system

Abstract

The system according to this embodiment aims to optimize energy management, security, and comfort throughout the entire house. [Solution] The system according to the embodiment comprises an energy management unit, a security monitoring unit, and a comfort adjustment unit. The energy management unit collects energy usage data. The security monitoring unit collects security data. The comfort adjustment unit automatically adjusts the home environment based on the data collected by the energy management unit and the security monitoring unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the overall energy management, security, and optimization of comfort in a house have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to optimize the overall energy management, security, and comfort of a house.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an energy management unit, a security monitoring unit, and a comfort adjustment unit. The energy management unit collects energy usage data. The security monitoring unit collects security data. The comfort adjustment unit automatically adjusts the home environment based on the data collected by the energy management unit and the security monitoring unit. [Effects of the Invention]

[0007] The system according to this embodiment can optimize energy management, security, and comfort throughout the entire house. [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 such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving 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 embodiment of the present invention provides a smart home system that is an AI agent that connects smart devices throughout the house to optimize energy management, security, and comfort. This smart home system automatically adjusts the home environment to suit the user's lifestyle. For example, the smart home system connects all smart devices to optimize energy use, monitor security, and improve comfort through automatic adjustments. The smart home system automatically adjusts lighting, air conditioning, and security systems based on the user's lifestyle. This reduces energy waste, enhances home security, and provides a comfortable living environment. The smart home market is growing rapidly and is projected to reach hundreds of billions of dollars by 2025. Advanced AI-powered functions are attracting particular attention, and the demand for smart homes is surging due to the proliferation of smart devices. Advances in AI technology are enabling advanced functions that were not possible with conventional smart home systems. This system aims to provide smart home users with a safer, more energy-efficient, and more comfortable living environment. The vision is to support all households in living a stress-free and convenient life. As a result, the smart home system can optimize energy management, security, and comfort, and automatically adjust the home environment to suit the user's lifestyle.

[0029] The smart home system according to this embodiment comprises an energy management unit, a security monitoring unit, and a comfort adjustment unit. The energy management unit collects energy usage data. Energy usage data includes, but is not limited to, electricity consumption and gas usage. The energy management unit collects electricity consumption in real time using, for example, a smart meter. The energy management unit can also collect data using a sensor that measures gas usage. Furthermore, the energy management unit can monitor the amount of electricity generated by a solar power generation system. For example, the energy management unit collects electricity consumption in real time using a smart meter and optimizes energy use. It collects data using a sensor that measures gas usage to improve energy efficiency. It monitors the amount of electricity generated by a solar power generation system to achieve energy self-sufficiency. The security monitoring unit collects security data. Security data includes, but is not limited to, video from surveillance cameras and data from door sensors. The security monitoring unit monitors the area around the house using, for example, surveillance cameras. The security monitoring unit can also detect the opening and closing of doors using door sensors. Furthermore, the security monitoring unit can also detect suspicious movements using motion sensors. For example, the security monitoring unit can monitor the surroundings of the house using surveillance cameras and detect anomalies. It can enhance security by detecting the opening and closing of doors using door sensors. It can detect suspicious movements using motion sensors and issue alerts. The comfort adjustment unit automatically adjusts the home environment based on data collected by the energy management unit and the security monitoring unit. The comfort adjustment unit automatically adjusts lighting, air conditioning, and security systems, for example. For example, the comfort adjustment unit adjusts the brightness of the lights and sets the temperature of the air conditioning. The comfort adjustment unit can also automatically turn the security system on and off. For example, the comfort adjustment unit adjusts the brightness of the lights to provide a comfortable environment. It sets the temperature of the air conditioning to improve energy efficiency. It automatically turns the security system on and off to enhance security.As a result, the smart home system according to this embodiment can optimize energy management, security, and comfort, and automatically adjust the home environment to suit the user's lifestyle.

[0030] The Energy Management Department collects energy usage data. This data includes, but is not limited to, electricity consumption and gas usage. For example, the Energy Management Department collects electricity consumption in real time using smart meters. Smart meters monitor the usage of each power-consuming device in the home in detail and transmit the data to a central management system. This allows the Energy Management Department to understand the consumption patterns of each device and implement optimal control to suppress peak electricity usage. The Energy Management Department can also collect data using sensors that measure gas usage. Gas sensors accurately measure gas flow rate and usage time, which can be used to detect gas leaks and optimize usage. Furthermore, the Energy Management Department can monitor the power generation of solar power systems. Since solar power generation data fluctuates depending on weather and sunlight conditions, real-time monitoring is important. The Energy Management Department integrates this data and develops strategies to optimize energy use in the home. For example, the Energy Management Department collects electricity consumption in real time using smart meters and optimizes energy use. By collecting data using sensors to measure gas usage, energy efficiency can be improved. The amount of electricity generated by the solar power system is monitored to achieve energy self-sufficiency. This allows the energy management unit to efficiently manage household energy use, contributing to reduced energy costs and a lower environmental impact.

[0031] The security monitoring unit collects security data. This security data includes, but is not limited to, video from surveillance cameras and data from door sensors. For example, the security monitoring unit monitors the area around the house using surveillance cameras. Surveillance cameras capture high-resolution video in real time and detect unusual movements or intruders. This allows the security monitoring unit to constantly monitor the situation around the house and issue an alert immediately if an anomaly occurs. The security monitoring unit can also detect the opening and closing of doors using door sensors. Door sensors monitor the open / closed state of doors in real time and issue an alarm if there is an unauthorized intrusion. Furthermore, the security monitoring unit can also detect suspicious movements using motion sensors. Motion sensors sense movement inside and around the house and issue an alert immediately if there is unusual movement. For example, the security monitoring unit monitors the area around the house using surveillance cameras and detects anomalies. It enhances security by detecting the opening and closing of doors using door sensors. It detects suspicious movements using motion sensors and issues an alert. This allows the security monitoring unit to enhance security both inside and outside homes, ensuring the safety of residents. Furthermore, the security monitoring unit centrally manages the collected data and provides information to enable a rapid response in the event of an anomaly. For example, if an anomaly is detected, the security monitoring unit will issue an alarm and notify residents and security companies. This enables a swift response and minimizes damage.

[0032] The comfort adjustment unit automatically adjusts the home environment based on data collected by the energy management unit and the security monitoring unit. For example, the comfort adjustment unit automatically adjusts lighting, air conditioning, and security systems. When adjusting lighting brightness, it considers the room's brightness, time of day, and the residents' activities to set the optimal brightness. For example, it dims the lights at night to provide a relaxing environment. Similarly, when setting the air conditioning temperature, it considers the room temperature, humidity, and the residents' comfort to set the optimal temperature. For example, it moderately lowers the room temperature in summer and moderately raises it in winter to provide a comfortable environment. Furthermore, the comfort adjustment unit can automatically turn the security system on and off. For example, it can turn the security system on when residents leave and off when they return, enhancing security. Thus, the comfort adjustment unit adjusts lighting brightness to provide a comfortable environment, sets air conditioning temperature to improve energy efficiency, and automatically turns the security system on and off to enhance security. In addition, the comfort adjustment unit can customize the home environment according to the residents' lifestyles and preferences. For example, the lighting color and brightness can be adjusted according to the residents' preferences, and the air conditioning settings can be changed. This allows the comfort control unit to provide an optimal environment tailored to the residents' lifestyles, supporting a comfortable life.

