system

Abstract

The system according to this embodiment aims to automatically optimize the home environment based on the user's preferences and lifestyle patterns. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a control unit, an adjustment unit, and a reduction unit. The data collection unit collects user behavior data. The analysis unit analyzes the data collected by the data collection unit to understand the user's preferences and lifestyle patterns. The control unit controls the device based on the analysis results obtained by the analysis unit. The adjustment unit automatically adjusts the home environment according to the scenes of being at home, going out, and sleeping. The reduction unit reduces power consumption.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of 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 conventional technology, the home environment has not been fully automatically optimized based on the user's preferences and lifestyle patterns, and there is room for improvement.

[0005] The system according to the embodiment aims to automatically optimize the home environment based on the user's preferences and lifestyle patterns.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a control unit, an adjustment unit, and a reduction unit. The data collection unit collects user behavior data. The analysis unit analyzes the data collected by the data collection unit to understand the user's preferences and lifestyle patterns. The control unit controls the device based on the analysis results obtained by the analysis unit. The adjustment unit automatically adjusts the home environment according to the scenes of being at home, going out, and sleeping. The reduction unit reduces power consumption. [Effects of the Invention]

[0007] The system according to this embodiment can automatically optimize the home environment based on the user's preferences and lifestyle patterns. [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 a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 agent system according to an embodiment of the present invention is a system that learns the user's preferences and lifestyle patterns and automatically optimizes the settings of smart home devices such as lighting, temperature, and music. This AI agent system collects and analyzes user behavior data to understand the user's preferences and lifestyle patterns. Based on the analysis results, it controls devices and provides optimal environmental settings. It also automatically adjusts the home environment according to scenes such as when the user is at home, away from home, or sleeping, and aims to reduce power consumption. For example, when the user goes out, the lights and air conditioner are automatically turned off, and when the user goes to sleep, the temperature is adjusted to a comfortable range. This reduces power consumption and improves the user's quality of life. Furthermore, the AI ​​agent understands the user's preferences using generative AI and controls home devices using IoT technology. For example, if the user instructs, "Dim the living room lights," the AI ​​agent understands the instruction and dims the living room lights. The AI ​​agent also predicts the user's behavior patterns and provides optimal environmental settings. For example, if the user wakes up at 7:00 every morning, the AI ​​agent sets the lights to gradually brighten at 6:50 and music to play at 7:00. This AI agent also contributes to reducing electricity costs and CO2 emissions. For example, it can reduce electricity costs by an average of 10% per month and CO2 emissions by approximately 5% per year. Furthermore, it aims to improve overall life satisfaction by enhancing the user's quality of life. In this way, the AI ​​agent system can optimize the user's living environment and reduce power consumption.

[0029] The AI ​​agent system according to this embodiment comprises a data collection unit, an analysis unit, a control unit, an adjustment unit, and a reduction unit. The data collection unit collects user behavior data. The data collection unit can collect data such as the user's movement history, applications used, and power consumption. The data collection unit collects data from devices such as smartphones and smartwatches. The data collection unit can also collect data using sensors in the home. For example, the data collection unit can obtain the user's movement history from GPS data and data on applications used from a smartphone. The analysis unit analyzes the data collected by the data collection unit to understand the user's preferences and lifestyle patterns. The analysis unit analyzes the data using data mining or machine learning algorithms, for example. The analysis unit can analyze the user's food preferences and sleep patterns, for example. The analysis unit extracts patterns from the user's behavior data using data mining techniques and predicts the user's preferences using machine learning algorithms. The control unit controls the device based on the analysis results obtained by the analysis unit. The control unit can, for example, turn the device on and off or adjust the temperature. The control unit, for example, turns on the lights and adjusts the air conditioner temperature when the user enters the living room. The control unit can also control devices based on user instructions. For example, if the user instructs, "Dim the living room lights," the control unit understands the instruction and dims the living room lights. The adjustment unit automatically adjusts the home environment according to the scene of being at home, going out, and going to bed. For example, the adjustment unit automatically turns off the lights and air conditioner when the user goes out and adjusts the temperature to a comfortable range when going to bed. The adjustment unit can also predict the user's behavior patterns and provide optimal environmental settings. For example, if the user wakes up at 7:00 every morning, the adjustment unit will set the lights to gradually brighten at 6:50 and play music at 7:00. The reduction unit reduces power consumption. For example, the reduction unit turns off infrequently used devices and uses energy-efficient devices. For example, the reduction unit can reduce electricity bills by an average of 10% per month and CO2 emissions by approximately 5% per year.As a result, the AI ​​agent system according to this embodiment can provide an optimal smart home environment by collecting, analyzing, controlling, adjusting, and reducing user behavior data.

[0030] The data collection unit collects user behavior data. For example, it can collect data such as user movement history, application usage, and power consumption. Specifically, it collects data from devices such as smartphones and smartwatches. It uses the GPS function of smartphones to obtain user movement history and monitor application usage. Smartwatches can collect health data such as heart rate, steps, and exercise levels. It can also collect data using sensors within the home. For example, it can obtain environmental data from temperature sensors, humidity sensors, and illuminance sensors installed in the home. Furthermore, it can collect real-time power consumption data within the home using smart meters. This allows the data collection unit to collect a wide range of data from various devices, enabling a detailed understanding of user behavior and the environment. The collected data is transmitted to a cloud server and securely stored. The frequency and accuracy of data collection can be adjusted according to user settings and system requirements. For example, movement history can be set to be acquired every minute, while power consumption data can be acquired in real time. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes data collected by the data collection unit to understand user preferences and lifestyle patterns. For example, the analysis unit uses data mining and machine learning algorithms to analyze the data. Specifically, it extracts patterns from user behavior data and uses machine learning algorithms to predict user preferences. For instance, it can analyze a user's food preferences and sleep patterns. To analyze food preferences, it collects data such as the applications the user uses, search history, and purchase history, and uses this data to predict user preferences. To analyze sleep patterns, it analyzes heart rate and sleep duration data collected from smartwatches to understand the quality and patterns of the user's sleep. The analysis unit integrates this data to gain a detailed understanding of the user's lifestyle patterns. Furthermore, the analysis unit can utilize historical data and statistical information to evaluate long-term trends and risks. For example, based on historical data, it can predict user behavior patterns during specific times of day or seasons and formulate future countermeasures. Additionally, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The control unit controls the devices based on the analysis results obtained by the analysis unit. For example, the control unit can turn devices on and off and adjust the temperature. Specifically, it can turn on the lights and adjust the air conditioner temperature when the user enters the living room. This includes automatic control based on the user's location information and behavior patterns. For example, when the user enters the living room, the lights automatically turn on and the air conditioner is set to a comfortable temperature. It can also control devices based on user instructions. For example, if the user instructs, "Dim the lights in the living room," the control unit understands the instruction and dims the lights in the living room. The control unit can understand user instructions and perform appropriate control using speech recognition technology and natural language processing technology. Furthermore, the control unit can coordinate and control multiple devices at once. For example, if the user instructs, "I want to watch a movie," it will automatically set up the optimal environment for watching a movie by dimming the lights, turning on the TV, and adjusting the sound system. In this way, the control unit can realize device control that makes the user's life more comfortable and convenient.

