that utilizes both solar power and wind power. This promotes the use of renewable energy by promoting the use of solar power and wind power. Some or all of the above processing in the service provider may be performed using, for example, a generating AI, or not using a generating AI. For example, the service provider can use a generating AI to generate plans that promote the use of solar power and wind power.

[0037] The data collection unit can select the optimal collection method by referring to past energy consumption history when collecting energy consumption data from households and businesses. For example, the data collection unit can analyze past energy consumption history and focus on collecting data during peak hours. For example, the data collection unit can also collect data on specific days of the week or time periods based on past energy consumption history. For example, the data collection unit can collect data during periods of large fluctuations in energy consumption by referring to past energy consumption history. This enables efficient data collection by selecting the optimal collection method by referring to past energy consumption history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past energy consumption history into a generating AI and have the generating AI select the optimal collection method.

[0038] The data collection unit can filter energy data based on the user's current living and working conditions. For example, if the user is working from home, the data collection unit will focus on collecting household energy consumption data. If the user is out, for example, the data collection unit can omit household energy consumption data and collect corporate energy consumption data. If the user is on holiday, for example, the data collection unit can collect household energy consumption data in detail. This enables data collection tailored to the user's living and working conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's living and working conditions into a generating AI and have the generating AI perform the filtering.

[0039] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting energy data. For example, if the user is in an urban area, the data collection unit will prioritize the collection of energy data for urban areas. For example, if the user is in a suburban area, the data collection unit can also prioritize the collection of energy data for suburban areas. For example, if the user is in a specific region, the data collection unit can also prioritize the collection of energy data for that region. This enables efficient data collection by prioritizing the collection of highly relevant data based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI select highly relevant data.

[0040] The data collection unit can analyze the user's social media activity and collect relevant data when collecting energy data. For example, if a user makes a post about energy on social media, the data collection unit can collect data based on the content of that post. For example, if a user participates in a specific energy-related event on social media, the data collection unit can also collect data related to that event. For example, if a user expresses an opinion about energy consumption on social media, the data collection unit can also collect data based on that opinion. This enables efficient data collection by collecting relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0041] The analysis unit can identify peak energy consumption times during energy data analysis and generate plans that prioritize the use of renewable energy during those times. For example, the analysis unit can generate plans that prioritize the use of solar power during peak energy consumption times. The analysis unit can also generate plans that prioritize the use of wind power during peak energy consumption times. The analysis unit can also generate plans that utilize battery storage during peak energy consumption times. This improves energy efficiency by prioritizing the use of renewable energy during peak energy consumption times. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input energy data into a generation AI and have the generation AI identify peak times and generate plans.

[0042] The analysis unit can apply different analysis algorithms to each category of energy consumption when analyzing energy data. For example, the analysis unit can apply a specific algorithm to electricity consumption data. For example, the analysis unit can apply a different algorithm to gas consumption data. For example, the analysis unit can apply yet another algorithm to renewable energy generation data. By applying different analysis algorithms to each category of energy consumption, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input energy data into a generative AI and have the generative AI perform analysis for each category.

[0043] The analysis unit can determine the priority of energy data analysis based on the submission date of energy consumption. For example, the analysis unit may prioritize the analysis of energy data with an approaching submission deadline. For example, the analysis unit may postpone the analysis of energy data with a distant submission deadline. For example, the analysis unit may quickly analyze energy data whose submission deadline has passed. This enables efficient analysis by determining the priority of analysis based on the submission date of energy consumption. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input energy data into a generating AI and have the generating AI perform the determination of priority based on submission date.

[0044] The analysis unit can adjust the order of analysis based on the relationships between energy consumption when analyzing energy data. For example, the analysis unit can prioritize the analysis of highly relevant energy data. For example, the analysis unit can postpone the analysis of less relevant energy data. For example, the analysis unit can group highly relevant energy data together for analysis. This allows for efficient analysis by adjusting the order of analysis based on the relationships between energy consumption. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input energy data into a generative AI and have the generative AI execute the analysis in a relationship-based order.

[0045] The service provider can generate plans that include promoting the use of renewable energy and reducing energy costs when providing energy utilization optimization plans. For example, the service provider can generate plans that promote the use of solar power. The service provider can also generate plans that promote the use of wind power. The service provider can also generate plans that include reducing energy costs. By providing plans that include promoting the use of renewable energy and reducing energy costs, sustainable energy management becomes possible. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or not using a generation AI. For example, the service provider can use a generation AI to generate plans that include promoting the use of renewable energy and reducing energy costs.

