System

Abstract

An object of a system according to an embodiment is to support selection of a device having high energy efficiency and design of an optimal energy use schedule.SOLUTION: A system according to an embodiment includes a household appliance selection part, a sensor data analysis part, an energy saving proposal part, a schedule design part, and a power cooperation part. The home appliance selection unit provides advice on selection and setting of home appliances and illumination devices with high energy efficiency. The sensor data analysis unit analyzes sensor data and weather forecast. The energy saving proposal unit makes an optimal energy saving proposal based on the data analyzed by the sensor data analysis unit. The schedule design unit designs an optimal energy use schedule on the basis of the content proposed by the energy saving proposal unit. The power coordination unit promotes efficient power utilization in coordination with a power supply company based on the schedule designed by the schedule design unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately select energy-efficient appliances or design optimal energy usage schedules for homes and businesses, and there is room for improvement.

[0005] The system according to the embodiment aims to support the selection of energy-efficient appliances and the design of an optimal energy usage schedule. [Means for solving the problem]

[0006] The system according to the embodiment includes a home appliance selection unit, a sensor data analysis unit, an energy saving proposal unit, a schedule design unit, and a power coordination unit. The home appliance selection unit provides advice on selecting and configuring energy-efficient home appliances and lighting devices. The sensor data analysis unit analyzes sensor data and weather forecasts. The energy saving proposal unit makes optimal energy saving proposals based on the data analyzed by the sensor data analysis unit. The schedule design unit designs an optimal energy use schedule based on the content proposed by the energy saving proposal unit. The power coordination unit promotes efficient power use in cooperation with power supply companies based on the schedule designed by the schedule design unit. [Effects of the Invention]

[0007] The system according to the embodiment can assist in selecting energy-efficient appliances and designing an optimal energy usage schedule. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) An energy saving support system according to an embodiment of the present invention provides advice on the selection and settings of energy-efficient home appliances and lighting equipment for homes and businesses, monitors energy usage using sensor data and weather forecasts, makes optimal energy saving proposals, designs optimal energy usage schedules, and promotes efficient power usage in cooperation with power supply companies. As a result, the energy saving support system can realize more efficient energy usage in homes and businesses and contribute to building a sustainable society.

[0029] An energy-saving support system according to an embodiment includes a home appliance selection unit, a sensor data analysis unit, an energy-saving proposal unit, a schedule design unit, and a power coordination unit. The home appliance selection unit provides advice on selecting and configuring energy-efficient home appliances and lighting devices. For example, the generation AI analyzes a user's usage and proposes optimal air conditioner temperature settings and lighting brightness. The generation AI can also recommend Energy Star certified products and high-efficiency LED lighting. The sensor data analysis unit analyzes sensor data and weather forecasts. For example, the generation AI analyzes data from temperature and humidity sensors to predict fluctuations in energy usage. The generation AI can also forecast weather using data from the Japan Meteorological Agency and private weather services. The energy-saving proposal unit makes optimal energy-saving proposals based on the data analyzed by the sensor data analysis unit. For example, the generation AI identifies time periods with high energy usage and makes specific proposals for reducing energy usage during those time periods. The generation AI can also provide energy consumption reduction targets and specific action proposals. The schedule design unit designs an optimal energy usage schedule based on the proposals made by the energy-saving proposal unit. For example, the generation AI designs an energy usage schedule taking peak shifting and time shifting into consideration. The generation AI can also generate a schedule based on the user's energy usage patterns and data from the power supply company. The power coordination unit promotes efficient power usage through coordination with the power supply company based on the schedule designed by the schedule design unit. For example, the generation AI analyzes data provided by the power supply company and suggests optimal energy usage methods to the user. The generation AI can also achieve efficient power usage by utilizing demand response programs and real-time price linkage. As a result, the energy saving support system according to the embodiment can achieve more efficient energy usage in homes and businesses and contribute to building a sustainable society. For example, energy usage can be reduced by providing advice on selecting and setting up energy-efficient home appliances and lighting equipment.In addition, by utilizing sensor data and weather forecasts, energy usage can be monitored in real time and optimal energy-saving proposals can be provided.Furthermore, efficient power usage can be promoted by designing optimal energy usage schedules and coordinating with power supply companies.

[0030] The home appliance selection unit can analyze the user's lifestyle patterns in detail and automatically adjust the optimal home appliance settings for each time period. For example, the generation AI analyzes the user's lifestyle patterns and automatically adjusts the optimal home appliance settings for each time period, such as setting the air conditioner temperature higher in the morning and lower in the evening. The generation AI can also adjust the brightness and color temperature of lighting based on the user's behavioral data. Furthermore, the generation AI can automatically control the on / off of home appliances in accordance with the user's lifestyle rhythm. This makes it possible to automatically adjust the optimal home appliance settings according to the user's lifestyle patterns.

[0031] The home appliance selection unit can take into account the life cycle cost of the product and make suggestions that maximize long-term energy-saving effects. For example, when purchasing a home appliance, the generation AI considers not only the initial cost but also operating and maintenance costs, and suggests the product with the best long-term cost performance. The generation AI can also calculate the life cycle cost of the product and present the optimal option to the user. Furthermore, the generation AI can evaluate the energy efficiency and durability of the product and make suggestions that maximize long-term energy-saving effects. This makes it possible to make suggestions that maximize long-term energy-saving effects.

[0032] The home appliance selection unit can take into account the user's health data and make suggestions to support a healthy lifestyle. For example, the generation AI in the home appliance selection unit can analyze the user's heart rate data and suggest lighting settings that will help them relax when they are under stress. The generation AI can also suggest the optimal bedroom environment based on the user's sleep data. Furthermore, the generation AI can adjust the temperature and airflow of the air conditioner based on the user's exercise data. This makes it possible to make suggestions that take into account the user's health data.

[0033] The home appliance selection unit can work in conjunction with a smart home system to achieve cooperative operation with other devices. For example, the generation AI in the home appliance selection unit can work in conjunction with the smart home system to collectively control devices such as air conditioners, lighting, and curtains to provide an optimal indoor environment. The generation AI can also work in conjunction with security systems and entertainment systems through the smart home system. Furthermore, the generation AI can also optimize home appliance settings based on data from the smart home system. This allows the unit to work in conjunction with the smart home system to achieve cooperative operation with other devices.

[0034] The sensor data analysis unit can combine sensor data with weather forecast data to predict long-term trends in energy usage. For example, the generation AI in the sensor data analysis unit can combine data from temperature and humidity sensors with weather forecast data to predict long-term trends in energy usage. The generation AI can also build a prediction model for energy usage based on trend analysis of past data. Furthermore, the generation AI can also make suggestions for optimizing energy usage based on the predicted trends. This makes it possible to predict long-term trends in energy usage.

[0035] The sensor data analysis unit can build a system that detects abnormal energy usage patterns based on monitoring data and issues early warnings. For example, the sensor data analysis unit uses a generation AI to analyze sensor data in real time and issue a warning to the user if it detects an abnormal energy usage pattern. The generation AI can also detect abnormalities in energy usage using an anomaly detection algorithm. Furthermore, the generation AI can identify abnormal energy usage patterns based on threshold settings. This makes it possible to detect abnormal energy usage patterns early and issue warnings.

[0036] The sensor data analysis unit can compare the monitoring data with the energy usage pattern of the entire region and make energy-saving proposals for each region. For example, the generation AI in the sensor data analysis unit compares the energy usage data of the entire region with the user's data and makes energy-saving proposals for each region. The generation AI can also provide optimal energy-saving proposals by taking into account the region's climate characteristics and energy consumption patterns. Furthermore, the generation AI can also suggest specific actions to the user based on the region's energy usage pattern. This makes it possible to make energy-saving proposals for each region.

