System

Abstract

An object of a system according to an embodiment is to identify wasteful use of energy and propose an optimal operation method.SOLUTION: A system includes an energy use data collection unit, an external environment data collection unit, an energy use pattern prediction unit, a waste use specification unit, and an operation method proposal unit. The energy usage data collector is configured to collect energy usage data. The external environment data collection unit collects external environment data. The energy use pattern predictor predicts an energy use pattern based on the data collected by the energy use data collector and the external environment data collector. The waste use specifying unit specifies waste energy use based on the energy use pattern predicted by the energy use pattern predicting unit. The operation method proposal unit proposes an optimum operation method based on the wasteful energy use specified by the wasteful use specification unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not efficiently identify wasteful energy use and propose optimal operation methods, so there is room for improvement.

[0005] The system according to the embodiment aims to identify wasteful energy use and propose optimal operation methods. [Means for solving the problem]

[0006] The system according to the embodiment includes an energy usage data collection unit, an external environment data collection unit, an energy usage pattern prediction unit, a wasteful usage identification unit, and an operation method proposal unit. The energy usage data collection unit collects energy usage data. The external environment data collection unit collects external environment data. The energy usage pattern prediction unit predicts an energy usage pattern based on data collected by the energy usage data collection unit and the external environment data collection unit. The wasteful usage identification unit identifies wasteful energy usage based on the energy usage pattern predicted by the energy usage pattern prediction unit. The operation method proposal unit proposes an optimal operation method based on the wasteful energy usage identified by the wasteful usage identification unit. [Effects of the Invention]

[0007] The system according to the embodiment can identify wasteful energy use and propose optimal operation methods. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The energy manager system according to an embodiment of the present invention utilizes generative AI as an energy manager to predict and optimize energy usage patterns of individuals, households, and businesses, and provides advice on saving and energy conservation. As a result, the energy manager system can achieve more efficient energy use and energy conservation.

[0029] An energy manager system according to an embodiment includes an energy usage data collection unit, an external environment data collection unit, an energy usage pattern prediction unit, a wasteful usage identification unit, and an operation method proposal unit. The energy usage data collection unit collects energy usage data, such as electricity consumption, gas consumption, and water consumption. The energy usage data collection unit also collects household electricity consumption data and can analyze which home appliances are used most frequently during certain times of the day. The external environment data collection unit collects external environment data, such as temperature, humidity, and weather forecasts. The external environment data collection unit also collects temperature data and can analyze it to optimize air conditioner usage patterns. The energy usage pattern prediction unit predicts energy usage patterns based on data collected by the energy usage data collection unit and the external environment data collection unit. For example, it predicts power consumption for the next week based on past data. The wasteful usage identification unit identifies wasteful energy usage based on the energy usage pattern predicted by the energy usage pattern prediction unit. For example, if unnecessary power is consumed at night, it identifies the cause. The operation method suggestion unit proposes an optimal operation method based on the wasteful energy use identified by the wasteful use identification unit. For example, it proposes a method to reduce power consumption by appropriately adjusting the temperature setting of an air conditioner. This enables the energy manager system according to the embodiment to achieve more efficient energy use and energy savings.

[0030] The energy usage data collection unit can monitor the usage status of home appliances in real time and analyze usage frequency and usage time in detail. For example, the energy usage data collection unit attaches sensors to each home appliance to monitor usage status in real time. For example, it records the power consumption of refrigerators and air conditioners in seconds and analyzes usage frequency and usage time in detail. The energy usage data collection unit also uses smart plugs to collect power consumption data for each home appliance and analyzes usage frequency and usage time. For example, it understands usage patterns of televisions and washing machines and identifies wasteful usage. The energy usage data collection unit also transmits home appliance usage status to the cloud in real time, and the generation AI analyzes the data. For example, it analyzes the usage time and temperature setting of an air conditioner and suggests optimal usage methods. This allows for a detailed understanding of the usage status of each home appliance and identifies wasteful energy consumption.

[0031] The energy usage data collection unit can collect resident behavioral pattern data in addition to energy usage data and analyze the relationship between behavior and energy usage. The energy usage data collection unit collects behavioral pattern data from residents' smartphones or wearable devices, for example, and integrates and analyzes it with energy usage data. For example, it analyzes the relationship between the time of day a resident is at home and electricity consumption. The energy usage data collection unit also uses motion sensors installed in the home to record residents' movement patterns and associates them with energy usage data. For example, it analyzes electricity consumption in a specific room and the length of time a resident is in that room. The energy usage data collection unit also utilizes a smart home system to collect and analyze resident behavioral patterns and energy usage data in real time. For example, it analyzes the time of day a resident is in their bedroom and their air conditioner usage pattern. This allows for the analysis of the relationship between a resident's behavioral patterns and energy usage, enabling more accurate optimization of energy usage.

[0032] The energy usage data collection unit can obtain detailed data by utilizing smart meters and IoT devices. For example, the energy usage data collection unit installs a smart meter and collects power consumption data for the entire household in real time. For example, it records and analyzes detailed power consumption for each time period. The energy usage data collection unit also attaches IoT devices to each home appliance and collects individual power consumption data. For example, it records detailed power consumption for refrigerators and air conditioners to identify wasteful use. The energy usage data collection unit also links the smart meter and IoT devices to integrate and analyze energy usage data for the entire household. For example, it compares overall power consumption with that of each home appliance and suggests optimal usage methods. In this way, more detailed energy usage data can be obtained by utilizing smart meters and IoT devices.

[0033] The energy usage data collection unit can compare energy usage data with those of households and businesses to set benchmarks. For example, the energy usage data collection unit collects energy usage data from other households and businesses and compares it with the energy usage of the home. For example, it compares power consumption with that of households of the same size to set a benchmark. The energy usage data collection unit also collects energy usage data by region and analyzes energy usage patterns for the entire region. For example, it compares the average power consumption of the region with the consumption of the home. The energy usage data collection unit also sets a benchmark for energy efficiency based on the energy usage data of other households and businesses. For example, it uses data from highly energy-efficient households as a reference to optimize the energy usage of the home. In this way, by comparing with other households and businesses, it is possible to set an energy usage benchmark and use it as an index for optimization.

[0034] The external environment data collection unit collects detailed environmental data such as temperature, humidity, wind speed, and sunshine hours, and can use the data to optimize energy use. The external environment data collection unit collects detailed environmental data such as temperature, humidity, wind speed, and sunshine hours, and uses the data to optimize energy use. For example, it may suggest reducing the use of the air conditioner on days with long sunshine hours. The external environment data collection unit also collects external environment data in real time and analyzes energy use patterns. For example, it may recommend opening windows for natural ventilation on days with strong winds. The external environment data collection unit also optimizes energy use based on the detailed environmental data. For example, it may recommend using a dehumidifier and suggest reducing the use of the air conditioner on days with high humidity. In this way, energy use can be optimized by collecting detailed environmental data.