[0046] The service provider, when providing an energy utilization optimization plan, can propose plans that promote the use of solar power and wind power. For example, the service provider can propose a plan that promotes the use of solar power. The service provider can also propose a plan that promotes the use of wind power. The service provider can also propose a plan that utilizes both solar power and wind power. This promotes the use of renewable energy by promoting the use of solar power and wind power. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can use a generative AI to generate plans that promote the use of solar power and wind power.

[0047] The service provider can prioritize energy utilization optimization plans based on the timing of energy consumption data submission. For example, the service provider may prioritize plans based on energy consumption data with an approaching submission deadline. The service provider may also postpone plans based on energy consumption data with a distant submission deadline. The service provider may also promptly provide plans based on energy consumption data whose submission deadline has passed. This enables efficient plan provision by prioritizing plans based on the timing of energy consumption data submission. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input energy consumption data into a generative AI and have the generative AI prioritize plans based on submission timing.

[0048] The service provider can adjust the order of energy consumption optimization plans based on the relevance of energy consumption. For example, the service provider may prioritize plans based on highly relevant energy consumption data. For example, the service provider may postpone plans based on less relevant energy consumption data. For example, the service provider may group highly relevant energy consumption data and provide plans based on those groups. This allows for efficient plan provision by adjusting the order of plans based on the relevance of energy consumption. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input energy consumption data into a generative AI and have the generative AI execute the order of plans based on relevance.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The analysis unit can generate an optimal energy use plan by considering seasonal variations in energy consumption when analyzing energy data. For example, the analysis unit can generate a plan that improves the energy efficiency of heating because heating use increases in winter. For example, the analysis unit can also generate a plan that improves the energy efficiency of cooling because cooling use increases in summer. For example, the analysis unit can also generate a plan that maximizes the use of renewable energy because energy consumption is relatively low in spring and autumn. In this way, energy efficiency can be improved by providing an optimal energy use plan that is tailored to seasonal energy consumption patterns.

[0051] The service provider can customize energy utilization optimization plans based on the user's lifestyle. For example, if a user works from home, the service provider can provide a plan that optimizes energy consumption during that time. If a user frequently goes out, the service provider can also provide a plan that minimizes energy consumption while out. If a user has a nocturnal lifestyle, the service provider can also provide a plan that optimizes energy consumption at night. By providing an optimal energy utilization plan tailored to the user's lifestyle, energy efficiency can be improved.

[0052] The analysis unit can improve the accuracy of its analysis by referring to the user's past energy consumption history when analyzing energy data. For example, the analysis unit can build a model to predict future energy consumption based on past energy consumption history. For example, the analysis unit can also identify energy consumption trends by analyzing past energy consumption history. For example, the analysis unit can identify peak energy consumption times by referring to past energy consumption history. By improving the accuracy of the analysis by referring to past energy consumption history, it is possible to provide a more accurate energy usage plan.

[0053] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting energy data. For example, if the user is in an urban area, the data collection unit will prioritize collecting energy data for urban areas. If the user is in a suburban area, the data collection unit can also prioritize collecting energy data for suburban areas. If the user is in a specific region, the data collection unit can also prioritize collecting energy data for that region. This enables efficient data collection by prioritizing the collection of highly relevant data based on the user's geographical location.

[0054] The analysis unit can apply different analysis algorithms to each category of energy consumption when analyzing energy data. For example, the analysis unit can apply a specific algorithm to electricity consumption data. For example, the analysis unit can apply a different algorithm to gas consumption data. For example, the analysis unit can apply yet another algorithm to renewable energy generation data. By applying different analysis algorithms to each category of energy consumption, the accuracy of the analysis is improved.

