system

Abstract

The system according to this embodiment aims to efficiently manage energy consumption in homes and offices and to conserve electricity. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, and an adjustment unit. The data collection unit collects data related to energy consumption. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes an optimal energy use plan based on the analysis results obtained by the analysis unit. The adjustment unit automatically adjusts energy consumption based on the energy use plan proposed by the proposal unit.

Description

Technical Field

[0004] ,

[0006] , , ,

[0005] , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, the energy consumption of homes and offices has not been efficiently managed to save electricity, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently manage the energy consumption of homes and offices and save electricity.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and an adjustment unit. The data collection unit collects data related to energy consumption. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes an optimal energy use plan based on the analysis results obtained by the analysis unit. The adjustment unit automatically adjusts energy consumption based on the energy use plan proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently manage energy consumption in homes and offices and save electricity. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI voice assistant system according to an embodiment of the present invention is a system that efficiently manages energy consumption in homes and offices, and provides energy-saving suggestions and automatic adjustments. This system collects real-time data on energy consumption in homes and offices and analyzes historical consumption patterns to propose an optimal energy usage plan. Furthermore, it automatically adjusts energy consumption based on user needs. This mechanism is expected to reduce annual energy costs by up to 20%, and contribute to the environment by reducing CO2 emissions and optimizing energy consumption. For example, the AI ​​voice assistant system collects real-time data on energy consumption in homes and offices. For example, it collects data such as electricity usage, gas usage, temperature, and humidity through sensors. This data is input into the AI ​​voice assistant system. Next, the AI ​​voice assistant system analyzes the collected data and compares it with historical consumption patterns. For example, it compares current usage with past electricity usage data and predicts peak consumption. This allows for an understanding of energy consumption trends. Furthermore, the AI ​​voice assistant system proposes an optimal energy usage plan based on the analysis results. For example, it suggests adjusting the air conditioner temperature setting during specific time periods to avoid peak electricity usage. Furthermore, to reduce unnecessary power consumption, the system will suggest automatically turning off unused electrical appliances. Next, the AI ​​voice assistant system will automatically adjust energy consumption based on user needs. For example, if a user instructs the system to "save energy," the AI ​​voice assistant system will automatically adjust the air conditioner's temperature setting and turn off unnecessary electrical appliances. If a user instructs the system to "maintain a comfortable temperature," the AI ​​voice assistant system will adjust the air conditioner's temperature setting to an optimal range. This system efficiently manages energy consumption in homes and offices, providing energy-saving suggestions and automatic adjustments. As a result, it is expected that annual energy costs will be reduced by 20%, contributing to environmental protection through reduced CO2 emissions and optimized energy consumption.Furthermore, for household and office users who are conscious of energy consumption but lack the time, the Energy Assistant Agent is an ideal solution. This allows the AI ​​voice assistant system to efficiently manage energy consumption in homes and offices, providing energy-saving suggestions and automatic adjustments.

[0029] The AI ​​voice assistant system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, and an adjustment unit. The data collection unit collects data related to energy consumption. The data collection unit collects data such as electricity usage, gas usage, temperature, and humidity through sensors. For example, the data collection unit uses a smart meter to measure electricity usage, a gas meter to measure gas usage, and a temperature and humidity sensor to measure temperature and humidity. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the collected data in real time to understand energy consumption trends. For example, the analysis unit compares current usage with past electricity usage data to predict peak consumption. For example, the analysis unit uses AI to analyze the collected data and understand energy consumption trends. The proposal unit proposes an optimal energy usage plan based on the analysis results obtained by the analysis unit. For example, the proposal unit suggests adjusting the air conditioner's temperature setting during specific time periods to avoid peak electricity usage. The suggestion unit, for example, suggests automatically turning off the power to unused home appliances in order to reduce unnecessary power consumption. The suggestion unit, for example, uses AI to suggest an optimal energy usage plan. The adjustment unit automatically adjusts energy consumption based on the energy usage plan suggested by the suggestion unit. The adjustment unit automatically adjusts energy consumption based on user needs, for example. For example, if the user instructs the adjustment unit to "save energy," the adjustment unit automatically adjusts the air conditioner's temperature setting and turns off the power to unnecessary home appliances. For example, if the user instructs the adjustment unit to "maintain a comfortable temperature," the adjustment unit adjusts the air conditioner's temperature setting to an optimal range. As a result, the AI ​​voice assistant system according to this embodiment can efficiently manage energy consumption in homes and offices and provide energy-saving suggestions and automatic adjustments.

[0030] The data collection unit collects data related to energy consumption. For example, it collects data such as electricity usage, gas usage, temperature, and humidity through sensors. Specifically, it uses smart meters to measure electricity usage. Smart meters measure electricity consumption in homes and offices in real time and transmit the data to a central database. Gas meters are used to measure gas usage. Gas meters accurately measure gas usage and transmit the data to the data collection unit. Temperature and humidity sensors are used to measure temperature and humidity. Temperature and humidity sensors monitor indoor temperature and humidity in real time and transmit the data to the data collection unit. These sensors are installed throughout homes and offices to collect detailed data on energy consumption. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance. Furthermore, the data collection unit uses encryption technology to protect the data in order to ensure its security. This prevents unauthorized access to the collected data and improves the reliability of the system.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data in real time to understand energy consumption trends. Specifically, it compares current usage with past electricity usage data and predicts peak consumption. The analysis unit uses AI to analyze the collected data and understand energy consumption trends. The AI ​​uses machine learning algorithms to learn patterns from past data and predict future energy consumption. For example, the AI ​​analyzes past electricity usage data to understand consumption trends during specific times of day or seasons. This allows the analysis unit to predict peak energy consumption and take appropriate measures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. For example, it can detect sudden increases in electricity consumption or abnormal fluctuations in gas usage and notify users. This allows the analysis unit to not only understand the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system. Furthermore, the analysis unit uses data visualization tools to display energy consumption trends and anomalies in graphs and charts, making them intuitively understandable to users. This allows users to grasp the status of their energy consumption and take appropriate measures.