[0037] The sensor data analysis unit can analyze seasonal energy usage patterns based on weather forecast data and make optimal energy-saving suggestions for each season. For example, the generation AI in the sensor data analysis unit can analyze seasonal energy usage patterns based on weather forecast data and make optimal energy-saving suggestions. The generation AI can also provide suggestions for optimizing energy usage for each season, taking into account differences in consumption patterns between summer and winter. Furthermore, the generation AI can make specific action suggestions to the user based on seasonal energy usage patterns. This makes it possible to make optimal energy-saving suggestions for each season.

[0038] The energy saving suggestion unit can analyze the user's energy usage history in detail and make individually customized energy saving suggestions. For example, the generation AI in the energy saving suggestion unit analyzes the user's past energy usage data and makes individually customized energy saving suggestions. The generation AI can also provide the user with optimal energy saving suggestions based on smart meter data and past electricity usage records. Furthermore, the generation AI can also suggest specific actions based on the user's energy usage history. This makes it possible to make individually customized energy saving suggestions.

[0039] The energy-saving suggestion unit can numerically indicate the energy usage reduction effect in the proposal content and present specific benefits to the user. For example, the generation AI in the energy-saving suggestion unit can numerically indicate the effect of the energy-saving proposal and present specific benefits to the user. For example, it can specifically indicate how much annual electricity bills can be saved by implementing the proposal. The generation AI can also clarify the units of reduction effect and comparison standards to explain it to the user in an easy-to-understand manner. Furthermore, the generation AI can numerically indicate the contribution to the environment by implementing the proposal content. This makes it possible to numerically indicate the energy usage reduction effect and present specific benefits to the user.

[0040] The energy-saving proposal unit can compare the proposal with other users' success stories and share best practices. For example, the generation AI analyzes other users' success stories and compares the proposal content to share best practices. For example, the energy-saving proposal unit makes proposals based on success stories of users in the same area. The generation AI can also collect performance data of other users and define best practices. Furthermore, the generation AI can show specific success stories to the user to increase the reliability of the proposal content. This allows the proposal to be compared with other users' success stories and best practices to be shared.

[0041] The energy-saving suggestion unit can expand the content of the suggestion to consider not only the reduction of energy use but also the contribution to the environment. For example, the generation AI expands the content of the energy-saving suggestion to consider not only the reduction of energy use but also the contribution to the environment. For example, it shows how much CO2 emissions can be reduced by implementing the suggestion. The generation AI can also provide an index for evaluating the contribution to the environment. Furthermore, the generation AI can specifically show the contribution to the environment to the user, increasing the value of the suggestion. This makes it possible to make suggestions that consider not only the reduction of energy use but also the contribution to the environment.

[0042] The schedule design unit can automatically generate an energy usage schedule that matches the user's lifestyle rhythm. For example, the generation AI analyzes the user's lifestyle rhythm and automatically generates an energy usage schedule that matches the user's lifestyle rhythm, such as turning on the air conditioner in the morning and turning it off in the evening. The generation AI can also provide an optimized energy usage schedule based on the user's daily behavior patterns. Furthermore, the generation AI can customize the energy usage schedule based on the user's activity data for each time period. This makes it possible to automatically generate an energy usage schedule that matches the user's lifestyle rhythm.

[0043] The schedule design unit can take energy price fluctuations into account and propose cost-efficient energy usage. For example, the generation AI analyzes energy price fluctuation data and proposes cost-efficient energy usage schedules. For example, it can propose using home appliances during times when electricity rates are low. The generation AI can also predict energy price fluctuations based on past price data and prediction models. Furthermore, the generation AI can also show the user specific cost-efficient energy usage methods. This allows the generation AI to propose cost-efficient energy usage, taking energy price fluctuations into account.

[0044] The schedule design unit can adjust with the energy usage patterns of other members in the household to maximize overall efficiency. For example, the schedule design unit uses a generation AI to analyze the energy usage patterns of other members in the household and propose a schedule to maximize overall efficiency. For example, it can suggest turning off home appliances when all family members are out. The generation AI can also provide an optimized energy usage schedule based on the behavior patterns of each member in the household. Furthermore, the generation AI can also provide a specific approach for adjusting the energy usage patterns in the household to maximize overall efficiency. This allows it to adjust with the energy usage patterns of other members in the household to maximize overall efficiency.

[0045] The schedule design unit can link with the energy usage patterns of the entire region to increase the energy conservation effects of the entire region. For example, the generation AI analyzes the energy usage patterns of the entire region and proposes a schedule to increase the energy conservation effects of the entire region. For example, it makes a proposal to avoid energy usage during peak hours throughout the entire region. The generation AI can also provide an optimized energy usage schedule for the entire region, taking into account the region's climate characteristics and energy consumption patterns. Furthermore, the generation AI can suggest specific actions to the user based on the energy usage patterns of the region. This allows the schedule design unit to link with the energy usage patterns of the entire region and increase the energy conservation effects of the entire region.

[0046] The power coordination unit can analyze real-time data from the power supply company and propose optimal energy usage methods. For example, the generation AI in the power coordination unit analyzes real-time data provided by the power supply company and proposes optimal energy usage methods. For example, it suggests using home appliances during times when the power supply is stable. The generation AI can also provide an optimized energy usage schedule based on real-time data. Furthermore, the generation AI can also suggest specific actions to the user based on power supply status and demand forecast data. This makes it possible to analyze real-time data from the power supply company and propose optimal energy usage methods.

[0047] The power coordination unit can perform highly accurate demand forecasts and ensure stability of supply. For example, the generation AI performs highly accurate demand forecasts based on data provided by power supply companies, ensuring stability of supply. For example, it analyzes past data and predicts peak demand. The generation AI can also build a demand forecasting model and evaluate the stability of supply. Furthermore, the generation AI can provide a specific approach for optimizing coordination with power supply companies based on the demand forecast. This allows highly accurate demand forecasts to ensure stability of supply.

[0048] The power coordination unit can improve overall energy efficiency by linking coordination with power supply companies with other energy supply sources. For example, the generation AI in the power coordination unit can link coordination with power supply companies with renewable energy supply sources to improve overall energy efficiency. For example, it can optimize energy use based on solar power generation data. The generation AI can also analyze data from other energy supply sources to provide an optimized energy use schedule. Furthermore, the generation AI can provide a specific approach for improving overall energy efficiency based on the type of energy supply source and the method of linkage. This allows coordination with power supply companies to be linked with other energy supply sources to improve overall energy efficiency.

[0049] The power coordination unit provides a dashboard for visually showing the effects of coordination to the user, thereby increasing the transparency of energy use. The power coordination unit, for example, has a generation AI provide a dashboard for visually showing the effects of coordination to the user, thereby increasing the transparency of energy use. For example, the generation AI can display energy usage and energy-saving effects in graphs. The generation AI can also visualize energy usage status based on real-time data. Furthermore, the generation AI can provide the user with specific approaches for increasing the transparency of energy use. This allows the generation AI to provide a dashboard for visually showing the effects of coordination to the user, thereby increasing the transparency of energy use.

[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 home appliance selection unit can analyze the user's lifestyle patterns in detail and automatically adjust the optimal home appliance settings for each time period. For example, the generation AI can analyze the user's lifestyle patterns and automatically adjust the optimal home appliance settings for each time period, such as setting the air conditioner temperature higher in the morning and lower in the evening. The generation AI can also adjust the brightness and color temperature of lighting based on the user's behavioral data. Furthermore, the generation AI can automatically control the on / off of home appliances in accordance with the user's lifestyle rhythm. This makes it possible to automatically adjust the optimal home appliance settings according to the user's lifestyle patterns.

[0052] The home appliance selection unit can take into account the life cycle cost of the product and make suggestions that maximize long-term energy-saving effects. For example, when purchasing a home appliance, the generation AI considers not only the initial cost but also operating and maintenance costs to suggest the product with the best long-term cost performance. The generation AI can also calculate the life cycle cost of the product and present the optimal option to the user. Furthermore, the generation AI can evaluate the energy efficiency and durability of the product and make suggestions that maximize long-term energy-saving effects. This makes it possible to make suggestions that maximize long-term energy-saving effects.