[0055] The data collection unit can analyze users' social media activity and collect relevant data when collecting energy data. For example, if a user makes a post about energy on social media, the data collection unit can collect data based on the content of that post. For example, if a user participates in a specific energy-related event on social media, the data collection unit can also collect data related to that event. For example, if a user expresses an opinion about energy consumption on social media, the data collection unit can also collect data based on that opinion. This enables efficient data collection by collecting relevant data based on users' social media activity.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The collection unit collects energy data. The collection unit collects energy consumption data from homes and businesses using sensors and smart meters. For example, it collects data such as electricity consumption, gas usage, and renewable energy generation. Furthermore, it can collect energy data in real time using smart meters, and it can also collect environmental data that affects energy consumption using temperature sensors. Step 2: The analysis unit analyzes the energy data collected by the collection unit. The analysis unit uses a generation AI to analyze energy consumption patterns, identify peak electricity consumption times, and generate a plan that prioritizes the use of renewable energy during those times. It can also analyze seasonal variations in energy consumption and generate optimal energy use plans for each season. Furthermore, it can use machine learning algorithms to build a predictive model for energy consumption and predict future energy consumption. Step 3: The service provider provides an energy utilization optimization plan based on the results analyzed by the analysis unit. The service provider provides an optimization plan that includes promoting the use of renewable energy and reducing energy costs. For example, it can provide a plan that promotes the use of solar and wind power, or a plan that includes improving energy efficiency and reducing consumption. Furthermore, it can also provide a plan that prioritizes the use of renewable energy during peak energy consumption hours.

[0058] (Example of form 2) An energy management system according to an embodiment of the present invention is a system that optimizes energy use in homes and businesses by analyzing energy data using generative AI. This energy management system collects energy data, and the generative AI analyzes that data to provide an energy use optimization plan. This plan includes promoting the use of renewable energy and reducing energy costs. This enables sustainable energy management and cost reduction. For example, the energy management system collects detailed energy consumption data from homes and businesses using sensors and smart meters. For example, it collects data such as electricity consumption, gas usage, and renewable energy generation. This allows for an overall understanding of energy consumption. Next, the energy management system uses generative AI to analyze the collected energy data. The generative AI analyzes energy consumption patterns and generates an energy use optimization plan. For example, it can identify peak electricity consumption times and propose a plan that prioritizes the use of renewable energy during those times. This improves energy efficiency and reduces costs. Furthermore, the energy management system provides an energy use optimization plan based on the results of the analysis by the generative AI. This plan includes promoting the use of renewable energy and reducing energy costs. For example, plans can be proposed to promote the use of solar and wind power generation, thereby reducing energy costs. This enables sustainable energy management and cost reduction. For instance, households can reduce energy costs by using renewable energy during peak electricity consumption hours. Businesses can reduce costs by analyzing energy consumption patterns and improving energy efficiency. Furthermore, this system provides solutions to challenges such as rising energy costs and the need for renewable energy. Rising energy costs are a global concern, causing economic and social problems. Renewable energy is attracting attention as a sustainable energy source, and can promote long-term energy stability while improving environmental issues.This system enables reduced energy costs and promotes the use of renewable energy, resulting in sustainable energy management. Thus, the energy management system can achieve sustainable energy management and cost reduction.

[0059] The energy management system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects energy data. For example, the collection unit collects energy consumption data from households and businesses using sensors or smart meters. For example, the collection unit collects data such as electricity consumption, gas usage, and renewable energy generation. For example, the collection unit can collect energy data in real time using smart meters. For example, the collection unit can also collect environmental data that affects energy consumption using temperature sensors. The analysis unit analyzes the energy data collected by the collection unit. For example, the analysis unit analyzes energy consumption patterns using generative AI. For example, the analysis unit identifies peak time periods for electricity consumption and generates a plan that prioritizes the use of renewable energy during those times. For example, the analysis unit can also analyze seasonal fluctuations in energy consumption and generate optimal energy use plans for each season. For example, the analysis unit can build a predictive model for energy consumption using machine learning algorithms and predict future energy consumption. The provision unit provides an optimized energy use plan based on the results analyzed by the analysis unit. The service provider offers, for example, optimization plans that include promoting the use of renewable energy and reducing energy costs. The service provider offers, for example, plans that promote the use of solar and wind power generation. The service provider can also offer, for example, plans that include improving energy efficiency and reducing consumption. The service provider can also offer, for example, plans that prioritize the use of renewable energy during peak energy consumption hours. As a result, the energy management system according to the embodiment can optimize energy use by collecting and analyzing energy data and providing optimization plans.