[0033] The energy management unit can collect energy usage data and perform optimizations to reduce energy waste. For example, the energy management unit can collect energy usage data and perform peak shifting. For example, the energy management unit can adjust energy usage to avoid peak times. The energy management unit can also analyze energy usage data to improve energy efficiency. For example, the energy management unit can analyze energy usage data to identify energy-inefficient devices and perform optimizations. Furthermore, the energy management unit can understand energy consumption patterns based on energy usage data and perform optimizations. For example, the energy management unit can identify peak times for energy consumption based on energy usage data and optimize energy use. In this way, energy efficiency is improved by collecting energy usage data and performing optimizations to reduce energy waste. Some or all of the above processes in the energy management unit may be performed using AI, for example, or without AI. For example, the energy management unit can input energy usage data into a generating AI and have the generating AI perform energy usage optimizations.

[0034] The security monitoring unit can collect security data and enhance home security. For example, the security monitoring unit can collect security data and implement an alert system. For example, the security monitoring unit can monitor surveillance camera footage in real time and issue an alert if an anomaly is detected. The security monitoring unit can also install security cameras and monitor the area around the house. For example, the security monitoring unit can install security cameras and monitor the area around the house 24 hours a day. Furthermore, the security monitoring unit can analyze security data and identify security risks. For example, the security monitoring unit can analyze security data to identify areas with high security risks and strengthen monitoring. In this way, by collecting security data and enhancing home security, the safety of the home is improved. Some or all of the above processes in the security monitoring unit may be performed using AI, for example, or not using AI. For example, the security monitoring unit can input security data into a generating AI and have the generating AI perform the identification of security risks.

[0035] The comfort adjustment unit can automatically adjust lighting, air conditioning, and security systems based on data collected by the energy management unit and the security monitoring unit. For example, the comfort adjustment unit can automatically adjust lighting, air conditioning, and security systems to provide a comfortable living environment. For example, it can adjust the brightness of the lighting and set the temperature of the air conditioning. The comfort adjustment unit can also automatically turn security systems on and off. For example, it can adjust the brightness of the lighting to provide a comfortable environment, set the temperature of the air conditioning to improve energy efficiency, and automatically turn security systems on and off to enhance security. In this way, by automatically adjusting lighting, air conditioning, and security systems, a comfortable living environment is provided. Some or all of the above-described processes in the comfort adjustment unit may be performed using AI, for example, or without AI. For example, the comfort adjustment unit can input data collected by the energy management unit and the security monitoring unit into a generating AI, and have the generating AI perform the adjustments to the lighting, air conditioning, and security systems.

[0036] The comfort adjustment unit can automatically adjust the home environment based on the user's lifestyle. For example, the comfort adjustment unit adjusts lighting and air conditioning based on the user's lifestyle. For example, the comfort adjustment unit learns the user's behavior patterns and adjusts the brightness of the lighting. The comfort adjustment unit can also set the air conditioning temperature based on the user's preferences. For example, the comfort adjustment unit learns the user's behavior patterns and automatically adjusts the brightness of the lighting. It sets the air conditioning temperature based on the user's preferences to provide a comfortable environment. Furthermore, the comfort adjustment unit can also adjust the security system based on the user's lifestyle. For example, the comfort adjustment unit automatically turns the security system on and off based on the user's lifestyle. In this way, by automatically adjusting the home environment based on the user's lifestyle, a more comfortable living environment is provided. Some or all of the above processing in the comfort adjustment unit may be performed using AI, for example, or without AI. For example, the comfort adjustment unit can input user lifestyle data into a generating AI and have the generating AI perform the automatic adjustment of the home environment.

[0037] The energy management unit can improve the accuracy of optimization by meticulously recording the frequency and time of use of each device when collecting energy usage data. For example, the energy management unit can record the frequency of use of each device and optimize the energy usage of the most frequently used devices. The energy management unit can also record the time of use of each device and optimize energy usage during peak hours. For example, the energy management unit can analyze the usage history of each device to understand energy usage patterns and optimize accordingly. This improves the accuracy of energy usage optimization by meticulously recording the frequency and time of use of each device. Some or all of the above processes in the energy management unit may be performed using AI, for example, or without AI. For example, the energy management unit can input the usage data of each device into a generating AI and have the generating AI perform energy usage optimization.

[0038] The energy management unit can predict and optimize energy consumption by referring to weather data when collecting energy usage data. For example, the energy management unit can refer to weather data to optimize heating energy use during cold weather. The energy management unit can also refer to weather data to optimize cooling energy use during hot weather. The energy management unit can also refer to weather data to predict and optimize energy use in accordance with changes in weather. This makes it possible to optimize energy use by predicting energy consumption by referring to weather data. Some or all of the above processing in the energy management unit may be performed using AI, for example, or without AI. For example, the energy management unit can input weather data into a generating AI and have the generating AI perform energy consumption prediction and optimization.

[0039] The energy management unit can reflect region-specific energy consumption patterns by considering the user's geographical location when collecting energy usage data. For example, the energy management unit can optimize energy use according to the local climate based on the user's geographical location. The energy management unit can also analyze and optimize regional energy consumption patterns based on the user's geographical location. For example, the energy management unit can optimize energy use by considering the local energy supply situation based on the user's geographical location. This makes it possible to optimize energy use by reflecting region-specific energy consumption patterns by considering the user's geographical location. Some or all of the above processing in the energy management unit may be performed using AI, for example, or without AI. For example, the energy management unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of reflecting region-specific energy consumption patterns.

[0040] The energy management unit can analyze users' social media activity when collecting energy usage data to understand trends in energy consumption. For example, the energy management unit can analyze users' social media activity to understand and optimize trends in energy consumption. The energy management unit can also analyze users' social media activity to understand their interests regarding energy consumption and optimize accordingly. For example, the energy management unit can analyze users' social media activity to collect and optimize information regarding energy consumption. This makes it possible to understand and optimize trends in energy consumption by analyzing users' social media activity. Some or all of the above processing in the energy management unit may be performed using AI, for example, or without AI. For example, the energy management unit can input user social media data into a generating AI and have the generating AI perform the task of understanding trends in energy consumption.

[0041] The security monitoring unit can improve the accuracy of security enhancements by meticulously recording the anomaly detection history of each device when collecting security data. For example, the security monitoring unit can record the anomaly detection history of each device, analyze the patterns of anomalies, and enhance security. For example, the security monitoring unit can also enhance security by understanding the frequency of anomalies based on the anomaly detection history of each device. For example, the security monitoring unit can also enhance security by referring to the anomaly detection history of each device, identifying the cause of the anomaly. This improves the accuracy of security enhancements by meticulously recording the anomaly detection history of each device. Some or all of the above processes in the security monitoring unit may be performed using AI, for example, or without AI. For example, the security monitoring unit can input the anomaly detection data of each device into a generating AI and have the generating AI perform anomaly pattern analysis.

[0042] The security monitoring unit can predict security risks and enhance monitoring by referring to nearby crime data when collecting security data. For example, the security monitoring unit can refer to nearby crime data and enhance security monitoring in areas with a high crime rate. For example, the security monitoring unit can predict security risks during specific time periods based on nearby crime data and enhance monitoring. For example, the security monitoring unit can analyze nearby crime data to understand crime trends and enhance security monitoring. This makes it possible to enhance security monitoring by predicting security risks by referring to nearby crime data. Some or all of the above processes in the security monitoring unit may be performed using AI, for example, or without AI. For example, the security monitoring unit can input nearby crime data into a generating AI and have the generating AI perform security risk predictions.