[0053] The home appliance selection unit can take into account the user's health data and make suggestions to support a healthy lifestyle. For example, the generation AI can analyze the user's heart rate data and suggest lighting settings that will help them relax when they are under stress. The generation AI can also suggest the optimal bedroom environment based on the user's sleep data. Furthermore, the generation AI can adjust the temperature and airflow of the air conditioner based on the user's exercise data. This makes it possible to make suggestions that take into account the user's health data.

[0054] The home appliance selection unit can work in conjunction with a smart home system to achieve cooperative operation with other devices. For example, the generation AI can work in conjunction with a smart home system to collectively control devices such as air conditioners, lighting, and curtains to provide an optimal indoor environment. The generation AI can also work in conjunction with security systems and entertainment systems through the smart home system. Furthermore, the generation AI can also optimize home appliance settings based on data from the smart home system. This allows the generation AI to work in conjunction with a smart home system to achieve cooperative operation with other devices.

[0055] The sensor data analysis unit can combine sensor data with weather forecast data to predict long-term trends in energy usage. For example, the generation AI can combine data from temperature and humidity sensors with weather forecast data to predict long-term trends in energy usage. The generation AI can also build a prediction model for energy usage based on trend analysis of past data. Furthermore, the generation AI can also make suggestions for optimizing energy usage based on the predicted trends. This makes it possible to predict long-term trends in energy usage.

[0056] The sensor data analysis unit can build a system that detects abnormal energy usage patterns based on monitoring data and issues early warnings. For example, the generation AI can analyze sensor data in real time and issue a warning to the user if it detects an abnormal energy usage pattern. The generation AI can also detect abnormalities in energy usage using an anomaly detection algorithm. Furthermore, the generation AI can identify abnormal energy usage patterns based on threshold settings. This allows for early detection of abnormal energy usage patterns and the issuance of warnings.

[0057] The sensor data analysis unit can compare the monitoring data with the energy usage patterns of the entire region and make energy-saving proposals for each region. For example, the generation AI can compare the energy usage data of the entire region with the user's data and make energy-saving proposals for each region. The generation AI can also provide optimal energy-saving proposals by taking into account the region's climate characteristics and energy consumption patterns. Furthermore, the generation AI can also suggest specific actions to the user based on the region's energy usage patterns. This makes it possible to make energy-saving proposals for each region.

[0058] The sensor data analysis unit can analyze seasonal energy usage patterns based on weather forecast data and make optimal energy-saving suggestions for each season. For example, the generation AI can analyze seasonal energy usage patterns based on weather forecast data and make optimal energy-saving suggestions. The generation AI can also provide suggestions for optimizing energy usage for each season, taking into account differences in consumption patterns between summer and winter. Furthermore, the generation AI can make specific action suggestions to the user based on seasonal energy usage patterns. This makes it possible to make optimal energy-saving suggestions for each season.

[0059] The energy saving suggestion unit can analyze the user's energy usage history in detail and make individually customized energy saving suggestions. For example, the generation AI can analyze the user's past energy usage data and make individually customized energy saving suggestions. The generation AI can also provide the user with optimal energy saving suggestions based on smart meter data and past electricity usage records. Furthermore, the generation AI can also make specific action suggestions based on the user's energy usage history. This makes it possible to make individually customized energy saving suggestions.

[0060] The energy-saving suggestion unit can numerically indicate the energy usage reduction effect of the proposal and present specific benefits to the user. For example, the generation AI can numerically indicate the effect of the energy-saving proposal and present specific benefits to the user. For example, it can specifically indicate how much annual electricity bills can be saved by implementing the proposal. The generation AI can also clarify the units of reduction effect and comparison standards to provide an easy-to-understand explanation to the user. Furthermore, the generation AI can numerically indicate the contribution to the environment by implementing the proposal. This makes it possible to numerically indicate the energy usage reduction effect and present specific benefits to the user.

[0061] The energy-saving proposal unit can compare the proposal with other users' success stories and share best practices. For example, the generation AI can analyze other users' success stories and compare the proposal to share best practices. For example, the generation AI can make proposals based on success stories of users in the same area. The generation AI can also collect performance data from other users and define best practices. Furthermore, the generation AI can show specific success stories to the user to increase the reliability of the proposal. This allows the proposal to be compared with other users' success stories and best practices to be shared.

[0062] The energy-saving suggestion unit can expand the content of the suggestions to take into account not only the reduction of energy use but also the contribution to the environment. For example, the generation AI expands the content of the energy-saving suggestions to take into account not only the reduction of energy use but also the contribution to the environment. For example, it shows how much CO2 emissions can be reduced by implementing the suggestion. The generation AI can also provide indicators for evaluating the contribution to the environment. Furthermore, the generation AI can specifically show the contribution to the environment to the user, increasing the value of the suggestion. This makes it possible to make suggestions that take into account not only the reduction of energy use but also the contribution to the environment.

[0063] The schedule design unit can automatically generate an energy usage schedule that matches the user's lifestyle rhythm. For example, the generation AI analyzes the user's lifestyle rhythm and automatically generates an energy usage schedule that matches the user's lifestyle rhythm, such as turning on the air conditioner in the morning and turning it off in the evening. The generation AI can also provide an optimized energy usage schedule based on the user's daily behavior patterns. Furthermore, the generation AI can customize the energy usage schedule based on the user's activity data for each time period. This makes it possible to automatically generate an energy usage schedule that matches the user's lifestyle rhythm.

[0064] The schedule design unit can take energy price fluctuations into account and propose cost-efficient energy usage. For example, the generation AI analyzes energy price fluctuation data and proposes a cost-efficient energy usage schedule. For example, it suggests using home appliances during times when electricity rates are low. The generation AI can also predict energy price fluctuations based on past price data and prediction models. Furthermore, the generation AI can also show the user specific cost-efficient energy usage methods. This allows the system to propose cost-efficient energy usage, taking energy price fluctuations into account.

[0065] The schedule design unit can adjust the energy usage patterns of other members in the household to maximize overall efficiency. For example, the generation AI can analyze the energy usage patterns of other members in the household and propose a schedule to maximize overall efficiency. For example, it can suggest turning off home appliances when all family members are out. The generation AI can also provide an optimized energy usage schedule based on the behavior patterns of each member in the household. Furthermore, the generation AI can also provide a specific approach to adjusting the energy usage patterns in the household to maximize overall efficiency. This allows the energy usage patterns of other members in the household to be adjusted to maximize overall efficiency.

[0066] The schedule design unit can link with the energy usage patterns of the entire region to increase the energy conservation effects of the entire region. For example, the generation AI analyzes the energy usage patterns of the entire region and proposes a schedule to increase the energy conservation effects of the entire region. For example, it makes a proposal to avoid energy usage during peak hours throughout the entire region. The generation AI can also provide an optimized energy usage schedule for the entire region, taking into account the region's climate characteristics and energy consumption patterns. Furthermore, the generation AI can also suggest specific actions to the user based on the region's energy usage patterns. This allows the schedule to be linked with the energy usage patterns of the entire region to increase the energy conservation effects of the entire region.

[0067] The power coordination unit can analyze real-time data from power supply companies and propose optimal energy usage methods. For example, the generation AI analyzes real-time data provided by power supply companies and proposes optimal energy usage methods. For example, it suggests using home appliances during times when the power supply is stable. The generation AI can also provide an optimized energy usage schedule based on real-time data. Furthermore, the generation AI can also make specific action suggestions to users based on power supply status and demand forecast data. This makes it possible to analyze real-time data from power supply companies and propose optimal energy usage methods.