[0060] The data collection unit collects energy data. For example, it collects energy consumption data from homes and businesses using sensors and smart meters. Specifically, it obtains detailed electricity consumption data through smart plugs attached to each electrical appliance in the home and energy monitoring devices installed in each department of a company. This allows for accurate identification of which devices consume how much energy at what time of day. Furthermore, it also collects gas usage data in real time using sensors connected to gas meters. For renewable energy generation, it obtains data from power generation measurement devices installed in solar and wind power generation systems. This allows the data collection unit to centrally collect a wide range of energy data, including electricity consumption, gas usage, and renewable energy generation. The data collection unit can also collect environmental data that affects energy consumption using temperature sensors. For example, it collects data such as indoor and outdoor temperature, humidity, and solar radiation, and uses this environmental data as basic data to analyze the impact of this environmental data on energy consumption. This allows the data collection unit to grasp the detailed situation of energy consumption and provide accurate data to the analysis and provision units. Furthermore, the data collection unit can transmit this data to a cloud server, allowing for real-time data storage and management. This enables the data collection unit to efficiently and accurately collect energy data, improving the overall system performance.

[0061] The analysis unit analyzes the energy data collected by the collection unit. For example, the analysis unit uses generative AI to analyze energy consumption patterns. Specifically, based on the collected energy data, it extracts the characteristics of each household's and company's energy consumption and classifies the consumption patterns. The generative AI learns from past data and can detect energy consumption trends and anomalies. For example, it can identify peak electricity consumption times and generate a plan that prioritizes the use of renewable energy during those times. This can shift the peak of electricity consumption, leading to reduced energy costs and stabilization of the power supply. Furthermore, the analysis unit can analyze seasonal fluctuations in energy consumption and generate optimal energy use plans for each season. For example, it can propose efficient energy use methods tailored to periods of increased energy consumption, such as summer and winter. The analysis unit can also use machine learning algorithms to build predictive models for energy consumption and forecast future energy consumption. This can be useful for energy supply planning and demand forecasting. For example, based on past data, it can predict increases and decreases in energy consumption during specific time periods or seasons and formulate appropriate energy supply plans. This allows the analysis unit to optimize and improve the efficiency of energy consumption, thereby enhancing the overall system performance.

[0062] The service provider offers energy utilization optimization plans based on the results analyzed by the analysis department. Specifically, it provides optimization plans that include promoting the use of renewable energy and reducing energy costs. For example, it offers plans that promote the use of solar and wind power. This maximizes the use of renewable energy and reduces the use of fossil fuels. The service provider can also offer plans that include improving energy efficiency and reducing consumption. For example, it can offer plans that prioritize the use of renewable energy during peak energy consumption hours. This shifts the peak of electricity consumption, reducing energy costs and stabilizing the power supply. Furthermore, the service provider has an interface for providing energy utilization optimization plans to users. For example, it provides users with information on their energy consumption status and optimization plans through a smartphone app or web portal. This allows users to understand their energy consumption status in real time and select the optimal energy utilization method. The service provider can collect feedback from users and continuously improve the accuracy and effectiveness of the optimization plans. For example, it can customize the optimization plan according to the user's energy consumption patterns and environmental conditions, proposing more effective energy utilization methods. This allows the service provider to offer users the optimal energy utilization method and achieve energy utilization optimization.

[0063] The data collection unit can collect energy consumption data from households and businesses using sensors and smart meters. For example, the data collection unit can collect electricity consumption data from households using smart meters. For example, the data collection unit can also collect gas usage data from businesses using sensors. For example, the data collection unit can also collect renewable energy generation data using smart meters. This allows for the optimization of energy use by collecting detailed energy consumption data from households and businesses. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from smart meters into a generating AI and have the generating AI perform data analysis.

[0064] The analysis unit can analyze energy data using algorithms that analyze energy consumption patterns. For example, the analysis unit can analyze energy consumption patterns using machine learning algorithms. The analysis unit can also analyze energy consumption patterns using statistical analysis algorithms. The analysis unit can also analyze energy consumption patterns using generative AI. By analyzing energy consumption patterns, it becomes possible to optimize energy use. Some or all of the above-described processes in the analysis unit may be performed using generative AI, for example, or without using generative AI. For example, the analysis unit can input energy data into a generative AI and have the generative AI perform the analysis of energy consumption patterns.

[0065] The service provider can provide optimization plans that include promoting the use of renewable energy and reducing energy costs. For example, the service provider can provide a plan that promotes the use of solar power. For example, the service provider can also provide a plan that promotes the use of wind power. For example, the service provider can also provide a plan that includes improving energy efficiency and reducing consumption. This enables sustainable energy management by providing optimization plans that include promoting the use of renewable energy and reducing energy costs. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can use a generative AI to generate optimization plans that include promoting the use of renewable energy and reducing energy costs.