[0035] The external environment data collection unit can combine external environment data and energy usage data to analyze seasonal energy usage patterns. The external environment data collection unit, for example, combines external environment data and energy usage data to analyze seasonal energy usage patterns. For example, it analyzes the difference in power consumption between summer and winter. The external environment data collection unit also analyzes seasonal energy usage patterns and suggests optimal energy usage methods. For example, it appropriately adjusts the heating setting temperature in winter. The external environment data collection unit also predicts seasonal energy usage patterns based on the external environment data. For example, it recommends natural ventilation in spring and suggests reducing air conditioning use. In this way, by analyzing seasonal energy usage patterns, more accurate energy usage optimization is possible.

[0036] The external environment data collection unit can collect external environment data for each region and analyze energy usage patterns specific to the region. The external environment data collection unit collects environmental data such as temperature, humidity, wind speed, and sunshine hours for each region and analyzes energy usage patterns specific to the region. For example, it analyzes that heating is used more in cold regions. The external environment data collection unit also analyzes energy usage patterns specific to the region based on the energy usage data for each region. For example, it analyzes the difference in electricity consumption between urban and suburban areas. The external environment data collection unit also predicts energy usage patterns specific to the region based on the external environment data for each region. For example, it recommends natural ventilation, taking into account that coastal regions often have strong winds. In this way, by analyzing energy usage patterns for each region, it becomes possible to optimize energy usage specific to the region.

[0037] The external environment data collection unit can integrate the external environment data with other datasets to gain new insights into energy use. For example, the external environment data collection unit can integrate the external environment data with traffic data to gain new insights into energy use. For example, it can analyze that electricity consumption increases during times of heavy traffic. The external environment data collection unit can also integrate the external environment data with population data to gain new insights into energy use. For example, it can analyze electricity consumption patterns in densely populated areas. The external environment data collection unit can also integrate the external environment data with other datasets to gain new insights into energy use. For example, it can analyze the relationship between temperature and traffic volume and suggest energy use optimization. In this way, new insights into energy use can be gained by integrating the external environment data with other datasets.

[0038] The energy usage pattern prediction unit can make predictions that take into account future events in addition to energy usage data. The energy usage pattern prediction unit, for example, combines past energy usage data with future event data to predict energy usage patterns. For example, it takes into account that electricity consumption increases on holidays and special event days. The energy usage pattern prediction unit also predicts energy usage patterns based on future event data. For example, it predicts that air conditioner use will increase on days when specific events are held. The energy usage pattern prediction unit also predicts energy usage patterns by integrating past data and future event data. For example, it takes into account that electricity consumption increases on special event days and suggests optimal energy usage methods. This makes it possible to predict energy usage patterns that take future events into account.

[0039] The energy usage pattern prediction unit incorporates an anomaly detection algorithm into the prediction of energy usage patterns, enabling early detection of abnormal energy usage. The energy usage pattern prediction unit, for example, incorporates an anomaly detection algorithm into energy usage data to detect abnormal energy usage early. For example, it detects power consumption that deviates significantly from normal patterns. The energy usage pattern prediction unit also uses an anomaly detection algorithm to predict energy usage patterns. For example, it issues an alert when abnormal power consumption occurs. The energy usage pattern prediction unit also combines the energy usage data with the anomaly detection algorithm to detect abnormal energy usage early and propose countermeasures. For example, when abnormal power consumption occurs, it identifies the cause and proposes improvement measures. This enables early detection of abnormal energy usage.

[0040] The energy usage pattern prediction unit can predict energy usage patterns on different time scales to provide detailed predictions. The energy usage pattern prediction unit predicts energy usage patterns on different time scales, such as daily, weekly, and monthly, to provide detailed predictions. For example, the energy usage pattern prediction unit predicts daily power consumption patterns and performs short-term optimization. The energy usage pattern prediction unit also predicts energy usage patterns on different time scales to provide detailed predictions. For example, the energy usage pattern prediction unit predicts weekly power consumption patterns and performs medium-term optimization. The energy usage pattern prediction unit also predicts energy usage patterns on different time scales to provide detailed predictions. For example, the energy usage pattern prediction unit predicts monthly power consumption patterns and performs long-term optimization. This makes it possible to predict detailed energy usage patterns on different time scales.

[0041] The energy usage pattern prediction unit can compare the predicted energy usage pattern with other households and businesses to improve the prediction accuracy. The energy usage pattern prediction unit predicts the energy usage pattern based on, for example, the energy usage data of other households and businesses, and improves the prediction accuracy. For example, the prediction accuracy is improved by comparing the power consumption with households of the same size. The energy usage pattern prediction unit also collects energy usage data of other households and businesses and uses it to predict the energy usage pattern. For example, the prediction accuracy is improved based on the power consumption pattern of the entire region. The energy usage pattern prediction unit also integrates the energy usage data of other households and businesses to predict the energy usage pattern. For example, the prediction accuracy is improved by referring to data from households with high energy efficiency. In this way, the prediction accuracy of the energy usage pattern can be improved by comparing with other households and businesses.

[0042] The wasteful use identification unit analyzes the usage status of home appliances in detail and can identify the cause of wasteful energy consumption by a specific home appliance. The wasteful use identification unit, for example, analyzes the usage status of each home appliance in detail and can identify the cause of wasteful energy consumption by a specific home appliance. For example, it can identify that increased power consumption is caused by frequent opening and closing of a refrigerator door. The wasteful use identification unit can also identify the cause of wasteful energy consumption based on the usage data of the home appliances. For example, it can identify that increased power consumption is caused by an air conditioner temperature setting being inappropriate. The wasteful use identification unit can also analyze the usage status of each home appliance in detail and can identify the cause of wasteful energy consumption. For example, it can identify that increased power consumption is caused by a television being left on for long periods of time. This makes it possible to identify the cause of wasteful energy consumption by each home appliance.

[0043] The wasteful use identification unit can combine energy usage data and external environmental data to identify wasteful energy usage under specific environmental conditions. The wasteful use identification unit, for example, combines energy usage data and external environmental data to identify wasteful energy usage under specific environmental conditions. For example, it identifies that increased power consumption is caused by an air conditioner temperature setting being inappropriate on a hot day. The wasteful use identification unit also identifies the cause of wasteful energy consumption based on external environmental data. For example, it identifies that increased power consumption is caused by inappropriate use of a dehumidifier on a humid day. The wasteful use identification unit also analyzes energy usage data and external environmental data to identify the cause of wasteful energy consumption. For example, it identifies that increased power consumption is caused by leaving a window open on a day with strong winds. This makes it possible to identify wasteful energy usage under specific environmental conditions.

[0044] The wasteful use identifying unit introduces an anomaly detection algorithm to identify wasteful energy use, enabling early detection of abnormal energy use. The wasteful use identifying unit, for example, introduces an anomaly detection algorithm into energy usage data to detect abnormal energy use early. For example, it detects power consumption that deviates significantly from normal patterns. The wasteful use identifying unit also uses the anomaly detection algorithm to identify wasteful energy consumption early. For example, it issues an alert when abnormal power consumption occurs. The wasteful use identifying unit also combines the energy usage data with the anomaly detection algorithm to detect abnormal energy use early and propose countermeasures. For example, when abnormal power consumption occurs, it identifies the cause and proposes improvement measures. This makes it possible to detect abnormal energy use early.