[0068] The power coordination unit can perform highly accurate demand forecasts and ensure stability of supply. For example, the generation AI can perform highly accurate demand forecasts based on data provided by power supply companies, thereby ensuring stability of supply. For example, it can analyze past data and predict peak demand. The generation AI can also build a demand forecasting model and evaluate the stability of supply. Furthermore, the generation AI can provide a specific approach for optimizing coordination with power supply companies based on the demand forecast. This allows highly accurate demand forecasts to ensure stability of supply.

[0069] The power coordination unit can link coordination with power supply companies with other energy supply sources to improve overall energy efficiency. For example, the generation AI can link coordination with power supply companies with renewable energy sources to improve overall energy efficiency. For example, it can optimize energy use based on solar power generation data. The generation AI can also analyze data from other energy supply sources to provide an optimized energy use schedule. Furthermore, the generation AI can provide specific approaches to improve overall energy efficiency based on the type of energy supply source and the method of linkage. This allows coordination with power supply companies to link with other energy supply sources to improve overall energy efficiency.

[0070] The power coordination unit can provide a dashboard to visually show the effects of coordination to the user, thereby increasing the transparency of energy use. For example, the generation AI can provide a dashboard to visually show the effects of coordination to the user, thereby increasing the transparency of energy use. For example, the generation AI can display energy usage and energy-saving effects in graphs. The generation AI can also visualize energy usage status based on real-time data. Furthermore, the generation AI can provide the user with specific approaches to increasing the transparency of energy use. This can provide a dashboard to visually show the effects of coordination to the user, thereby increasing the transparency of energy use.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The home appliance selection unit provides advice on selecting and configuring energy-efficient home appliances and lighting. For example, the generation AI analyzes the user's usage and suggests optimal air conditioner temperature settings and lighting brightness. The generation AI can also recommend Energy Star certified products and high-efficiency LED lighting. Step 2: The sensor data analysis unit analyzes the sensor data and weather forecasts. For example, the generation AI analyzes data from temperature and humidity sensors to predict fluctuations in energy usage. The generation AI can also use data from the Japan Meteorological Agency and private weather services to make weather forecasts. Step 3: The energy-saving suggestion unit makes optimal energy-saving suggestions based on the data analyzed by the sensor data analysis unit. For example, the generation AI identifies time periods with high energy usage and makes specific suggestions for reducing energy usage during those periods. The generation AI can also provide energy consumption reduction targets and specific action suggestions. Step 4: The schedule design unit designs an optimal energy usage schedule based on the content proposed by the energy saving proposal unit. For example, the generation AI designs an energy usage schedule taking into account peak shifts and time shifts. The generation AI can also generate a schedule based on the user's energy usage patterns and data from the power supply company. Step 5: The power coordination unit promotes efficient power usage through coordination with the power supply company based on the schedule designed by the schedule design unit. For example, the generation AI analyzes data provided by the power supply company and suggests optimal energy usage methods to users. The generation AI can also achieve efficient power usage by utilizing demand response programs and real-time price linkage.

[0073] (Example 2) An energy saving support system according to an embodiment of the present invention provides advice on the selection and settings of energy-efficient home appliances and lighting equipment for homes and businesses, monitors energy usage using sensor data and weather forecasts, makes optimal energy saving proposals, designs optimal energy usage schedules, and promotes efficient power usage in cooperation with power supply companies. As a result, the energy saving support system can realize more efficient energy usage in homes and businesses and contribute to building a sustainable society.

[0074] An energy-saving support system according to an embodiment includes a home appliance selection unit, a sensor data analysis unit, an energy-saving proposal unit, a schedule design unit, and a power coordination unit. The home appliance selection unit provides advice on selecting and configuring energy-efficient home appliances and lighting devices. For example, the generation AI analyzes a user's usage and proposes optimal air conditioner temperature settings and lighting brightness. The generation AI can also recommend Energy Star certified products and high-efficiency LED lighting. The sensor data analysis unit analyzes sensor data and weather forecasts. For example, the generation AI analyzes data from temperature and humidity sensors to predict fluctuations in energy usage. The generation AI can also forecast weather using data from the Japan Meteorological Agency and private weather services. The energy-saving proposal unit makes optimal energy-saving proposals based on the data analyzed by the sensor data analysis unit. For example, the generation AI identifies time periods with high energy usage and makes specific proposals for reducing energy usage during those time periods. The generation AI can also provide energy consumption reduction targets and specific action proposals. The schedule design unit designs an optimal energy usage schedule based on the proposals made by the energy-saving proposal unit. For example, the generation AI designs an energy usage schedule taking peak shifting and time shifting into consideration. The generation AI can also generate a schedule based on the user's energy usage patterns and data from the power supply company. The power coordination unit promotes efficient power usage through coordination with the power supply company based on the schedule designed by the schedule design unit. For example, the generation AI analyzes data provided by the power supply company and suggests optimal energy usage methods to the user. The generation AI can also achieve efficient power usage by utilizing demand response programs and real-time price linkage. As a result, the energy saving support system according to the embodiment can achieve more efficient energy usage in homes and businesses and contribute to building a sustainable society. For example, energy usage can be reduced by providing advice on selecting and setting up energy-efficient home appliances and lighting equipment.In addition, by utilizing sensor data and weather forecasts, energy usage can be monitored in real time and optimal energy-saving proposals can be provided.Furthermore, efficient power usage can be promoted by designing optimal energy usage schedules and coordinating with power supply companies.

[0075] The home appliance selection unit can analyze the user's lifestyle patterns in detail and automatically adjust the optimal home appliance settings for each time period. For example, the generation AI analyzes the user's lifestyle patterns and automatically adjusts the optimal home appliance settings for each time period, such as setting the air conditioner temperature higher in the morning and lower in the evening. The generation AI can also adjust the brightness and color temperature of lighting based on the user's behavioral data. Furthermore, the generation AI can automatically control the on / off of home appliances in accordance with the user's lifestyle rhythm. This makes it possible to automatically adjust the optimal home appliance settings according to the user's lifestyle patterns.

[0076] The home appliance selection unit can take into account the life cycle cost of the product and make suggestions that maximize long-term energy-saving effects. For example, when purchasing a home appliance, the generation AI considers not only the initial cost but also operating and maintenance costs, and suggests the product with the best long-term cost performance. The generation AI can also calculate the life cycle cost of the product and present the optimal option to the user. Furthermore, the generation AI can evaluate the energy efficiency and durability of the product and make suggestions that maximize long-term energy-saving effects. This makes it possible to make suggestions that maximize long-term energy-saving effects.

[0077] The home appliance selection unit suggests home appliance settings according to the user's emotional state, achieving both comfort and energy conservation. For example, the generation AI in the home appliance selection unit analyzes the user's emotional state in real time and suggests relaxing lighting color temperature and brightness when stress levels are high. The generation AI can also adjust the temperature and airflow of the air conditioner based on the user's emotional data. Furthermore, the generation AI can adjust environmental elements such as music and fragrance according to the user's emotional state. This allows the unit to suggest home appliance settings according to the user's emotional state, achieving both comfort and energy conservation.

[0078] The home appliance selection unit can take into account the user's health data and make suggestions to support a healthy lifestyle. For example, the generation AI in the home appliance selection unit can analyze the user's heart rate data and suggest lighting settings that will help them relax when they are under stress. The generation AI can also suggest the optimal bedroom environment based on the user's sleep data. Furthermore, the generation AI can adjust the temperature and airflow of the air conditioner based on the user's exercise data. This makes it possible to make suggestions that take into account the user's health data.

[0079] The home appliance selection unit can work in conjunction with a smart home system to achieve cooperative operation with other devices. For example, the generation AI in the home appliance selection unit can work in conjunction with the smart home system to collectively control devices such as air conditioners, lighting, and curtains to provide an optimal indoor environment. The generation AI can also work in conjunction with security systems and entertainment systems through the smart home system. Furthermore, the generation AI can also optimize home appliance settings based on data from the smart home system. This allows the unit to work in conjunction with the smart home system to achieve cooperative operation with other devices.