[0066] The service provider can provide plans that promote the use of solar power and wind power. For example, the service provider can provide a plan that promotes the use of solar power. The service provider can also provide a plan that promotes the use of wind power. The service provider can also provide a plan that utilizes both solar power and wind power. This promotes the use of renewable energy by promoting the use of solar power and wind power. Some or all of the above processing in the service provider may be performed using, for example, a generating AI, or not using a generating AI. For example, the service provider can use a generating AI to generate plans that promote the use of solar power and wind power.

[0067] The data collection unit can estimate the user's emotions and adjust the timing of energy data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can collect energy data at night to reduce the user's burden. For example, if the user is relaxed, the data collection unit can collect energy data in real time to obtain detailed data. For example, if the user is in a hurry, the data collection unit can collect energy data in a short time and start analysis quickly. This reduces the user's burden by adjusting the timing of energy data collection 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, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0068] The data collection unit can select the optimal collection method by referring to past energy consumption history when collecting energy consumption data from households and businesses. For example, the data collection unit can analyze past energy consumption history and focus on collecting data during peak hours. For example, the data collection unit can also collect data on specific days of the week or time periods based on past energy consumption history. For example, the data collection unit can collect data during periods of large fluctuations in energy consumption by referring to past energy consumption history. This enables efficient data collection by selecting the optimal collection method by referring to past energy consumption history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past energy consumption history into a generating AI and have the generating AI select the optimal collection method.

[0069] The data collection unit can filter energy data based on the user's current living and working conditions. For example, if the user is working from home, the data collection unit will focus on collecting household energy consumption data. If the user is out, for example, the data collection unit can omit household energy consumption data and collect corporate energy consumption data. If the user is on holiday, for example, the data collection unit can collect household energy consumption data in detail. This enables data collection tailored to the user's living and working conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's living and working conditions into a generating AI and have the generating AI perform the filtering.

[0070] The data collection unit can estimate the user's emotions and determine the priority of energy data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone the collection of less important data. For example, if the user is relaxed, the data collection unit can collect all data equally. For example, if the user is in a hurry, the data collection unit can prioritize the collection of highly important data. This enables efficient data collection by prioritizing energy data 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 is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0071] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting energy data. For example, if the user is in an urban area, the data collection unit will prioritize the collection of energy data for urban areas. For example, if the user is in a suburban area, the data collection unit can also prioritize the collection of energy data for suburban areas. For example, if the user is in a specific region, the data collection unit can also prioritize the collection of energy data for that region. This enables efficient data collection by prioritizing the collection of highly relevant data based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI select highly relevant data.

[0072] The data collection unit can analyze the user's social media activity and collect relevant data when collecting energy data. For example, if a user makes a post about energy on social media, the data collection unit can collect data based on the content of that post. For example, if a user participates in a specific energy-related event on social media, the data collection unit can also collect data related to that event. For example, if a user expresses an opinion about energy consumption on social media, the data collection unit can also collect data based on that opinion. This enables efficient data collection by collecting relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0073] The analysis unit can estimate the user's emotions and adjust the analysis method of energy consumption patterns based on the estimated user emotions. For example, if the user is stressed, the analysis unit can use a simplified analysis method. For example, if the user is relaxed, the analysis unit can also use a detailed analysis method. For example, if the user is in a hurry, the analysis unit can also use a rapid analysis method. This allows for efficient analysis by adjusting the analysis method 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, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0074] The analysis unit can identify peak energy consumption times during energy data analysis and generate plans that prioritize the use of renewable energy during those times. For example, the analysis unit can generate plans that prioritize the use of solar power during peak energy consumption times. The analysis unit can also generate plans that prioritize the use of wind power during peak energy consumption times. The analysis unit can also generate plans that utilize battery storage during peak energy consumption times. This improves energy efficiency by prioritizing the use of renewable energy during peak energy consumption times. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input energy data into a generation AI and have the generation AI identify peak times and generate plans.

[0075] The analysis unit can apply different analysis algorithms to each category of energy consumption when analyzing energy data. For example, the analysis unit can apply a specific algorithm to electricity consumption data. For example, the analysis unit can apply a different algorithm to gas consumption data. For example, the analysis unit can apply yet another algorithm to renewable energy generation data. By applying different analysis algorithms to each category of energy consumption, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input energy data into a generative AI and have the generative AI perform analysis for each category.

