System

Abstract

To provide a system according to an embodiment that optimizes the use of energy in a house or building.SOLUTION: A system comprises a collection unit, an analysis unit, and an optimization unit. The collection unit collects an energy use pattern. The analysis unit analyzes the data collected by the collection unit. The optimization unit performs settings of energy supply or energy saving on the basis of an analysis result obtained by the analysis 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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Existing technologies leave room for improvement as they fall short in collecting and analyzing data to optimize energy use in homes and buildings.

[0005] The system according to the embodiment aims to optimize the energy usage in a home or building. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and an optimization unit. The collection unit collects energy usage patterns. The analysis unit analyzes the data collected by the collection unit. The optimization unit performs energy supply or energy-saving settings based on the analysis results obtained by the analysis unit. Effect of the Invention

[0007] An embodiment of the system can optimize energy usage in a home or building. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the 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 specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A smart home system according to an embodiment of the present invention is a system that optimizes the energy use of a home or building. This smart home system uses a generating AI to collect and analyze data on energy use patterns and external environments, and performs optimal energy supply and energy-saving settings. For example, the smart home system collects power consumption data and weather information in the home, and analyzes energy use patterns based on these data. Based on the analysis results, the smart home system automatically adjusts the temperature settings of the air conditioner and the on / off of the lights, reducing energy waste. In addition, the smart home system optimizes the use of the solar power generation system and the storage battery in response to changes in the external environment. This system can be expected to improve the efficiency of energy use in homes and buildings, and reduce energy costs. Furthermore, the burden on the environment is reduced, contributing to the realization of a sustainable society. As a result, the smart home system can improve the efficiency of energy use in homes and buildings, and reduce energy costs.

[0029] The smart home system according to the embodiment includes a collection unit, an analysis unit, and an optimization unit. The collection unit collects energy usage patterns. The collection unit can collect, for example, power consumption data in the home. The collection unit collects the power consumption data in real time using a sensor. The collection unit can also collect weather information. For example, the collection unit obtains weather information from the Internet and collects data such as temperature, humidity, and precipitation. The collection unit can also collect behavior patterns of residents. For example, the collection unit detects the time the residents are at home and the types of home appliances they use using a sensor, and collects behavior patterns. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the energy usage patterns using, for example, data mining technology. The analysis unit identifies consumption patterns during peak hours and consumption patterns during off-peak hours based on the collected data. The optimization unit performs energy supply and energy-saving settings based on the analysis results obtained by the analysis unit. The optimization unit automatically adjusts, for example, the temperature setting of an air conditioner and the on / off of lights. The optimization unit can also optimize the use of the photovoltaic power generation system and the storage battery in response to changes in the external environment. For example, the optimization unit adjusts the amount of power generated by the photovoltaic power generation system in response to changes in weather, and optimizes the charging and discharging of the storage battery. This allows the smart home system according to the embodiment to improve the efficiency of energy use in homes and buildings and reduce energy costs.

[0030] The collection unit collects energy usage patterns. Specifically, sensors installed on each home appliance and power supply line are used to collect power consumption data in the home. These sensors collect power consumption data in real time and send it to a central database. For example, sensors can be attached to major home appliances such as refrigerators, air conditioners, washing machines, and lighting, and the power consumption of each can be monitored individually. This makes it possible to grasp in detail how much power each home appliance consumes at what time of day. In addition, the collection unit obtains weather information through the Internet and collects data such as temperature, humidity, and precipitation. This makes it possible to evaluate the impact of changes in the external environment on energy use in the home. Furthermore, the collection unit uses motion sensors and door sensors to collect residents' behavior patterns. These sensors detect the time when residents are at home and the usage status of specific home appliances, and record the behavior patterns in detail. For example, it is possible to collect information such as when residents start using air conditioners and at what time of day they turn on the lights. This allows the collection unit to centrally collect various data on energy use in the home and provide it to the analysis unit and optimization unit. The collection unit updates this data frequently and always keeps the latest information, thereby improving the accuracy and reliability of the entire system.

[0031] The analysis unit analyzes the data collected by the collection unit. Specifically, the analysis unit uses data mining technology to analyze energy usage patterns and identify consumption patterns during peak hours and off-peak hours. For example, a machine learning algorithm can be used to extract trends in consumption patterns from past data and predict future consumption patterns. The analysis unit makes specific proposals to improve the efficiency of energy usage based on these data. For example, if energy consumption is high during a specific time period, the priority of home appliances used during that time period can be reviewed. The analysis unit also analyzes the relationship between the behavioral patterns of residents and energy consumption to optimize energy usage in accordance with the residents' lifestyles. For example, by adjusting the temperature setting of the air conditioner during times when the residents are at home, energy consumption can be reduced while maintaining comfort. Furthermore, the analysis unit takes into account data on the external environment and evaluates the impact of weather changes on energy usage. For example, since it is predicted that the use of air conditioners will increase on days with high temperatures, it is possible to predict the energy consumption of that day in advance and take appropriate measures. As a result, the analysis unit can analyze the collected data from multiple angles and provide specific insights for improving energy usage efficiency and reducing costs.

[0032] The optimization unit performs energy supply and energy-saving settings based on the analysis results obtained by the analysis unit. Specifically, it automatically adjusts the temperature setting of the air conditioner and the on / off of the lights. For example, based on the results of the analysis unit, it is possible to optimize the set temperature of the air conditioner and reduce wasteful energy consumption. In addition, it is possible to automatically control the on / off of the lights according to the behavioral patterns of the residents and prevent unnecessary power consumption. Furthermore, the optimization unit can also optimize the use of the solar power generation system and the storage battery in response to changes in the external environment. For example, it is possible to efficiently use energy by adjusting the amount of power generated by the solar power generation system in response to changes in weather and optimizing the charging and discharging of the storage battery. Specifically, since the amount of power generated by the solar power generation system increases on sunny days, surplus power can be stored in the storage battery. On the other hand, on cloudy or rainy days, it is possible to prioritize the supply of power from the storage battery and minimize the purchase of power from outside. As a result, the optimization unit can improve the efficiency of energy use in the home and reduce energy costs. Furthermore, the optimization unit can collect feedback from residents and continuously improve the system settings in order to optimize energy use while maintaining the comfort of the residents. For example, if a resident is dissatisfied with the temperature setting of the air conditioner, the temperature setting can be adjusted based on that feedback to improve comfort. In this way, the optimization unit can achieve both efficient energy use and resident comfort.