[0032] The proposal department proposes an optimal energy usage plan based on the analysis results obtained by the analysis department. For example, the proposal department may suggest adjusting the air conditioner temperature setting during specific times to avoid peak power consumption. Specifically, it uses AI to predict peak energy consumption and proposes reducing power consumption by setting the air conditioner temperature slightly higher during those times. It also proposes automatically turning off unused appliances to reduce unnecessary power consumption. For example, the AI ​​analyzes the usage of appliances and proposes turning off appliances that have not been used for a long time. Furthermore, the proposal department makes customized suggestions based on the user's lifestyle and needs to optimize energy consumption. For example, if a user wants to "save energy," the AI ​​makes specific suggestions to minimize energy consumption. On the other hand, if a user wants to "maintain a comfortable temperature," the AI ​​makes suggestions to optimize energy consumption while maintaining comfort. In this way, the proposal department can provide a flexible energy usage plan that meets the user's needs and improve the efficiency of energy consumption. Furthermore, the proposal department evaluates the effectiveness of the suggestions and builds a feedback loop for continuous improvement. This allows the proposal department to consistently provide the optimal energy usage plan and improve the overall system performance.

[0033] The adjustment unit automatically adjusts energy consumption based on the energy usage plan proposed by the proposal unit. For example, the adjustment unit automatically adjusts energy consumption based on user needs. Specifically, if the user instructs that they "want to save energy," it automatically adjusts the air conditioner's temperature setting and turns off unnecessary appliances. For example, the AI ​​reduces energy consumption by setting the air conditioner's temperature slightly higher and turning off appliances that have not been used for a long time. Also, if the user instructs that they "want to maintain a comfortable temperature," it adjusts the air conditioner's temperature setting to an optimal range. For example, the AI ​​monitors the room temperature and humidity and adjusts the air conditioner's temperature setting to maintain comfort. This allows the adjustment unit to flexibly adjust energy consumption according to user needs. Furthermore, the adjustment unit monitors the results of energy consumption adjustments in real time and modifies the adjustments as needed. For example, it optimizes the adjustments based on the energy consumption status and user feedback. This allows the adjustment unit to always perform optimal energy consumption adjustments and improve the overall system performance. In addition, the adjustment unit records the results of energy consumption adjustments so that the analysis unit and proposal unit can refer to them later. This allows the adjustment unit to improve energy efficiency and enhance the overall reliability and safety of the system.

[0034] The data collection unit can collect data such as electricity usage, gas usage, temperature, and humidity through sensors. For example, the data collection unit uses a smart meter to measure electricity usage. For example, the data collection unit uses a gas meter to measure gas usage. For example, the data collection unit uses a temperature and humidity sensor to measure temperature and humidity. This allows for accurate data collection by collecting energy consumption data through sensors. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from sensors into a generating AI and have the generating AI perform data analysis.

[0035] The analysis unit can analyze the collected data and compare it with historical consumption patterns. For example, the analysis unit can analyze the collected data in real time to understand energy consumption trends. For example, the analysis unit can compare current usage with past electricity usage data to predict peak consumption. For example, the analysis unit can use AI to analyze the collected data and understand energy consumption trends. This allows for understanding energy consumption trends by comparing them with historical consumption patterns. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the data analysis.

[0036] The suggestion unit can make suggestions to adjust the air conditioner's temperature setting during specific time periods to avoid peak power consumption. For example, the suggestion unit can make suggestions to adjust the air conditioner's temperature setting during specific time periods to avoid peak power consumption. The suggestion unit can, for example, use AI to propose an optimal energy usage plan. This makes it possible to optimize energy consumption by making suggestions to avoid peak power consumption. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can have a generating AI execute suggestions to adjust the air conditioner's temperature setting.

[0037] The suggestion unit can propose automatically turning off the power to unused electrical appliances in order to reduce unnecessary power consumption. For example, the suggestion unit can propose automatically turning off the power to unused electrical appliances in order to reduce unnecessary power consumption. The suggestion unit can propose an optimal energy usage plan using AI, for example. This reduces energy waste by making suggestions to reduce unnecessary power consumption. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can have a generating AI execute a suggestion to turn off the power to electrical appliances.

[0038] The adjustment unit can automatically adjust energy consumption based on user needs. For example, if the user instructs the adjustment unit to "save energy," it will automatically adjust the air conditioner's temperature setting and turn off unnecessary appliances. For example, if the user instructs the adjustment unit to "maintain a comfortable temperature," it will adjust the air conditioner's temperature setting to an optimal range. In this way, by automatically adjusting energy consumption based on user needs, energy can be saved while maintaining a comfortable environment. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can have a generating AI perform the process of adjusting the air conditioner's temperature setting.