[0043] The security monitoring unit can reflect region-specific security risks by considering the user's geographical location information when collecting security data. For example, the security monitoring unit can reflect security risks by considering the crime rate in the region based on the user's geographical location information. For example, the security monitoring unit can also analyze and reflect regional security risks based on the user's geographical location information. For example, the security monitoring unit can reflect risks by considering regional security measures based on the user's geographical location information. This improves the accuracy of security monitoring by reflecting region-specific security risks by considering the user's geographical location information. Some or all of the above processing in the security monitoring unit may be performed using AI, for example, or without AI. For example, the security monitoring unit can input the user's geographical location information into a generating AI and have the generating AI perform the reflection of region-specific security risks.

[0044] The security monitoring unit can analyze users' social media activity when collecting security data and grasp security trends. For example, the security monitoring unit can analyze users' social media activity, grasp security trends, and strengthen monitoring. The security monitoring unit can also analyze users' social media activity, grasp security concerns, and strengthen monitoring. For example, the security monitoring unit can analyze users' social media activity, collect security information, and strengthen monitoring. This allows for the identification of security trends and strengthening of monitoring through the analysis of users' social media activity. Some or all of the above-described processes in the security monitoring unit may be performed using AI, or not. For example, the security monitoring unit can input user social media data into a generating AI and have the generating AI identify security trends.

[0045] The comfort adjustment unit can select the optimal adjustment method by referring to the user's past environmental setting history when adjusting comfort. For example, the comfort adjustment unit can refer to the user's past environmental setting history and select the optimal lighting and air conditioning settings. For example, the comfort adjustment unit can also select the optimal adjustment method for a specific time period based on the user's past environmental setting history. For example, the comfort adjustment unit can analyze the user's past environmental setting history and select the most comfortable adjustment method. In this way, by referring to the user's past environmental setting history, the optimal adjustment method can be selected and comfort can be improved. Some or all of the above processing in the comfort adjustment unit may be performed using AI, for example, or without AI. For example, the comfort adjustment unit can input the user's past environmental setting history data into a generating AI and have the generating AI perform the selection of the optimal adjustment method.

[0046] The comfort adjustment unit can perform optimal environmental settings by referring to the user's health data during comfort adjustment. For example, the comfort adjustment unit can refer to the user's health data and set the optimal temperature and humidity. For example, the comfort adjustment unit can also perform optimal environmental settings for a specific health condition based on the user's health data. For example, the comfort adjustment unit can analyze the user's health data and perform the healthiest environmental settings. In this way, by referring to the user's health data, the optimal environmental settings can be performed, providing a healthy living environment. Some or all of the above processing in the comfort adjustment unit may be performed using AI, for example, or without AI. For example, the comfort adjustment unit can input the user's health data into a generating AI and have the generating AI perform the optimal environmental settings.

[0047] The comfort adjustment unit can reflect region-specific comfort requirements by considering the user's geographical location information during comfort adjustment. For example, the comfort adjustment unit can adjust comfort according to the local climate based on the user's geographical location information. The comfort adjustment unit can also adjust comfort according to local culture and customs based on the user's geographical location information. For example, the comfort adjustment unit can adjust comfort considering the local energy supply situation based on the user's geographical location information. This improves comfort by reflecting region-specific comfort requirements by considering the user's geographical location information. Some or all of the above processing in the comfort adjustment unit may be performed using AI, for example, or without AI. For example, the comfort adjustment unit can input the user's geographical location information into a generating AI and have the generating AI perform the reflection of region-specific comfort requirements.

[0048] The comfort adjustment unit can analyze the user's social media activity and grasp comfort-related trends when adjusting comfort. For example, the comfort adjustment unit can analyze the user's social media activity, grasp comfort-related trends, and make adjustments. The comfort adjustment unit can also analyze the user's social media activity, grasp comfort-related interests, and make adjustments. The comfort adjustment unit can also analyze the user's social media activity, collect comfort-related information, and make adjustments. In this way, by analyzing the user's social media activity, it is possible to grasp comfort-related trends and improve comfort. Some or all of the above processing in the comfort adjustment unit may be performed using AI, for example, or without AI. For example, the comfort adjustment unit can input the user's social media data into a generating AI and have the generating AI perform the task of grasping comfort-related trends.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The energy management department can optimize energy use by taking into account the user's health condition when collecting energy usage data. For example, if a user provides health checkup data, the energy management department can adjust energy use based on that data. If a user has high blood pressure, the energy management department can appropriately adjust the air conditioning temperature to provide a comfortable environment. Also, if a user has allergies, the energy management department can optimize the use of air purifiers to promote the removal of allergens. Furthermore, the energy management department can adjust the brightness and color temperature of lighting according to the user's health condition to provide a healthy living environment. This makes it possible to optimize energy use while considering the user's health condition, thereby providing a healthier living environment.

[0051] The security monitoring unit can detect the movements of the user's pets while collecting security data, providing functions to ensure the pets' safety. For example, the security monitoring unit monitors the pet's movements and issues an alert if the pet approaches a dangerous area. If the pet goes out into the yard, the security monitoring unit can check if the yard gate is closed and automatically close it if it is open. Also, if the pet gets lost inside the house, the security monitoring unit can locate the pet and notify the user. Furthermore, by analyzing the pet's movements and understanding its behavior patterns, the security monitoring unit can detect abnormal movements and respond quickly. This ensures the safety of the pets and provides peace of mind to the user.

[0052] The energy management unit can optimize energy use by considering the user's life events when collecting energy usage data. For example, if a user welcomes a new family member, the energy management unit can adjust energy use based on that data. If there is a newborn, the energy management unit can appropriately adjust the air conditioning temperature to provide a comfortable environment. Also, if a user moves, the energy management unit can learn the energy usage patterns of the new home and optimize accordingly. Furthermore, if a user takes an extended vacation, the energy management unit can reduce the use of lighting and air conditioning to minimize energy consumption. This enables the optimization of energy use that takes into account the user's life events, providing a more comfortable living environment.

[0053] The security monitoring unit can provide a function to verify the safety of visitors by performing facial recognition when collecting security data. For example, the security monitoring unit recognizes a visitor's face and compares it with pre-registered facial data. If the visitor is registered, the security monitoring unit can verify the visitor's safety and notify the user. If the visitor is not registered, the security monitoring unit can also record the visitor's face and notify the user. Furthermore, by analyzing the visitor's facial recognition data and understanding the visitor's behavior patterns, the security monitoring unit can detect abnormal behavior and respond quickly. This allows for verification of visitor safety and provides users with peace of mind.

[0054] The energy management unit can optimize energy use by taking into account the user's hobbies and preferences when collecting energy usage data. For example, if the user enjoys watching movies, the energy management unit can optimize energy use during movie viewing. During movie viewing, the lighting can be dimmed and the air conditioning temperature can be appropriately adjusted. Also, if the user enjoys cooking, the energy management unit can optimize energy use during cooking and adjust the kitchen lighting and air conditioning. Furthermore, if the user enjoys reading, the energy management unit can optimize energy use during reading and adjust the brightness and color temperature of the lighting. This makes it possible to optimize energy use while considering the user's hobbies and preferences, providing a more comfortable living environment.