[0033] The collection unit can collect power consumption data. The power consumption data includes, for example, consumption by time period and consumption by device, but is not limited to such examples. The collection unit, for example, collects power consumption data in real time using a sensor. The sensor measures the power consumption of each device and transmits the data to the collection unit. The collection unit can also collect power consumption data of the entire home using a smart meter. The smart meter measures power consumption in the home in real time and transmits the data to the collection unit. Furthermore, the collection unit can also collect data by manual input. For example, a resident manually inputs power consumption data and transmits the data to the collection unit. This allows the collection unit to collect power consumption data. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input power consumption data acquired from a sensor to a generation AI and have the generation AI perform analysis of the data.

[0034] The collection unit can collect weather information. The weather information includes, for example, temperature, humidity, precipitation, etc., but is not limited to such examples. The collection unit, for example, acquires weather information from the Internet. The collection unit acquires weather information in real time from a weather data providing service and collects the data. The collection unit can also collect weather information using a weather sensor. The weather sensor measures data such as temperature, humidity, precipitation, etc., and transmits the data to the collection unit. Furthermore, the collection unit can also collect weather information by manual input. For example, a resident manually inputs weather information and transmits the data to the collection unit. This allows the collection unit to collect weather information. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input weather information acquired from a weather sensor to a generation AI and have the generation AI execute analysis of the data.

[0035] The collection unit can collect the behavioral patterns of the residents. The behavioral patterns of the residents include, for example, the time at home and the type of home appliances used, but are not limited to such examples. The collection unit, for example, collects the behavioral patterns of the residents using a sensor. The sensor detects the time at home of the residents and the type of home appliances used, and transmits the data to the collection unit. The collection unit can also collect the behavioral patterns of the residents using a smart device. The smart device monitors the behavior of the residents in real time and transmits the data to the collection unit. Furthermore, the collection unit can also collect the behavioral patterns by manual input. For example, the resident manually inputs the behavioral pattern and transmits the data to the collection unit. This allows the collection unit to collect the behavioral patterns of the residents. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the behavioral pattern data acquired from the sensor to the generation AI, and cause the generation AI to perform analysis of the data.

[0036] The analysis unit can analyze the energy usage pattern based on the collected data. The energy usage pattern includes, for example, a consumption pattern during peak hours and a consumption pattern during off-peak hours, but is not limited to such examples. The analysis unit analyzes the energy usage pattern using, for example, a data mining technique. The analysis unit identifies the consumption pattern during peak hours and the consumption pattern during off-peak hours based on the collected data. The analysis unit can also analyze the energy usage pattern using a statistical analysis method. For example, the analysis unit analyzes the energy usage pattern using a regression analysis. Furthermore, the analysis unit can also analyze the energy usage pattern using a machine learning algorithm. For example, the analysis unit analyzes the energy usage pattern using a clustering algorithm. This allows the analysis unit to analyze the energy usage pattern based on the collected data. A part or all of the above-mentioned processing in the analysis unit is performed using, for example, a generation AI. For example, the analysis unit can input the collected data to the generation AI and cause the generation AI to execute the analysis of the energy usage pattern.

[0037] The optimization unit can automatically adjust the temperature setting of the air conditioner and the on / off of the lights. The temperature setting of the air conditioner includes, for example, a cooling setting in summer and a heating setting in winter, but is not limited to such examples. The optimization unit automatically adjusts, for example, the temperature setting of the air conditioner. The optimization unit optimizes the temperature setting of the air conditioner based on the collected data. The optimization unit can also automatically adjust the on / off of the lights. The on / off of the lights includes, for example, lighting control for each room, control by time period, etc., but is not limited to such examples. The optimization unit optimizes the on / off of the lights based on the collected data. For example, the optimization unit adjusts the on / off of the lights based on the time the resident is at home. This allows the optimization unit to automatically adjust the temperature setting of the air conditioner and the on / off of the lights. A part or all of the above-mentioned processing in the optimization unit is performed using, for example, a generation AI. For example, the optimization unit can input the collected data to the generation AI and cause the generation AI to execute optimization of the temperature setting of the air conditioner and the on / off of the lights.

[0038] The optimization unit can optimize the use of the photovoltaic power generation system and the storage battery in response to changes in the external environment. Examples of changes in the external environment include, but are not limited to, changes in weather and power supply conditions. For example, the optimization unit adjusts the amount of power generated by the photovoltaic power generation system in response to changes in weather. The optimization unit optimizes the amount of power generated by the photovoltaic power generation system based on the collected weather information. The optimization unit can also optimize the use of the storage battery. Examples of storage batteries include, but are not limited to, capacity and charge / discharge cycles. The optimization unit optimizes the charge / discharge of the storage battery based on the collected data. For example, the optimization unit adjusts the charge / discharge of the storage battery in response to the power supply conditions. This allows the optimization unit to optimize the use of the photovoltaic power generation system and the storage battery in response to changes in the external environment. A part or all of the above-mentioned processing in the optimization unit is performed using, for example, a generation AI. For example, the optimization unit can input the collected data to the generation AI and cause the generation AI to execute optimization of the use of the photovoltaic power generation system and the storage battery.

[0039] The collection unit can analyze the resident's past behavioral patterns and select the optimal data collection method. For example, if the resident is often active at night, the collection unit collects energy usage data at night. If the resident is often at home on weekends, the collection unit can concentrate data collection on weekends. In addition, if the resident has an irregular life pattern, the collection unit can dynamically adjust the optimal collection timing based on past data. This allows the collection unit to select the optimal data collection method based on the resident's past behavioral patterns. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the resident's past behavioral pattern data to the generation AI and cause the generation AI to select the optimal data collection method.

[0040] The collection unit can filter the energy usage data based on the resident's current activity status and areas of interest when collecting the energy usage data. For example, when the resident is watching television, the collection unit focuses on collecting energy usage data for that time period. When the resident is cooking, the collection unit can preferentially collect energy usage data for the kitchen. In addition, when the resident is out, the collection unit can also collect energy usage data for the entire house. This allows the collection unit to filter data based on the resident's current activity status and areas of interest. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the resident's current activity status data to the generation AI and cause the generation AI to filter the energy usage data.

[0041] When collecting energy usage data, the collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the resident. For example, when the resident is in the living room, the collection unit preferentially collects energy usage data of the living room. When the resident is in the bedroom, the collection unit can preferentially collect energy usage data of the bedroom. In addition, when the resident is out, the collection unit can also collect energy usage data of the entire house. This allows the collection unit to preferentially collect highly relevant data based on the geographical location information of the resident. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information data of the resident to the generation AI and cause the generation AI to collect highly relevant data.