[0039] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can determine the most efficient sensor placement from past data collection history. For example, the data collection unit can optimize the frequency of data collection based on past data collection history. For example, the data collection unit can analyze past data collection history and select a method to concentrate data collection during specific time periods. In this way, the optimal collection method can be selected by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0040] The data collection unit can filter data based on the user's current activity status and environment during data collection. For example, if the user is active, the data collection unit can temporarily stop data collection and resume it after the activity is finished. For example, if the user's environment is noisy, the data collection unit can remove noise before collecting data. For example, if the user is performing a specific activity, the data collection unit can prioritize collecting only the data related to that activity. This allows for more accurate data collection by filtering data based on the user's activity status and environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user activity status and environment data into a generating AI and have the generating AI perform data filtering.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of energy consumption data related to that region. For example, if the user is on the move, the data collection unit will pre-collect energy consumption data for the destination. For example, if the user is inside a specific building, the data collection unit will prioritize the collection of energy consumption data for that building. In this way, by considering the user's geographical location information, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0042] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user shares information about energy consumption on social media, the data collection unit can collect data based on that information. For example, the data collection unit can analyze a user's interest in energy consumption from their social media activity and collect relevant data. For example, if a user exhibits a specific energy consumption pattern on social media, the data collection unit can collect data related to that pattern. In this way, relevant data can be collected by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on important data. For example, the analysis unit performs a simplified analysis on less important data. For example, the analysis unit determines the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a specific analysis algorithm to electricity usage data. For example, the analysis unit applies a different analysis algorithm to gas usage data. For example, the analysis unit applies yet another different analysis algorithm to temperature and humidity data. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0045] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may perform analysis of current data based on past data. For example, the analysis unit may adjust the order of analysis according to the data collection timing. This allows for prioritizing the analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. For example, the analysis unit may determine the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0047] The proposal unit can adjust the level of detail of its proposals based on the importance of the energy use plan. For example, the proposal unit will provide detailed proposals for important energy use plans. For example, it will provide simplified proposals for less important energy use plans. The proposal unit will also determine the priority of proposals based on the importance of the energy use plan. This allows for efficient proposals by adjusting the level of detail of proposals based on the importance of the energy use plan. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the energy use plan into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0048] The proposal unit can apply different proposal algorithms depending on the category of the energy use plan when making a proposal. For example, the proposal unit applies a specific proposal algorithm to electricity use plans. For example, the proposal unit applies a different proposal algorithm to gas use plans. For example, the proposal unit applies yet another proposal algorithm to energy use plans related to temperature and humidity. By applying different proposal algorithms depending on the category of the energy use plan, more accurate proposals can be made. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the category of the energy use plan into a generating AI and have the generating AI execute the application of different proposal algorithms.

[0049] The proposal department can determine the priority of proposals based on the submission timing of energy use plans. For example, the proposal department will prioritize proposals for energy use plans with approaching submission deadlines. For example, the proposal department will postpone proposals for energy use plans with later submission deadlines. For example, the proposal department will adjust the order of proposals according to the submission timing. This enables efficient proposals by determining the priority of proposals based on the submission timing of energy use plans. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the submission timing of energy use plans into a generating AI and have the generating AI perform the determination of proposal priority.

[0050] The proposal unit can adjust the order of proposals based on the relevance of the energy use plans during the proposal process. For example, the proposal unit may prioritize proposing highly relevant energy use plans. For example, the proposal unit may postpone less relevant energy use plans. For example, the proposal unit may determine the order of proposals according to the relevance of the energy use plans. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the energy use plans. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of the energy use plans into a generating AI and have the generating AI perform the adjustment of the proposal order.

[0051] The adjustment unit can analyze the user's past energy consumption behavior during adjustment and select the optimal adjustment method. For example, the adjustment unit selects the optimal adjustment method based on the user's past energy consumption behavior. For example, the adjustment unit analyzes the user's past energy consumption patterns and proposes an efficient adjustment method. For example, the adjustment unit selects an adjustment method that optimizes energy consumption by referring to the user's past energy consumption behavior. In this way, the optimal adjustment method can be selected by analyzing the user's past energy consumption behavior. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's past energy consumption behavior data into a generating AI and have the generating AI select the optimal adjustment method.

[0052] The adjustment unit can customize the means of adjustment based on the user's current living situation during the adjustment process. For example, if the user is at home, the adjustment unit will make adjustments to optimize energy consumption. For example, if the user is out, the adjustment unit will make adjustments to reduce unnecessary energy consumption. For example, the adjustment unit will make customized adjustments to optimize energy consumption according to the user's living situation. This enables efficient adjustment by customizing the means of adjustment based on the user's current living situation. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's current living situation data into a generating AI and have the generating AI perform the customization of the means of adjustment.

[0053] The adjustment unit can select the optimal adjustment method by considering the user's geographical location information during the adjustment process. For example, if the user is in a specific region, the adjustment unit will perform adjustments based on energy consumption data related to that region. For example, if the user is on the move, the adjustment unit will perform adjustments based on energy consumption data of the destination. For example, if the user is inside a specific building, the adjustment unit will perform adjustments based on energy consumption data of that building. In this way, the optimal adjustment method can be selected by considering the user's geographical location information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal adjustment method.

[0054] The adjustment unit can analyze the user's social media activity during adjustment and propose adjustment measures. For example, if the user shares information about energy consumption on social media, the adjustment unit will make adjustments based on that information. For example, the adjustment unit will analyze the user's interest in energy consumption from their social media activity and propose relevant adjustment measures. For example, if the user exhibits a specific energy consumption pattern on social media, the adjustment unit will propose adjustment measures related to that pattern. In this way, relevant adjustment measures can be proposed by analyzing the user's social media activity. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of adjustment measures.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The data collection unit can adjust the frequency of data collection by considering the user's past energy consumption patterns. For example, it can predict peak energy consumption from past data and increase the frequency of data collection during those times. Conversely, it can decrease the frequency of data collection during times of low energy consumption. This allows for efficient data collection by considering past energy consumption patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past energy consumption data into a generating AI and have the generating AI adjust the frequency of data collection.

[0057] The analysis unit can determine the priority of analysis based on the reliability of the data during the analysis process. For example, it can prioritize the analysis of highly reliable data and postpone the analysis of less reliable data. This allows for more accurate analysis by determining the priority of analysis based on data reliability. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the reliability of the data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0058] The proposal unit can adjust the content of its proposals by considering the user's past energy usage history. For example, it can make similar proposals based on energy-saving measures the user has taken in the past. Conversely, it can avoid proposals that were ineffective in the past. This makes it possible to make more effective proposals by considering the user's past energy usage history. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the user's past energy usage history into a generating AI and have the generating AI adjust the content of the proposals.