[0055] The security monitoring unit can adjust the intensity of security monitoring when collecting security data, taking into account the user's family structure. For example, if there are young children in the family, the security monitoring unit will increase the intensity of security monitoring. To ensure that young children can stay safe in the house, the monitoring range of security cameras can be expanded and door and window sensors can be strengthened. Also, if there are elderly people in the family, the security monitoring unit can adjust the intensity of security monitoring to ensure their safety. Furthermore, if there are pets in the family, the security monitoring unit can monitor the pets' movements and adjust the intensity of security monitoring to ensure their safety. This makes it possible to adjust the intensity of security monitoring considering the user's family structure, providing a safer living environment.

[0056] The energy management department can optimize energy use by considering the user's occupation and working hours when collecting energy usage data. For example, if a user is working from home, the energy management department can optimize energy use during working hours. During home work, it can optimize the use of lighting and air conditioning to provide a comfortable working environment. If a user is on a shift work schedule, the energy management department can also optimize energy use according to the shift schedule and adjust the use of lighting and air conditioning. Furthermore, if a user is away from home for an extended period, the energy management department can reduce the use of lighting and air conditioning to minimize energy consumption while the user is away. This enables the optimization of energy use considering the user's occupation and working hours, providing a more comfortable living environment.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The Energy Management Department collects energy usage data. This data includes electricity consumption, gas consumption, and power generation from the solar power system. The Energy Management Department collects electricity consumption in real time using smart meters and gas consumption using sensors. It can also monitor the power generation from the solar power system. Step 2: The security monitoring unit collects security data. This security data includes video from surveillance cameras, data from door sensors, and data from motion sensors. The security monitoring unit uses surveillance cameras to monitor the area around the house, door sensors to detect when doors are opened and closed, and motion sensors to detect suspicious movements. Step 3: The comfort adjustment unit automatically adjusts the home environment based on data collected by the energy management unit and the security monitoring unit. The comfort adjustment unit adjusts the brightness of the lighting, sets the temperature of the air conditioning, and automatically turns the security system on and off. This provides a comfortable environment, improves energy efficiency, and enhances security.

[0059] (Example of form 2) An embodiment of the present invention provides a smart home system that is an AI agent that connects smart devices throughout the house to optimize energy management, security, and comfort. This smart home system automatically adjusts the home environment to suit the user's lifestyle. For example, the smart home system connects all smart devices to optimize energy use, monitor security, and improve comfort through automatic adjustments. The smart home system automatically adjusts lighting, air conditioning, and security systems based on the user's lifestyle. This reduces energy waste, enhances home security, and provides a comfortable living environment. The smart home market is growing rapidly and is projected to reach hundreds of billions of dollars by 2025. Advanced AI-powered functions are attracting particular attention, and the demand for smart homes is surging due to the proliferation of smart devices. Advances in AI technology are enabling advanced functions that were not possible with conventional smart home systems. This system aims to provide smart home users with a safer, more energy-efficient, and more comfortable living environment. The vision is to support all households in living a stress-free and convenient life. As a result, the smart home system can optimize energy management, security, and comfort, and automatically adjust the home environment to suit the user's lifestyle.

[0060] The smart home system according to this embodiment comprises an energy management unit, a security monitoring unit, and a comfort adjustment unit. The energy management unit collects energy usage data. Energy usage data includes, but is not limited to, electricity consumption and gas usage. The energy management unit collects electricity consumption in real time using, for example, a smart meter. The energy management unit can also collect data using a sensor that measures gas usage. Furthermore, the energy management unit can monitor the amount of electricity generated by a solar power generation system. For example, the energy management unit collects electricity consumption in real time using a smart meter and optimizes energy use. It collects data using a sensor that measures gas usage to improve energy efficiency. It monitors the amount of electricity generated by a solar power generation system to achieve energy self-sufficiency. The security monitoring unit collects security data. Security data includes, but is not limited to, video from surveillance cameras and data from door sensors. The security monitoring unit monitors the area around the house using, for example, surveillance cameras. The security monitoring unit can also detect the opening and closing of doors using door sensors. Furthermore, the security monitoring unit can also detect suspicious movements using motion sensors. For example, the security monitoring unit can monitor the surroundings of the house using surveillance cameras and detect anomalies. It can enhance security by detecting the opening and closing of doors using door sensors. It can detect suspicious movements using motion sensors and issue alerts. The comfort adjustment unit automatically adjusts the home environment based on data collected by the energy management unit and the security monitoring unit. The comfort adjustment unit automatically adjusts lighting, air conditioning, and security systems, for example. For example, the comfort adjustment unit adjusts the brightness of the lights and sets the temperature of the air conditioning. The comfort adjustment unit can also automatically turn the security system on and off. For example, the comfort adjustment unit adjusts the brightness of the lights to provide a comfortable environment. It sets the temperature of the air conditioning to improve energy efficiency. It automatically turns the security system on and off to enhance security.As a result, the smart home system according to this embodiment can optimize energy management, security, and comfort, and automatically adjust the home environment to suit the user's lifestyle.

[0061] The Energy Management Department collects energy usage data. This data includes, but is not limited to, electricity consumption and gas usage. For example, the Energy Management Department collects electricity consumption in real time using smart meters. Smart meters monitor the usage of each power-consuming device in the home in detail and transmit the data to a central management system. This allows the Energy Management Department to understand the consumption patterns of each device and implement optimal control to suppress peak electricity usage. The Energy Management Department can also collect data using sensors that measure gas usage. Gas sensors accurately measure gas flow rate and usage time, which can be used to detect gas leaks and optimize usage. Furthermore, the Energy Management Department can monitor the power generation of solar power systems. Since solar power generation data fluctuates depending on weather and sunlight conditions, real-time monitoring is important. The Energy Management Department integrates this data and develops strategies to optimize energy use in the home. For example, the Energy Management Department collects electricity consumption in real time using smart meters and optimizes energy use. By collecting data using sensors to measure gas usage, energy efficiency can be improved. The amount of electricity generated by the solar power system is monitored to achieve energy self-sufficiency. This allows the energy management unit to efficiently manage household energy use, contributing to reduced energy costs and a lower environmental impact.

[0062] The security monitoring unit collects security data. This security data includes, but is not limited to, video from surveillance cameras and data from door sensors. For example, the security monitoring unit monitors the area around the house using surveillance cameras. Surveillance cameras capture high-resolution video in real time and detect unusual movements or intruders. This allows the security monitoring unit to constantly monitor the situation around the house and issue an alert immediately if an anomaly occurs. The security monitoring unit can also detect the opening and closing of doors using door sensors. Door sensors monitor the open / closed state of doors in real time and issue an alarm if there is an unauthorized intrusion. Furthermore, the security monitoring unit can also detect suspicious movements using motion sensors. Motion sensors sense movement inside and around the house and issue an alert immediately if there is unusual movement. For example, the security monitoring unit monitors the area around the house using surveillance cameras and detects anomalies. It enhances security by detecting the opening and closing of doors using door sensors. It detects suspicious movements using motion sensors and issues an alert. This allows the security monitoring unit to enhance security both inside and outside homes, ensuring the safety of residents. Furthermore, the security monitoring unit centrally manages the collected data and provides information to enable a rapid response in the event of an anomaly. For example, if an anomaly is detected, the security monitoring unit will issue an alarm and notify residents and security companies. This enables a swift response and minimizes damage.