[0042] The collection unit can analyze the social media activity of the resident and collect related data when collecting the energy usage data. For example, if the resident posts about energy usage on social media, the collection unit collects data based on the content of the posts. If the resident shows a specific energy usage pattern on social media, the collection unit can collect data based on the pattern. In addition, if the resident shows an interest in energy usage on social media, the collection unit can also collect data based on the interest. This allows the collection unit to collect related data based on the social media activity of the resident. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the social media activity data of the resident to the generation AI and cause the generation AI to collect related data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the energy usage data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can perform a simplified analysis on data with low importance. The analysis unit can also perform an analysis with an appropriate level of detail on data with medium importance. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the energy usage data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance of the energy usage data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the energy usage data during analysis. For example, the analysis unit applies a specific algorithm to power consumption data for analysis. The analysis unit can apply a different algorithm to weather information for analysis. The analysis unit can also apply yet another algorithm to resident behavior patterns for analysis. This allows the analysis unit to apply an appropriate analysis algorithm depending on the category of the energy usage data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of energy usage data to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0045] During the analysis, the analysis unit can determine the priority of the analysis based on the collection time of the energy usage data. For example, the analysis unit prioritizes the analysis of the most recently collected data. The analysis unit can analyze the data by focusing on the current data while referring to the past data. The analysis unit can also analyze the data collected in a specific period with a focus on the data. This allows the analysis unit to determine the priority of the analysis based on the collection time of the energy usage data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the collection time of the energy usage data to the generation AI and cause the generation AI to determine the priority of the analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the energy usage data during analysis. For example, the analysis unit preferentially analyzes data with high relevance. The analysis unit can analyze data with medium relevance next. The analysis unit can also analyze data with low relevance last. This allows the analysis unit to adjust the order of analysis based on the relevance of the energy usage data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the relevance of the energy usage data to the generation AI and cause the generation AI to adjust the order of analysis.

[0047] During optimization, the optimization unit can select an optimal optimization method by analyzing the resident's past energy usage pattern. For example, the optimization unit selects an optimal optimization method based on the resident's past energy usage pattern. The optimization unit can select a method that minimizes energy consumption from the resident's past energy usage pattern. The optimization unit can also analyze the resident's past energy usage pattern and select the most efficient optimization method. This allows the optimization unit to select an optimal optimization method based on the resident's past energy usage pattern. A part or all of the above-mentioned processing in the optimization unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the optimization unit can input the resident's past energy usage pattern data to the generation AI and cause the generation AI to select the optimal optimization method.

[0048] The optimization unit can customize the optimization means based on the current living situation of the resident at the time of optimization. For example, when the resident is at home, the optimization unit applies the optimal energy usage method when the resident is at home. When the resident is out, the optimization unit can apply the optimal energy usage method when the resident is out. In addition, when the resident is performing a specific activity, the optimization unit can also apply the optimal energy usage method for that activity. In this way, the optimization unit can provide the optimal energy usage method based on the current living situation of the resident. A part or all of the above-mentioned processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input the current living situation data of the resident to the generation AI and cause the generation AI to execute customization of the optimization means.

[0049] The optimization unit can select the optimal optimization method in consideration of the geographical location information of the resident during optimization. For example, when the resident is in the living room, the optimization unit selects a method for optimizing energy usage in the living room. When the resident is in the bedroom, the optimization unit can select a method for optimizing energy usage in the bedroom. In addition, when the resident is out, the optimization unit can also select a method for optimizing energy usage in the entire house. This allows the optimization unit to provide an optimal energy usage method based on the geographical location information of the resident. A part or all of the above-mentioned processing in the optimization unit may be performed, for example, using AI, or may be performed without using AI. For example, the optimization unit can input the geographical location information data of the resident to the generation AI and cause the generation AI to select the optimal optimization method.

[0050] During optimization, the optimization unit can analyze the social media activity of the resident to suggest optimization measures. For example, if the resident posts about energy use on social media, the optimization unit suggests optimization measures based on the content of the posts. If the resident shows a specific energy usage pattern on social media, the optimization unit can suggest optimization measures based on the pattern. In addition, if the resident shows an interest in energy use on social media, the optimization unit can also suggest optimization measures based on the interest. In this way, the optimization unit can suggest an optimal energy usage method based on the resident's social media activity. Some or all of the above-mentioned processing in the optimization unit may be performed, for example, using AI, or may be performed without using AI. For example, the optimization unit can input the resident's social media activity data to the generation AI and cause the generation AI to execute the proposal of optimization measures.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] When collecting energy usage data within the home, the collection unit can monitor the health condition of the resident and use the data to analyze the energy usage pattern. For example, the collection unit can measure the resident's heart rate and body temperature with a sensor to grasp the health condition. If the resident is healthy, the collection unit can collect a normal energy usage pattern. If the resident is not feeling well, the collection unit can collect data to adjust the energy usage pattern and provide a comfortable environment. This allows the collection unit to collect energy usage data based on the resident's health condition and perform a more accurate analysis.

[0053] When analyzing energy usage patterns based on collected data, the analysis unit can customize the analysis according to the resident's lifestyle. For example, if the resident lives a nocturnal lifestyle, the analysis unit will focus on analyzing nighttime energy usage patterns. If the resident is mostly active during the day, the analysis unit can analyze daytime energy usage patterns in detail. In addition, if the resident is often at home on weekends, the analysis unit can identify weekend energy usage patterns and suggest optimal energy supply methods. This allows the analysis unit to analyze energy usage patterns according to the resident's lifestyle.

[0054] The optimization unit can customize energy supply and energy-saving settings based on the resident's preferences. For example, the optimization unit sets the resident's preferred room temperature and lighting brightness in advance, and adjusts the air conditioner and lighting based on the settings. If a resident often uses a specific home appliance during a specific time period, the optimization unit can optimize energy supply to suit that time period. In addition, if a resident wishes to reduce energy consumption, the optimization unit can strengthen energy-saving settings and achieve reductions in energy costs. This allows the optimization unit to perform energy supply and energy-saving settings based on the resident's preferences.

[0055] When collecting energy usage data, the collection unit can filter the data based on the resident's hobbies and interests. For example, if a resident likes listening to music, the collection unit can focus on collecting energy usage data from audio equipment. If a resident enjoys cooking, the collection unit can preferentially collect energy usage data from the kitchen. Also, if a resident likes watching sports, the collection unit can collect energy usage data from televisions and related equipment. This allows the collection unit to filter energy usage data based on the resident's hobbies and interests and collect more relevant data.