[0059] The adjustment unit can monitor the user's current energy consumption in real time during adjustment and make optimal adjustments. For example, if current energy consumption is at its peak, it can adjust by turning off the power to unnecessary appliances. Conversely, if energy consumption is low, it can adjust the air conditioner's temperature setting to prioritize comfort. This enables efficient adjustment by monitoring energy consumption in real time. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input current energy consumption data into a generating AI and have the generating AI perform the adjustments.

[0060] The data collection unit can adjust the timing of data collection, taking into account the user's daily rhythm. For example, the frequency of data collection can be reduced when the user is sleeping. Conversely, the frequency of data collection can be increased during times when the user is active. This allows for efficient data collection by considering the user's daily rhythm. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's daily rhythm data into a generating AI and have the generating AI adjust the timing of data collection.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects data related to energy consumption. For example, it collects data such as electricity usage, gas usage, temperature, and humidity through sensors. The data collection unit uses a smart meter to measure electricity usage, a gas meter to measure gas usage, and a temperature and humidity sensor to measure temperature and humidity. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes the collected data in real time to understand energy consumption trends. It compares current usage with past electricity usage data to predict peak consumption. It uses AI to analyze the collected data and understand energy consumption trends. Step 3: The proposal unit proposes an optimal energy usage plan based on the analysis results obtained by the analysis unit. For example, it may suggest adjusting the air conditioner temperature setting during specific times to avoid peak power consumption. It may also suggest automatically turning off unused electrical appliances to reduce unnecessary power consumption. AI is used to propose the optimal energy usage plan. Step 4: The adjustment unit automatically adjusts energy consumption based on the energy usage plan proposed by the suggestion unit. For example, it automatically adjusts energy consumption based on user needs. If the user instructs "I want to save energy," it automatically adjusts the air conditioner's temperature setting and turns off unnecessary appliances. If the user instructs "I want to maintain a comfortable temperature," it adjusts the air conditioner's temperature setting to the optimal range.

[0063] (Example of form 2) An AI voice assistant system according to an embodiment of the present invention is a system that efficiently manages energy consumption in homes and offices, and provides energy-saving suggestions and automatic adjustments. This system collects real-time data on energy consumption in homes and offices and analyzes historical consumption patterns to propose an optimal energy usage plan. Furthermore, it automatically adjusts energy consumption based on user needs. This mechanism is expected to reduce annual energy costs by up to 20%, and contribute to the environment by reducing CO2 emissions and optimizing energy consumption. For example, the AI ​​voice assistant system collects real-time data on energy consumption in homes and offices. For example, it collects data such as electricity usage, gas usage, temperature, and humidity through sensors. This data is input into the AI ​​voice assistant system. Next, the AI ​​voice assistant system analyzes the collected data and compares it with historical consumption patterns. For example, it compares current usage with past electricity usage data and predicts peak consumption. This allows for an understanding of energy consumption trends. Furthermore, the AI ​​voice assistant system proposes an optimal energy usage plan based on the analysis results. For example, it suggests adjusting the air conditioner temperature setting during specific time periods to avoid peak electricity usage. Furthermore, to reduce unnecessary power consumption, the system will suggest automatically turning off unused electrical appliances. Next, the AI ​​voice assistant system will automatically adjust energy consumption based on user needs. For example, if a user instructs the system to "save energy," the AI ​​voice assistant system will automatically adjust the air conditioner's temperature setting and turn off unnecessary electrical appliances. If a user instructs the system to "maintain a comfortable temperature," the AI ​​voice assistant system will adjust the air conditioner's temperature setting to an optimal range. This system efficiently manages energy consumption in homes and offices, providing energy-saving suggestions and automatic adjustments. As a result, it is expected that annual energy costs will be reduced by 20%, contributing to environmental protection through reduced CO2 emissions and optimized energy consumption.Furthermore, for household and office users who are conscious of energy consumption but lack the time, the Energy Assistant Agent is an ideal solution. This allows the AI ​​voice assistant system to efficiently manage energy consumption in homes and offices, providing energy-saving suggestions and automatic adjustments.

[0064] The AI ​​voice assistant system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, and an adjustment unit. The data collection unit collects data related to energy consumption. The data collection unit collects data such as electricity usage, gas usage, temperature, and humidity through sensors. For example, the data collection unit uses a smart meter to measure electricity usage, a gas meter to measure gas usage, and a temperature and humidity sensor to measure temperature and humidity. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the collected data in real time to understand energy consumption trends. For example, the analysis unit compares current usage with past electricity usage data to predict peak consumption. For example, the analysis unit uses AI to analyze the collected data and understand energy consumption trends. The proposal unit proposes an optimal energy usage plan based on the analysis results obtained by the analysis unit. For example, the proposal unit suggests adjusting the air conditioner's temperature setting during specific time periods to avoid peak electricity usage. The suggestion unit, for example, suggests automatically turning off the power to unused home appliances in order to reduce unnecessary power consumption. The suggestion unit, for example, uses AI to suggest an optimal energy usage plan. The adjustment unit automatically adjusts energy consumption based on the energy usage plan suggested by the suggestion unit. The adjustment unit automatically adjusts energy consumption based on user needs, for example. For example, if the user instructs the adjustment unit to "save energy," the adjustment unit automatically adjusts the air conditioner's temperature setting and turns off the power to unnecessary home appliances. For example, if the user instructs the adjustment unit to "maintain a comfortable temperature," the adjustment unit adjusts the air conditioner's temperature setting to an optimal range. As a result, the AI ​​voice assistant system according to this embodiment can efficiently manage energy consumption in homes and offices and provide energy-saving suggestions and automatic adjustments.