[0045] The wasteful use identification unit can compare with other households and businesses to identify wasteful energy use and set benchmarks. The wasteful use identification unit identifies wasteful energy use and sets benchmarks, for example, based on energy usage data of other households and businesses. For example, it compares power consumption with households of the same size to identify wasteful use. The wasteful use identification unit also collects energy usage data of other households and businesses to identify the causes of wasteful energy consumption. For example, it identifies wasteful use based on power consumption patterns across an entire region. The wasteful use identification unit also integrates energy usage data of other households and businesses to identify the causes of wasteful energy consumption. For example, it identifies wasteful use by referring to data from highly energy-efficient households. In this way, it is possible to identify wasteful energy use and set benchmarks by comparing with other households and businesses.

[0046] The operation method suggestion unit can propose the optimal operation method for the home appliance in detail and provide specific settings and usage methods. The operation method suggestion unit, for example, can propose the optimal operation method for each home appliance in detail and provide specific settings and usage methods. For example, it can propose a method for appropriately adjusting the set temperature of an air conditioner. The operation method suggestion unit can also propose the optimal operation method based on usage data of the home appliance. For example, it can provide a method for optimizing the temperature setting of a refrigerator. The operation method suggestion unit can also analyze the usage status of each home appliance and propose the optimal operation method. For example, it can provide a method for optimizing the usage time of a washing machine. This makes it possible to propose the optimal operation method for each home appliance in detail and provide specific settings and usage methods.

[0047] The operation method proposal unit can combine energy usage data and external environmental data to propose the optimal operation method under specific environmental conditions. The operation method proposal unit, for example, combines energy usage data and external environmental data to propose the optimal operation method under specific environmental conditions. For example, it proposes a method to appropriately adjust the set temperature of an air conditioner on a hot day. The operation method proposal unit also proposes the optimal operation method based on the external environmental data. For example, it recommends using a dehumidifier on a humid day. The operation method proposal unit also analyzes the energy usage data and external environmental data to propose the optimal operation method. For example, it recommends opening windows to allow natural ventilation on a day with strong winds. In this way, it is possible to propose the optimal operation method under specific environmental conditions.

[0048] The operation method proposal unit can propose optimal operation methods on different time scales and provide detailed advice. The operation method proposal unit can propose optimal operation methods on different time scales, such as daily, weekly, and monthly, and provide detailed advice. For example, it can propose a daily air conditioner setting temperature and perform short-term optimization. The operation method proposal unit can also propose optimal operation methods on different time scales and provide detailed advice. For example, it can propose an optimal operation method based on a weekly power consumption pattern. The operation method proposal unit can also propose optimal operation methods on different time scales and provide detailed advice. For example, it can propose an optimal operation method based on a monthly power consumption pattern. This makes it possible to propose optimal operation methods on different time scales and provide detailed advice.

[0049] The operation method proposal unit can compare the optimal operation method with other households and businesses and set a benchmark. The operation method proposal unit proposes the optimal operation method and sets a benchmark, for example, based on energy usage data of other households and businesses. For example, it compares power consumption with households of the same size and proposes the optimal operation method. The operation method proposal unit also collects energy usage data of other households and businesses and proposes the optimal operation method. For example, it proposes the optimal operation method based on the power consumption pattern of the entire region. The operation method proposal unit also integrates energy usage data of other households and businesses and proposes the optimal operation method. For example, it proposes the optimal operation method based on data from highly energy-efficient households. In this way, it is possible to set a benchmark for the optimal operation method by comparing with other households and businesses.

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

[0051] The energy usage data collection unit can collect resident health data in addition to energy usage data and analyze the relationship between health status and energy usage. For example, it can collect resident heart rate and sleep data and integrate it with the energy usage data for analysis. For example, it can analyze the difference in power consumption when a resident is in good health and when they are in poor health. The energy usage data collection unit can also suggest energy usage optimization based on the health data in accordance with the resident's health status. For example, it can adjust the air conditioner's temperature setting when the resident is in poor health, providing a comfortable environment. The energy usage data collection unit can also suggest energy usage optimization based on the resident's health status, based on the health data. For example, it can suggest reducing energy consumption when the resident is in good health. This makes it possible to optimize energy usage based on the resident's health status.

[0052] The energy usage data collection unit can collect resident hobby and preference data in addition to energy usage data and analyze the relationship between hobbies and preferences and energy usage. For example, it can collect resident music and movie viewing history and integrate it with energy usage data for analysis. For example, it can analyze the electricity consumption pattern when a resident is watching a movie. The energy usage data collection unit can also suggest energy usage optimization in accordance with the resident's hobby and preference based on the hobby and preference data. For example, it can provide a comfortable environment by dimming the lights when watching a movie. The energy usage data collection unit can also suggest energy usage optimization in accordance with the resident's hobby and preference based on the hobby and preference data. For example, it can provide a comfortable environment by adjusting the air conditioner's temperature setting when listening to music. This makes it possible to optimize energy usage based on the resident's hobby and preference.

[0053] The energy usage data collection unit can collect residents' social activity data in addition to energy usage data and analyze the relationship between social activity and energy usage. For example, it can collect residents' social media posts and message exchanges and integrate them with energy usage data for analysis. For example, it can analyze electricity consumption patterns when residents are interacting with friends. The energy usage data collection unit also proposes energy usage optimization in accordance with the resident's social activity based on the social activity data. For example, it can provide a comfortable environment by brightening the lights when residents are interacting with friends. The energy usage data collection unit also proposes energy usage optimization in accordance with the resident's social activity based on the social activity data. For example, it can provide a comfortable environment by adjusting the air conditioner's temperature setting when residents are using social media. This makes it possible to optimize energy usage based on the resident's social activity.

[0054] The energy usage data collection unit can collect resident dietary data in addition to energy usage data and analyze the relationship between diet and energy usage. For example, it can collect the contents of residents' meals and calorie intake, and integrate and analyze them with the energy usage data. For example, it can analyze the patterns of electricity consumption when residents are eating. The energy usage data collection unit also proposes optimization of energy usage in accordance with the resident's meals based on the dietary data. For example, it can provide a comfortable environment by brightening the lights while residents are eating. The energy usage data collection unit also proposes optimization of energy usage in accordance with the resident's meals based on the dietary data. For example, it can provide a comfortable environment by adjusting the air conditioner's temperature setting while residents are eating. This makes it possible to optimize energy usage based on the resident's meals.