[0056] When analyzing the energy usage pattern, the analysis unit can perform the analysis taking into account the resident's future plans and schedules. For example, if the resident plans to go on a trip, the analysis unit can predict the energy usage pattern during that period and propose an optimal energy supply method. If the resident plans to move, the analysis unit can simulate the energy usage pattern in the new residence and provide optimal energy settings. In addition, if the resident plans to purchase a new home appliance, the analysis unit can also analyze the energy usage pattern of the home appliance in advance and propose an optimal energy supply method. This allows the analysis unit to analyze the energy usage pattern based on the resident's future plans and schedules.

[0057] The process flow of the first embodiment will be briefly described below.

[0058] Step 1: The collection unit collects energy usage patterns. The collection unit uses sensors to collect power consumption data within the home in real time. The collection unit also obtains weather information from the Internet and collects data such as temperature, humidity, and precipitation. Furthermore, the collection unit uses sensors to detect the times when residents are at home and the types of home appliances they use, and collects behavioral patterns. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses data mining techniques to analyze energy usage patterns and identify peak consumption patterns and off-peak consumption patterns. Step 3: The optimization unit performs energy supply and energy-saving settings based on the analysis results obtained by the analysis unit. The optimization unit automatically adjusts the temperature settings of the air conditioner and the on / off of lights, and optimizes the use of the solar power generation system and storage batteries in response to changes in the external environment.

[0059] (Example 2) A smart home system according to an embodiment of the present invention is a system that optimizes the energy use of a home or building. This smart home system uses a generating AI to collect and analyze data on energy use patterns and external environments, and performs optimal energy supply and energy-saving settings. For example, the smart home system collects power consumption data and weather information in the home, and analyzes energy use patterns based on these data. Based on the analysis results, the smart home system automatically adjusts the temperature settings of the air conditioner and the on / off of the lights, reducing energy waste. In addition, the smart home system optimizes the use of the solar power generation system and the storage battery in response to changes in the external environment. This system can be expected to improve the efficiency of energy use in homes and buildings, and reduce energy costs. Furthermore, the burden on the environment is reduced, contributing to the realization of a sustainable society. As a result, the smart home system can improve the efficiency of energy use in homes and buildings, and reduce energy costs.

[0060] The smart home system according to the embodiment includes a collection unit, an analysis unit, and an optimization unit. The collection unit collects energy usage patterns. The collection unit can collect, for example, power consumption data in the home. The collection unit collects the power consumption data in real time using a sensor. The collection unit can also collect weather information. For example, the collection unit obtains weather information from the Internet and collects data such as temperature, humidity, and precipitation. The collection unit can also collect behavior patterns of residents. For example, the collection unit detects the time the residents are at home and the types of home appliances they use using a sensor, and collects behavior patterns. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the energy usage patterns using, for example, data mining technology. The analysis unit identifies consumption patterns during peak hours and consumption patterns during off-peak hours based on the collected data. The optimization unit performs energy supply and energy-saving settings based on the analysis results obtained by the analysis unit. The optimization unit automatically adjusts, for example, the temperature setting of an air conditioner and the on / off of lights. The optimization unit can also optimize the use of the photovoltaic power generation system and the storage battery in response to changes in the external environment. For example, the optimization unit adjusts the amount of power generated by the photovoltaic power generation system in response to changes in weather, and optimizes the charging and discharging of the storage battery. This allows the smart home system according to the embodiment to improve the efficiency of energy use in homes and buildings and reduce energy costs.

[0061] The collection unit collects energy usage patterns. Specifically, sensors installed on each home appliance and power supply line are used to collect power consumption data in the home. These sensors collect power consumption data in real time and send it to a central database. For example, sensors can be attached to major home appliances such as refrigerators, air conditioners, washing machines, and lighting, and the power consumption of each can be monitored individually. This makes it possible to grasp in detail how much power each home appliance consumes at what time of day. In addition, the collection unit obtains weather information through the Internet and collects data such as temperature, humidity, and precipitation. This makes it possible to evaluate the impact of changes in the external environment on energy use in the home. Furthermore, the collection unit uses motion sensors and door sensors to collect residents' behavior patterns. These sensors detect the time when residents are at home and the usage status of specific home appliances, and record the behavior patterns in detail. For example, it is possible to collect information such as when residents start using air conditioners and at what time of day they turn on the lights. This allows the collection unit to centrally collect various data on energy use in the home and provide it to the analysis unit and optimization unit. The collection unit updates this data frequently and always keeps the latest information, thereby improving the accuracy and reliability of the entire system.

[0062] The analysis unit analyzes the data collected by the collection unit. Specifically, the analysis unit uses data mining technology to analyze energy usage patterns and identify consumption patterns during peak hours and off-peak hours. For example, a machine learning algorithm can be used to extract trends in consumption patterns from past data and predict future consumption patterns. The analysis unit makes specific proposals to improve the efficiency of energy usage based on these data. For example, if energy consumption is high during a specific time period, the priority of home appliances used during that time period can be reviewed. The analysis unit also analyzes the relationship between the behavioral patterns of residents and energy consumption to optimize energy usage in accordance with the residents' lifestyles. For example, by adjusting the temperature setting of the air conditioner during times when the residents are at home, energy consumption can be reduced while maintaining comfort. Furthermore, the analysis unit takes into account data on the external environment and evaluates the impact of weather changes on energy usage. For example, since it is predicted that the use of air conditioners will increase on days with high temperatures, it is possible to predict the energy consumption of that day in advance and take appropriate measures. As a result, the analysis unit can analyze the collected data from multiple angles and provide specific insights for improving energy usage efficiency and reducing costs.

[0063] The optimization unit performs energy supply and energy-saving settings based on the analysis results obtained by the analysis unit. Specifically, it automatically adjusts the temperature setting of the air conditioner and the on / off of the lights. For example, based on the results of the analysis unit, it is possible to optimize the set temperature of the air conditioner and reduce wasteful energy consumption. In addition, it is possible to automatically control the on / off of the lights according to the behavioral patterns of the residents and prevent unnecessary power consumption. Furthermore, the optimization unit can also optimize the use of the solar power generation system and the storage battery in response to changes in the external environment. For example, it is possible to efficiently use energy by adjusting the amount of power generated by the solar power generation system in response to changes in weather and optimizing the charging and discharging of the storage battery. Specifically, since the amount of power generated by the solar power generation system increases on sunny days, surplus power can be stored in the storage battery. On the other hand, on cloudy or rainy days, it is possible to prioritize the supply of power from the storage battery and minimize the purchase of power from outside. As a result, the optimization unit can improve the efficiency of energy use in the home and reduce energy costs. Furthermore, the optimization unit can collect feedback from residents and continuously improve the system settings in order to optimize energy use while maintaining the comfort of the residents. For example, if a resident is dissatisfied with the temperature setting of the air conditioner, the temperature setting can be adjusted based on that feedback to improve comfort. In this way, the optimization unit can achieve both efficient energy use and resident comfort.