[0065] The data collection unit collects data related to energy consumption. For example, it collects data such as electricity usage, gas usage, temperature, and humidity through sensors. Specifically, it uses smart meters to measure electricity usage. Smart meters measure electricity consumption in homes and offices in real time and transmit the data to a central database. Gas meters are used to measure gas usage. Gas meters accurately measure gas usage and transmit the data to the data collection unit. Temperature and humidity sensors are used to measure temperature and humidity. Temperature and humidity sensors monitor indoor temperature and humidity in real time and transmit the data to the data collection unit. These sensors are installed throughout homes and offices to collect detailed data on energy consumption. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance. Furthermore, the data collection unit uses encryption technology to protect the data in order to ensure its security. This prevents unauthorized access to the collected data and improves the reliability of the system.

[0066] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data in real time to understand energy consumption trends. Specifically, it compares current usage with past electricity usage data and predicts peak consumption. The analysis unit uses AI to analyze the collected data and understand energy consumption trends. The AI ​​uses machine learning algorithms to learn patterns from past data and predict future energy consumption. For example, the AI ​​analyzes past electricity usage data to understand consumption trends during specific times of day or seasons. This allows the analysis unit to predict peak energy consumption and take appropriate measures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. For example, it can detect sudden increases in electricity consumption or abnormal fluctuations in gas usage and notify users. This allows the analysis unit to not only understand the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system. Furthermore, the analysis unit uses data visualization tools to display energy consumption trends and anomalies in graphs and charts, making them intuitively understandable to users. This allows users to grasp the status of their energy consumption and take appropriate measures.

[0067] The proposal department proposes an optimal energy usage plan based on the analysis results obtained by the analysis department. For example, the proposal department may suggest adjusting the air conditioner temperature setting during specific times to avoid peak power consumption. Specifically, it uses AI to predict peak energy consumption and proposes reducing power consumption by setting the air conditioner temperature slightly higher during those times. It also proposes automatically turning off unused appliances to reduce unnecessary power consumption. For example, the AI ​​analyzes the usage of appliances and proposes turning off appliances that have not been used for a long time. Furthermore, the proposal department makes customized suggestions based on the user's lifestyle and needs to optimize energy consumption. For example, if a user wants to "save energy," the AI ​​makes specific suggestions to minimize energy consumption. On the other hand, if a user wants to "maintain a comfortable temperature," the AI ​​makes suggestions to optimize energy consumption while maintaining comfort. In this way, the proposal department can provide a flexible energy usage plan that meets the user's needs and improve the efficiency of energy consumption. Furthermore, the proposal department evaluates the effectiveness of the suggestions and builds a feedback loop for continuous improvement. This allows the proposal department to consistently provide the optimal energy usage plan and improve the overall system performance.

[0068] The adjustment unit automatically adjusts energy consumption based on the energy usage plan proposed by the proposal unit. For example, the adjustment unit automatically adjusts energy consumption based on user needs. Specifically, if the user instructs that they "want to save energy," it automatically adjusts the air conditioner's temperature setting and turns off unnecessary appliances. For example, the AI ​​reduces energy consumption by setting the air conditioner's temperature slightly higher and turning off appliances that have not been used for a long time. Also, if the user instructs that they "want to maintain a comfortable temperature," it adjusts the air conditioner's temperature setting to an optimal range. For example, the AI ​​monitors the room temperature and humidity and adjusts the air conditioner's temperature setting to maintain comfort. This allows the adjustment unit to flexibly adjust energy consumption according to user needs. Furthermore, the adjustment unit monitors the results of energy consumption adjustments in real time and modifies the adjustments as needed. For example, it optimizes the adjustments based on the energy consumption status and user feedback. This allows the adjustment unit to always perform optimal energy consumption adjustments and improve the overall system performance. In addition, the adjustment unit records the results of energy consumption adjustments so that the analysis unit and proposal unit can refer to them later. This allows the adjustment unit to improve energy efficiency and enhance the overall reliability and safety of the system.

[0069] The data collection unit can collect data such as electricity usage, gas usage, temperature, and humidity through sensors. For example, the data collection unit uses a smart meter to measure electricity usage. For example, the data collection unit uses a gas meter to measure gas usage. For example, the data collection unit uses a temperature and humidity sensor to measure temperature and humidity. This allows for accurate data collection by collecting energy consumption data through sensors. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from sensors into a generating AI and have the generating AI perform data analysis.

[0070] The analysis unit can analyze the collected data and compare it with historical consumption patterns. For example, the analysis unit can analyze the collected data in real time to understand energy consumption trends. For example, the analysis unit can compare current usage with past electricity usage data to predict peak consumption. For example, the analysis unit can use AI to analyze the collected data and understand energy consumption trends. This allows for understanding energy consumption trends by comparing them with historical consumption patterns. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the data analysis.

[0071] The suggestion unit can make suggestions to adjust the air conditioner's temperature setting during specific time periods to avoid peak power consumption. For example, the suggestion unit can make suggestions to adjust the air conditioner's temperature setting during specific time periods to avoid peak power consumption. The suggestion unit can, for example, use AI to propose an optimal energy usage plan. This makes it possible to optimize energy consumption by making suggestions to avoid peak power consumption. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can have a generating AI execute suggestions to adjust the air conditioner's temperature setting.

[0072] The suggestion unit can propose automatically turning off the power to unused electrical appliances in order to reduce unnecessary power consumption. For example, the suggestion unit can propose automatically turning off the power to unused electrical appliances in order to reduce unnecessary power consumption. The suggestion unit can propose an optimal energy usage plan using AI, for example. This reduces energy waste by making suggestions to reduce unnecessary power consumption. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can have a generating AI execute a suggestion to turn off the power to electrical appliances.