[0055] The energy usage data collection unit can collect resident exercise data in addition to energy usage data and analyze the relationship between exercise and energy usage. For example, it can collect the amount and duration of resident exercise and integrate this with the energy usage data for analysis. For example, it can analyze the patterns of electricity consumption when a resident is exercising. The energy usage data collection unit can also suggest optimization of energy usage in accordance with the resident's exercise based on the exercise data. For example, it can adjust the temperature setting of the air conditioner when the resident is exercising, thereby providing a comfortable environment. The energy usage data collection unit can also suggest optimization of energy usage in accordance with the resident's exercise based on the exercise data. For example, it can brighten the lights when the resident is exercising, thereby providing a comfortable environment. This makes it possible to optimize energy usage based on the resident's exercise.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The energy usage data collection unit collects energy usage data. For example, it collects electricity consumption, gas consumption, water consumption, etc. It can also collect household electricity consumption data and analyze which home appliances are used most frequently at which times. Step 2: The external environment data collection unit collects external environment data, such as temperature, humidity, and weather forecasts. It can also collect temperature data and perform analysis to optimize air conditioner usage patterns. Step 3: The energy usage pattern prediction unit predicts an energy usage pattern based on the data collected by the energy usage data collection unit and the external environment data collection unit. For example, it predicts the amount of power consumption for the next week based on past data. Step 4: The wasteful use identification unit identifies wasteful energy use based on the energy use pattern predicted by the energy use pattern prediction unit. For example, if unnecessary power is being consumed at night, the unit identifies the cause. Step 5: The operation method suggestion unit proposes an optimal operation method based on the wasteful energy use identified by the wasteful use identification unit. For example, it proposes a way to reduce power consumption by appropriately adjusting the temperature setting of an air conditioner.

[0058] (Example 2) The energy manager system according to an embodiment of the present invention utilizes generative AI as an energy manager to predict and optimize energy usage patterns of individuals, households, and businesses, and provides advice on saving and energy conservation. As a result, the energy manager system can achieve more efficient energy use and energy conservation.

[0059] An energy manager system according to an embodiment includes an energy usage data collection unit, an external environment data collection unit, an energy usage pattern prediction unit, a wasteful usage identification unit, and an operation method proposal unit. The energy usage data collection unit collects energy usage data, such as electricity consumption, gas consumption, and water consumption. The energy usage data collection unit also collects household electricity consumption data and can analyze which home appliances are used most frequently during certain times of the day. The external environment data collection unit collects external environment data, such as temperature, humidity, and weather forecasts. The external environment data collection unit also collects temperature data and can analyze it to optimize air conditioner usage patterns. The energy usage pattern prediction unit predicts energy usage patterns based on data collected by the energy usage data collection unit and the external environment data collection unit. For example, it predicts power consumption for the next week based on past data. The wasteful usage identification unit identifies wasteful energy usage based on the energy usage pattern predicted by the energy usage pattern prediction unit. For example, if unnecessary power is consumed at night, it identifies the cause. The operation method suggestion unit proposes an optimal operation method based on the wasteful energy use identified by the wasteful use identification unit. For example, it proposes a method to reduce power consumption by appropriately adjusting the temperature setting of an air conditioner. This enables the energy manager system according to the embodiment to achieve more efficient energy use and energy savings.

[0060] The energy usage data collection unit can monitor the usage status of home appliances in real time and analyze usage frequency and usage time in detail. For example, the energy usage data collection unit attaches sensors to each home appliance to monitor usage status in real time. For example, it records the power consumption of refrigerators and air conditioners in seconds and analyzes usage frequency and usage time in detail. The energy usage data collection unit also uses smart plugs to collect power consumption data for each home appliance and analyzes usage frequency and usage time. For example, it understands usage patterns of televisions and washing machines and identifies wasteful usage. The energy usage data collection unit also transmits home appliance usage status to the cloud in real time, and the generation AI analyzes the data. For example, it analyzes the usage time and temperature setting of an air conditioner and suggests optimal usage methods. This allows for a detailed understanding of the usage status of each home appliance and identifies wasteful energy consumption.

[0061] The energy usage data collection unit can collect resident behavioral pattern data in addition to energy usage data and analyze the relationship between behavior and energy usage. The energy usage data collection unit collects behavioral pattern data from residents' smartphones or wearable devices, for example, and integrates and analyzes it with energy usage data. For example, it analyzes the relationship between the time of day a resident is at home and electricity consumption. The energy usage data collection unit also uses motion sensors installed in the home to record residents' movement patterns and associates them with energy usage data. For example, it analyzes electricity consumption in a specific room and the length of time a resident is in that room. The energy usage data collection unit also utilizes a smart home system to collect and analyze resident behavioral patterns and energy usage data in real time. For example, it analyzes the time of day a resident is in their bedroom and their air conditioner usage pattern. This allows for the analysis of the relationship between a resident's behavioral patterns and energy usage, enabling more accurate optimization of energy usage.

[0062] The energy usage data collection unit can use the emotion estimation function to analyze the relationship between the resident's emotional state and energy usage and propose energy usage optimization based on the emotion. The energy usage data collection unit, for example, analyzes the resident's facial expressions and voice to estimate the resident's emotional state. For example, it adjusts lighting and air conditioning settings to provide a relaxing environment when the resident is under high stress. The energy usage data collection unit also optimizes energy consumption during times when the resident is relaxing based on the emotion estimation data. For example, it optimizes the air conditioning setting temperature when the resident is relaxing. The energy usage data collection unit also uses the emotion estimation function to propose energy usage optimization according to the resident's emotional state. For example, it dims the lights when the resident is tired to provide a relaxing environment. This makes it possible to optimize energy usage based on the resident's emotional state.

[0063] The energy usage data collection unit can obtain detailed data by utilizing smart meters and IoT devices. For example, the energy usage data collection unit installs a smart meter and collects power consumption data for the entire household in real time. For example, it records and analyzes detailed power consumption for each time period. The energy usage data collection unit also attaches IoT devices to each home appliance and collects individual power consumption data. For example, it records detailed power consumption for refrigerators and air conditioners to identify wasteful use. The energy usage data collection unit also links the smart meter and IoT devices to integrate and analyze energy usage data for the entire household. For example, it compares overall power consumption with that of each home appliance and suggests optimal usage methods. In this way, more detailed energy usage data can be obtained by utilizing smart meters and IoT devices.

[0064] The energy usage data collection unit can compare energy usage data with those of households and businesses to set benchmarks. For example, the energy usage data collection unit collects energy usage data from other households and businesses and compares it with the energy usage of the home. For example, it compares power consumption with that of households of the same size to set a benchmark. The energy usage data collection unit also collects energy usage data by region and analyzes energy usage patterns for the entire region. For example, it compares the average power consumption of the region with the consumption of the home. The energy usage data collection unit also sets a benchmark for energy efficiency based on the energy usage data of other households and businesses. For example, it uses data from highly energy-efficient households as a reference to optimize the energy usage of the home. In this way, by comparing with other households and businesses, it is possible to set an energy usage benchmark and use it as an index for optimization.

[0065] The energy usage data collection unit can use the emotion estimation function to analyze how residents feel about energy use and provide advice based on their emotions. For example, the energy usage data collection unit analyzes the residents' emotional state in real time to understand their emotions about energy use. For example, if a resident feels stressed when energy use is high, the energy usage data collection unit identifies the cause. The energy usage data collection unit also provides advice based on the emotion estimation data that will help the resident feel positive about energy use. For example, it recommends the use of energy-efficient home appliances. The energy usage data collection unit also suggests optimizing energy use according to the resident's emotional state. For example, it provides advice to provide a relaxing environment when energy use is high. This makes it possible to optimize energy use based on the resident's emotions.