[0064] The collection unit can collect power consumption data. The power consumption data includes, for example, consumption by time period and consumption by device, but is not limited to such examples. The collection unit, for example, collects power consumption data in real time using a sensor. The sensor measures the power consumption of each device and transmits the data to the collection unit. The collection unit can also collect power consumption data of the entire home using a smart meter. The smart meter measures power consumption in the home in real time and transmits the data to the collection unit. Furthermore, the collection unit can also collect data by manual input. For example, a resident manually inputs power consumption data and transmits the data to the collection unit. This allows the collection unit to collect power consumption data. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input power consumption data acquired from a sensor to a generation AI and have the generation AI perform analysis of the data.

[0065] The collection unit can collect weather information. The weather information includes, for example, temperature, humidity, precipitation, etc., but is not limited to such examples. The collection unit, for example, acquires weather information from the Internet. The collection unit acquires weather information in real time from a weather data providing service and collects the data. The collection unit can also collect weather information using a weather sensor. The weather sensor measures data such as temperature, humidity, precipitation, etc., and transmits the data to the collection unit. Furthermore, the collection unit can also collect weather information by manual input. For example, a resident manually inputs weather information and transmits the data to the collection unit. This allows the collection unit to collect weather information. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input weather information acquired from a weather sensor to a generation AI and have the generation AI execute analysis of the data.

[0066] The collection unit can collect the behavioral patterns of the residents. The behavioral patterns of the residents include, for example, the time at home and the type of home appliances used, but are not limited to such examples. The collection unit, for example, collects the behavioral patterns of the residents using a sensor. The sensor detects the time at home of the residents and the type of home appliances used, and transmits the data to the collection unit. The collection unit can also collect the behavioral patterns of the residents using a smart device. The smart device monitors the behavior of the residents in real time and transmits the data to the collection unit. Furthermore, the collection unit can also collect the behavioral patterns by manual input. For example, the resident manually inputs the behavioral pattern and transmits the data to the collection unit. This allows the collection unit to collect the behavioral patterns of the residents. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the behavioral pattern data acquired from the sensor to the generation AI, and cause the generation AI to perform analysis of the data.

[0067] The analysis unit can analyze the energy usage pattern based on the collected data. The energy usage pattern includes, for example, a consumption pattern during peak hours and a consumption pattern during off-peak hours, but is not limited to such examples. The analysis unit analyzes the energy usage pattern using, for example, a data mining technique. The analysis unit identifies the consumption pattern during peak hours and the consumption pattern during off-peak hours based on the collected data. The analysis unit can also analyze the energy usage pattern using a statistical analysis method. For example, the analysis unit analyzes the energy usage pattern using a regression analysis. Furthermore, the analysis unit can also analyze the energy usage pattern using a machine learning algorithm. For example, the analysis unit analyzes the energy usage pattern using a clustering algorithm. This allows the analysis unit to analyze the energy usage pattern based on the collected data. A part or all of the above-mentioned processing in the analysis unit is performed using, for example, a generation AI. For example, the analysis unit can input the collected data to the generation AI and cause the generation AI to execute the analysis of the energy usage pattern.

[0068] The optimization unit can automatically adjust the temperature setting of the air conditioner and the on / off of the lights. The temperature setting of the air conditioner includes, for example, a cooling setting in summer and a heating setting in winter, but is not limited to such examples. The optimization unit automatically adjusts, for example, the temperature setting of the air conditioner. The optimization unit optimizes the temperature setting of the air conditioner based on the collected data. The optimization unit can also automatically adjust the on / off of the lights. The on / off of the lights includes, for example, lighting control for each room, control by time period, etc., but is not limited to such examples. The optimization unit optimizes the on / off of the lights based on the collected data. For example, the optimization unit adjusts the on / off of the lights based on the time the resident is at home. This allows the optimization unit to automatically adjust the temperature setting of the air conditioner and the on / off of the lights. A part or all of the above-mentioned processing in the optimization unit is performed using, for example, a generation AI. For example, the optimization unit can input the collected data to the generation AI and cause the generation AI to execute optimization of the temperature setting of the air conditioner and the on / off of the lights.

[0069] The optimization unit can optimize the use of the photovoltaic power generation system and the storage battery in response to changes in the external environment. Examples of changes in the external environment include, but are not limited to, changes in weather and power supply conditions. For example, the optimization unit adjusts the amount of power generated by the photovoltaic power generation system in response to changes in weather. The optimization unit optimizes the amount of power generated by the photovoltaic power generation system based on the collected weather information. The optimization unit can also optimize the use of the storage battery. Examples of storage batteries include, but are not limited to, capacity and charge / discharge cycles. The optimization unit optimizes the charge / discharge of the storage battery based on the collected data. For example, the optimization unit adjusts the charge / discharge of the storage battery in response to the power supply conditions. This allows the optimization unit to optimize the use of the photovoltaic power generation system and the storage battery in response to changes in the external environment. A part or all of the above-mentioned processing in the optimization unit is performed using, for example, a generation AI. For example, the optimization unit can input the collected data to the generation AI and cause the generation AI to execute optimization of the use of the photovoltaic power generation system and the storage battery.

[0070] The collection unit can estimate the user's emotion and adjust the collection timing of the energy usage data based on the estimated user's emotion. For example, when the user is relaxed, the collection unit collects the energy usage data at a normal collection timing. When the user is stressed, the collection unit can delay the collection timing to reduce the burden on the user. In addition, when the user is in a hurry, the collection unit can also advance the collection timing to quickly acquire data. Thereby, the collection unit can reduce the burden on the user by adjusting the collection timing based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the collection timing of the energy usage data.

[0071] The collection unit can analyze the resident's past behavioral patterns and select the optimal data collection method. For example, if the resident is often active at night, the collection unit collects energy usage data at night. If the resident is often at home on weekends, the collection unit can concentrate data collection on weekends. In addition, if the resident has an irregular life pattern, the collection unit can dynamically adjust the optimal collection timing based on past data. This allows the collection unit to select the optimal data collection method based on the resident's past behavioral patterns. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the resident's past behavioral pattern data to the generation AI and cause the generation AI to select the optimal data collection method.