[0033] The adjustment unit automatically adjusts the home environment according to the situation, whether the user is at home, out, or sleeping. For example, when the user leaves the house, the adjustment unit automatically turns off the lights and air conditioner, and when the user goes to sleep, it adjusts the temperature to a comfortable range. Specifically, it can predict the user's behavior patterns and provide the optimal environment settings. For example, if the user wakes up at 7:00 every morning, the adjustment unit can be set to gradually brighten the lights at 6:50 and play music at 7:00. This allows the user to wake up naturally. Also, when the user leaves the house, the lights and air conditioner are automatically turned off, preventing energy waste. Furthermore, the adjustment unit can also provide individually customized environment settings based on the user's preferences and past data. For example, if the user prefers a specific temperature or lighting brightness, it will remember these settings and provide a similar environment on subsequent visits. In this way, the adjustment unit can provide an optimal environment tailored to the user's lifestyle and support a comfortable life.

[0034] The reduction unit reduces electricity consumption. For example, it turns off infrequently used devices and uses more energy-efficient devices. Specifically, it monitors the usage of devices in the home and automatically turns off infrequently used devices. For example, it detects lights and appliances that have not been used for a long time and turns them off to reduce electricity consumption. It can also be set to prioritize the use of more energy-efficient devices. For example, it uses energy-efficient LED lighting and replaces old fluorescent lights. Furthermore, the reduction unit can analyze electricity consumption data and suggest optimal usage patterns to reduce electricity bills. For example, it suggests refraining from using devices during certain times to avoid peak electricity usage. In this way, the reduction unit can efficiently manage electricity consumption and reduce electricity bills. In addition, the reduction unit also contributes to reducing CO2 emissions. For example, by reducing electricity consumption, it is possible to reduce CO2 emissions by approximately 5% per year. In this way, the reduction unit can contribute to environmental protection and support sustainable living.

[0035] The data collection unit can analyze the user's past behavioral data and select the optimal data collection method. The data collection unit can customize the data collection method based on, for example, actions the user has frequently performed in the past. The data collection unit can analyze the user's past behavioral patterns and select the most efficient data collection method. The data collection unit can suggest the optimal data collection method for a specific time period based on the user's past behavioral data. This allows the most efficient data collection method to be selected by analyzing past behavioral data. Some or all of the above processes in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's past behavioral data into a generating AI and have the generating AI select the optimal data collection method.

[0036] The data collection unit can filter behavioral data based on the user's current activities and areas of interest. For example, the data collection unit can collect only relevant data based on the user's current activities. For example, the data collection unit can select the data to collect based on the user's areas of interest. For example, the data collection unit can grasp the user's current activities in real time and adjust the data to be collected. This allows the data to be filtered based on the current activities and areas of interest, thereby collecting only relevant data. 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 current activity data into a generating AI and have the generating AI perform the filtering.

[0037] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting behavioral data. For example, if the user is in a specific location, the data collection unit will prioritize the collection of data related to that location. For example, the data collection unit can select highly relevant data based on the user's current location. For example, the data collection unit can grasp the user's geographical location in real time and adjust the data to be collected. This allows for the priority collection of highly relevant data by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the selection of highly relevant data.

[0038] The data collection unit can analyze a user's social media activity and collect relevant data when collecting behavioral data. For example, the data collection unit can collect relevant data based on information shared by the user on social media. For example, the data collection unit can analyze a user's social media activity and select data based on their interests. For example, the data collection unit can grasp the content of a user's social media posts in real time and adjust the data to be collected. This allows relevant data to be collected by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the behavioral data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. The analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the behavioral data. 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 importance of the behavioral data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the category of behavioral data during analysis. For example, the analysis unit can apply a lighting-specific analysis algorithm to lighting data. For example, the analysis unit can apply a temperature-specific analysis algorithm to temperature data. For example, the analysis unit can apply a music-specific analysis algorithm to music data. By applying different analysis algorithms depending on the category of behavioral data, the accuracy of the analysis is improved. 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 categories of behavioral data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0041] The analysis unit can determine the priority of analysis based on the timing of behavioral data collection during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may analyze the most recent data while referring to past data. For example, the analysis unit may dynamically adjust the priority of analysis according to the timing of data collection. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the timing of behavioral data collection. 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 may input the timing of behavioral data collection to a generating AI and have the generating AI determine the priority of analysis.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the behavioral data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. For example, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the behavioral data. 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 relevance of the behavioral data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0043] The control unit can adjust the level of detail of the control based on the importance of the analysis results when controlling a device. For example, the control unit can perform detailed device control based on analysis results of high importance. For example, the control unit can perform simplified device control based on analysis results of low importance. For example, the control unit can dynamically adjust the level of detail of the control according to the importance of the analysis results. This enables efficient device control by adjusting the level of detail of the control based on the importance of the analysis results. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the importance of the analysis results into a generating AI and have the generating AI perform the adjustment of the level of detail of the control.

[0044] The control unit can apply different control algorithms depending on the device category when controlling a device. For example, the control unit can apply a control algorithm specifically for lighting to a lighting device. For example, the control unit can apply a control algorithm specifically for temperature to a temperature device. For example, the control unit can apply a control algorithm specifically for music to a music device. By applying different control algorithms depending on the device category, the accuracy of the control is improved. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the device category into a generating AI and have the generating AI execute the application of different control algorithms.

[0045] The control unit can determine the control priority based on the frequency of device use during device control. For example, the control unit can prioritize the control of devices that are used frequently. For example, the control unit can postpone the control of devices that are used infrequently. For example, the control unit can dynamically adjust the control priority according to the frequency of device use. This enables efficient device control by determining the control priority based on the frequency of device use. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input device usage frequency data into a generating AI and have the generating AI perform the determination of the control priority.

[0046] The control unit can determine the control priority based on the frequency of device use during device control. For example, the control unit can prioritize the control of devices that are used frequently. For example, the control unit can postpone the control of devices that are used infrequently. For example, the control unit can dynamically adjust the control priority according to the frequency of device use. This enables efficient device control by determining the control priority based on the frequency of device use. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input device usage frequency data into a generating AI and have the generating AI perform the determination of the control priority.