[0066] The external environment data collection unit collects detailed environmental data such as temperature, humidity, wind speed, and sunshine hours, and can use the data to optimize energy use. The external environment data collection unit collects detailed environmental data such as temperature, humidity, wind speed, and sunshine hours, and uses the data to optimize energy use. For example, it may suggest reducing the use of the air conditioner on days with long sunshine hours. The external environment data collection unit also collects external environment data in real time and analyzes energy use patterns. For example, it may recommend opening windows for natural ventilation on days with strong winds. The external environment data collection unit also optimizes energy use based on the detailed environmental data. For example, it may recommend using a dehumidifier and suggest reducing the use of the air conditioner on days with high humidity. In this way, energy use can be optimized by collecting detailed environmental data.

[0067] The external environment data collection unit can combine external environment data and energy usage data to analyze seasonal energy usage patterns. The external environment data collection unit, for example, combines external environment data and energy usage data to analyze seasonal energy usage patterns. For example, it analyzes the difference in power consumption between summer and winter. The external environment data collection unit also analyzes seasonal energy usage patterns and suggests optimal energy usage methods. For example, it appropriately adjusts the heating setting temperature in winter. The external environment data collection unit also predicts seasonal energy usage patterns based on the external environment data. For example, it recommends natural ventilation in spring and suggests reducing air conditioning use. In this way, by analyzing seasonal energy usage patterns, more accurate energy usage optimization is possible.

[0068] The external environment data collection unit can use the emotion estimation function to analyze the impact of changes in the external environment on the emotions of residents and propose optimization of energy use based on their emotions. The external environment data collection unit, for example, combines external environment data and emotion estimation data to analyze the impact of environmental changes on residents' emotions. For example, it analyzes that stress increases on hot days. The external environment data collection unit also proposes optimization of energy use in accordance with changes in the external environment based on the emotion estimation data. For example, on days with high humidity, it recommends using a dehumidifier to provide a comfortable environment. The external environment data collection unit also analyzes the external environment data and emotion estimation data to optimize energy use based on the emotions of residents. For example, on days with strong winds, it recommends opening windows for natural ventilation to provide a relaxing environment. This makes it possible to analyze the impact of changes in the external environment and optimize energy use based on their emotions.

[0069] The external environment data collection unit can collect external environment data for each region and analyze energy usage patterns specific to the region. The external environment data collection unit collects environmental data such as temperature, humidity, wind speed, and sunshine hours for each region and analyzes energy usage patterns specific to the region. For example, it analyzes that heating is used more in cold regions. The external environment data collection unit also analyzes energy usage patterns specific to the region based on the energy usage data for each region. For example, it analyzes the difference in electricity consumption between urban and suburban areas. The external environment data collection unit also predicts energy usage patterns specific to the region based on the external environment data for each region. For example, it recommends natural ventilation, taking into account that coastal regions often have strong winds. In this way, by analyzing energy usage patterns for each region, it becomes possible to optimize energy usage specific to the region.

[0070] The external environment data collection unit can integrate the external environment data with other datasets to gain new insights into energy use. For example, the external environment data collection unit can integrate the external environment data with traffic data to gain new insights into energy use. For example, it can analyze that electricity consumption increases during times of heavy traffic. The external environment data collection unit can also integrate the external environment data with population data to gain new insights into energy use. For example, it can analyze electricity consumption patterns in densely populated areas. The external environment data collection unit can also integrate the external environment data with other datasets to gain new insights into energy use. For example, it can analyze the relationship between temperature and traffic volume and suggest energy use optimization. In this way, new insights into energy use can be gained by integrating the external environment data with other datasets.

[0071] The external environment data collection unit can use the emotion estimation function to analyze the emotional impact of changes in the external environment on the resident's energy use and provide advice based on the emotion. The external environment data collection unit, for example, combines external environment data and emotion estimation data to analyze the emotional impact of changes in the environment on the resident's energy use. For example, it analyzes that air conditioner use increases on hot days. The external environment data collection unit also suggests optimizing energy use in response to changes in the external environment based on the emotion estimation data. For example, on days with high humidity, it recommends using a dehumidifier to provide a comfortable environment. The external environment data collection unit also analyzes the external environment data and emotion estimation data to optimize energy use based on the resident's emotion. For example, on days with strong winds, it recommends opening windows for natural ventilation to provide a relaxing environment. This makes it possible to analyze the emotional impact of changes in the external environment on the resident's energy use and provide advice based on the emotion.

[0072] The energy usage pattern prediction unit can make predictions that take into account future events in addition to energy usage data. The energy usage pattern prediction unit, for example, combines past energy usage data with future event data to predict energy usage patterns. For example, it takes into account that electricity consumption increases on holidays and special event days. The energy usage pattern prediction unit also predicts energy usage patterns based on future event data. For example, it predicts that air conditioner use will increase on days when specific events are held. The energy usage pattern prediction unit also predicts energy usage patterns by integrating past data and future event data. For example, it takes into account that electricity consumption increases on special event days and suggests optimal energy usage methods. This makes it possible to predict energy usage patterns that take future events into account.

[0073] The energy usage pattern prediction unit incorporates an anomaly detection algorithm into the prediction of energy usage patterns, enabling early detection of abnormal energy usage. The energy usage pattern prediction unit, for example, incorporates an anomaly detection algorithm into energy usage data to detect abnormal energy usage early. For example, it detects power consumption that deviates significantly from normal patterns. The energy usage pattern prediction unit also uses an anomaly detection algorithm to predict energy usage patterns. For example, it issues an alert when abnormal power consumption occurs. The energy usage pattern prediction unit also combines the energy usage data with the anomaly detection algorithm to detect abnormal energy usage early and propose countermeasures. For example, when abnormal power consumption occurs, it identifies the cause and proposes improvement measures. This enables early detection of abnormal energy usage.

[0074] The energy usage pattern prediction unit can use the emotion estimation function to predict energy usage patterns taking into account the emotional state of the resident. The energy usage pattern prediction unit predicts energy usage patterns taking into account the emotional state of the resident, for example, based on emotion estimation data. For example, it predicts that air conditioner use will increase when stress is high. The energy usage pattern prediction unit also analyzes the emotional state of the resident in real time and predicts energy usage patterns based on that data. For example, it predicts that lighting use will decrease when the resident is relaxed. The energy usage pattern prediction unit also uses the emotion estimation function to predict energy usage patterns based on the emotional state of the resident. For example, it predicts that power consumption will increase when the resident is tired. This makes it possible to predict energy usage patterns taking into account the emotional state of the resident.