[0072] The collection unit can filter the energy usage data based on the resident's current activity status and areas of interest when collecting the energy usage data. For example, when the resident is watching television, the collection unit focuses on collecting energy usage data for that time period. When the resident is cooking, the collection unit can preferentially collect energy usage data for the kitchen. In addition, when the resident is out, the collection unit can also collect energy usage data for the entire house. This allows the collection unit to filter data based on the resident's current activity status and areas of interest. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the resident's current activity status data to the generation AI and cause the generation AI to filter the energy usage data.

[0073] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. For example, when the user is relaxed, the collection unit collects data with normal priority. When the user is stressed, the collection unit can preferentially collect data with high importance. In addition, when the user is in a hurry, the collection unit can also prioritize data that can be collected quickly. In this way, the collection unit can prioritize important data by determining the priority of data based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the data.

[0074] When collecting energy usage data, the collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the resident. For example, when the resident is in the living room, the collection unit preferentially collects energy usage data of the living room. When the resident is in the bedroom, the collection unit can preferentially collect energy usage data of the bedroom. In addition, when the resident is out, the collection unit can also collect energy usage data of the entire house. This allows the collection unit to preferentially collect highly relevant data based on the geographical location information of the resident. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information data of the resident to the generation AI and cause the generation AI to collect highly relevant data.

[0075] The collection unit can analyze the social media activity of the resident and collect related data when collecting the energy usage data. For example, if the resident posts about energy usage on social media, the collection unit collects data based on the content of the posts. If the resident shows a specific energy usage pattern on social media, the collection unit can collect data based on the pattern. In addition, if the resident shows an interest in energy usage on social media, the collection unit can also collect data based on the interest. This allows the collection unit to collect related data based on the social media activity of the resident. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the social media activity data of the resident to the generation AI and cause the generation AI to collect related data.

[0076] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. For example, when the user is relaxed, the analysis unit provides a detailed analysis result. When the user is stressed, the analysis unit can provide a concise analysis result that covers the main points. In addition, when the user is in a hurry, the analysis unit can also provide the analysis result in a format that can be quickly understood. In this way, the analysis unit can provide an analysis result that is easy for the user to understand by adjusting the expression method of the analysis based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the expression method of the analysis.

[0077] The analysis unit can adjust the level of detail of the analysis based on the importance of the energy usage data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can perform a simplified analysis on data with low importance. The analysis unit can also perform an analysis with an appropriate level of detail on data with medium importance. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the energy usage data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance of the energy usage data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0078] The analysis unit can apply different analysis algorithms depending on the category of the energy usage data during analysis. For example, the analysis unit applies a specific algorithm to power consumption data for analysis. The analysis unit can apply a different algorithm to weather information for analysis. The analysis unit can also apply yet another algorithm to resident behavior patterns for analysis. This allows the analysis unit to apply an appropriate analysis algorithm depending on the category of the energy usage data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of energy usage data to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0079] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, when the user is relaxed, the analysis unit performs a detailed analysis. When the user is stressed, the analysis unit can perform a concise analysis. In addition, when the user is in a hurry, the analysis unit can perform a short analysis that can be quickly understood. In this way, the analysis unit can provide an analysis result of an appropriate length for the user by adjusting the length of the analysis based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the length of the analysis.

[0080] During the analysis, the analysis unit can determine the priority of the analysis based on the collection time of the energy usage data. For example, the analysis unit prioritizes the analysis of the most recently collected data. The analysis unit can analyze the data by focusing on the current data while referring to the past data. The analysis unit can also analyze the data collected in a specific period with a focus on the data. This allows the analysis unit to determine the priority of the analysis based on the collection time of the energy usage data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the collection time of the energy usage data to the generation AI and cause the generation AI to determine the priority of the analysis.

[0081] The analysis unit can adjust the order of analysis based on the relevance of the energy usage data during analysis. For example, the analysis unit preferentially analyzes data with high relevance. The analysis unit can analyze data with medium relevance next. The analysis unit can also analyze data with low relevance last. This allows the analysis unit to adjust the order of analysis based on the relevance of the energy usage data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the relevance of the energy usage data to the generation AI and cause the generation AI to adjust the order of analysis.

[0082] The optimization unit can estimate the user's emotion and adjust the optimization method based on the estimated user's emotion. For example, when the user is relaxed, the optimization unit applies a normal optimization method. When the user is stressed, the optimization unit can apply an optimization method that minimizes energy consumption. In addition, when the user is in a hurry, the optimization unit can also apply a method for quickly optimizing. In this way, the optimization unit can provide an optimal energy usage method for the user by adjusting the optimization method based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the optimization unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the optimization unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the optimization method.

[0083] During optimization, the optimization unit can select an optimal optimization method by analyzing the resident's past energy usage pattern. For example, the optimization unit selects an optimal optimization method based on the resident's past energy usage pattern. The optimization unit can select a method that minimizes energy consumption from the resident's past energy usage pattern. The optimization unit can also analyze the resident's past energy usage pattern and select the most efficient optimization method. This allows the optimization unit to select an optimal optimization method based on the resident's past energy usage pattern. A part or all of the above-mentioned processing in the optimization unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the optimization unit can input the resident's past energy usage pattern data to the generation AI and cause the generation AI to select the optimal optimization method.

[0084] The optimization unit can customize the optimization means based on the current living situation of the resident at the time of optimization. For example, when the resident is at home, the optimization unit applies the optimal energy usage method when the resident is at home. When the resident is out, the optimization unit can apply the optimal energy usage method when the resident is out. In addition, when the resident is performing a specific activity, the optimization unit can also apply the optimal energy usage method for that activity. In this way, the optimization unit can provide the optimal energy usage method based on the current living situation of the resident. A part or all of the above-mentioned processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input the current living situation data of the resident to the generation AI and cause the generation AI to execute customization of the optimization means.

[0085] The optimization unit can estimate the user's emotion and determine the priority of optimization based on the estimated user's emotion. For example, when the user is relaxed, the optimization unit performs optimization with normal priority. When the user is stressed, the optimization unit can prioritize optimization with high importance. In addition, when the user is in a hurry, the optimization unit can also perform optimization quickly. Thereby, the optimization unit can prioritize important optimization by determining the priority of optimization based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the optimization unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the optimization unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of optimization.

[0086] The optimization unit can select the optimal optimization method in consideration of the geographical location information of the resident during optimization. For example, when the resident is in the living room, the optimization unit selects a method for optimizing energy usage in the living room. When the resident is in the bedroom, the optimization unit can select a method for optimizing energy usage in the bedroom. In addition, when the resident is out, the optimization unit can also select a method for optimizing energy usage in the entire house. This allows the optimization unit to provide an optimal energy usage method based on the geographical location information of the resident. A part or all of the above-mentioned processing in the optimization unit may be performed, for example, using AI, or may be performed without using AI. For example, the optimization unit can input the geographical location information data of the resident to the generation AI and cause the generation AI to select the optimal optimization method.