[0073] The adjustment unit can automatically adjust energy consumption based on user needs. For example, if the user instructs the adjustment unit to "save energy," it will automatically adjust the air conditioner's temperature setting and turn off unnecessary appliances. For example, if the user instructs the adjustment unit to "maintain a comfortable temperature," it will adjust the air conditioner's temperature setting to an optimal range. In this way, by automatically adjusting energy consumption based on user needs, energy can be saved while maintaining a comfortable environment. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can have a generating AI perform the process of adjusting the air conditioner's temperature setting.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is relaxed, the data collection unit can reduce the frequency of data collection to lessen the user's burden. For example, if the user is stressed, the data collection unit can adjust the timing of data collection to a time when the user is less active. For example, if the user is in a hurry, the data collection unit can collect data quickly to provide the necessary information immediately. In this way, the user's burden can be reduced by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0075] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can determine the most efficient sensor placement from past data collection history. For example, the data collection unit can optimize the frequency of data collection based on past data collection history. For example, the data collection unit can analyze past data collection history and select a method to concentrate data collection during specific time periods. In this way, the optimal collection method can be selected by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0076] The data collection unit can filter data based on the user's current activity status and environment during data collection. For example, if the user is active, the data collection unit can temporarily stop data collection and resume it after the activity is finished. For example, if the user's environment is noisy, the data collection unit can remove noise before collecting data. For example, if the user is performing a specific activity, the data collection unit can prioritize collecting only the data related to that activity. This allows for more accurate data collection by filtering data based on the user's activity status and environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user activity status and environment data into a generating AI and have the generating AI perform data filtering.

[0077] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit will prioritize collecting detailed data on energy consumption. If the user is stressed, the data collection unit will prioritize collecting only important data and postpone the collection of other data. If the user is in a hurry, the data collection unit will prioritize collecting data that is immediately needed. This allows for the priority collection of important data by determining the priority of data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data.

[0078] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of energy consumption data related to that region. For example, if the user is on the move, the data collection unit will pre-collect energy consumption data for the destination. For example, if the user is inside a specific building, the data collection unit will prioritize the collection of energy consumption data for that building. In this way, by considering the user's geographical location information, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0079] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user shares information about energy consumption on social media, the data collection unit can collect data based on that information. For example, the data collection unit can analyze a user's interest in energy consumption from their social media activity and collect relevant data. For example, if a user exhibits a specific energy consumption pattern on social media, the data collection unit can collect data related to that pattern. In this way, relevant data can be collected by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is stressed, the analysis unit provides concise and to-the-point analysis results. For example, if the user is in a hurry, the analysis unit provides visually easy-to-understand analysis results for quick comprehension. In this way, by adjusting the presentation of the analysis based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on important data. For example, the analysis unit performs a simplified analysis on less important data. For example, the analysis unit determines the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a specific analysis algorithm to electricity usage data. For example, the analysis unit applies a different analysis algorithm to gas usage data. For example, the analysis unit applies yet another different analysis algorithm to temperature and humidity data. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0083] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is stressed, the analysis unit provides concise and to-the-point analysis results. For example, if the user is in a hurry, the analysis unit provides short analysis results that can be quickly understood. By adjusting the length of the analysis based on the user's emotions, the analysis unit can provide analysis results of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0084] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may perform analysis of current data based on past data. For example, the analysis unit may adjust the order of analysis according to the data collection timing. This allows for prioritizing the analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the priority of analysis.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. For example, the analysis unit may determine the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0086] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, the suggestion unit will provide detailed suggestions. If the user is stressed, the suggestion unit will provide concise and to-the-point suggestions. If the user is in a hurry, the suggestion unit will provide visually clear suggestions that can be quickly understood. By adjusting the way suggestions are presented based on the user's emotions, the suggestion unit can provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0087] The proposal unit can adjust the level of detail of its proposals based on the importance of the energy use plan. For example, the proposal unit will provide detailed proposals for important energy use plans. For example, it will provide simplified proposals for less important energy use plans. The proposal unit will also determine the priority of proposals based on the importance of the energy use plan. This allows for efficient proposals by adjusting the level of detail of proposals based on the importance of the energy use plan. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the energy use plan into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0088] The proposal unit can apply different proposal algorithms depending on the category of the energy use plan when making a proposal. For example, the proposal unit applies a specific proposal algorithm to electricity use plans. For example, the proposal unit applies a different proposal algorithm to gas use plans. For example, the proposal unit applies yet another proposal algorithm to energy use plans related to temperature and humidity. By applying different proposal algorithms depending on the category of the energy use plan, more accurate proposals can be made. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the category of the energy use plan into a generating AI and have the generating AI execute the application of different proposal algorithms.

[0089] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit will provide detailed suggestions. If the user is stressed, the suggestion unit will provide concise and to-the-point suggestions. If the user is in a hurry, the suggestion unit will provide short suggestions that can be quickly understood. By adjusting the length of suggestions based on the user's emotions, the suggestion unit can provide suggestions of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of the suggestions.

[0090] The proposal department can determine the priority of proposals based on the submission timing of energy use plans. For example, the proposal department will prioritize proposals for energy use plans with approaching submission deadlines. For example, the proposal department will postpone proposals for energy use plans with later submission deadlines. For example, the proposal department will adjust the order of proposals according to the submission timing. This enables efficient proposals by determining the priority of proposals based on the submission timing of energy use plans. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the submission timing of energy use plans into a generating AI and have the generating AI perform the determination of proposal priority.

[0091] The proposal unit can adjust the order of proposals based on the relevance of the energy use plans during the proposal process. For example, the proposal unit may prioritize proposing highly relevant energy use plans. For example, the proposal unit may postpone less relevant energy use plans. For example, the proposal unit may determine the order of proposals according to the relevance of the energy use plans. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the energy use plans. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of the energy use plans into a generating AI and have the generating AI perform the adjustment of the proposal order.