[0075] The energy usage pattern prediction unit can predict energy usage patterns on different time scales to provide detailed predictions. The energy usage pattern prediction unit predicts energy usage patterns on different time scales, such as daily, weekly, and monthly, to provide detailed predictions. For example, the energy usage pattern prediction unit predicts daily power consumption patterns and performs short-term optimization. The energy usage pattern prediction unit also predicts energy usage patterns on different time scales to provide detailed predictions. For example, the energy usage pattern prediction unit predicts weekly power consumption patterns and performs medium-term optimization. The energy usage pattern prediction unit also predicts energy usage patterns on different time scales to provide detailed predictions. For example, the energy usage pattern prediction unit predicts monthly power consumption patterns and performs long-term optimization. This makes it possible to predict detailed energy usage patterns on different time scales.

[0076] The energy usage pattern prediction unit can compare the predicted energy usage pattern with other households and businesses to improve the prediction accuracy. The energy usage pattern prediction unit predicts the energy usage pattern based on, for example, the energy usage data of other households and businesses, and improves the prediction accuracy. For example, the prediction accuracy is improved by comparing the power consumption with households of the same size. The energy usage pattern prediction unit also collects energy usage data of other households and businesses and uses it to predict the energy usage pattern. For example, the prediction accuracy is improved based on the power consumption pattern of the entire region. The energy usage pattern prediction unit also integrates the energy usage data of other households and businesses to predict the energy usage pattern. For example, the prediction accuracy is improved by referring to data from households with high energy efficiency. In this way, the prediction accuracy of the energy usage pattern can be improved by comparing with other households and businesses.

[0077] The energy usage pattern prediction unit can use the emotion estimation function to predict energy usage patterns based on the emotional state of the resident and provide emotion-based advice. The energy usage pattern prediction unit, for example, predicts energy usage patterns based on the emotional state of the resident based on emotion estimation data and provides emotion-based advice. For example, it provides a relaxing environment when the resident is under high stress. The energy usage pattern prediction unit also analyzes the emotional state of the resident in real time, predicts energy usage patterns based on the data, and provides emotion-based advice. For example, it suggests reducing the use of lights when the resident is relaxed. The energy usage pattern prediction unit also uses the emotion estimation function to predict energy usage patterns based on the emotional state of the resident and provides emotion-based advice. For example, it suggests reducing power consumption when the resident is tired. This makes it possible to predict energy usage patterns and provide advice based on the emotional state of the resident.

[0078] The wasteful use identification unit analyzes the usage status of home appliances in detail and can identify the cause of wasteful energy consumption by a specific home appliance. The wasteful use identification unit, for example, analyzes the usage status of each home appliance in detail and can identify the cause of wasteful energy consumption by a specific home appliance. For example, it can identify that increased power consumption is caused by frequent opening and closing of a refrigerator door. The wasteful use identification unit can also identify the cause of wasteful energy consumption based on the usage data of the home appliances. For example, it can identify that increased power consumption is caused by an air conditioner temperature setting being inappropriate. The wasteful use identification unit can also analyze the usage status of each home appliance in detail and can identify the cause of wasteful energy consumption. For example, it can identify that increased power consumption is caused by a television being left on for long periods of time. This makes it possible to identify the cause of wasteful energy consumption by each home appliance.

[0079] The wasteful use identification unit can combine energy usage data and external environmental data to identify wasteful energy usage under specific environmental conditions. The wasteful use identification unit, for example, combines energy usage data and external environmental data to identify wasteful energy usage under specific environmental conditions. For example, it identifies that increased power consumption is caused by an air conditioner temperature setting being inappropriate on a hot day. The wasteful use identification unit also identifies the cause of wasteful energy consumption based on external environmental data. For example, it identifies that increased power consumption is caused by inappropriate use of a dehumidifier on a humid day. The wasteful use identification unit also analyzes energy usage data and external environmental data to identify the cause of wasteful energy consumption. For example, it identifies that increased power consumption is caused by leaving a window open on a day with strong winds. This makes it possible to identify wasteful energy usage under specific environmental conditions.

[0080] The wasteful use identification unit uses the emotion estimation function to analyze the impact of the resident's emotional state on wasteful energy use and can propose improvement measures based on the emotion. The wasteful use identification unit analyzes the impact of the resident's emotional state on wasteful energy use, for example, based on emotion estimation data. For example, it analyzes that air conditioner use increases when stress is high. The wasteful use identification unit also analyzes the resident's emotional state in real time and identifies the cause of wasteful energy consumption based on that data. For example, it analyzes that lighting use increases when the resident is relaxing. The wasteful use identification unit also uses the emotion estimation function to propose optimization of energy use based on the resident's emotional state. For example, it suggests reducing power consumption when the resident is tired. In this way, it is possible to analyze the impact of the resident's emotional state on wasteful energy use and propose improvement measures based on the emotion.

[0081] The wasteful use identifying unit introduces an anomaly detection algorithm to identify wasteful energy use, enabling early detection of abnormal energy use. The wasteful use identifying unit, for example, introduces an anomaly detection algorithm into energy usage data to detect abnormal energy use early. For example, it detects power consumption that deviates significantly from normal patterns. The wasteful use identifying unit also uses the anomaly detection algorithm to identify wasteful energy consumption early. For example, it issues an alert when abnormal power consumption occurs. The wasteful use identifying unit also combines the energy usage data with the anomaly detection algorithm to detect abnormal energy use early and propose countermeasures. For example, when abnormal power consumption occurs, it identifies the cause and proposes improvement measures. This makes it possible to detect abnormal energy use early.

[0082] The wasteful use identification unit can compare with other households and businesses to identify wasteful energy use and set benchmarks. The wasteful use identification unit identifies wasteful energy use and sets benchmarks, for example, based on energy usage data of other households and businesses. For example, it compares power consumption with households of the same size to identify wasteful use. The wasteful use identification unit also collects energy usage data of other households and businesses to identify the causes of wasteful energy consumption. For example, it identifies wasteful use based on power consumption patterns across an entire region. The wasteful use identification unit also integrates energy usage data of other households and businesses to identify the causes of wasteful energy consumption. For example, it identifies wasteful use by referring to data from highly energy-efficient households. In this way, it is possible to identify wasteful energy use and set benchmarks by comparing with other households and businesses.

[0083] The wasteful use identification unit uses the emotion estimation function to identify wasteful energy usage based on the emotional state of the resident and can propose improvement measures based on the emotion. The wasteful use identification unit identifies wasteful energy usage based on the emotional state of the resident, for example, based on emotion estimation data. For example, it determines that air conditioner use increases when stress is high. The wasteful use identification unit also analyzes the emotional state of the resident in real time and identifies the cause of wasteful energy consumption based on that data. For example, it determines that lighting use increases when the resident is relaxing. The wasteful use identification unit also uses the emotion estimation function to propose energy usage optimization based on the emotional state of the resident. For example, it suggests reducing power consumption when the resident is tired. This makes it possible to identify wasteful energy usage and propose improvement measures based on the emotional state of the resident.