[0087] During optimization, the optimization unit can analyze the social media activity of the resident to suggest optimization measures. For example, if the resident posts about energy use on social media, the optimization unit suggests optimization measures based on the content of the posts. If the resident shows a specific energy usage pattern on social media, the optimization unit can suggest optimization measures based on the pattern. In addition, if the resident shows an interest in energy use on social media, the optimization unit can also suggest optimization measures based on the interest. In this way, the optimization unit can suggest an optimal energy usage method based on the resident's social media activity. Some or all of the above-mentioned processing in the optimization unit may be performed, for example, using AI, or may be performed without using AI. For example, the optimization unit can input the resident's social media activity data to the generation AI and cause the generation AI to execute the proposal of optimization measures.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] When collecting energy usage data within the home, the collection unit can monitor the health condition of the resident and use the data to analyze the energy usage pattern. For example, the collection unit can measure the resident's heart rate and body temperature with a sensor to grasp the health condition. If the resident is healthy, the collection unit can collect a normal energy usage pattern. If the resident is not feeling well, the collection unit can collect data to adjust the energy usage pattern and provide a comfortable environment. This allows the collection unit to collect energy usage data based on the resident's health condition and perform a more accurate analysis.

[0090] When analyzing energy usage patterns based on collected data, the analysis unit can customize the analysis according to the resident's lifestyle. For example, if the resident lives a nocturnal lifestyle, the analysis unit will focus on analyzing nighttime energy usage patterns. If the resident is mostly active during the day, the analysis unit can analyze daytime energy usage patterns in detail. In addition, if the resident is often at home on weekends, the analysis unit can identify weekend energy usage patterns and suggest optimal energy supply methods. This allows the analysis unit to analyze energy usage patterns according to the resident's lifestyle.

[0091] The optimization unit can customize energy supply and energy-saving settings based on the resident's preferences. For example, the optimization unit sets the resident's preferred room temperature and lighting brightness in advance, and adjusts the air conditioner and lighting based on the settings. If a resident often uses a specific home appliance during a specific time period, the optimization unit can optimize energy supply to suit that time period. In addition, if a resident wishes to reduce energy consumption, the optimization unit can strengthen energy-saving settings and achieve reductions in energy costs. This allows the optimization unit to perform energy supply and energy-saving settings based on the resident's preferences.

[0092] When collecting energy usage data, the collection unit can filter the data based on the resident's hobbies and interests. For example, if a resident likes listening to music, the collection unit can focus on collecting energy usage data from audio equipment. If a resident enjoys cooking, the collection unit can preferentially collect energy usage data from the kitchen. Also, if a resident likes watching sports, the collection unit can collect energy usage data from televisions and related equipment. This allows the collection unit to filter energy usage data based on the resident's hobbies and interests and collect more relevant data.

[0093] When analyzing the energy usage pattern, the analysis unit can perform the analysis taking into account the resident's future plans and schedules. For example, if the resident plans to go on a trip, the analysis unit can predict the energy usage pattern during that period and propose an optimal energy supply method. If the resident plans to move, the analysis unit can simulate the energy usage pattern in the new residence and provide optimal energy settings. In addition, if the resident plans to purchase a new home appliance, the analysis unit can also analyze the energy usage pattern of the home appliance in advance and propose an optimal energy supply method. This allows the analysis unit to analyze the energy usage pattern based on the resident's future plans and schedules.

[0094] The collection unit can estimate the user's emotion and adjust the collection method of the energy usage data based on the estimated user's emotion. For example, when the user is relaxed, the collection unit collects energy usage data using a normal collection method. When the user is stressed, the collection unit can change the collection method to reduce the burden on the user. Also, when the user is in a hurry, the collection unit can apply a method for quickly collecting data. In this way, the collection unit can adjust the collection method of the energy usage data based on the user's emotion and reduce the burden on the user.

[0095] The analysis unit can estimate the user's emotions and adjust the method of providing the analysis results based on the estimated user's emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can provide concise analysis results that are to the point when the user is stressed. The analysis unit can also provide analysis results in a format that can be quickly understood when the user is in a hurry. In this way, the analysis unit can adjust the method of providing the analysis results based on the user's emotions and provide analysis results that are easy for the user to understand.

[0096] The optimization unit can estimate the user's emotion and determine the priority of energy supply based on the estimated user's emotion. For example, when the user is relaxed, the optimization unit supplies energy with normal priority. When the user is stressed, the optimization unit can prioritize energy supply with high importance. In addition, when the user is in a hurry, the optimization unit can also supply energy quickly. In this way, the optimization unit can determine the priority of energy supply based on the user's emotion and prioritize important energy supply.

[0097] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. For example, when the user is relaxed, the collection unit collects data with normal priority. When the user is stressed, the collection unit can preferentially collect data of high importance. In addition, when the user is in a hurry, the collection unit can also prioritize data that can be collected quickly. In this way, the collection unit can preferentially collect important data by determining the priority of data based on the user's emotions.

[0098] The optimization unit can estimate the user's emotion and adjust the optimization method based on the estimated user's emotion. For example, the optimization unit applies a normal optimization method when the user is relaxed. The optimization unit can apply an optimization method that minimizes energy consumption when the user is stressed. The optimization unit can also apply a method for rapid optimization when the user is in a hurry. In this way, the optimization unit can provide an optimal energy usage method for the user by adjusting the optimization method based on the user's emotion.

[0099] The process flow of the second embodiment will be briefly described below.

[0100] Step 1: The collection unit collects energy usage patterns. The collection unit uses sensors to collect power consumption data within the home in real time. The collection unit also obtains weather information from the Internet and collects data such as temperature, humidity, and precipitation. Furthermore, the collection unit uses sensors to detect the times when residents are at home and the types of home appliances they use, and collects behavioral patterns. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses data mining techniques to analyze energy usage patterns and identify peak consumption patterns and off-peak consumption patterns. Step 3: The optimization unit performs energy supply and energy-saving settings based on the analysis results obtained by the analysis unit. The optimization unit automatically adjusts the temperature settings of the air conditioner and the on / off of lights, and optimizes the use of the solar power generation system and storage batteries in response to changes in the external environment.