[0076] The analysis unit can estimate the user's emotions and adjust the display method of the energy consumption pattern analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple display method. For example, if the user is relaxed, the analysis unit can also provide a detailed display method. For example, if the user is in a hurry, the analysis unit can also provide a concise display method. By adjusting the display method of the analysis results according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0077] The analysis unit can determine the priority of energy data analysis based on the submission date of energy consumption. For example, the analysis unit may prioritize the analysis of energy data with an approaching submission deadline. For example, the analysis unit may postpone the analysis of energy data with a distant submission deadline. For example, the analysis unit may quickly analyze energy data whose submission deadline has passed. This enables efficient analysis by determining the priority of analysis based on the submission date of energy consumption. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input energy data into a generating AI and have the generating AI perform the determination of priority based on submission date.

[0078] The analysis unit can adjust the order of analysis based on the relationships between energy consumption when analyzing energy data. For example, the analysis unit can prioritize the analysis of highly relevant energy data. For example, the analysis unit can postpone the analysis of less relevant energy data. For example, the analysis unit can group highly relevant energy data together for analysis. This allows for efficient analysis by adjusting the order of analysis based on the relationships between energy consumption. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input energy data into a generative AI and have the generative AI execute the analysis in a relationship-based order.

[0079] The service provider can estimate the user's emotions and adjust the presentation of the energy utilization optimization plan based on the estimated emotions. For example, if the user is stressed, the service provider can provide a simple presentation. For example, if the user is relaxed, the service provider can provide a detailed presentation. For example, if the user is in a hurry, the service provider can provide a concise presentation. By adjusting the presentation of the plan according to the user's emotions, the service provider can provide a plan that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the service provider may be performed using a generative AI, for example, or without a generative AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0080] The service provider can generate plans that include promoting the use of renewable energy and reducing energy costs when providing energy utilization optimization plans. For example, the service provider can generate plans that promote the use of solar power. The service provider can also generate plans that promote the use of wind power. The service provider can also generate plans that include reducing energy costs. By providing plans that include promoting the use of renewable energy and reducing energy costs, sustainable energy management becomes possible. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or not using a generation AI. For example, the service provider can use a generation AI to generate plans that include promoting the use of renewable energy and reducing energy costs.

[0081] The service provider, when providing an energy utilization optimization plan, can propose plans that promote the use of solar power and wind power. For example, the service provider can propose a plan that promotes the use of solar power. The service provider can also propose a plan that promotes the use of wind power. The service provider can also propose a plan that utilizes both solar power and wind power. This promotes the use of renewable energy by promoting the use of solar power and wind power. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can use a generative AI to generate plans that promote the use of solar power and wind power.

[0082] The service provider can estimate the user's emotions and adjust the length of the energy utilization optimization plan based on the estimated emotions. For example, if the user is stressed, the service provider can provide a short, concise plan. If the user is relaxed, the service provider can also provide a longer plan with detailed explanations. If the user is in a hurry, the service provider can also provide a short plan that can be executed quickly. By adjusting the length of the plan according to the user's emotions, the service provider can provide the optimal plan for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0083] The service provider can prioritize energy utilization optimization plans based on the timing of energy consumption data submission. For example, the service provider may prioritize plans based on energy consumption data with an approaching submission deadline. The service provider may also postpone plans based on energy consumption data with a distant submission deadline. The service provider may also promptly provide plans based on energy consumption data whose submission deadline has passed. This enables efficient plan provision by prioritizing plans based on the timing of energy consumption data submission. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input energy consumption data into a generative AI and have the generative AI prioritize plans based on submission timing.

[0084] The service provider can adjust the order of energy consumption optimization plans based on the relevance of energy consumption. For example, the service provider may prioritize plans based on highly relevant energy consumption data. For example, the service provider may postpone plans based on less relevant energy consumption data. For example, the service provider may group highly relevant energy consumption data and provide plans based on those groups. This allows for efficient plan provision by adjusting the order of plans based on the relevance of energy consumption. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input energy consumption data into a generative AI and have the generative AI execute the order of plans based on relevance.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The analysis unit can generate an optimal energy use plan by considering seasonal variations in energy consumption when analyzing energy data. For example, the analysis unit can generate a plan that improves the energy efficiency of heating because heating use increases in winter. For example, the analysis unit can also generate a plan that improves the energy efficiency of cooling because cooling use increases in summer. For example, the analysis unit can also generate a plan that maximizes the use of renewable energy because energy consumption is relatively low in spring and autumn. In this way, energy efficiency can be improved by providing an optimal energy use plan that is tailored to seasonal energy consumption patterns.