[0063] The comfort adjustment unit automatically adjusts the home environment based on data collected by the energy management unit and the security monitoring unit. For example, the comfort adjustment unit automatically adjusts lighting, air conditioning, and security systems. When adjusting lighting brightness, it considers the room's brightness, time of day, and the residents' activities to set the optimal brightness. For example, it dims the lights at night to provide a relaxing environment. Similarly, when setting the air conditioning temperature, it considers the room temperature, humidity, and the residents' comfort to set the optimal temperature. For example, it moderately lowers the room temperature in summer and moderately raises it in winter to provide a comfortable environment. Furthermore, the comfort adjustment unit can automatically turn the security system on and off. For example, it can turn the security system on when residents leave and off when they return, enhancing security. Thus, the comfort adjustment unit adjusts lighting brightness to provide a comfortable environment, sets air conditioning temperature to improve energy efficiency, and automatically turns the security system on and off to enhance security. In addition, the comfort adjustment unit can customize the home environment according to the residents' lifestyles and preferences. For example, the lighting color and brightness can be adjusted according to the residents' preferences, and the air conditioning settings can be changed. This allows the comfort control unit to provide an optimal environment tailored to the residents' lifestyles, supporting a comfortable life.

[0064] The energy management unit can collect energy usage data and perform optimizations to reduce energy waste. For example, the energy management unit can collect energy usage data and perform peak shifting. For example, the energy management unit can adjust energy usage to avoid peak times. The energy management unit can also analyze energy usage data to improve energy efficiency. For example, the energy management unit can analyze energy usage data to identify energy-inefficient devices and perform optimizations. Furthermore, the energy management unit can understand energy consumption patterns based on energy usage data and perform optimizations. For example, the energy management unit can identify peak times for energy consumption based on energy usage data and optimize energy use. In this way, energy efficiency is improved by collecting energy usage data and performing optimizations to reduce energy waste. Some or all of the above processes in the energy management unit may be performed using AI, for example, or without AI. For example, the energy management unit can input energy usage data into a generating AI and have the generating AI perform energy usage optimizations.

[0065] The security monitoring unit can collect security data and enhance home security. For example, the security monitoring unit can collect security data and implement an alert system. For example, the security monitoring unit can monitor surveillance camera footage in real time and issue an alert if an anomaly is detected. The security monitoring unit can also install security cameras and monitor the area around the house. For example, the security monitoring unit can install security cameras and monitor the area around the house 24 hours a day. Furthermore, the security monitoring unit can analyze security data and identify security risks. For example, the security monitoring unit can analyze security data to identify areas with high security risks and strengthen monitoring. In this way, by collecting security data and enhancing home security, the safety of the home is improved. Some or all of the above processes in the security monitoring unit may be performed using AI, for example, or not using AI. For example, the security monitoring unit can input security data into a generating AI and have the generating AI perform the identification of security risks.

[0066] The comfort adjustment unit can automatically adjust lighting, air conditioning, and security systems based on data collected by the energy management unit and the security monitoring unit. For example, the comfort adjustment unit can automatically adjust lighting, air conditioning, and security systems to provide a comfortable living environment. For example, it can adjust the brightness of the lighting and set the temperature of the air conditioning. The comfort adjustment unit can also automatically turn security systems on and off. For example, it can adjust the brightness of the lighting to provide a comfortable environment, set the temperature of the air conditioning to improve energy efficiency, and automatically turn security systems on and off to enhance security. In this way, by automatically adjusting lighting, air conditioning, and security systems, a comfortable living environment is provided. Some or all of the above-described processes in the comfort adjustment unit may be performed using AI, for example, or without AI. For example, the comfort adjustment unit can input data collected by the energy management unit and the security monitoring unit into a generating AI, and have the generating AI perform the adjustments to the lighting, air conditioning, and security systems.

[0067] The comfort adjustment unit can automatically adjust the home environment based on the user's lifestyle. For example, the comfort adjustment unit adjusts lighting and air conditioning based on the user's lifestyle. For example, the comfort adjustment unit learns the user's behavior patterns and adjusts the brightness of the lighting. The comfort adjustment unit can also set the air conditioning temperature based on the user's preferences. For example, the comfort adjustment unit learns the user's behavior patterns and automatically adjusts the brightness of the lighting. It sets the air conditioning temperature based on the user's preferences to provide a comfortable environment. Furthermore, the comfort adjustment unit can also adjust the security system based on the user's lifestyle. For example, the comfort adjustment unit automatically turns the security system on and off based on the user's lifestyle. In this way, by automatically adjusting the home environment based on the user's lifestyle, a more comfortable living environment is provided. Some or all of the above processing in the comfort adjustment unit may be performed using AI, for example, or without AI. For example, the comfort adjustment unit can input user lifestyle data into a generating AI and have the generating AI perform the automatic adjustment of the home environment.

[0068] The energy management unit can estimate the user's emotions and adjust the energy usage optimization method based on the estimated user emotions. For example, if the user is stressed, the energy management unit can optimize the energy use of lighting and air conditioning to provide a relaxing environment. For example, if the user is relaxed, the energy management unit can also reduce the use of lighting and air conditioning to minimize energy consumption. For example, if the user is in a hurry, the energy management unit can also optimize energy usage to quickly supply the necessary energy. This allows for more comfortable energy management by adjusting the energy usage optimization method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the energy management unit may be performed using AI or not using AI. For example, the energy management unit can input user emotion data into a generative AI and have the generative AI adjust the energy usage optimization method.

[0069] The energy management unit can improve the accuracy of optimization by meticulously recording the frequency and time of use of each device when collecting energy usage data. For example, the energy management unit can record the frequency of use of each device and optimize the energy usage of the most frequently used devices. The energy management unit can also record the time of use of each device and optimize energy usage during peak hours. For example, the energy management unit can analyze the usage history of each device to understand energy usage patterns and optimize accordingly. This improves the accuracy of energy usage optimization by meticulously recording the frequency and time of use of each device. Some or all of the above processes in the energy management unit may be performed using AI, for example, or without AI. For example, the energy management unit can input the usage data of each device into a generating AI and have the generating AI perform energy usage optimization.

[0070] The energy management unit can predict and optimize energy consumption by referring to weather data when collecting energy usage data. For example, the energy management unit can refer to weather data to optimize heating energy use during cold weather. The energy management unit can also refer to weather data to optimize cooling energy use during hot weather. The energy management unit can also refer to weather data to predict and optimize energy use in accordance with changes in weather. This makes it possible to optimize energy use by predicting energy consumption by referring to weather data. Some or all of the above processing in the energy management unit may be performed using AI, for example, or without AI. For example, the energy management unit can input weather data into a generating AI and have the generating AI perform energy consumption prediction and optimization.

[0071] The energy management unit can estimate the user's emotions and determine energy usage priorities based on those emotions. For example, if the user is relaxed, the energy management unit can optimize energy usage by prioritizing comfort. If the user is stressed, the energy management unit can also optimize energy usage by prioritizing stress reduction. If the user is in a hurry, the energy management unit can also optimize energy usage by prioritizing quick response. This allows for more comfortable energy management by determining energy usage priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 energy management unit may be performed using AI or not. For example, the energy management unit can input user emotion data into a generative AI and have the generative AI determine energy usage priorities.