[0047] The control unit can adjust the control order based on the relationships between devices during device control. For example, the control unit can prioritize controlling highly relevant devices. For example, the control unit can postpone controlling less relevant devices. For example, the control unit can dynamically adjust the control order according to the relationships between devices. This enables efficient device control by adjusting the control order based on the relationships between devices. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input device relationship data into a generating AI and have the generating AI perform the adjustment of the control order.

[0048] The adjustment unit can analyze the user's past behavior patterns and select the optimal adjustment method when adjusting the environment. For example, the adjustment unit can select the optimal adjustment method based on the user's preferred environmental settings in the past. For example, the adjustment unit can analyze the user's past behavior patterns and select the most efficient adjustment method. For example, the adjustment unit can propose the optimal adjustment method for a specific time period based on the user's past behavior data. In this way, the optimal adjustment method can be selected by analyzing past behavior patterns. 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 behavior data into a generating AI and have the generating AI select the optimal adjustment method.

[0049] The adjustment unit can customize the means of adjustment based on the user's current living situation when adjusting the environment. For example, if the user is at home, the adjustment unit can adjust the lighting and temperature to a comfortable range. For example, if the user is out, the adjustment unit can turn off devices to reduce energy consumption. For example, the adjustment unit can grasp the user's current living situation in real time and dynamically customize the means of adjustment. This allows the system to provide the user with an optimal environment by customizing the means of adjustment based on the current living situation. 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 current living situation data into a generating AI and have the generating AI perform the customization of the means of adjustment.

[0050] The adjustment unit can select the optimal adjustment method when adjusting the environment, taking into account the user's geographical location information. For example, if the user is in a specific location, the adjustment unit will prioritize adjusting the environment settings related to that location. For example, the adjustment unit can select the optimal adjustment method based on the user's current location. For example, the adjustment unit can grasp the user's geographical location information in real time and dynamically customize the means of adjustment. This allows the adjustment unit to select the optimal adjustment method by taking geographical location information into consideration. 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 data into a generating AI and have the generating AI select the optimal adjustment method.

[0051] The adjustment unit can analyze the user's social media activity and propose adjustment methods during environment adjustment. For example, the adjustment unit can propose relevant environment settings based on information shared by the user on social media. For example, the adjustment unit can analyze the user's social media activity and select adjustment methods based on their interests. For example, the adjustment unit can grasp the content of the user's social media posts in real time and dynamically propose adjustment methods. This allows it to propose relevant environment settings by analyzing 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 methods.

[0052] The reduction unit can analyze the user's past consumption data to select the optimal reduction method when reducing electricity consumption. For example, the reduction unit can select the optimal reduction method based on the user's past electricity consumption data. For example, the reduction unit can analyze the user's past consumption patterns to select the most efficient reduction method. For example, the reduction unit can propose the optimal reduction method for a specific time period based on the user's past consumption data. In this way, the optimal reduction method can be selected by analyzing past consumption data. Some or all of the above processing in the reduction unit may be performed using AI, for example, or without AI. For example, the reduction unit can input the user's past consumption data into a generating AI and have the generating AI select the optimal reduction method.

[0053] The power reduction unit can customize the power reduction measures based on the user's current living situation when reducing power consumption. For example, if the user is at home, the power reduction unit can reduce power consumption by adjusting lighting and temperature to a comfortable range. For example, if the user is out, the power reduction unit can turn off devices to reduce energy consumption. For example, the power reduction unit can grasp the user's current living situation in real time and dynamically customize the power reduction measures. This enables efficient power reduction by customizing the power reduction measures based on the current living situation. Some or all of the above processing in the power reduction unit may be performed using AI, for example, or without AI. For example, the power reduction unit can input the user's current living situation data into a generating AI and have the generating AI perform the customization of the power reduction measures.

[0054] The reduction unit can select the optimal reduction method when reducing power consumption, taking into account the user's geographical location information. For example, if the user is in a specific location, the reduction unit will prioritize selecting a power reduction method related to that location. For example, the reduction unit can select the optimal reduction method based on the user's current location. For example, the reduction unit can grasp the user's geographical location information in real time and dynamically customize the reduction methods. This allows the optimal reduction method to be selected by considering geographical location information. Some or all of the above processing in the reduction unit may be performed using AI, for example, or without AI. For example, the reduction unit can input the user's geographical location information data into a generating AI and have the generating AI select the optimal reduction method.

[0055] The reduction unit can analyze the user's social media activity and propose reduction measures when reducing power consumption. For example, the reduction unit can propose relevant power reduction methods based on information shared by the user on social media. For example, the reduction unit can analyze the user's social media activity and select reduction measures based on their interests. For example, the reduction unit can grasp the content of the user's social media posts in real time and dynamically propose reduction measures. In this way, by analyzing social media activity, it can propose relevant power reduction methods. Some or all of the above processing in the reduction unit may be performed using AI, for example, or without AI. For example, the reduction unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of reduction measures.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The analysis unit can adjust the level of detail in the analysis of behavioral data based on the importance of the data. For example, it can perform a detailed analysis on highly important data, and a simplified analysis on less important data. Furthermore, it can dynamically adjust the level of detail in the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail in the analysis based on the importance of the behavioral data.

[0058] The control unit can determine the control priority based on the frequency of device use during device control. For example, it can prioritize the control of frequently used devices, while delaying the control of less frequently used devices. Furthermore, it can dynamically adjust the control priority according to the frequency of device use. This enables efficient device control by determining the control priority based on the frequency of device use.

[0059] The adjustment unit can select the optimal adjustment method by analyzing the user's past behavior patterns during environment adjustment. For example, it can select the optimal adjustment method based on the user's preferred environment settings in the past. It can also select the most efficient adjustment method by analyzing the user's past behavior patterns. Furthermore, it can suggest the optimal adjustment method for a specific time period based on the user's past behavior data. In this way, the optimal adjustment method can be selected by analyzing past behavior patterns.

[0060] The power reduction unit can analyze the user's past consumption data to select the optimal power reduction method when reducing electricity consumption. For example, it can select the optimal reduction method based on the user's past electricity consumption data. It can also analyze the user's past consumption patterns to select the most efficient reduction method. Furthermore, it can suggest the optimal reduction method for a specific time period based on the user's past consumption data. In this way, the optimal reduction method can be selected by analyzing past consumption data.