[0087] The service provider can customize energy utilization optimization plans based on the user's lifestyle. For example, if a user works from home, the service provider can provide a plan that optimizes energy consumption during that time. If a user frequently goes out, the service provider can also provide a plan that minimizes energy consumption while out. If a user has a nocturnal lifestyle, the service provider can also provide a plan that optimizes energy consumption at night. By providing an optimal energy utilization plan tailored to the user's lifestyle, energy efficiency can be improved.

[0088] The data collection unit can adjust the frequency and timing of energy data collection based on the user's health condition. For example, if the user is unwell, the data collection unit can reduce the frequency of data collection to lessen the user's burden. If the user is healthy, for example, the data collection unit can increase the frequency of data collection to obtain more detailed data. If the user is elderly, for example, the data collection unit can adjust the timing of data collection to match the user's lifestyle. This reduces the user's burden and enables efficient data collection by collecting data according to the user's health condition.

[0089] The analysis unit can improve the accuracy of its analysis by referring to the user's past energy consumption history when analyzing energy data. For example, the analysis unit can build a model to predict future energy consumption based on past energy consumption history. For example, the analysis unit can also identify energy consumption trends by analyzing past energy consumption history. For example, the analysis unit can identify peak energy consumption times by referring to past energy consumption history. By improving the accuracy of the analysis by referring to past energy consumption history, it is possible to provide a more accurate energy usage plan.

[0090] When providing an energy utilization optimization plan, the service provider can estimate the user's emotions and adjust the plan based on those emotions. For example, if the user is stressed, the service provider can provide a simple and easy-to-implement plan. If the user is relaxed, the service provider can also provide a plan with detailed explanations. If the user is in a hurry, the service provider can also provide a short plan that can be implemented quickly. By adjusting the plan content according to the user's emotions, the service provider can provide the optimal plan for the user.

[0091] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting energy data. For example, if the user is in an urban area, the data collection unit will prioritize collecting energy data for urban areas. If the user is in a suburban area, the data collection unit can also prioritize collecting energy data for suburban areas. If the user is in a specific region, the data collection unit can also prioritize collecting energy data for that region. This enables efficient data collection by prioritizing the collection of highly relevant data based on the user's geographical location.

[0092] The analysis unit can apply different analysis algorithms to each category of energy consumption when analyzing energy data. For example, the analysis unit can apply a specific algorithm to electricity consumption data. For example, the analysis unit can apply a different algorithm to gas consumption data. For example, the analysis unit can apply yet another algorithm to renewable energy generation data. By applying different analysis algorithms to each category of energy consumption, the accuracy of the analysis is improved.

[0093] When providing an energy utilization optimization plan, the service provider can estimate the user's emotions and adjust the way the plan is presented based on those emotions. For example, if the user is stressed, the service provider can provide a simple presentation. If the user is relaxed, for example, the service provider can provide a more detailed presentation. If the user is in a hurry, for example, the service provider can provide a more concise presentation. By adjusting the presentation of the plan according to the user's emotions, the service provider can provide a plan that is easy for the user to understand.

[0094] The data collection unit can analyze users' social media activity and collect relevant data when collecting energy data. For example, if a user makes a post about energy on social media, the data collection unit can collect data based on the content of that post. For example, if a user participates in a specific energy-related event on social media, the data collection unit can also collect data related to that event. For example, if a user expresses an opinion about energy consumption on social media, the data collection unit can also collect data based on that opinion. This enables efficient data collection by collecting relevant data based on users' social media activity.