[0080] The home appliance selection unit can suggest the color temperature and brightness of lighting that will make the user feel most relaxed. For example, the generation AI in the home appliance selection unit analyzes the user's emotional state and suggests a relaxing color temperature (e.g., warm colors) and brightness. The generation AI can also suggest optimal lighting settings based on the user's behavioral data. Furthermore, the generation AI can customize the color temperature and brightness of lighting according to the user's preferences. This makes it possible to suggest the color temperature and brightness of lighting that will make the user feel most relaxed.

[0081] The sensor data analysis unit can combine sensor data with weather forecast data to predict long-term trends in energy usage. For example, the generation AI in the sensor data analysis unit can combine data from temperature and humidity sensors with weather forecast data to predict long-term trends in energy usage. The generation AI can also build a prediction model for energy usage based on trend analysis of past data. Furthermore, the generation AI can also make suggestions for optimizing energy usage based on the predicted trends. This makes it possible to predict long-term trends in energy usage.

[0082] The sensor data analysis unit can build a system that detects abnormal energy usage patterns based on monitoring data and issues early warnings. For example, the sensor data analysis unit uses a generation AI to analyze sensor data in real time and issue a warning to the user if it detects an abnormal energy usage pattern. The generation AI can also detect abnormalities in energy usage using an anomaly detection algorithm. Furthermore, the generation AI can identify abnormal energy usage patterns based on threshold settings. This makes it possible to detect abnormal energy usage patterns early and issue warnings.

[0083] The sensor data analysis unit can analyze the impact of a user's emotional state on energy usage and make energy-saving suggestions based on their emotions. For example, the generation AI in the sensor data analysis unit analyzes the user's emotional data, identifies a tendency for energy usage to increase when stress levels are high, and proposes countermeasures. The generation AI can also use its emotion estimation function to monitor the user's emotional state in real time and predict fluctuations in energy usage. Furthermore, the generation AI can make energy-saving suggestions based on their emotions and optimize the user's energy usage. This makes it possible to make energy-saving suggestions based on the user's emotional state.

[0084] The sensor data analysis unit can compare the monitoring data with the energy usage pattern of the entire region and make energy-saving proposals for each region. For example, the generation AI in the sensor data analysis unit compares the energy usage data of the entire region with the user's data and makes energy-saving proposals for each region. The generation AI can also provide optimal energy-saving proposals by taking into account the region's climate characteristics and energy consumption patterns. Furthermore, the generation AI can also suggest specific actions to the user based on the region's energy usage pattern. This makes it possible to make energy-saving proposals for each region.

[0085] The sensor data analysis unit can analyze seasonal energy usage patterns based on weather forecast data and make optimal energy-saving suggestions for each season. For example, the generation AI in the sensor data analysis unit can analyze seasonal energy usage patterns based on weather forecast data and make optimal energy-saving suggestions. The generation AI can also provide suggestions for optimizing energy usage for each season, taking into account differences in consumption patterns between summer and winter. Furthermore, the generation AI can make specific action suggestions to the user based on seasonal energy usage patterns. This makes it possible to make optimal energy-saving suggestions for each season.

[0086] The sensor data analysis unit can suggest energy usage patterns that cause the user the least stress. For example, the generation AI in the sensor data analysis unit analyzes the user's emotional data and suggests energy usage patterns that cause the least stress. The generation AI can also suggest comfortable temperature settings and lighting adjustments based on the user's emotional state. Furthermore, the generation AI can suggest energy usage patterns that reduce stress based on the user's behavioral data. This makes it possible to suggest energy usage patterns that cause the user the least stress.

[0087] The energy saving suggestion unit can analyze the user's energy usage history in detail and make individually customized energy saving suggestions. For example, the generation AI in the energy saving suggestion unit analyzes the user's past energy usage data and makes individually customized energy saving suggestions. The generation AI can also provide the user with optimal energy saving suggestions based on smart meter data and past electricity usage records. Furthermore, the generation AI can also suggest specific actions based on the user's energy usage history. This makes it possible to make individually customized energy saving suggestions.

[0088] The energy-saving suggestion unit can numerically indicate the energy usage reduction effect in the proposal content and present specific benefits to the user. For example, the generation AI in the energy-saving suggestion unit can numerically indicate the effect of the energy-saving proposal and present specific benefits to the user. For example, it can specifically indicate how much annual electricity bills can be saved by implementing the proposal. The generation AI can also clarify the units of reduction effect and comparison standards to explain it to the user in an easy-to-understand manner. Furthermore, the generation AI can numerically indicate the contribution to the environment by implementing the proposal content. This makes it possible to numerically indicate the energy usage reduction effect and present specific benefits to the user.

[0089] The energy-saving suggestion unit generates energy-saving suggestions that are most acceptable to the user, thereby increasing the acceptability of the suggestions. For example, the generation AI in the energy-saving suggestion unit analyzes the user's emotional data and generates the most acceptable energy-saving suggestions. For example, it prioritizes suggestions that evoke strong positive emotions. The generation AI can also adjust the content of the suggestions based on user feedback. Furthermore, the generation AI can provide specific approaches to increase the acceptability of the suggestions based on the user's behavioral data. This allows the generation of energy-saving suggestions that are most acceptable to the user, thereby increasing the acceptability of the suggestions.

[0090] The energy-saving proposal unit can compare the proposal with other users' success stories and share best practices. For example, the generation AI analyzes other users' success stories and compares the proposal content to share best practices. For example, the energy-saving proposal unit makes proposals based on success stories of users in the same area. The generation AI can also collect performance data of other users and define best practices. Furthermore, the generation AI can show specific success stories to the user to increase the reliability of the proposal content. This allows the proposal to be compared with other users' success stories and best practices to be shared.

[0091] The energy-saving suggestion unit can expand the content of the suggestion to consider not only the reduction of energy use but also the contribution to the environment. For example, the generation AI expands the content of the energy-saving suggestion to consider not only the reduction of energy use but also the contribution to the environment. For example, it shows how much CO2 emissions can be reduced by implementing the suggestion. The generation AI can also provide an index for evaluating the contribution to the environment. Furthermore, the generation AI can specifically show the contribution to the environment to the user, increasing the value of the suggestion. This makes it possible to make suggestions that consider not only the reduction of energy use but also the contribution to the environment.

[0092] The energy-saving suggestion unit can make energy-saving suggestions that motivate the user the most. For example, the generation AI analyzes the user's emotional data and makes energy-saving suggestions that motivate the user the most. For example, it prioritizes suggestions that evoke strong positive emotions. The generation AI can also provide specific approaches to increase motivation based on user questionnaire surveys and behavioral data. Furthermore, the generation AI can specifically show the user the benefits of the suggestions to increase motivation. This makes it possible to make energy-saving suggestions that motivate the user the most.

[0093] The schedule design unit can automatically generate an energy usage schedule that matches the user's lifestyle rhythm. For example, the generation AI analyzes the user's lifestyle rhythm and automatically generates an energy usage schedule that matches the user's lifestyle rhythm, such as turning on the air conditioner in the morning and turning it off in the evening. The generation AI can also provide an optimized energy usage schedule based on the user's daily behavior patterns. Furthermore, the generation AI can customize the energy usage schedule based on the user's activity data for each time period. This makes it possible to automatically generate an energy usage schedule that matches the user's lifestyle rhythm.

[0094] The schedule design unit can take energy price fluctuations into account and propose cost-efficient energy usage. For example, the generation AI analyzes energy price fluctuation data and proposes cost-efficient energy usage schedules. For example, it can propose using home appliances during times when electricity rates are low. The generation AI can also predict energy price fluctuations based on past price data and prediction models. Furthermore, the generation AI can also show the user specific cost-efficient energy usage methods. This allows the generation AI to propose cost-efficient energy usage, taking energy price fluctuations into account.