[0072] The energy management unit can reflect region-specific energy consumption patterns by considering the user's geographical location when collecting energy usage data. For example, the energy management unit can optimize energy use according to the local climate based on the user's geographical location. The energy management unit can also analyze and optimize regional energy consumption patterns based on the user's geographical location. For example, the energy management unit can optimize energy use by considering the local energy supply situation based on the user's geographical location. This makes it possible to optimize energy use by reflecting region-specific energy consumption patterns by considering the user's geographical location. Some or all of the above processing in the energy management unit may be performed using AI, for example, or without AI. For example, the energy management unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of reflecting region-specific energy consumption patterns.

[0073] The energy management unit can analyze users' social media activity when collecting energy usage data to understand trends in energy consumption. For example, the energy management unit can analyze users' social media activity to understand and optimize trends in energy consumption. The energy management unit can also analyze users' social media activity to understand their interests regarding energy consumption and optimize accordingly. For example, the energy management unit can analyze users' social media activity to collect and optimize information regarding energy consumption. This makes it possible to understand and optimize trends in energy consumption by analyzing users' social media activity. Some or all of the above processing in the energy management unit may be performed using AI, for example, or without AI. For example, the energy management unit can input user social media data into a generating AI and have the generating AI perform the task of understanding trends in energy consumption.

[0074] The security monitoring unit can estimate the user's emotions and adjust the intensity of security monitoring based on the estimated emotions. For example, if the user is feeling anxious, the security monitoring unit can increase the intensity of security monitoring. For example, if the user is relaxed, the security monitoring unit can also return the intensity of security monitoring to normal. For example, if the user is in a hurry, the security monitoring unit can perform security monitoring that requires a quick response. This allows for more appropriate security management by adjusting the intensity of security monitoring based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the security monitoring unit may be performed using AI or not using AI. For example, the security monitoring unit can input user emotion data into a generative AI and have the generative AI adjust the intensity of security monitoring.

[0075] The security monitoring unit can improve the accuracy of security enhancements by meticulously recording the anomaly detection history of each device when collecting security data. For example, the security monitoring unit can record the anomaly detection history of each device, analyze the patterns of anomalies, and enhance security. For example, the security monitoring unit can also enhance security by understanding the frequency of anomalies based on the anomaly detection history of each device. For example, the security monitoring unit can also enhance security by referring to the anomaly detection history of each device, identifying the cause of the anomaly. This improves the accuracy of security enhancements by meticulously recording the anomaly detection history of each device. Some or all of the above processes in the security monitoring unit may be performed using AI, for example, or without AI. For example, the security monitoring unit can input the anomaly detection data of each device into a generating AI and have the generating AI perform anomaly pattern analysis.

[0076] The security monitoring unit can predict security risks and enhance monitoring by referring to nearby crime data when collecting security data. For example, the security monitoring unit can refer to nearby crime data and enhance security monitoring in areas with a high crime rate. For example, the security monitoring unit can predict security risks during specific time periods based on nearby crime data and enhance monitoring. For example, the security monitoring unit can analyze nearby crime data to understand crime trends and enhance security monitoring. This makes it possible to enhance security monitoring by predicting security risks by referring to nearby crime data. Some or all of the above processes in the security monitoring unit may be performed using AI, for example, or without AI. For example, the security monitoring unit can input nearby crime data into a generating AI and have the generating AI perform security risk predictions.

[0077] The security monitoring unit can estimate the user's emotions and prioritize security alerts based on those emotions. For example, if the user is feeling anxious, the security monitoring unit will prioritize notifying important security alerts. For example, if the user is relaxed, the security monitoring unit may also notify regular security alerts. For example, if the user is in a hurry, the security monitoring unit may also prioritize notifying security alerts that require immediate attention. This allows for more appropriate security management by prioritizing security alerts based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the security monitoring unit may be performed using AI or not. For example, the security monitoring unit can input user emotion data into a generative AI and have the generative AI determine the priority of security alerts.

[0078] The security monitoring unit can reflect region-specific security risks by considering the user's geographical location information when collecting security data. For example, the security monitoring unit can reflect security risks by considering the crime rate in the region based on the user's geographical location information. For example, the security monitoring unit can also analyze and reflect regional security risks based on the user's geographical location information. For example, the security monitoring unit can reflect risks by considering regional security measures based on the user's geographical location information. This improves the accuracy of security monitoring by reflecting region-specific security risks by considering the user's geographical location information. Some or all of the above processing in the security monitoring unit may be performed using AI, for example, or without AI. For example, the security monitoring unit can input the user's geographical location information into a generating AI and have the generating AI perform the reflection of region-specific security risks.

[0079] The security monitoring unit can analyze users' social media activity when collecting security data and grasp security trends. For example, the security monitoring unit can analyze users' social media activity, grasp security trends, and strengthen monitoring. The security monitoring unit can also analyze users' social media activity, grasp security concerns, and strengthen monitoring. For example, the security monitoring unit can analyze users' social media activity, collect security information, and strengthen monitoring. This allows for the identification of security trends and strengthening of monitoring through the analysis of users' social media activity. Some or all of the above-described processes in the security monitoring unit may be performed using AI, or not. For example, the security monitoring unit can input user social media data into a generating AI and have the generating AI identify security trends.

[0080] The comfort adjustment unit can estimate the user's emotions and change the way it adjusts lighting and air conditioning based on the estimated emotions. For example, if the user is relaxed, the comfort adjustment unit can change the lighting to a warm color and set the air conditioning to a comfortable temperature. For example, if the user is stressed, the comfort adjustment unit can also change the lighting to a calming color and set the air conditioning to a relaxing temperature. For example, if the user is in a hurry, the comfort adjustment unit can brighten the lighting and quickly adjust the air conditioning. In this way, a more comfortable environment can be provided by changing the way it adjusts lighting and air conditioning based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the comfort adjustment unit may be performed using AI, for example, or without AI. For example, the comfort adjustment unit can input user emotion data into a generating AI and have the AI ​​execute changes to the lighting and air conditioning settings.

[0081] The comfort adjustment unit can select the optimal adjustment method by referring to the user's past environmental setting history when adjusting comfort. For example, the comfort adjustment unit can refer to the user's past environmental setting history and select the optimal lighting and air conditioning settings. For example, the comfort adjustment unit can also select the optimal adjustment method for a specific time period based on the user's past environmental setting history. For example, the comfort adjustment unit can analyze the user's past environmental setting history and select the most comfortable adjustment method. In this way, by referring to the user's past environmental setting history, the optimal adjustment method can be selected and comfort can be improved. Some or all of the above processing in the comfort adjustment unit may be performed using AI, for example, or without AI. For example, the comfort adjustment unit can input the user's past environmental setting history data into a generating AI and have the generating AI perform the selection of the optimal adjustment method.

[0082] The comfort adjustment unit can perform optimal environmental settings by referring to the user's health data during comfort adjustment. For example, the comfort adjustment unit can refer to the user's health data and set the optimal temperature and humidity. For example, the comfort adjustment unit can also perform optimal environmental settings for a specific health condition based on the user's health data. For example, the comfort adjustment unit can analyze the user's health data and perform the healthiest environmental settings. In this way, by referring to the user's health data, the optimal environmental settings can be performed, providing a healthy living environment. Some or all of the above processing in the comfort adjustment unit may be performed using AI, for example, or without AI. For example, the comfort adjustment unit can input the user's health data into a generating AI and have the generating AI perform the optimal environmental settings.