[0084] The operation method suggestion unit can propose the optimal operation method for the home appliance in detail and provide specific settings and usage methods. The operation method suggestion unit, for example, can propose the optimal operation method for each home appliance in detail and provide specific settings and usage methods. For example, it can propose a method for appropriately adjusting the set temperature of an air conditioner. The operation method suggestion unit can also propose the optimal operation method based on usage data of the home appliance. For example, it can provide a method for optimizing the temperature setting of a refrigerator. The operation method suggestion unit can also analyze the usage status of each home appliance and propose the optimal operation method. For example, it can provide a method for optimizing the usage time of a washing machine. This makes it possible to propose the optimal operation method for each home appliance in detail and provide specific settings and usage methods.

[0085] The operation method proposal unit can combine energy usage data and external environmental data to propose the optimal operation method under specific environmental conditions. The operation method proposal unit, for example, combines energy usage data and external environmental data to propose the optimal operation method under specific environmental conditions. For example, it proposes a method to appropriately adjust the set temperature of an air conditioner on a hot day. The operation method proposal unit also proposes the optimal operation method based on the external environmental data. For example, it recommends using a dehumidifier on a humid day. The operation method proposal unit also analyzes the energy usage data and external environmental data to propose the optimal operation method. For example, it recommends opening windows to allow natural ventilation on a day with strong winds. In this way, it is possible to propose the optimal operation method under specific environmental conditions.

[0086] The operation method suggestion unit uses the emotion estimation function to suggest an optimal operation method based on the emotional state of the resident and provide advice based on the emotion. The operation method suggestion unit suggests an optimal operation method based on the emotional state of the resident, for example, based on the emotion estimation data. For example, when stress is high, the operation method suggestion unit adjusts the settings of lighting and air conditioning to provide a relaxing environment. The operation method suggestion unit also analyzes the emotional state of the resident in real time and suggests an optimal operation method based on the data. For example, it suggests reducing the use of lights when the resident is relaxed. The operation method suggestion unit also uses the emotion estimation function to suggest an optimal operation method based on the emotional state of the resident. For example, it suggests reducing power consumption when the resident is tired. In this way, it is possible to suggest an optimal operation method based on the emotional state of the resident and provide advice based on the emotion.

[0087] The operation method proposal unit can propose optimal operation methods on different time scales and provide detailed advice. The operation method proposal unit can propose optimal operation methods on different time scales, such as daily, weekly, and monthly, and provide detailed advice. For example, it can propose a daily air conditioner setting temperature and perform short-term optimization. The operation method proposal unit can also propose optimal operation methods on different time scales and provide detailed advice. For example, it can propose an optimal operation method based on a weekly power consumption pattern. The operation method proposal unit can also propose optimal operation methods on different time scales and provide detailed advice. For example, it can propose an optimal operation method based on a monthly power consumption pattern. This makes it possible to propose optimal operation methods on different time scales and provide detailed advice.

[0088] The operation method proposal unit can compare the optimal operation method with other households and businesses and set a benchmark. The operation method proposal unit proposes the optimal operation method and sets a benchmark, for example, based on energy usage data of other households and businesses. For example, it compares power consumption with households of the same size and proposes the optimal operation method. The operation method proposal unit also collects energy usage data of other households and businesses and proposes the optimal operation method. For example, it proposes the optimal operation method based on the power consumption pattern of the entire region. The operation method proposal unit also integrates energy usage data of other households and businesses and proposes the optimal operation method. For example, it proposes the optimal operation method based on data from highly energy-efficient households. In this way, it is possible to set a benchmark for the optimal operation method by comparing with other households and businesses.

[0089] The operation method suggestion unit uses the emotion estimation function to suggest an optimal operation method based on the emotional state of the resident and provide advice based on the emotion. The operation method suggestion unit suggests an optimal operation method based on the emotional state of the resident, for example, based on the emotion estimation data. For example, when stress is high, the operation method suggestion unit adjusts the settings of lighting and air conditioning to provide a relaxing environment. The operation method suggestion unit also analyzes the emotional state of the resident in real time and suggests an optimal operation method based on the data. For example, it suggests reducing the use of lights when the resident is relaxed. The operation method suggestion unit also uses the emotion estimation function to suggest an optimal operation method based on the emotional state of the resident. For example, it suggests reducing power consumption when the resident is tired. In this way, it is possible to suggest an optimal operation method based on the emotional state of the resident and provide advice based on the emotion.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The energy usage data collection unit can collect resident health data in addition to energy usage data and analyze the relationship between health status and energy usage. For example, it can collect resident heart rate and sleep data and integrate it with the energy usage data for analysis. For example, it can analyze the difference in power consumption when a resident is in good health and when they are in poor health. The energy usage data collection unit can also suggest energy usage optimization based on the health data in accordance with the resident's health status. For example, it can adjust the air conditioner's temperature setting when the resident is in poor health, providing a comfortable environment. The energy usage data collection unit can also suggest energy usage optimization based on the resident's health status, based on the health data. For example, it can suggest reducing energy consumption when the resident is in good health. This makes it possible to optimize energy usage based on the resident's health status.

[0092] The energy usage data collection unit can collect resident hobby and preference data in addition to energy usage data and analyze the relationship between hobbies and preferences and energy usage. For example, it can collect resident music and movie viewing history and integrate it with energy usage data for analysis. For example, it can analyze the electricity consumption pattern when a resident is watching a movie. The energy usage data collection unit can also suggest energy usage optimization in accordance with the resident's hobby and preference based on the hobby and preference data. For example, it can provide a comfortable environment by dimming the lights when watching a movie. The energy usage data collection unit can also suggest energy usage optimization in accordance with the resident's hobby and preference based on the hobby and preference data. For example, it can provide a comfortable environment by adjusting the air conditioner's temperature setting when listening to music. This makes it possible to optimize energy usage based on the resident's hobby and preference.

[0093] The energy usage data collection unit can collect residents' social activity data in addition to energy usage data and analyze the relationship between social activity and energy usage. For example, it can collect residents' social media posts and message exchanges and integrate them with energy usage data for analysis. For example, it can analyze electricity consumption patterns when residents are interacting with friends. The energy usage data collection unit also proposes energy usage optimization in accordance with the resident's social activity based on the social activity data. For example, it can provide a comfortable environment by brightening the lights when residents are interacting with friends. The energy usage data collection unit also proposes energy usage optimization in accordance with the resident's social activity based on the social activity data. For example, it can provide a comfortable environment by adjusting the air conditioner's temperature setting when residents are using social media. This makes it possible to optimize energy usage based on the resident's social activity.

[0094] The energy usage data collection unit can collect resident dietary data in addition to energy usage data and analyze the relationship between diet and energy usage. For example, it can collect the contents of residents' meals and calorie intake, and integrate and analyze them with the energy usage data. For example, it can analyze the patterns of electricity consumption when residents are eating. The energy usage data collection unit also proposes optimization of energy usage in accordance with the resident's meals based on the dietary data. For example, it can provide a comfortable environment by brightening the lights while residents are eating. The energy usage data collection unit also proposes optimization of energy usage in accordance with the resident's meals based on the dietary data. For example, it can provide a comfortable environment by adjusting the air conditioner's temperature setting while residents are eating. This makes it possible to optimize energy usage based on the resident's meals.