[0061] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting behavioral data. For example, if a user is in a specific location, it can prioritize the collection of data related to that location. It can also select highly relevant data based on the user's current location. Furthermore, it can grasp the user's geographical location in real time and adjust the data collected accordingly. As a result, by considering geographical location, it can prioritize the collection of highly relevant data.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects user behavior data. The data collection unit can collect data such as the user's movement history, applications used, and power consumption. The data collection unit collects data from devices such as smartphones and smartwatches. The data collection unit can also collect data using sensors in the home. For example, the data collection unit can obtain the user's movement history from GPS data and data on applications used from a smartphone. Step 2: The analysis unit analyzes the data collected by the collection unit to understand the user's preferences and lifestyle patterns. The analysis unit analyzes the data using, for example, data mining or machine learning algorithms. For example, the analysis unit can analyze the user's eating preferences and sleep patterns. For example, the analysis unit uses data mining techniques to extract patterns from the user's behavioral data and uses machine learning algorithms to predict the user's preferences. Step 3: The control unit controls the device based on the analysis results obtained by the analysis unit. The control unit can, for example, turn the device on or off or adjust the temperature. For example, the control unit can turn on the lights and adjust the air conditioner temperature when the user enters the living room. The control unit can also control the device based on user instructions. For example, if the user instructs, "Dim the lights in the living room," the control unit understands the instruction and dims the lights in the living room. Step 4: The adjustment unit automatically adjusts the home environment according to the situation, whether the user is at home, out, or sleeping. For example, the adjustment unit automatically turns off the lights and air conditioner when the user leaves the house, and adjusts the temperature to a comfortable range when the user goes to sleep. The adjustment unit can also predict the user's behavior patterns and provide the optimal environment settings. For example, if the user wakes up at 7:00 every morning, the adjustment unit will set the lights to gradually brighten at 6:50 and music to play at 7:00. Step 5: The reduction unit reduces power consumption. For example, the reduction unit turns off infrequently used devices and uses energy-efficient devices. For example, the reduction unit can reduce electricity costs by an average of 10% per month and CO2 emissions by approximately 5% per year.

[0064] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that learns the user's preferences and lifestyle patterns and automatically optimizes the settings of smart home devices such as lighting, temperature, and music. This AI agent system collects and analyzes user behavior data to understand the user's preferences and lifestyle patterns. Based on the analysis results, it controls devices and provides optimal environmental settings. It also automatically adjusts the home environment according to scenes such as when the user is at home, away from home, or sleeping, and aims to reduce power consumption. For example, when the user goes out, the lights and air conditioner are automatically turned off, and when the user goes to sleep, the temperature is adjusted to a comfortable range. This reduces power consumption and improves the user's quality of life. Furthermore, the AI ​​agent understands the user's preferences using generative AI and controls home devices using IoT technology. For example, if the user instructs, "Dim the living room lights," the AI ​​agent understands the instruction and dims the living room lights. The AI ​​agent also predicts the user's behavior patterns and provides optimal environmental settings. For example, if the user wakes up at 7:00 every morning, the AI ​​agent sets the lights to gradually brighten at 6:50 and music to play at 7:00. This AI agent also contributes to reducing electricity costs and CO2 emissions. For example, it can reduce electricity costs by an average of 10% per month and CO2 emissions by approximately 5% per year. Furthermore, it aims to improve overall life satisfaction by enhancing the user's quality of life. In this way, the AI ​​agent system can optimize the user's living environment and reduce power consumption.

[0065] The AI ​​agent system according to this embodiment comprises a data collection unit, an analysis unit, a control unit, an adjustment unit, and a reduction unit. The data collection unit collects user behavior data. The data collection unit can collect data such as the user's movement history, applications used, and power consumption. The data collection unit collects data from devices such as smartphones and smartwatches. The data collection unit can also collect data using sensors in the home. For example, the data collection unit can obtain the user's movement history from GPS data and data on applications used from a smartphone. The analysis unit analyzes the data collected by the data collection unit to understand the user's preferences and lifestyle patterns. The analysis unit analyzes the data using data mining or machine learning algorithms, for example. The analysis unit can analyze the user's food preferences and sleep patterns, for example. The analysis unit extracts patterns from the user's behavior data using data mining techniques and predicts the user's preferences using machine learning algorithms. The control unit controls the device based on the analysis results obtained by the analysis unit. The control unit can, for example, turn the device on and off or adjust the temperature. The control unit, for example, turns on the lights and adjusts the air conditioner temperature when the user enters the living room. The control unit can also control devices based on user instructions. For example, if the user instructs, "Dim the living room lights," the control unit understands the instruction and dims the living room lights. The adjustment unit automatically adjusts the home environment according to the scene of being at home, going out, and going to bed. For example, the adjustment unit automatically turns off the lights and air conditioner when the user goes out and adjusts the temperature to a comfortable range when going to bed. The adjustment unit can also predict the user's behavior patterns and provide optimal environmental settings. For example, if the user wakes up at 7:00 every morning, the adjustment unit will set the lights to gradually brighten at 6:50 and play music at 7:00. The reduction unit reduces power consumption. For example, the reduction unit turns off infrequently used devices and uses energy-efficient devices. For example, the reduction unit can reduce electricity bills by an average of 10% per month and CO2 emissions by approximately 5% per year.As a result, the AI ​​agent system according to this embodiment can provide an optimal smart home environment by collecting, analyzing, controlling, adjusting, and reducing user behavior data.

[0066] The data collection unit collects user behavior data. For example, it can collect data such as user movement history, application usage, and power consumption. Specifically, it collects data from devices such as smartphones and smartwatches. It uses the GPS function of smartphones to obtain user movement history and monitor application usage. Smartwatches can collect health data such as heart rate, steps, and exercise levels. It can also collect data using sensors within the home. For example, it can obtain environmental data from temperature sensors, humidity sensors, and illuminance sensors installed in the home. Furthermore, it can collect real-time power consumption data within the home using smart meters. This allows the data collection unit to collect a wide range of data from various devices, enabling a detailed understanding of user behavior and the environment. The collected data is transmitted to a cloud server and securely stored. The frequency and accuracy of data collection can be adjusted according to user settings and system requirements. For example, movement history can be set to be acquired every minute, while power consumption data can be acquired in real time. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0067] The analysis unit analyzes data collected by the data collection unit to understand user preferences and lifestyle patterns. For example, the analysis unit uses data mining and machine learning algorithms to analyze the data. Specifically, it extracts patterns from user behavior data and uses machine learning algorithms to predict user preferences. For instance, it can analyze a user's food preferences and sleep patterns. To analyze food preferences, it collects data such as the applications the user uses, search history, and purchase history, and uses this data to predict user preferences. To analyze sleep patterns, it analyzes heart rate and sleep duration data collected from smartwatches to understand the quality and patterns of the user's sleep. The analysis unit integrates this data to gain a detailed understanding of the user's lifestyle patterns. Furthermore, the analysis unit can utilize historical data and statistical information to evaluate long-term trends and risks. For example, based on historical data, it can predict user behavior patterns during specific times of day or seasons and formulate future countermeasures. Additionally, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0068] The control unit controls the devices based on the analysis results obtained by the analysis unit. For example, the control unit can turn devices on and off and adjust the temperature. Specifically, it can turn on the lights and adjust the air conditioner temperature when the user enters the living room. This includes automatic control based on the user's location information and behavior patterns. For example, when the user enters the living room, the lights automatically turn on and the air conditioner is set to a comfortable temperature. It can also control devices based on user instructions. For example, if the user instructs, "Dim the lights in the living room," the control unit understands the instruction and dims the lights in the living room. The control unit can understand user instructions and perform appropriate control using speech recognition technology and natural language processing technology. Furthermore, the control unit can coordinate and control multiple devices at once. For example, if the user instructs, "I want to watch a movie," it will automatically set up the optimal environment for watching a movie by dimming the lights, turning on the TV, and adjusting the sound system. In this way, the control unit can realize device control that makes the user's life more comfortable and convenient.