[0095] The schedule design unit can propose an energy usage schedule that corresponds to the user's emotional state, thereby reducing stress. In the schedule design unit, for example, the generation AI analyzes the user's emotional data and proposes an energy usage schedule that reduces stress. For example, it may suggest using the air conditioner during times when the user is most likely to relax. The generation AI can also provide an optimized energy usage schedule based on the user's emotional state. Furthermore, the generation AI can also propose an energy usage schedule that reduces stress based on the user's behavioral data. This allows the system to propose an energy usage schedule that corresponds to the user's emotional state, thereby reducing stress.

[0096] The schedule design unit can adjust with the energy usage patterns of other members in the household to maximize overall efficiency. For example, the schedule design unit uses a generation AI to analyze the energy usage patterns of other members in the household and propose a schedule to maximize overall efficiency. For example, it can suggest turning off home appliances when all family members are out. The generation AI can also provide an optimized energy usage schedule based on the behavior patterns of each member in the household. Furthermore, the generation AI can also provide a specific approach for adjusting the energy usage patterns in the household to maximize overall efficiency. This allows it to adjust with the energy usage patterns of other members in the household to maximize overall efficiency.

[0097] The schedule design unit can link with the energy usage patterns of the entire region to increase the energy conservation effects of the entire region. For example, the generation AI analyzes the energy usage patterns of the entire region and proposes a schedule to increase the energy conservation effects of the entire region. For example, it makes a proposal to avoid energy usage during peak hours throughout the entire region. The generation AI can also provide an optimized energy usage schedule for the entire region, taking into account the region's climate characteristics and energy consumption patterns. Furthermore, the generation AI can suggest specific actions to the user based on the energy usage patterns of the region. This allows the schedule design unit to link with the energy usage patterns of the entire region and increase the energy conservation effects of the entire region.

[0098] The schedule design unit can propose an energy usage schedule that makes the user feel most comfortable. For example, the generation AI analyzes the user's emotional data and proposes an energy usage schedule that makes the user feel most comfortable. For example, it may suggest using the air conditioner during times when the user is most comfortable relaxing. The generation AI can also suggest comfortable temperature settings and lighting adjustments based on the user's behavioral data. Furthermore, the generation AI can customize the energy usage schedule according to the user's preferences. This allows it to propose an energy usage schedule that makes the user feel most comfortable.

[0099] The power coordination unit can analyze real-time data from the power supply company and propose optimal energy usage methods. For example, the generation AI in the power coordination unit analyzes real-time data provided by the power supply company and proposes optimal energy usage methods. For example, it suggests using home appliances during times when the power supply is stable. The generation AI can also provide an optimized energy usage schedule based on real-time data. Furthermore, the generation AI can also suggest specific actions to the user based on power supply status and demand forecast data. This makes it possible to analyze real-time data from the power supply company and propose optimal energy usage methods.

[0100] The power coordination unit can perform highly accurate demand forecasts and ensure stability of supply. For example, the generation AI performs highly accurate demand forecasts based on data provided by power supply companies, ensuring stability of supply. For example, it analyzes past data and predicts peak demand. The generation AI can also build a demand forecasting model and evaluate the stability of supply. Furthermore, the generation AI can provide a specific approach for optimizing coordination with power supply companies based on the demand forecast. This allows highly accurate demand forecasts to ensure stability of supply.

[0101] The power coordination unit makes energy usage proposals that are most convincing to the user, thereby increasing the effectiveness of coordination. In the power coordination unit, for example, the generation AI analyzes the user's emotional data and makes the most convincing energy usage proposals. For example, it prioritizes proposals that evoke strong positive emotions. The generation AI can also adjust the content of the proposals based on user feedback. Furthermore, the generation AI can provide specific approaches to increase the effectiveness of coordination based on user behavior data. This makes it possible to make energy usage proposals that are most convincing to the user, thereby increasing the effectiveness of coordination.

[0102] The power coordination unit can improve overall energy efficiency by linking coordination with power supply companies with other energy supply sources. For example, the generation AI in the power coordination unit can link coordination with power supply companies with renewable energy supply sources to improve overall energy efficiency. For example, it can optimize energy use based on solar power generation data. The generation AI can also analyze data from other energy supply sources to provide an optimized energy use schedule. Furthermore, the generation AI can provide a specific approach for improving overall energy efficiency based on the type of energy supply source and the method of linkage. This allows coordination with power supply companies to be linked with other energy supply sources to improve overall energy efficiency.

[0103] The power coordination unit provides a dashboard for visually showing the effects of coordination to the user, thereby increasing the transparency of energy use. The power coordination unit, for example, has a generation AI provide a dashboard for visually showing the effects of coordination to the user, thereby increasing the transparency of energy use. For example, the generation AI can display energy usage and energy-saving effects in graphs. The generation AI can also visualize energy usage status based on real-time data. Furthermore, the generation AI can provide the user with specific approaches for increasing the transparency of energy use. This allows the generation AI to provide a dashboard for visually showing the effects of coordination to the user, thereby increasing the transparency of energy use.

[0104] The power coordination unit can make energy usage suggestions that will make the user most cooperative. For example, the generation AI analyzes the user's emotional data and makes energy usage suggestions that will make the user most cooperative. For example, it prioritizes suggestions that evoke strong positive emotions. The generation AI can also adjust the content of the suggestions based on user feedback. Furthermore, the generation AI can provide specific approaches for becoming cooperative based on the user's behavioral data. This makes it possible to make energy usage suggestions that will make the user most cooperative.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The home appliance selection unit can analyze the user's lifestyle patterns in detail and automatically adjust the optimal home appliance settings for each time period. For example, the generation AI can analyze the user's lifestyle patterns and automatically adjust the optimal home appliance settings for each time period, such as setting the air conditioner temperature higher in the morning and lower in the evening. The generation AI can also adjust the brightness and color temperature of lighting based on the user's behavioral data. Furthermore, the generation AI can automatically control the on / off of home appliances in accordance with the user's lifestyle rhythm. This makes it possible to automatically adjust the optimal home appliance settings according to the user's lifestyle patterns.

[0107] The home appliance selection unit can take into account the life cycle cost of the product and make suggestions that maximize long-term energy-saving effects. For example, when purchasing a home appliance, the generation AI considers not only the initial cost but also operating and maintenance costs to suggest the product with the best long-term cost performance. The generation AI can also calculate the life cycle cost of the product and present the optimal option to the user. Furthermore, the generation AI can evaluate the energy efficiency and durability of the product and make suggestions that maximize long-term energy-saving effects. This makes it possible to make suggestions that maximize long-term energy-saving effects.

[0108] The home appliance selection unit suggests home appliance settings according to the user's emotional state, achieving both comfort and energy conservation. For example, the generation AI analyzes the user's emotional state in real time and suggests relaxing lighting color temperature and brightness when stress levels are high. The generation AI can also adjust the temperature and airflow of the air conditioner based on the user's emotional data. Furthermore, the generation AI can adjust environmental elements such as music and fragrance according to the user's emotional state. This allows the system to suggest home appliance settings according to the user's emotional state, achieving both comfort and energy conservation.

[0109] The home appliance selection unit can take into account the user's health data and make suggestions to support a healthy lifestyle. For example, the generation AI can analyze the user's heart rate data and suggest lighting settings that will help them relax when they are under stress. The generation AI can also suggest the optimal bedroom environment based on the user's sleep data. Furthermore, the generation AI can adjust the temperature and airflow of the air conditioner based on the user's exercise data. This makes it possible to make suggestions that take into account the user's health data.