[0095] The analysis unit can estimate the user's emotions when analyzing energy data and adjust the analysis method of energy consumption patterns based on the estimated user emotions. For example, if the user is stressed, the analysis unit will use a simplified analysis method. For example, if the user is relaxed, the analysis unit can also use a detailed analysis method. For example, if the user is in a hurry, the analysis unit can also use a rapid analysis method. This allows for efficient analysis by adjusting the analysis method according to the user's emotions.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The collection unit collects energy data. The collection unit collects energy consumption data from homes and businesses using sensors and smart meters. For example, it collects data such as electricity consumption, gas usage, and renewable energy generation. Furthermore, it can collect energy data in real time using smart meters, and it can also collect environmental data that affects energy consumption using temperature sensors. Step 2: The analysis unit analyzes the energy data collected by the collection unit. The analysis unit uses a generation AI to analyze energy consumption patterns, identify peak electricity consumption times, and generate a plan that prioritizes the use of renewable energy during those times. It can also analyze seasonal variations in energy consumption and generate optimal energy use plans for each season. Furthermore, it can use machine learning algorithms to build a predictive model for energy consumption and predict future energy consumption. Step 3: The service provider provides an energy utilization optimization plan based on the results analyzed by the analysis unit. The service provider provides an optimization plan that includes promoting the use of renewable energy and reducing energy costs. For example, it can provide a plan that promotes the use of solar and wind power, or a plan that includes improving energy efficiency and reducing consumption. Furthermore, it can also provide a plan that prioritizes the use of renewable energy during peak energy consumption hours.

[0098] 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.

[0099] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0100] 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.

[0101] Each of the multiple elements described above, including the data collection unit, analysis unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects energy data using sensors and smart meters of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes energy consumption patterns using generated AI. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates an optimization plan, which is provided to the user through the output device 40 of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.).

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0116] 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.

[0117] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented in, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects energy data using the sensors and smart meter of the smart glasses 214. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes energy consumption patterns using generated AI. The data provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates an optimization plan, which is provided to the user through the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] 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.

[0133] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects energy data using sensors and smart meters of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes energy consumption patterns using generated AI. The data provision unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates an optimization plan, which is provided to the user through the display 343 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0150] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects energy data using sensors and smart meters on the robot 414. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes energy consumption patterns using generated AI. The data provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates an optimization plan, which is provided to the user through the speaker 240 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] (Note 1) A data collection unit that collects energy data, An analysis unit analyzes the energy data collected by the aforementioned collection unit, The system includes a provisioning unit that provides an energy utilization optimization plan based on the results analyzed by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect energy consumption data from homes and businesses using sensors and smart meters. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We analyze energy data using algorithms that analyze energy consumption patterns. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, We provide optimization plans that include promoting the use of renewable energy and reducing energy costs. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, We offer plans to promote the use of solar and wind power generation. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of energy data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting energy consumption data from households and businesses, the optimal collection method is selected by referring to past energy consumption history. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting energy data, filtering is performed based on the user's current lifestyle and work situation. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and determines the priority of energy data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting energy data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting energy data, analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, We estimate the user's emotions and adjust the analysis method of energy consumption patterns based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, When analyzing energy data, the system identifies peak energy consumption periods and generates plans that prioritize the use of renewable energy during those times. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, When analyzing energy data, different analysis algorithms are applied to each category of energy consumption. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and adjusts how the energy consumption pattern analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, When analyzing energy data, the priority of the analysis is determined based on when the energy consumption data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, When analyzing energy data, adjust the order of analysis based on the relationships between energy consumption. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the energy usage optimization plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing energy utilization optimization plans, we generate plans that include promoting the use of renewable energy and reducing energy costs. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing energy utilization optimization plans, we propose plans that promote the use of solar and wind power. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the energy usage optimization plan based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing energy utilization optimization plans, we prioritize plans based on when energy consumption data is submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing an energy utilization optimization plan, we adjust the order of the plan based on the relevance of energy consumption. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0170] 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 data collection unit that collects energy data, An analysis unit analyzes the energy data collected by the aforementioned collection unit, The system includes a provisioning unit that provides an energy utilization optimization plan based on the results analyzed by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect energy consumption data from homes and businesses using sensors and smart meters. The system according to feature 1.

3. The aforementioned analysis unit, We analyze energy data using algorithms that analyze energy consumption patterns. The system according to feature 1.

4. The aforementioned supply unit is, We provide optimization plans that include promoting the use of renewable energy and reducing energy costs. The system according to feature 1.

5. The aforementioned supply unit is, We offer plans to promote the use of solar and wind power generation. The system according to feature 1.

6. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of energy data collection based on the estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is When collecting energy consumption data from households and businesses, the optimal collection method is selected by referring to past energy consumption history. The system according to feature 1.

8. The aforementioned collection unit is When collecting energy data, filtering is performed based on the user's current lifestyle and work situation. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and determines the priority of energy data to collect based on the estimated user emotions. The system according to feature 1.

10. The aforementioned collection unit is When collecting energy data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system according to feature 1.

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