[0069] The adjustment unit automatically adjusts the home environment according to the situation, whether the user is at home, out, or sleeping. For example, when the user leaves the house, the adjustment unit automatically turns off the lights and air conditioner, and when the user goes to sleep, it adjusts the temperature to a comfortable range. Specifically, it can predict the user's behavior patterns and provide the optimal environment settings. For example, if the user wakes up at 7:00 every morning, the adjustment unit can be set to gradually brighten the lights at 6:50 and play music at 7:00. This allows the user to wake up naturally. Also, when the user leaves the house, the lights and air conditioner are automatically turned off, preventing energy waste. Furthermore, the adjustment unit can also provide individually customized environment settings based on the user's preferences and past data. For example, if the user prefers a specific temperature or lighting brightness, it will remember these settings and provide a similar environment on subsequent visits. In this way, the adjustment unit can provide an optimal environment tailored to the user's lifestyle and support a comfortable life.

[0070] The reduction unit reduces electricity consumption. For example, it turns off infrequently used devices and uses more energy-efficient devices. Specifically, it monitors the usage of devices in the home and automatically turns off infrequently used devices. For example, it detects lights and appliances that have not been used for a long time and turns them off to reduce electricity consumption. It can also be set to prioritize the use of more energy-efficient devices. For example, it uses energy-efficient LED lighting and replaces old fluorescent lights. Furthermore, the reduction unit can analyze electricity consumption data and suggest optimal usage patterns to reduce electricity bills. For example, it suggests refraining from using devices during certain times to avoid peak electricity usage. In this way, the reduction unit can efficiently manage electricity consumption and reduce electricity bills. In addition, the reduction unit also contributes to reducing CO2 emissions. For example, by reducing electricity consumption, it is possible to reduce CO2 emissions by approximately 5% per year. In this way, the reduction unit can contribute to environmental protection and support sustainable living.

[0071] The data collection unit can estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the collection timing to lessen the user's burden. For example, if the user is relaxed, the data collection unit can increase the collection timing to obtain more detailed data. For example, if the user is in a hurry, the data collection unit can shorten the collection timing to quickly obtain data. In this way, by adjusting the collection timing according to the user's emotions, the user's burden can be reduced and more detailed data can be obtained. 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 without AI. For example, the data collection unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0072] The data collection unit can analyze the user's past behavioral data and select the optimal data collection method. The data collection unit can customize the data collection method based on, for example, actions the user has frequently performed in the past. The data collection unit can analyze the user's past behavioral patterns and select the most efficient data collection method. The data collection unit can suggest the optimal data collection method for a specific time period based on the user's past behavioral data. This allows the most efficient data collection method to be selected by analyzing past behavioral data. Some or all of the above processes in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's past behavioral data into a generating AI and have the generating AI select the optimal data collection method.

[0073] The data collection unit can filter behavioral data based on the user's current activities and areas of interest. For example, the data collection unit can collect only relevant data based on the user's current activities. For example, the data collection unit can select the data to collect based on the user's areas of interest. For example, the data collection unit can grasp the user's current activities in real time and adjust the data to be collected. This allows the data to be filtered based on the current activities and areas of interest, thereby collecting only relevant data. 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 current activity data into a generating AI and have the generating AI perform the filtering.

[0074] The data collection unit can estimate the user's emotions and determine the priority of behavioral data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit may postpone the collection of less important data. For example, if the user is relaxed, the data collection unit may prioritize the collection of detailed data. For example, if the user is in a hurry, the data collection unit may prioritize the collection of highly important data. This allows for the priority collection of important data by determining data priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0075] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting behavioral data. For example, if the user is in a specific location, the data collection unit will prioritize the collection of data related to that location. For example, the data collection unit can select highly relevant data based on the user's current location. For example, the data collection unit can grasp the user's geographical location in real time and adjust the data to be collected. This allows for the priority collection of highly relevant data by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the selection of highly relevant data.

[0076] The data collection unit can analyze a user's social media activity and collect relevant data when collecting behavioral data. For example, the data collection unit can collect relevant data based on information shared by the user on social media. For example, the data collection unit can analyze a user's social media activity and select data based on their interests. For example, the data collection unit can grasp the content of a user's social media posts in real time and adjust the data to be collected. This allows relevant data to be collected by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0077] 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 can provide detailed analysis results. For example, if the user is stressed, the analysis unit can provide concise analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, the analysis unit can provide 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 a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. 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 user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0078] The analysis unit can adjust the level of detail of the analysis based on the importance of the behavioral data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. The analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the behavioral data. 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 importance of the behavioral data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0079] The analysis unit can apply different analysis algorithms depending on the category of behavioral data during analysis. For example, the analysis unit can apply a lighting-specific analysis algorithm to lighting data. For example, the analysis unit can apply a temperature-specific analysis algorithm to temperature data. For example, the analysis unit can apply a music-specific analysis algorithm to music data. By applying different analysis algorithms depending on the category of behavioral data, the accuracy of the analysis is improved. 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 categories of behavioral data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0080] 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 can provide detailed analysis results. For example, if the user is stressed, the analysis unit can provide concise analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide appropriate analysis results for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a 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 the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0081] The analysis unit can determine the priority of analysis based on the timing of behavioral data collection during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may analyze the most recent data while referring to past data. For example, the analysis unit may dynamically adjust the priority of analysis according to the timing of data collection. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the timing of behavioral data collection. 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 may input the timing of behavioral data collection to a generating AI and have the generating AI determine the priority of analysis.