[0110] The home appliance selection unit can work in conjunction with a smart home system to achieve cooperative operation with other devices. For example, the generation AI can work in conjunction with a smart home system to collectively control devices such as air conditioners, lighting, and curtains to provide an optimal indoor environment. The generation AI can also work in conjunction with security systems and entertainment systems through the smart home system. Furthermore, the generation AI can also optimize home appliance settings based on data from the smart home system. This allows the generation AI to work in conjunction with a smart home system to achieve cooperative operation with other devices.

[0111] The sensor data analysis unit can combine sensor data with weather forecast data to predict long-term trends in energy usage. For example, the generation AI can combine data from temperature and humidity sensors with weather forecast data to predict long-term trends in energy usage. The generation AI can also build a prediction model for energy usage based on trend analysis of past data. Furthermore, the generation AI can also make suggestions for optimizing energy usage based on the predicted trends. This makes it possible to predict long-term trends in energy usage.

[0112] The sensor data analysis unit can build a system that detects abnormal energy usage patterns based on monitoring data and issues early warnings. For example, the generation AI can analyze sensor data in real time and issue a warning to the user if it detects an abnormal energy usage pattern. The generation AI can also detect abnormalities in energy usage using an anomaly detection algorithm. Furthermore, the generation AI can identify abnormal energy usage patterns based on threshold settings. This allows for early detection of abnormal energy usage patterns and the issuance of warnings.

[0113] The sensor data analysis unit can analyze the impact of a user's emotional state on energy usage and make energy-saving suggestions based on their emotions. For example, the generation AI can analyze a user's emotional data, identify a tendency for energy usage to increase when stress levels are high, and suggest countermeasures. The generation AI can also use its emotion estimation function to monitor the user's emotional state in real time and predict fluctuations in energy usage. Furthermore, the generation AI can make energy-saving suggestions based on their emotions and optimize the user's energy usage. This makes it possible to make energy-saving suggestions based on the user's emotional state.

[0114] The sensor data analysis unit can compare the monitoring data with the energy usage patterns of the entire region and make energy-saving proposals for each region. For example, the generation AI can compare the energy usage data of the entire region with the user's data and make energy-saving proposals for each region. The generation AI can also provide optimal energy-saving proposals by taking into account the region's climate characteristics and energy consumption patterns. Furthermore, the generation AI can also suggest specific actions to the user based on the region's energy usage patterns. This makes it possible to make energy-saving proposals for each region.

[0115] The sensor data analysis unit can analyze seasonal energy usage patterns based on weather forecast data and make optimal energy-saving suggestions for each season. For example, the generation AI can analyze seasonal energy usage patterns based on weather forecast data and make optimal energy-saving suggestions. The generation AI can also provide suggestions for optimizing energy usage for each season, taking into account differences in consumption patterns between summer and winter. Furthermore, the generation AI can make specific action suggestions to the user based on seasonal energy usage patterns. This makes it possible to make optimal energy-saving suggestions for each season.

[0116] The sensor data analysis unit can suggest energy usage patterns that cause the least stress to the user. For example, the generation AI can analyze the user's emotional data and suggest energy usage patterns that cause the least stress. The generation AI can also suggest comfortable temperature settings and lighting adjustments based on the user's emotional state. Furthermore, the generation AI can suggest energy usage patterns that reduce stress based on the user's behavioral data. This allows it to suggest energy usage patterns that cause the least stress to the user.

[0117] The energy saving suggestion unit can analyze the user's energy usage history in detail and make individually customized energy saving suggestions. For example, the generation AI can analyze the user's past energy usage data and make individually customized energy saving suggestions. The generation AI can also provide the user with optimal energy saving suggestions based on smart meter data and past electricity usage records. Furthermore, the generation AI can also make specific action suggestions based on the user's energy usage history. This makes it possible to make individually customized energy saving suggestions.

[0118] The energy-saving suggestion unit can numerically indicate the energy usage reduction effect of the proposal and present specific benefits to the user. For example, the generation AI can numerically indicate the effect of the energy-saving proposal and present specific benefits to the user. For example, it can specifically indicate how much annual electricity bills can be saved by implementing the proposal. The generation AI can also clarify the units of reduction effect and comparison standards to provide an easy-to-understand explanation to the user. Furthermore, the generation AI can numerically indicate the contribution to the environment by implementing the proposal. This makes it possible to numerically indicate the energy usage reduction effect and present specific benefits to the user.

[0119] The energy-saving suggestion unit generates energy-saving suggestions that are most acceptable to the user, thereby increasing the acceptability of the suggestions. For example, the generation AI analyzes the user's emotional data and generates the most acceptable energy-saving suggestions. For example, it prioritizes suggestions that evoke strong positive emotions. The generation AI can also adjust the content of the suggestions based on user feedback. Furthermore, the generation AI can provide specific approaches to increase the acceptability of the suggestions based on the user's behavioral data. This allows the generation of energy-saving suggestions that are most acceptable to the user, thereby increasing the acceptability of the suggestions.

[0120] The energy-saving proposal unit can compare the proposal with other users' success stories and share best practices. For example, the generation AI can analyze other users' success stories and compare the proposal to share best practices. For example, the generation AI can make proposals based on success stories of users in the same area. The generation AI can also collect performance data from other users and define best practices. Furthermore, the generation AI can show specific success stories to the user to increase the reliability of the proposal. This allows the proposal to be compared with other users' success stories and best practices to be shared.

[0121] The energy-saving suggestion unit can expand the content of the suggestions to take into account not only the reduction of energy use but also the contribution to the environment. For example, the generation AI expands the content of the energy-saving suggestions to take into account not only the reduction of energy use but also the contribution to the environment. For example, it shows how much CO2 emissions can be reduced by implementing the suggestion. The generation AI can also provide indicators for evaluating the contribution to the environment. Furthermore, the generation AI can specifically show the contribution to the environment to the user, increasing the value of the suggestion. This makes it possible to make suggestions that take into account not only the reduction of energy use but also the contribution to the environment.

[0122] The energy-saving suggestion unit can make energy-saving suggestions that motivate the user the most. For example, the generation AI can analyze the user's emotional data and make energy-saving suggestions that motivate the user the most. For example, it can prioritize suggestions that evoke strong positive emotions. The generation AI can also provide specific approaches to increase motivation based on user questionnaire surveys and behavioral data. Furthermore, the generation AI can specifically show the user the benefits of the suggestions to increase motivation. This makes it possible to make energy-saving suggestions that motivate the user the most.

[0123] The schedule design unit can automatically generate an energy usage schedule that matches the user's lifestyle rhythm. For example, the generation AI analyzes the user's lifestyle rhythm and automatically generates an energy usage schedule that matches the user's lifestyle rhythm, such as turning on the air conditioner in the morning and turning it off in the evening. The generation AI can also provide an optimized energy usage schedule based on the user's daily behavior patterns. Furthermore, the generation AI can customize the energy usage schedule based on the user's activity data for each time period. This makes it possible to automatically generate an energy usage schedule that matches the user's lifestyle rhythm.

[0124] The schedule design unit can take energy price fluctuations into account and propose cost-efficient energy usage. For example, the generation AI analyzes energy price fluctuation data and proposes a cost-efficient energy usage schedule. For example, it suggests using home appliances during times when electricity rates are low. The generation AI can also predict energy price fluctuations based on past price data and prediction models. Furthermore, the generation AI can also show the user specific cost-efficient energy usage methods. This allows the system to propose cost-efficient energy usage, taking energy price fluctuations into account.

[0125] The schedule design unit can propose an energy usage schedule that corresponds to the user's emotional state, thereby reducing stress. For example, the generation AI can analyze the user's emotional data and propose a low-stress energy usage schedule. For example, it can suggest using the air conditioner during times when the user is most likely to relax. The generation AI can also provide an optimized energy usage schedule based on the user's emotional state. Furthermore, the generation AI can also propose an energy usage schedule that reduces stress based on the user's behavioral data. This allows the system to propose an energy usage schedule that corresponds to the user's emotional state, thereby reducing stress.