[0101] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0103] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0104] Each of the multiple elements including the collection unit, analysis unit, and optimization unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects power consumption data and weather information using the sensor and Internet connection of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The optimization unit is realized, for example, by the control unit 46A of the smart device 14, and automatically adjusts the temperature setting of the air conditioner and the on / off of the lights. In addition, the optimization unit is also realized by the specific processing unit 290 of the data processing device 12, and optimizes the use of the solar power generation system and the storage battery. The correspondence between each unit and the device and the control unit is not limited to the above example, and various changes are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0108] 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.

[0109] 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 the voice according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0111] 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 performed in a secure state.

[0112] Fig. 4 shows an example of 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.

[0113] 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.

[0114] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 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 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0116] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0117] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0118] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0119] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements including the above-mentioned collection unit, analysis unit, and optimization unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects power consumption data and weather information using the sensor and Internet connection of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The optimization unit is realized, for example, by the control unit 46A of the smart glasses 214, and automatically adjusts the temperature setting of the air conditioner and the on / off of the lights. In addition, the optimization unit is also realized by the specific processing unit 290 of the data processing device 12, and optimizes the use of the solar power generation system and the storage battery. The correspondence between each unit and the device and the control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

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

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

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

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

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

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

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

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

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

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

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

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

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

[0136] Each of the multiple elements including the above-mentioned collection unit, analysis unit, and optimization unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects power consumption data and weather information using the sensor and Internet connection of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The optimization unit is realized, for example, by the control unit 46A of the headset type terminal 314, and automatically adjusts the temperature setting of the air conditioner and the on / off of the lights. In addition, the optimization unit is also realized by the specific processing unit 290 of the data processing device 12, and optimizes the use of the solar power generation system and the storage battery. The correspondence between each unit and the device and the control unit is not limited to the above-mentioned example, and various changes are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0140] 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. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] 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 the voice according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0143] 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 performed in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0145] 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.

[0146] 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.

[0147] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0148] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0149] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0150] 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 a voice indicating a user input for 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.

[0151] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0152] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0153] Each of the multiple elements including the above-mentioned collection unit, analysis unit, and optimization unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects power consumption data and weather information using the sensors and Internet connection of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The optimization unit is realized, for example, by the control unit 46A of the robot 414, and automatically adjusts the temperature setting of the air conditioner and the on / off of the lights. In addition, the optimization unit is also realized by the specific processing unit 290 of the data processing device 12, and optimizes the use of the solar power generation system and the storage battery. The correspondence between each unit and the device and the control unit is not limited to the above-mentioned example, and various changes are possible.

[0154] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0155] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0156] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0157] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0158] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0159] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0161] 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.

[0162] 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 Universal Serial Bus (USB) 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.

[0163] In addition, 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 upon request from the data processing device 12.

[0164] 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.

[0165] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0166] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0167] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0168] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0169] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0170] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0171] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0172] (Appendix 1) A collection unit for collecting energy usage patterns; an analysis unit that analyzes the data collected by the collection unit; an optimization unit that performs energy supply or energy saving settings based on the analysis results obtained by the analysis unit; A system characterized by: (Appendix 2) The collecting unit includes: Collecting power consumption data 2. The system of claim 1, (Appendix 3) The collecting unit includes: Collect weather information 2. The system of claim 1, (Appendix 4) The collecting unit includes: Collecting resident behavior patterns 2. The system of claim 1, (Appendix 5) The analysis unit is Analyzing energy usage patterns based on collected data 2. The system of claim 1, (Appendix 6) The optimization unit is Automatically adjust air conditioning temperature settings or turn lights on and off 2. The system of claim 1, (Appendix 7) The optimization unit is Optimize the use of photovoltaic power generation systems or storage batteries according to changes in the external environment 2. The system of claim 1, (Appendix 8) The collecting unit includes: To estimate a user's emotion and adjust a timing of collecting energy usage data based on the estimated user's emotion. 2. The system of claim 1, (Appendix 9) The collecting unit includes: Analyze residents' past behavioral patterns and choose the most appropriate data collection method 2. The system of claim 1, (Appendix 10) The collecting unit includes: Filtering energy usage data as it is collected based on a resident's current activities and areas of interest 2. The system of claim 1, (Appendix 11) The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting energy usage data, the geographic location of residents is taken into account to prioritize the collection of the most relevant data. 2. The system of claim 1, (Appendix 13) The collecting unit includes: Analyze residents' social media activity and collect relevant data when collecting energy usage data 2. The system of claim 1, (Appendix 14) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, adjust the level of detail in the analysis based on the importance of energy use data 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of energy usage data 2. The system of claim 1, (Appendix 17) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, prioritize analysis based on when energy use data was collected 2. The system of claim 1, (Appendix 19) The analysis unit is During analysis, adjust the analysis order based on the relevance of energy use data 2. The system of claim 1, (Appendix 20) The optimization unit is Infer user emotions and adjust optimization methods based on the inferred user emotions 2. The system of claim 1, (Appendix 21) The optimization unit is During optimization, the system analyzes residents' past energy usage patterns to select the best optimization method. 2. The system of claim 1, (Appendix 22) The optimization unit is During optimization, customize optimization measures based on residents' current living situations 2. The system of claim 1, (Appendix 23) The optimization unit is Infer user sentiment and determine optimization priorities based on the inferred user sentiment 2. The system of claim 1, (Appendix 24) The optimization unit is During optimization, consider the geographic location of residents to determine the best optimization method 2. The system of claim 1, (Appendix 25) The optimization unit is During optimization, analyze residents' social media activity to suggest optimization measures 2. The system of claim 1, [Explanation of symbols]

[0173] 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 collection unit for collecting energy usage patterns; an analysis unit that analyzes the data collected by the collection unit; an optimization unit that performs energy supply or energy saving settings based on the analysis results obtained by the analysis unit; A system characterized by:

2. The collecting unit includes: Collecting power consumption data 2. The system of claim 1 .

3. The collecting unit includes: Collect weather information 2. The system of claim 1 .

4. The collecting unit includes: Collecting resident behavior patterns 2. The system of claim 1 .

5. The analysis unit is Analyzing energy usage patterns based on the data collected by the collecting unit.

2. The system of claim 1 .

6. The optimization unit is Automatically adjust air conditioning temperature settings or turn lights on and off 2. The system of claim 1 .

7. The optimization unit is Optimize the use of photovoltaic power generation systems or storage batteries according to changes in the external environment 2. The system of claim 1 .

8. The collecting unit includes: To estimate a user's emotion and adjust a timing of collecting energy usage data based on the estimated user's emotion.

2. The system of claim 1 .

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