[0082] The analysis unit can adjust the order of analysis based on the relevance of the behavioral data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. For example, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the behavioral data. 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 relevance of the behavioral data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0083] The control unit can estimate the user's emotions and adjust the device control method based on the estimated user emotions. For example, if the user is relaxed, the control unit can adjust the lighting to a softer light. For example, if the user is stressed, the control unit can change the music to a quieter one. For example, if the user is in a hurry, the control unit can speed up the device's response time. This allows for a comfortable environment for the user by adjusting the device control method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the control unit may be performed using AI or not using AI. For example, the control unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0084] The control unit can adjust the level of detail of the control based on the importance of the analysis results when controlling a device. For example, the control unit can perform detailed device control based on analysis results of high importance. For example, the control unit can perform simplified device control based on analysis results of low importance. For example, the control unit can dynamically adjust the level of detail of the control according to the importance of the analysis results. This enables efficient device control by adjusting the level of detail of the control based on the importance of the analysis results. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the importance of the analysis results into a generating AI and have the generating AI perform the adjustment of the level of detail of the control.

[0085] The control unit can apply different control algorithms depending on the device category when controlling a device. For example, the control unit can apply a control algorithm specifically for lighting to a lighting device. For example, the control unit can apply a control algorithm specifically for temperature to a temperature device. For example, the control unit can apply a control algorithm specifically for music to a music device. By applying different control algorithms depending on the device category, the accuracy of the control is improved. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the device category into a generating AI and have the generating AI execute the application of different control algorithms.

[0086] The control unit can estimate the user's emotions and determine the priority of device control based on the estimated user emotions. For example, if the user is relaxed, the control unit may prioritize adjusting the lighting. For example, if the user is stressed, the control unit may prioritize adjusting the music. For example, if the user is in a hurry, the control unit may prioritize the device's response speed. By determining the priority of device control according to the user's emotions, a comfortable environment can be provided 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 control unit may be performed using AI or not using AI. For example, the control unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0087] The control unit can determine the control priority based on the frequency of device use during device control. For example, the control unit can prioritize the control of devices that are used frequently. For example, the control unit can postpone the control of devices that are used infrequently. For example, the control unit can dynamically adjust the control priority according to the frequency of device use. This enables efficient device control by determining the control priority based on the frequency of device use. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input device usage frequency data into a generating AI and have the generating AI perform the determination of the control priority.

[0088] The control unit can determine the control priority based on the frequency of device use during device control. For example, the control unit can prioritize the control of devices that are used frequently. For example, the control unit can postpone the control of devices that are used infrequently. For example, the control unit can dynamically adjust the control priority according to the frequency of device use. This enables efficient device control by determining the control priority based on the frequency of device use. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input device usage frequency data into a generating AI and have the generating AI perform the determination of the control priority.

[0089] The control unit can adjust the control order based on the relationships between devices during device control. For example, the control unit can prioritize controlling highly relevant devices. For example, the control unit can postpone controlling less relevant devices. For example, the control unit can dynamically adjust the control order according to the relationships between devices. This enables efficient device control by adjusting the control order based on the relationships between devices. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input device relationship data into a generating AI and have the generating AI perform the adjustment of the control order.

[0090] The adjustment unit can estimate the user's emotions and adjust the environmental adjustment method based on the estimated user emotions. For example, if the user is relaxed, the adjustment unit can adjust the lighting to a softer light. For example, if the user is stressed, the adjustment unit can change the music to a quieter one. For example, if the user is in a hurry, the adjustment unit can adjust the temperature to a comfortable range. In this way, by adjusting the environmental adjustment method according to the user's emotions, a comfortable environment can be provided for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. 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 user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0091] The adjustment unit can analyze the user's past behavior patterns and select the optimal adjustment method when adjusting the environment. For example, the adjustment unit can select the optimal adjustment method based on the user's preferred environmental settings in the past. For example, the adjustment unit can analyze the user's past behavior patterns and select the most efficient adjustment method. For example, the adjustment unit can propose the optimal adjustment method for a specific time period based on the user's past behavior data. In this way, the optimal adjustment method can be selected by analyzing past behavior patterns. 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 behavior data into a generating AI and have the generating AI select the optimal adjustment method.

[0092] The adjustment unit can customize the means of adjustment based on the user's current living situation when adjusting the environment. For example, if the user is at home, the adjustment unit can adjust the lighting and temperature to a comfortable range. For example, if the user is out, the adjustment unit can turn off devices to reduce energy consumption. For example, the adjustment unit can grasp the user's current living situation in real time and dynamically customize the means of adjustment. This allows the system to provide the user with an optimal environment by customizing the means of adjustment based on the current living situation. 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 current living situation data into a generating AI and have the generating AI perform the customization of the means of adjustment.

[0093] The adjustment unit can estimate the user's emotions and determine the priority of environmental adjustments based on the estimated user emotions. For example, if the user is relaxed, the adjustment unit may prioritize adjusting the lighting. For example, if the user is stressed, the adjustment unit may prioritize adjusting the music. For example, if the user is in a hurry, the adjustment unit may prioritize adjusting the temperature. In this way, by determining the priority of environmental adjustments according to the user's emotions, a comfortable environment can be provided for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The 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 without AI. For example, the adjustment unit can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0094] The adjustment unit can select the optimal adjustment method when adjusting the environment, taking into account the user's geographical location information. For example, if the user is in a specific location, the adjustment unit will prioritize adjusting the environment settings related to that location. For example, the adjustment unit can select the optimal adjustment method based on the user's current location. For example, the adjustment unit can grasp the user's geographical location information in real time and dynamically customize the means of adjustment. This allows the adjustment unit to select the optimal adjustment method by taking geographical location information into consideration. 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 data into a generating AI and have the generating AI select the optimal adjustment method.

[0095] The adjustment unit can analyze the user's social media activity and propose adjustment methods during environment adjustment. For example, the adjustment unit can propose relevant environment settings based on information shared by the user on social media. For example, the adjustment unit can analyze the user's social media activity and select adjustment methods based on their interests. For example, the adjustment unit can grasp the content of the user's social media posts in real time and dynamically propose adjustment methods. This allows it to propose relevant environment settings by analyzing 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 methods.

[0096] The power reduction unit can estimate the user's emotions and adjust the power reduction method based on the estimated user emotions. For example, if the user is relaxed, the power reduction unit can adjust the lighting to a softer light to reduce power consumption. For example, if the user is stressed, the power reduction unit can change the music to a quieter one to reduce power consumption. For example, if the user is in a hurry, the power reduction unit can speed up the device's response time to reduce power consumption. This allows for efficient power reduction by adjusting the power reduction method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the power reduction unit may be performed using AI or not using AI. For example, the power reduction unit can input user facial expression data into the generative AI and have the generative AI perform the user emotion estimation.

[0097] The reduction unit can analyze the user's past consumption data to select the optimal reduction method when reducing electricity consumption. For example, the reduction unit can select the optimal reduction method based on the user's past electricity consumption data. For example, the reduction unit can analyze the user's past consumption patterns to select the most efficient reduction method. For example, the reduction unit can propose the optimal reduction method for a specific time period based on the user's past consumption data. In this way, the optimal reduction method can be selected by analyzing past consumption data. Some or all of the above processing in the reduction unit may be performed using AI, for example, or without AI. For example, the reduction unit can input the user's past consumption data into a generating AI and have the generating AI select the optimal reduction method.