[0083] The comfort adjustment unit can estimate the user's emotions and determine the priority of comfort adjustments based on the estimated user emotions. For example, if the user is relaxed, the comfort adjustment unit will prioritize comfort adjustments. For example, if the user is stressed, the comfort adjustment unit can also prioritize stress reduction adjustments. For example, if the user is in a hurry, the comfort adjustment unit can also prioritize quick response adjustments. In this way, a more comfortable environment can be provided by determining the priority of comfort adjustments based on the user's emotions. 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 comfort adjustment unit may be performed using AI, for example, or without AI. For example, the comfort adjustment unit can input user emotion data into a generative AI and have the generative AI perform the determination of comfort adjustment priorities.

[0084] The comfort adjustment unit can reflect region-specific comfort requirements by considering the user's geographical location information during comfort adjustment. For example, the comfort adjustment unit can adjust comfort according to the local climate based on the user's geographical location information. The comfort adjustment unit can also adjust comfort according to local culture and customs based on the user's geographical location information. For example, the comfort adjustment unit can adjust comfort considering the local energy supply situation based on the user's geographical location information. This improves comfort by reflecting region-specific comfort requirements by considering the user's geographical location information. Some or all of the above processing in the comfort adjustment unit may be performed using AI, for example, or without AI. For example, the comfort adjustment unit can input the user's geographical location information into a generating AI and have the generating AI perform the reflection of region-specific comfort requirements.

[0085] The comfort adjustment unit can analyze the user's social media activity and grasp comfort-related trends when adjusting comfort. For example, the comfort adjustment unit can analyze the user's social media activity, grasp comfort-related trends, and make adjustments. The comfort adjustment unit can also analyze the user's social media activity, grasp comfort-related interests, and make adjustments. The comfort adjustment unit can also analyze the user's social media activity, collect comfort-related information, and make adjustments. In this way, by analyzing the user's social media activity, it is possible to grasp comfort-related trends and improve comfort. Some or all of the above processing in the comfort adjustment unit may be performed using AI, for example, or without AI. For example, the comfort adjustment unit can input the user's social media data into a generating AI and have the generating AI perform the task of grasping comfort-related trends.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The energy management department can optimize energy use by taking into account the user's health condition when collecting energy usage data. For example, if a user provides health checkup data, the energy management department can adjust energy use based on that data. If a user has high blood pressure, the energy management department can appropriately adjust the air conditioning temperature to provide a comfortable environment. Also, if a user has allergies, the energy management department can optimize the use of air purifiers to promote the removal of allergens. Furthermore, the energy management department can adjust the brightness and color temperature of lighting according to the user's health condition to provide a healthy living environment. This makes it possible to optimize energy use while considering the user's health condition, thereby providing a healthier living environment.

[0088] The security monitoring unit can detect the movements of the user's pets while collecting security data, providing functions to ensure the pets' safety. For example, the security monitoring unit monitors the pet's movements and issues an alert if the pet approaches a dangerous area. If the pet goes out into the yard, the security monitoring unit can check if the yard gate is closed and automatically close it if it is open. Also, if the pet gets lost inside the house, the security monitoring unit can locate the pet and notify the user. Furthermore, by analyzing the pet's movements and understanding its behavior patterns, the security monitoring unit can detect abnormal movements and respond quickly. This ensures the safety of the pets and provides peace of mind to the user.

[0089] The comfort adjustment unit can estimate the user's emotions and adjust music and scents based on those emotions. For example, if the user is relaxed, the comfort adjustment unit can play relaxing music and release a relaxing scent. If the user is stressed, the comfort adjustment unit can also play stress-reducing music and release a relaxing scent. If the user is in a hurry, the comfort adjustment unit can also play music that enhances concentration and release a refreshing scent. In this way, a more comfortable environment can be provided by adjusting music and scents based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the comfort adjustment unit may be performed using AI, for example, or without AI. For example, the comfort adjustment unit can input user emotion data into a generative AI and have the generative AI perform the music and scent adjustments.

[0090] The energy management unit can optimize energy use by considering the user's life events when collecting energy usage data. For example, if a user welcomes a new family member, the energy management unit can adjust energy use based on that data. If there is a newborn, the energy management unit can appropriately adjust the air conditioning temperature to provide a comfortable environment. Also, if a user moves, the energy management unit can learn the energy usage patterns of the new home and optimize accordingly. Furthermore, if a user takes an extended vacation, the energy management unit can reduce the use of lighting and air conditioning to minimize energy consumption. This enables the optimization of energy use that takes into account the user's life events, providing a more comfortable living environment.

[0091] The security monitoring unit can provide a function to verify the safety of visitors by performing facial recognition when collecting security data. For example, the security monitoring unit recognizes a visitor's face and compares it with pre-registered facial data. If the visitor is registered, the security monitoring unit can verify the visitor's safety and notify the user. If the visitor is not registered, the security monitoring unit can also record the visitor's face and notify the user. Furthermore, by analyzing the visitor's facial recognition data and understanding the visitor's behavior patterns, the security monitoring unit can detect abnormal behavior and respond quickly. This allows for verification of visitor safety and provides users with peace of mind.

[0092] The comfort adjustment unit can estimate the user's emotions and manage indoor plants based on those emotions. For example, if the user is relaxed, the comfort adjustment unit can adjust the watering and lighting of the plants to promote their growth. If the user is stressed, the comfort adjustment unit can rearrange the plants and place plants with relaxing effects in prominent locations. If the user is in a hurry, the comfort adjustment unit can minimize plant management and respond quickly. This allows for a more comfortable environment by managing indoor plants based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the comfort adjustment unit may be performed using AI or not. For example, the comfort adjustment unit can input user emotion data into a generative AI and have the generative AI manage the plants.

[0093] The energy management unit can optimize energy use by taking into account the user's hobbies and preferences when collecting energy usage data. For example, if the user enjoys watching movies, the energy management unit can optimize energy use during movie viewing. During movie viewing, the lighting can be dimmed and the air conditioning temperature can be appropriately adjusted. Also, if the user enjoys cooking, the energy management unit can optimize energy use during cooking and adjust the kitchen lighting and air conditioning. Furthermore, if the user enjoys reading, the energy management unit can optimize energy use during reading and adjust the brightness and color temperature of the lighting. This makes it possible to optimize energy use while considering the user's hobbies and preferences, providing a more comfortable living environment.

[0094] The security monitoring unit can adjust the intensity of security monitoring when collecting security data, taking into account the user's family structure. For example, if there are young children in the family, the security monitoring unit will increase the intensity of security monitoring. To ensure that young children can stay safe in the house, the monitoring range of security cameras can be expanded and door and window sensors can be strengthened. Also, if there are elderly people in the family, the security monitoring unit can adjust the intensity of security monitoring to ensure their safety. Furthermore, if there are pets in the family, the security monitoring unit can monitor the pets' movements and adjust the intensity of security monitoring to ensure their safety. This makes it possible to adjust the intensity of security monitoring considering the user's family structure, providing a safer living environment.

[0095] The comfort adjustment unit can estimate the user's emotions and adjust the room's acoustic environment based on the estimated emotions. For example, if the user is relaxed, the comfort adjustment unit can play relaxing music and adjust the volume appropriately. If the user is stressed, the comfort adjustment unit can also play music with stress-reducing effects and adjust the volume. If the user is in a hurry, the comfort adjustment unit can also play music that enhances concentration and adjust the volume. In this way, a more comfortable environment can be provided by adjusting the room's acoustic environment based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the comfort adjustment unit may be performed using AI, for example, or without AI. For example, the comfort adjustment unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the acoustic environment.