[0092] The adjustment unit can estimate the user's emotions and adjust the adjustment method based on the estimated user emotions. For example, if the user is relaxed, the adjustment unit makes detailed adjustments to optimize energy consumption. For example, if the user is stressed, the adjustment unit makes concise and quick adjustments. For example, if the user is in a hurry, the adjustment unit immediately adjusts energy consumption. This allows for optimal adjustments for the user by adjusting the adjustment method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI or not using AI. For example, the adjustment unit can input user emotion data into the generative AI and have the generative AI perform adjustments to the adjustment method.

[0093] The adjustment unit can analyze the user's past energy consumption behavior during adjustment and select the optimal adjustment method. For example, the adjustment unit selects the optimal adjustment method based on the user's past energy consumption behavior. For example, the adjustment unit analyzes the user's past energy consumption patterns and proposes an efficient adjustment method. For example, the adjustment unit selects an adjustment method that optimizes energy consumption by referring to the user's past energy consumption behavior. In this way, the optimal adjustment method can be selected by analyzing the user's past energy consumption behavior. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's past energy consumption behavior data into a generating AI and have the generating AI select the optimal adjustment method.

[0094] The adjustment unit can customize the means of adjustment based on the user's current living situation during the adjustment process. For example, if the user is at home, the adjustment unit will make adjustments to optimize energy consumption. For example, if the user is out, the adjustment unit will make adjustments to reduce unnecessary energy consumption. For example, the adjustment unit will make customized adjustments to optimize energy consumption according to the user's living situation. This enables efficient adjustment by customizing the means of adjustment based on the user's current living situation. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's current living situation data into a generating AI and have the generating AI perform the customization of the means of adjustment.

[0095] The adjustment unit can estimate the user's emotions and determine the priority of adjustments based on the estimated emotions. For example, if the user is relaxed, the adjustment unit will prioritize detailed adjustments. If the user is stressed, the adjustment unit will prioritize concise and quick adjustments. If the user is in a hurry, the adjustment unit will prioritize adjustments that are immediately needed. This allows for optimal adjustments for the user by determining the priority of adjustments based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input user emotion data into a generative AI and have the generative AI determine the priority of adjustments.

[0096] The adjustment unit can select the optimal adjustment method by considering the user's geographical location information during the adjustment process. For example, if the user is in a specific region, the adjustment unit will perform adjustments based on energy consumption data related to that region. For example, if the user is on the move, the adjustment unit will perform adjustments based on energy consumption data of the destination. For example, if the user is inside a specific building, the adjustment unit will perform adjustments based on energy consumption data of that building. In this way, the optimal adjustment method can be selected by considering the user's geographical location information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal adjustment method.

[0097] The adjustment unit can analyze the user's social media activity during adjustment and propose adjustment measures. For example, if the user shares information about energy consumption on social media, the adjustment unit will make adjustments based on that information. For example, the adjustment unit will analyze the user's interest in energy consumption from their social media activity and propose relevant adjustment measures. For example, if the user exhibits a specific energy consumption pattern on social media, the adjustment unit will propose adjustment measures related to that pattern. In this way, relevant adjustment measures can be proposed by analyzing the user's social media activity. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of adjustment measures.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The analysis unit can estimate the user's emotions and determine the priority of the analysis based on the estimated emotions. For example, if the user is relaxed, a detailed analysis can be prioritized. If the user is stressed, a concise and quick analysis can be prioritized. If the user is in a hurry, the analysis that is immediately needed can be prioritized. This allows for the optimal analysis for the user by determining the priority of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of the analysis.

[0100] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated emotions. For example, if the user is relaxed, suggestions can be made immediately. If the user is stressed, the timing of suggestions can be delayed. If the user is in a hurry, suggestions can be made quickly. By adjusting the timing of suggestions based on the user's emotions, suggestions can be made at the optimal time for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the timing of suggestions.

[0101] The adjustment unit can estimate the user's emotions and determine the frequency of adjustments based on the estimated emotions. For example, if the user is relaxed, the frequency of adjustments can be reduced. If the user is stressed, the frequency of adjustments can be increased. If the user is in a hurry, adjustments can be performed quickly. This allows adjustments to be performed at the optimal frequency for the user by determining the frequency of adjustments based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input user emotion data into a generative AI and have the generative AI determine the frequency of adjustments.

[0102] The data collection unit can estimate the user's emotions and adjust the data collection method based on the estimated emotions. For example, if the user is relaxed, detailed data can be collected. If the user is stressed, concise data can be collected. If the user is in a hurry, data can be collected quickly. This allows data to be collected in the most optimal way for the user by adjusting the data collection method based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the data collection method.

[0103] The suggestion unit can estimate the user's emotions and adjust the content of the suggestions based on the estimated emotions. For example, if the user is relaxed, it can provide detailed suggestions. If the user is stressed, it can provide concise suggestions. If the user is in a hurry, it can provide suggestions that can be quickly understood. In this way, by adjusting the content of suggestions based on the user's emotions, the system can provide suggestions that are optimal for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the content of the suggestions.

[0104] The data collection unit can adjust the frequency of data collection by considering the user's past energy consumption patterns. For example, it can predict peak energy consumption from past data and increase the frequency of data collection during those times. Conversely, it can decrease the frequency of data collection during times of low energy consumption. This allows for efficient data collection by considering past energy consumption patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past energy consumption data into a generating AI and have the generating AI adjust the frequency of data collection.

[0105] The analysis unit can determine the priority of analysis based on the reliability of the data during the analysis process. For example, it can prioritize the analysis of highly reliable data and postpone the analysis of less reliable data. This allows for more accurate analysis by determining the priority of analysis based on data reliability. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the reliability of the data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0106] The proposal unit can adjust the content of its proposals by considering the user's past energy usage history. For example, it can make similar proposals based on energy-saving measures the user has taken in the past. Conversely, it can avoid proposals that were ineffective in the past. This makes it possible to make more effective proposals by considering the user's past energy usage history. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the user's past energy usage history into a generating AI and have the generating AI adjust the content of the proposals.