[0098] The power reduction unit can customize the power reduction measures based on the user's current living situation when reducing power consumption. For example, if the user is at home, the power reduction unit can reduce power consumption by adjusting lighting and temperature to a comfortable range. For example, if the user is out, the power reduction unit can turn off devices to reduce energy consumption. For example, the power reduction unit can grasp the user's current living situation in real time and dynamically customize the power reduction measures. This enables efficient power reduction by customizing the power reduction measures based on the current living situation. Some or all of the above processing in the power reduction unit may be performed using AI, for example, or without AI. For example, the power reduction unit can input the user's current living situation data into a generating AI and have the generating AI perform the customization of the power reduction measures.

[0099] The power reduction unit can estimate the user's emotions and determine power reduction priorities based on the estimated emotions. For example, if the user is relaxed, the power reduction unit may prioritize adjusting the lighting. If the user is stressed, the power reduction unit may prioritize adjusting the music. If the user is in a hurry, the power reduction unit may prioritize the device's response speed. This enables efficient power reduction by determining power reduction priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 power reduction unit may be performed using AI or not. For example, the power reduction unit can input user facial expression data into the generative AI and have the generative AI perform the user's emotion estimation.

[0100] The reduction unit can select the optimal reduction method when reducing power consumption, taking into account the user's geographical location information. For example, if the user is in a specific location, the reduction unit will prioritize selecting a power reduction method related to that location. For example, the reduction unit can select the optimal reduction method based on the user's current location. For example, the reduction unit can grasp the user's geographical location information in real time and dynamically customize the reduction methods. This allows the optimal reduction method to be selected by considering geographical location information. Some or all of the above processing in the reduction unit may be performed using AI, for example, or without AI. For example, the reduction unit can input the user's geographical location information data into a generating AI and have the generating AI select the optimal reduction method.

[0101] The reduction unit can analyze the user's social media activity and propose reduction measures when reducing power consumption. For example, the reduction unit can propose relevant power reduction methods based on information shared by the user on social media. For example, the reduction unit can analyze the user's social media activity and select reduction measures based on their interests. For example, the reduction unit can grasp the content of the user's social media posts in real time and dynamically propose reduction measures. In this way, by analyzing social media activity, it can propose relevant power reduction methods. Some or all of the above processing in the reduction unit may be performed using AI, for example, or without AI. For example, the reduction unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of reduction measures.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The analysis unit can estimate the user's emotions and determine the priority of analysis based on those emotions. For example, if the user is stressed, the analysis unit can prioritize analyzing high-priority data and provide results quickly. If the user is relaxed, the analysis unit can perform a detailed analysis and provide useful information. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. In this way, by adjusting the priority of analysis according to the user's emotions, the system can provide the user with the most optimal analysis results.

[0104] The control unit can estimate the user's emotions and adjust the device's operating mode based on those emotions. For example, if the user is relaxed, the control unit can switch the device to energy-saving mode to reduce power consumption. If the user is stressed, the control unit can switch the device to comfort mode to alleviate the user's stress. Furthermore, if the user is in a hurry, the control unit can speed up the device's response time to enable quick operation. In this way, by adjusting the device's operating mode according to the user's emotions, the optimal environment for the user can be provided.

[0105] The adjustment unit can estimate the user's emotions and suggest environmental settings based on those emotions. For example, if the user is relaxed, the adjustment unit can adjust the lighting to a softer light to provide a relaxing environment. If the user is stressed, the adjustment unit can change the music to a quieter one to reduce stress. Furthermore, if the user is in a hurry, the adjustment unit can adjust the temperature to a comfortable range to support quick action. In this way, by suggesting environmental settings according to the user's emotions, a comfortable environment can be provided for the user.

[0106] The power reduction unit can estimate the user's emotions and adjust the power reduction method based on those emotions. For example, if the user is relaxed, the unit can adjust the lighting to a softer light to reduce power consumption. If the user is stressed, the unit can change the music to a quieter one to reduce power consumption. Furthermore, if the user is in a hurry, the unit can speed up the device's response time to reduce power consumption. This allows for efficient power reduction by adjusting the power reduction method according to the user's emotions.

[0107] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if the user is stressed, the unit can reduce the collection timing to lessen the user's burden. Conversely, if the user is relaxed, the unit can increase the collection timing to obtain more detailed data. Furthermore, if the user is in a hurry, the unit can shorten the collection timing to quickly acquire data. In this way, by adjusting the collection timing according to the user's emotions, the user's burden is reduced and more detailed data can be obtained.

[0108] The analysis unit can adjust the level of detail in the analysis of behavioral data based on the importance of the data. For example, it can perform a detailed analysis on highly important data, and a simplified analysis on less important data. Furthermore, it can dynamically adjust the level of detail in the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail in the analysis based on the importance of the behavioral data.

[0109] The control unit can determine the control priority based on the frequency of device use during device control. For example, it can prioritize the control of frequently used devices, while delaying the control of less frequently used devices. Furthermore, it can dynamically adjust the control priority according to the frequency of device use. This enables efficient device control by determining the control priority based on the frequency of device use.

[0110] The adjustment unit can select the optimal adjustment method by analyzing the user's past behavior patterns during environment adjustment. For example, it can select the optimal adjustment method based on the user's preferred environment settings in the past. It can also select the most efficient adjustment method by analyzing the user's past behavior patterns. Furthermore, it can suggest the optimal adjustment method for a specific time period based on the user's past behavior data. In this way, the optimal adjustment method can be selected by analyzing past behavior patterns.

[0111] The power reduction unit can analyze the user's past consumption data to select the optimal power reduction method when reducing electricity consumption. For example, it can select the optimal reduction method based on the user's past electricity consumption data. It can also analyze the user's past consumption patterns to select the most efficient reduction method. Furthermore, it can suggest the optimal reduction method for a specific time period based on the user's past consumption data. In this way, the optimal reduction method can be selected by analyzing past consumption data.