[0126] The schedule design unit can adjust the energy usage patterns of other members in the household to maximize overall efficiency. For example, the generation AI can analyze the energy usage patterns of other members in the household and propose a schedule to maximize overall efficiency. For example, it can suggest turning off home appliances when all family members are out. The generation AI can also provide an optimized energy usage schedule based on the behavior patterns of each member in the household. Furthermore, the generation AI can also provide a specific approach to adjusting the energy usage patterns in the household to maximize overall efficiency. This allows the energy usage patterns of other members in the household to be adjusted to maximize overall efficiency.

[0127] The schedule design unit can link with the energy usage patterns of the entire region to increase the energy conservation effects of the entire region. For example, the generation AI analyzes the energy usage patterns of the entire region and proposes a schedule to increase the energy conservation effects of the entire region. For example, it makes a proposal to avoid energy usage during peak hours throughout the entire region. The generation AI can also provide an optimized energy usage schedule for the entire region, taking into account the region's climate characteristics and energy consumption patterns. Furthermore, the generation AI can also suggest specific actions to the user based on the region's energy usage patterns. This allows the schedule to be linked with the energy usage patterns of the entire region to increase the energy conservation effects of the entire region.

[0128] The schedule design unit can propose an energy usage schedule that makes the user feel most comfortable. For example, the generation AI can analyze the user's emotional data and propose an energy usage schedule that makes the user feel most comfortable. For example, it can suggest using the air conditioner during times when the user is most comfortable relaxing. The generation AI can also suggest comfortable temperature settings and lighting adjustments based on the user's behavioral data. Furthermore, the generation AI can customize the energy usage schedule according to the user's preferences. This allows it to propose an energy usage schedule that makes the user feel most comfortable.

[0129] The power coordination unit can analyze real-time data from power supply companies and propose optimal energy usage methods. For example, the generation AI analyzes real-time data provided by power supply companies and proposes optimal energy usage methods. For example, it suggests using home appliances during times when the power supply is stable. The generation AI can also provide an optimized energy usage schedule based on real-time data. Furthermore, the generation AI can also make specific action suggestions to users based on power supply status and demand forecast data. This makes it possible to analyze real-time data from power supply companies and propose optimal energy usage methods.

[0130] The power coordination unit can perform highly accurate demand forecasts and ensure stability of supply. For example, the generation AI can perform highly accurate demand forecasts based on data provided by power supply companies, thereby ensuring stability of supply. For example, it can analyze past data and predict peak demand. The generation AI can also build a demand forecasting model and evaluate the stability of supply. Furthermore, the generation AI can provide a specific approach for optimizing coordination with power supply companies based on the demand forecast. This allows highly accurate demand forecasts to ensure stability of supply.

[0131] The power coordination unit can make energy usage proposals that are most convincing to the user, thereby increasing the effectiveness of coordination. For example, the generation AI can analyze the user's emotional data and make the most convincing energy usage proposals. For example, it can prioritize proposals that evoke strong positive emotions. The generation AI can also adjust the proposal content based on user feedback. Furthermore, the generation AI can provide specific approaches to increase the effectiveness of coordination based on user behavior data. This makes it possible to make the most convincing energy usage proposals to the user, thereby increasing the effectiveness of coordination.

[0132] The power coordination unit can link coordination with power supply companies with other energy supply sources to improve overall energy efficiency. For example, the generation AI can link coordination with power supply companies with renewable energy sources to improve overall energy efficiency. For example, it can optimize energy use based on solar power generation data. The generation AI can also analyze data from other energy supply sources to provide an optimized energy use schedule. Furthermore, the generation AI can provide specific approaches to improve overall energy efficiency based on the type of energy supply source and the method of linkage. This allows coordination with power supply companies to link with other energy supply sources to improve overall energy efficiency.

[0133] The power coordination unit can provide a dashboard to visually show the effects of coordination to the user, thereby increasing the transparency of energy use. For example, the generation AI can provide a dashboard to visually show the effects of coordination to the user, thereby increasing the transparency of energy use. For example, the generation AI can display energy usage and energy-saving effects in graphs. The generation AI can also visualize energy usage status based on real-time data. Furthermore, the generation AI can provide the user with specific approaches to increasing the transparency of energy use. This can provide a dashboard to visually show the effects of coordination to the user, thereby increasing the transparency of energy use.

[0134] The power cooperation unit can make energy usage suggestions that will encourage the user to be most cooperative. For example, the generation AI can analyze the user's emotional data and make energy usage suggestions that will encourage the user to be most cooperative. For example, it can prioritize suggestions that evoke strong positive emotions. The generation AI can also adjust the content of the suggestions based on user feedback. Furthermore, the generation AI can provide specific approaches for encouraging cooperation based on the user's behavioral data. This allows it to make energy usage suggestions that will encourage the user to be most cooperative.

[0135] The processing flow of the second embodiment will be briefly explained below.

[0136] Step 1: The home appliance selection unit provides advice on selecting and configuring energy-efficient home appliances and lighting. For example, the generation AI analyzes the user's usage and suggests optimal air conditioner temperature settings and lighting brightness. The generation AI can also recommend Energy Star certified products and high-efficiency LED lighting. Step 2: The sensor data analysis unit analyzes the sensor data and weather forecasts. For example, the generation AI analyzes data from temperature and humidity sensors to predict fluctuations in energy usage. The generation AI can also use data from the Japan Meteorological Agency and private weather services to make weather forecasts. Step 3: The energy-saving suggestion unit makes optimal energy-saving suggestions based on the data analyzed by the sensor data analysis unit. For example, the generation AI identifies time periods with high energy usage and makes specific suggestions for reducing energy usage during those periods. The generation AI can also provide energy consumption reduction targets and specific action suggestions. Step 4: The schedule design unit designs an optimal energy usage schedule based on the content proposed by the energy saving proposal unit. For example, the generation AI designs an energy usage schedule taking into account peak shifts and time shifts. The generation AI can also generate a schedule based on the user's energy usage patterns and data from the power supply company. Step 5: The power coordination unit promotes efficient power usage through coordination with the power supply company based on the schedule designed by the schedule design unit. For example, the generation AI analyzes data provided by the power supply company and suggests optimal energy usage methods to users. The generation AI can also achieve efficient power usage by utilizing demand response programs and real-time price linkage.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0141] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0150] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

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

[0156] 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.

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

[0158] The 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.

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

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

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

[0162] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] 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.

[0164] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0165] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0170] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0181] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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]

[0204] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A home appliance selection section provides advice on selecting and configuring energy-efficient home appliances and lighting devices; a sensor data analysis unit that analyzes sensor data and weather forecasts; an energy saving suggestion unit that makes optimal energy saving suggestions based on the data analyzed by the sensor data analysis unit; a schedule design unit that designs an optimal energy use schedule based on the content proposed by the energy saving proposal unit; a power cooperation unit that promotes efficient power use in cooperation with a power supply company based on the schedule designed by the schedule design unit; A system characterized by:

2. The home appliance selection unit Proposes settings for the home appliances according to the user's emotional state, achieving both comfort and energy savings.

2. The system of claim 1.

3. The sensor data analysis unit Combining the sensor data with weather forecast data to predict long-term trends in energy usage 2. The system of claim 1.

4. The energy saving suggestion unit Analyze the user's energy usage history in detail and provide individually customized energy-saving suggestions.

2. The system of claim 1.

5. The schedule design unit Automatically generate the energy usage schedule that matches the user's lifestyle 2. The system of claim 1.

6. The power coordination unit Analyzes real-time data from the power supplier and proposes optimal energy usage methods 2. The system of claim 1.

7. The sensor data analysis unit Analyze the impact of a user's emotional state on energy consumption and make energy-saving suggestions based on their emotions.

2. The system of claim 1.

8. The energy saving suggestion unit Make the energy-saving proposal that most motivates users 2. The system of claim 1.

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