[0096] The energy management department can optimize energy use by considering the user's occupation and working hours when collecting energy usage data. For example, if a user is working from home, the energy management department can optimize energy use during working hours. During home work, it can optimize the use of lighting and air conditioning to provide a comfortable working environment. If a user is on a shift work schedule, the energy management department can also optimize energy use according to the shift schedule and adjust the use of lighting and air conditioning. Furthermore, if a user is away from home for an extended period, the energy management department can reduce the use of lighting and air conditioning to minimize energy consumption while the user is away. This enables the optimization of energy use considering the user's occupation and working hours, providing a more comfortable living environment.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The Energy Management Department collects energy usage data. This data includes electricity consumption, gas consumption, and power generation from the solar power system. The Energy Management Department collects electricity consumption in real time using smart meters and gas consumption using sensors. It can also monitor the power generation from the solar power system. Step 2: The security monitoring unit collects security data. This security data includes video from surveillance cameras, data from door sensors, and data from motion sensors. The security monitoring unit uses surveillance cameras to monitor the area around the house, door sensors to detect when doors are opened and closed, and motion sensors to detect suspicious movements. Step 3: The comfort adjustment unit automatically adjusts the home environment based on data collected by the energy management unit and the security monitoring unit. The comfort adjustment unit adjusts the brightness of the lighting, sets the temperature of the air conditioning, and automatically turns the security system on and off. This provides a comfortable environment, improves energy efficiency, and enhances security.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] Each of the multiple elements described above, including the energy management unit, security monitoring unit, and comfort adjustment unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the energy management unit collects electricity consumption and gas usage using the smart meter and gas usage sensor of the smart device 14, and optimizes energy use using the specific processing unit 290 of the data processing unit 12. The security monitoring unit monitors the surroundings of the house using the surveillance camera and door sensor of the smart device 14 and detects abnormalities. The comfort adjustment unit controls the lighting and air conditioning systems of the smart device 14, and provides a comfortable environment using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the energy management unit, security monitoring unit, and comfort adjustment unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the energy management unit collects electricity consumption and gas usage using the smart meter and gas usage sensor of the smart glasses 214, and optimizes energy use using the specific processing unit 290 of the data processing unit 12. The security monitoring unit monitors the surroundings of the house using the surveillance camera and door sensor of the smart glasses 214 and detects abnormalities. The comfort adjustment unit controls the lighting and air conditioning systems of the smart glasses 214, and provides a comfortable environment using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] 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.

[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 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.

[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 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.

[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 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.

[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 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.

[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 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.

[0134] Each of the multiple elements described above, including the energy management unit, security monitoring unit, and comfort adjustment unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the energy management unit collects electricity consumption and gas usage using the smart meter and gas usage sensor of the headset terminal 314, and optimizes energy use using the specific processing unit 290 of the data processing unit 12. The security monitoring unit monitors the surroundings of the house using the surveillance camera and door sensor of the headset terminal 314 and detects abnormalities. The comfort adjustment unit controls the lighting and air conditioning systems of the headset terminal 314, and provides a comfortable environment using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[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 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.

[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 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).

[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] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the energy management unit, security monitoring unit, and comfort adjustment unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the energy management unit collects electricity consumption and gas usage using the robot 414's smart meter and gas usage sensors, and optimizes energy use using the specific processing unit 290 of the data processing unit 12. The security monitoring unit monitors the surroundings of the house using the robot 414's surveillance camera and door sensors and detects abnormalities. The comfort adjustment unit controls the robot 414's lighting and air conditioning systems, and provides a comfortable environment using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] (Note 1) The energy management department collects energy usage data, The security monitoring department collects security data, The system includes a comfort adjustment unit that automatically adjusts the home environment based on data collected by the energy management unit and the security monitoring unit. A system characterized by the following features. (Note 2) The aforementioned energy management unit, We collect energy usage data and optimize it to reduce energy waste. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned security monitoring unit, Collect security data to enhance home security The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned comfort adjustment unit is, Based on data collected by the Energy Management Department and the Security Monitoring Department, lighting, air conditioning, and security systems are automatically adjusted. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned comfort adjustment unit is, Automatically adjusts the home environment based on the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned energy management unit, It estimates the user's emotions and adjusts the energy usage optimization method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned energy management unit, When collecting energy usage data, the frequency and time of use of each device are recorded in detail to improve the accuracy of optimization. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned energy management unit, When collecting energy usage data, weather data is referenced to predict and optimize energy consumption. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned energy management unit, It estimates the user's emotions and prioritizes energy use based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned energy management unit, When collecting energy usage data, the system takes into account the user's geographical location to reflect region-specific energy consumption patterns. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned energy management unit, When collecting energy usage data, we analyze users' social media activity to understand trends in energy consumption. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned security monitoring unit, It estimates user sentiment and adjusts the intensity of security monitoring based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned security monitoring unit, When collecting security data, the anomaly detection history of each device is recorded in detail to improve the accuracy of security enhancements. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned security monitoring unit, When collecting security data, we refer to nearby crime data to predict security risks and enhance monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned security monitoring unit, It estimates user sentiment and prioritizes security alerts based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned security monitoring unit, When collecting security data, the system takes into account the user's geographical location to reflect region-specific security risks. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned security monitoring unit, When collecting security data, we analyze users' social media activity to understand security trends. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned comfort adjustment unit is, It estimates the user's emotions and changes the way lighting and air conditioning are adjusted based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned comfort adjustment unit is, When adjusting comfort settings, the system refers to the user's past preference history to select the optimal adjustment method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned comfort adjustment unit is, When adjusting comfort settings, the system uses the user's health data to optimize the environment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned comfort adjustment unit is, It estimates the user's emotions and determines the priority of comfort adjustments based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned comfort adjustment unit is, When adjusting comfort settings, the system takes into account the user's geographical location to reflect region-specific comfort requirements. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned comfort adjustment unit is, When adjusting comfort settings, analyze users' social media activity to understand comfort-related trends. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0171] 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. The energy management department collects energy usage data, The security monitoring department collects security data, The system includes a comfort adjustment unit that automatically adjusts the home environment based on data collected by the energy management unit and the security monitoring unit. A system characterized by the following features.

2. The aforementioned energy management unit, We collect energy usage data and optimize it to reduce energy waste. The system according to feature 1.

3. The aforementioned security monitoring unit, Collect security data to enhance home security The system according to feature 1.

4. The aforementioned comfort adjustment unit is, Based on data collected by the Energy Management Department and the Security Monitoring Department, lighting, air conditioning, and security systems are automatically adjusted. The system according to feature 1.

5. The aforementioned comfort adjustment unit is, Automatically adjusts the home environment based on the user's lifestyle. The system according to feature 1.

6. The aforementioned energy management unit, It estimates the user's emotions and adjusts the energy usage optimization method based on the estimated user emotions. The system according to feature 1.

7. The aforementioned energy management unit, When collecting energy usage data, the frequency and time of use of each device are recorded in detail to improve the accuracy of optimization. The system according to feature 1.

8. The aforementioned energy management unit, When collecting energy usage data, weather data is referenced to predict and optimize energy consumption. The system according to feature 1.

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