[0107] The adjustment unit can monitor the user's current energy consumption in real time during adjustment and make optimal adjustments. For example, if current energy consumption is at its peak, it can adjust by turning off the power to unnecessary appliances. Conversely, if energy consumption is low, it can adjust the air conditioner's temperature setting to prioritize comfort. This enables efficient adjustment by monitoring energy consumption in real time. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input current energy consumption data into a generating AI and have the generating AI perform the adjustments.

[0108] The data collection unit can adjust the timing of data collection, taking into account the user's daily rhythm. For example, the frequency of data collection can be reduced when the user is sleeping. Conversely, the frequency of data collection can be increased during times when the user is active. This allows for efficient data collection by considering the user's daily rhythm. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's daily rhythm data into a generating AI and have the generating AI adjust the timing of data collection.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The data collection unit collects data related to energy consumption. For example, it collects data such as electricity usage, gas usage, temperature, and humidity through sensors. The data collection unit uses a smart meter to measure electricity usage, a gas meter to measure gas usage, and a temperature and humidity sensor to measure temperature and humidity. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes the collected data in real time to understand energy consumption trends. It compares current usage with past electricity usage data to predict peak consumption. It uses AI to analyze the collected data and understand energy consumption trends. Step 3: The proposal unit proposes an optimal energy usage plan based on the analysis results obtained by the analysis unit. For example, it may suggest adjusting the air conditioner temperature setting during specific times to avoid peak power consumption. It may also suggest automatically turning off unused electrical appliances to reduce unnecessary power consumption. AI is used to propose the optimal energy usage plan. Step 4: The adjustment unit automatically adjusts energy consumption based on the energy usage plan proposed by the suggestion unit. For example, it automatically adjusts energy consumption based on user needs. If the user instructs "I want to save energy," it automatically adjusts the air conditioner's temperature setting and turns off unnecessary appliances. If the user instructs "I want to maintain a comfortable temperature," it adjusts the air conditioner's temperature setting to the optimal range.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and adjustment unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data such as power consumption, gas consumption, temperature, and humidity using the sensors of the smart device 14. The analysis unit analyzes the collected data in real time using the specific processing unit 290 of the data processing unit 12 to understand energy consumption trends. The proposal unit proposes an optimal energy usage plan using the specific processing unit 290 of the data processing unit 12. The adjustment unit automatically adjusts energy consumption based on the proposed energy usage plan using the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and adjustment unit, is implemented in, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data such as power consumption, gas consumption, temperature, and humidity using the sensors of the smart glasses 214. The analysis unit analyzes the collected data in real time, for example, by the specific processing unit 290 of the data processing unit 12, to understand energy consumption trends. The proposal unit proposes an optimal energy usage plan, for example, by the specific processing unit 290 of the data processing unit 12. The adjustment unit automatically adjusts energy consumption based on the proposed energy usage plan, for example, by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and adjustment unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data such as power consumption, gas consumption, temperature, and humidity using the sensors of the headset terminal 314. The analysis unit analyzes the collected data in real time using the specific processing unit 290 of the data processing unit 12 to understand energy consumption trends. The proposal unit proposes an optimal energy usage plan using the specific processing unit 290 of the data processing unit 12. The adjustment unit automatically adjusts energy consumption based on the proposed energy usage plan using the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and adjustment unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects data such as power consumption, gas consumption, temperature, and humidity using the sensors of the robot 414. The analysis unit analyzes the collected data in real time, for example, by the specific processing unit 290 of the data processing unit 12, to understand energy consumption trends. The proposal unit proposes an optimal energy usage plan, for example, by the specific processing unit 290 of the data processing unit 12. The adjustment unit automatically adjusts energy consumption based on the proposed energy usage plan, for example, by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) A data collection unit that collects data on energy consumption, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes an optimal energy usage plan. An adjustment unit that automatically adjusts energy consumption based on the energy use plan proposed by the aforementioned proposal unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned collection unit is Data such as electricity consumption, gas consumption, temperature, and humidity are collected through sensors. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed and compared with historical consumption patterns. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, To avoid peak electricity usage, we suggest adjusting the air conditioner temperature settings during specific time periods. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, To reduce unnecessary power consumption, we propose automatically turning off the power to unused electrical appliances. The system described in Appendix 1, characterized by the features described herein. (Note 6) The adjustment unit is, Automatically adjusts energy consumption based on user needs. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current activity status and environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the energy use plan. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the energy use plan. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting proposals, priority will be determined based on the timing of the submission of energy use plans. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on their relevance to the energy use plan. The system described in Appendix 1, characterized by the features described herein. (Note 25) The adjustment unit is, It estimates the user's emotions and adjusts the adjustment method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The adjustment unit is, During the adjustment process, the system analyzes the user's past energy consumption behavior to select the optimal adjustment method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The adjustment unit is, During the adjustment process, the adjustment methods are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The adjustment unit is, It estimates the user's emotions and determines the priority of adjustments based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The adjustment unit is, During the adjustment process, the optimal adjustment method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The adjustment unit is, During the adjustment process, we analyze users' social media activity and propose adjustment methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data on energy consumption, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes an optimal energy usage plan. An adjustment unit that automatically adjusts energy consumption based on the energy use plan proposed by the aforementioned proposal unit, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is Data such as electricity consumption, gas consumption, temperature, and humidity are collected through sensors. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed and compared with historical consumption patterns. The system according to feature 1.

4. The aforementioned proposal section is, To avoid peak electricity usage, we suggest adjusting the air conditioner temperature settings during specific time periods. The system according to feature 1.

5. The aforementioned proposal section is, To reduce unnecessary power consumption, we propose automatically turning off the power to unused electrical appliances. The system according to feature 1.

6. The adjustment unit is, Automatically adjusts energy consumption based on user needs. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system according to feature 1.

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