[0095] The energy usage data collection unit can collect resident exercise data in addition to energy usage data and analyze the relationship between exercise and energy usage. For example, it can collect the amount and duration of resident exercise and integrate this with the energy usage data for analysis. For example, it can analyze the patterns of electricity consumption when a resident is exercising. The energy usage data collection unit can also suggest optimization of energy usage in accordance with the resident's exercise based on the exercise data. For example, it can adjust the temperature setting of the air conditioner when the resident is exercising, thereby providing a comfortable environment. The energy usage data collection unit can also suggest optimization of energy usage in accordance with the resident's exercise based on the exercise data. For example, it can brighten the lights when the resident is exercising, thereby providing a comfortable environment. This makes it possible to optimize energy usage based on the resident's exercise.

[0096] The energy usage data collection unit can use the emotion estimation function to analyze the emotional state of the resident and propose energy usage optimization based on the emotion. For example, the emotional state can be estimated by analyzing the resident's facial expressions and voice. For example, the lighting and air conditioning settings can be adjusted to provide a relaxing environment when the resident is under high stress. The energy usage data collection unit also optimizes energy consumption during times when the resident is relaxed based on the emotion estimation data. For example, the air conditioner temperature setting can be optimized when the resident is relaxed. The energy usage data collection unit also uses the emotion estimation function to propose energy usage optimization according to the resident's emotional state. For example, when the resident is tired, the lights can be dimmed to provide a relaxing environment. This makes it possible to optimize energy usage based on the resident's emotional state.

[0097] The energy usage data collection unit can use the emotion estimation function to analyze the emotional state of the resident and propose energy usage optimization based on the emotion. For example, the emotional state can be estimated by analyzing the resident's facial expressions and voice. For example, the lighting and air conditioning settings can be adjusted to provide a relaxing environment when the resident is under high stress. The energy usage data collection unit also optimizes energy consumption during times when the resident is relaxed based on the emotion estimation data. For example, the air conditioner temperature setting can be optimized when the resident is relaxed. The energy usage data collection unit also uses the emotion estimation function to propose energy usage optimization according to the resident's emotional state. For example, when the resident is tired, the lights can be dimmed to provide a relaxing environment. This makes it possible to optimize energy usage based on the resident's emotional state.

[0098] The energy usage data collection unit can use the emotion estimation function to analyze the emotional state of the resident and propose energy usage optimization based on the emotion. For example, the emotional state can be estimated by analyzing the resident's facial expressions and voice. For example, the lighting and air conditioning settings can be adjusted to provide a relaxing environment when the resident is under high stress. The energy usage data collection unit also optimizes energy consumption during times when the resident is relaxed based on the emotion estimation data. For example, the air conditioner temperature setting can be optimized when the resident is relaxed. The energy usage data collection unit also uses the emotion estimation function to propose energy usage optimization according to the resident's emotional state. For example, when the resident is tired, the lights can be dimmed to provide a relaxing environment. This makes it possible to optimize energy usage based on the resident's emotional state.

[0099] The energy usage data collection unit can use the emotion estimation function to analyze the emotional state of the resident and propose energy usage optimization based on the emotion. For example, the emotional state can be estimated by analyzing the resident's facial expressions and voice. For example, the lighting and air conditioning settings can be adjusted to provide a relaxing environment when the resident is under high stress. The energy usage data collection unit also optimizes energy consumption during times when the resident is relaxed based on the emotion estimation data. For example, the air conditioner temperature setting can be optimized when the resident is relaxed. The energy usage data collection unit also uses the emotion estimation function to propose energy usage optimization according to the resident's emotional state. For example, when the resident is tired, the lights can be dimmed to provide a relaxing environment. This makes it possible to optimize energy usage based on the resident's emotional state.

[0100] The energy usage data collection unit can use the emotion estimation function to analyze the emotional state of the resident and propose energy usage optimization based on the emotion. For example, the emotional state can be estimated by analyzing the resident's facial expressions and voice. For example, the lighting and air conditioning settings can be adjusted to provide a relaxing environment when the resident is under high stress. The energy usage data collection unit also optimizes energy consumption during times when the resident is relaxed based on the emotion estimation data. For example, the air conditioner temperature setting can be optimized when the resident is relaxed. The energy usage data collection unit also uses the emotion estimation function to propose energy usage optimization according to the resident's emotional state. For example, when the resident is tired, the lights can be dimmed to provide a relaxing environment. This makes it possible to optimize energy usage based on the resident's emotional state.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The energy usage data collection unit collects energy usage data. For example, it collects electricity consumption, gas consumption, water consumption, etc. It can also collect household electricity consumption data and analyze which home appliances are used most frequently at which times. Step 2: The external environment data collection unit collects external environment data, such as temperature, humidity, and weather forecasts. It can also collect temperature data and perform analysis to optimize air conditioner usage patterns. Step 3: The energy usage pattern prediction unit predicts an energy usage pattern based on the data collected by the energy usage data collection unit and the external environment data collection unit. For example, it predicts the amount of power consumption for the next week based on past data. Step 4: The wasteful use identification unit identifies wasteful energy use based on the energy use pattern predicted by the energy use pattern prediction unit. For example, if unnecessary power is being consumed at night, the unit identifies the cause. Step 5: The operation method suggestion unit proposes an optimal operation method based on the wasteful energy use identified by the wasteful use identification unit. For example, it proposes a way to reduce power consumption by appropriately adjusting the temperature setting of an air conditioner.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] 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.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an energy usage data collection unit that collects energy usage data; an external environment data collection unit that collects external environment data; an energy usage pattern prediction unit that predicts an energy usage pattern based on the data collected by the energy usage data collection unit and the external environment data collection unit; a wasteful energy usage identifying unit that identifies wasteful energy usage based on the energy usage pattern predicted by the energy usage pattern predicting unit; an operation method proposing unit that proposes an optimal operation method based on the wasteful energy use identified by the wasteful use identifying unit; A system characterized by:

2. The energy usage data collection unit Real-time monitoring of home appliance usage and detailed analysis of usage frequency and duration 2. The system of claim 1.

3. The energy usage data collection unit In addition to the energy usage data, data on residents' behavioral patterns is collected and the relationship between behavior and the energy usage is analyzed.

2. The system of claim 1.

4. The energy usage data collection unit Analyze the relationship between the emotional state of residents and the energy usage, and propose optimization of the energy usage based on emotions.

2. The system of claim 1.

5. The energy usage data collection unit Utilizing smart meters and IoT devices to obtain detailed data 2. The system of claim 1.

6. The energy usage data collection unit Compare said energy usage data with homes and businesses to set benchmarks 2. The system of claim 1.

7. The energy usage data collection unit Analyzing residents' feelings about the energy usage and providing advice based on those feelings 2. The system of claim 1.

8. The external environment data collection unit Collect detailed environmental data such as temperature, humidity, wind speed, and sunshine hours to optimize energy usage.

2. The system of claim 1.

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