[0112] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting behavioral data. For example, if a user is in a specific location, it can prioritize the collection of data related to that location. It can also select highly relevant data based on the user's current location. Furthermore, it can grasp the user's geographical location in real time and adjust the data collected accordingly. As a result, by considering geographical location, it can prioritize the collection of highly relevant data.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The data collection unit collects user behavior data. The data collection unit can collect data such as the user's movement history, applications used, and power consumption. The data collection unit collects data from devices such as smartphones and smartwatches. The data collection unit can also collect data using sensors in the home. For example, the data collection unit can obtain the user's movement history from GPS data and data on applications used from a smartphone. Step 2: The analysis unit analyzes the data collected by the collection unit to understand the user's preferences and lifestyle patterns. The analysis unit analyzes the data using, for example, data mining or machine learning algorithms. For example, the analysis unit can analyze the user's eating preferences and sleep patterns. For example, the analysis unit uses data mining techniques to extract patterns from the user's behavioral data and uses machine learning algorithms to predict the user's preferences. Step 3: The control unit controls the device based on the analysis results obtained by the analysis unit. The control unit can, for example, turn the device on or off or adjust the temperature. For example, the control unit can turn on the lights and adjust the air conditioner temperature when the user enters the living room. The control unit can also control the device based on user instructions. For example, if the user instructs, "Dim the lights in the living room," the control unit understands the instruction and dims the lights in the living room. Step 4: The adjustment unit automatically adjusts the home environment according to the situation, whether the user is at home, out, or sleeping. For example, the adjustment unit automatically turns off the lights and air conditioner when the user leaves the house, and adjusts the temperature to a comfortable range when the user goes to sleep. The adjustment unit can also predict the user's behavior patterns and provide the optimal environment settings. For example, if the user wakes up at 7:00 every morning, the adjustment unit will set the lights to gradually brighten at 6:50 and music to play at 7:00. Step 5: The reduction unit reduces power consumption. For example, the reduction unit turns off infrequently used devices and uses energy-efficient devices. For example, the reduction unit can reduce electricity costs by an average of 10% per month and CO2 emissions by approximately 5% per year.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the data collection unit, analysis unit, control unit, adjustment unit, and reduction 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 user behavior data using the camera 42 and microphone 38B of the smart device 14 and transmits the data to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to understand the user's preferences and lifestyle patterns. The control unit is implemented, for example, by the control unit 46A of the smart device 14, and controls the device based on the analysis results. The adjustment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and automatically adjusts the home environment according to scenes such as when the user is at home, out, or sleeping. The reduction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and reduces power consumption. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the data collection unit, analysis unit, control unit, adjustment unit, and reduction unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects user behavior data using the camera 42 and microphone 238 of the smart glasses 214 and transmits the data to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to understand the user's preferences and lifestyle patterns. The control unit is implemented, for example, by the control unit 46A of the smart glasses 214, and controls the device based on the analysis results. The adjustment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and automatically adjusts the home environment according to the scenes of being at home, going out, and sleeping. The reduction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and reduces power consumption. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In 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.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0149] The data processing system 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.

[0150] Each of the multiple elements described above, including the data collection unit, analysis unit, control unit, adjustment unit, and reduction 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 user behavior data using the camera 42 and microphone 238 of the headset terminal 314 and transmits the data to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to understand the user's preferences and lifestyle patterns. The control unit is implemented, for example, by the control unit 46A of the headset terminal 314, and controls the device based on the analysis results. The adjustment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and automatically adjusts the home environment according to the scenes of being at home, going out, and sleeping. The reduction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and reduces power consumption. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] Each of the multiple elements described above, including the collection unit, analysis unit, control unit, adjustment unit, and reduction unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects user behavior data using the camera 42 and microphone 238 of the robot 414 and transmits the data to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to understand the user's preferences and lifestyle patterns. The control unit is implemented, for example, by the control unit 46A of the robot 414, and controls the device based on the analysis results. The adjustment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and automatically adjusts the home environment according to the scenes of being at home, going out, and sleeping. The reduction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and reduces power consumption. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] (Note 1) A data collection unit that collects user behavior data, The data collected by the aforementioned collection unit is analyzed by an analysis unit to understand the user's preferences and lifestyle patterns, A control unit that controls the device based on the analysis results obtained by the analysis unit, A control unit that automatically adjusts the home environment according to the situation of being at home, going out, or sleeping, It includes a power consumption reduction unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze users' past behavioral data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is When collecting behavioral data, filtering is performed based on the user's current activities and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and determines the priority of behavioral data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting behavioral data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting behavioral data, analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 8) 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 9) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 11) 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 12) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the behavioral data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The control unit, It estimates the user's emotions and adjusts the device's control methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The control unit, During device control, the level of detail of the control is adjusted based on the importance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 16) The control unit, When controlling a device, different control algorithms are applied depending on the device category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The control unit, It estimates the user's emotions and determines the priority of device controls based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The control unit, When controlling a device, the control priority is determined based on the frequency of device use. The system described in Appendix 1, characterized by the features described herein. (Note 19) The control unit, When controlling a device, the control priority is determined based on the frequency of device use. The system described in Appendix 1, characterized by the features described herein. (Note 20) The control unit, When controlling devices, the control order is adjusted based on the relationships between devices. The system described in Appendix 1, characterized by the features described herein. (Note 21) The adjustment unit is, It estimates the user's emotions and adjusts the environment settings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The adjustment unit is, When adjusting the environment, analyze the user's past behavior patterns to select the optimal adjustment method. The system described in Appendix 1, characterized by the features described herein. (Note 23) The adjustment unit is, During environment adjustment, 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 24) The adjustment unit is, It estimates user emotions and determines the priority of environmental adjustments based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The adjustment unit is, During environment adjustment, the optimal adjustment method is selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The adjustment unit is, During environment adjustments, we analyze users' social media activity and propose adjustment methods. The system described in Appendix 1, characterized by the features described herein. (Note 27) The reduction unit is, It estimates the user's emotions and adjusts the power saving method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The reduction unit is, When reducing electricity consumption, the system analyzes the user's past consumption data to select the optimal reduction method. The system described in Appendix 1, characterized by the features described herein. (Note 29) The reduction unit is, When reducing electricity consumption, customize the reduction methods based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 30) The reduction unit is, It estimates user emotions and determines power saving priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The reduction unit is, When reducing power consumption, the optimal reduction method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The reduction unit is, When reducing electricity consumption, we analyze users' social media activity and suggest ways to reduce it. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0187] 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 user behavior data, The data collected by the aforementioned collection unit is analyzed by an analysis unit to understand the user's preferences and lifestyle patterns, A control unit that controls the device based on the analysis results obtained by the analysis unit, A control unit that automatically adjusts the home environment according to the situation of being at home, going out, or sleeping, It includes a power consumption reduction unit. A system characterized by the following features.

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

3. The aforementioned collection unit is Analyze users' past behavioral data and select the optimal data collection method. The system according to feature 1.

4. The aforementioned collection unit is When collecting behavioral data, filtering is performed based on the user's current activities and areas of interest. The system according to feature 1.

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

6. The aforementioned collection unit is When collecting behavioral data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system according to feature 1.

7. The aforementioned collection unit is When collecting behavioral data, analyze users' social media activity and collect relevant data. The system according to feature 1.

8. 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 according to feature 1.

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