System

Abstract

A system according to an embodiment aims to ensure compatibility between devices of different manufacturers and to provide a comfortable and cost-effective home environment.SOLUTION: A system according to an embodiment includes an environmental information collection unit, a generated AI analysis unit, a pattern learning unit, a compatibility ensuring unit, a comfortable environment providing unit, and an environmental influence reduction unit. The environmental information collection unit collects data from various sensors. The generated data analysis unit analyzes the AI collected by the environmental information collection unit. The pattern learning unit learns the life pattern of the user based on the AI analyzed by the generated-data analysis unit. The compatibility ensuring unit ensures compatibility between devices of different manufacturers. The comfort environment provider provides a comfortable and cost-effective home environment. The environmental impact mitigator optimizes energy consumption.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the spread of smart homes has been hindered by incompatibility between manufacturers.

[0005] The system according to the embodiment aims to ensure compatibility between devices from different manufacturers and provide a comfortable and cost-effective home environment. [Means for solving the problem]

[0006] The system according to the embodiment includes an environmental information collection unit, a generative AI analysis unit, a pattern learning unit, a compatibility assurance unit, a comfortable environment provision unit, and an environmental impact reduction unit. The environmental information collection unit collects data from various sensors. The generative AI analysis unit analyzes the data collected by the environmental information collection unit. The pattern learning unit learns the user's lifestyle patterns based on the data analyzed by the generative AI analysis unit. The compatibility assurance unit ensures compatibility between devices from different manufacturers. The comfortable environment provision unit provides a comfortable and cost-effective home environment. The environmental impact reduction unit optimizes energy consumption. [Effects of the Invention]

[0007] The system according to the embodiment can ensure compatibility between devices from different manufacturers and provide a comfortable and cost-effective home environment. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

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

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

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The smart home system according to the embodiment of the present invention utilizes generative AI to analyze various environmental information and learn patterns, thereby providing a comfortable, low-cost, and environmentally friendly home environment.

[0029] A smart home system according to an embodiment includes an environmental information collection unit, a generation AI analysis unit, a pattern learning unit, a compatibility assurance unit, a comfortable environment provision unit, and an environmental impact reduction unit. The environmental information collection unit collects data from various sensors. For example, it uses temperature sensors, humidity sensors, light sensors, etc. to collect data such as outside temperature and humidity, electricity bills, and gas bills. The generation AI analysis unit analyzes the collected data. For example, the generation AI analyzes the data using a text generation AI (e.g., LLM) or a multimodal generation AI and proposes optimal settings for the living environment. The pattern learning unit learns the user's lifestyle patterns based on the analyzed data. For example, the generation AI learns a user's tendency to use the air conditioner during specific time periods and automatically adjusts the air conditioner to suit those time periods. The compatibility assurance unit ensures compatibility between devices from different manufacturers. For example, by supporting the global standard Matter, air conditioners and lighting from different manufacturers can be controlled by a single system. The comfortable environment provision unit provides a comfortable and low-cost home environment. For example, it reduces the use of the air conditioner during times when electricity rates are high and operates it during times when electricity rates are low. The environmental impact reduction unit optimizes energy consumption. For example, by optimizing the use of air conditioners and water heaters, energy consumption is reduced and carbon dioxide emissions are reduced. As a result, the smart home system according to the embodiment can provide a comfortable, low-cost, and environmentally friendly home environment.

[0030] The environmental information collecting unit can automatically adjust the set temperature of the air conditioner when the outside temperature is rising. For example, the environmental information collecting unit automatically adjusts the set temperature of the air conditioner when the outside temperature is high. For example, when the outside temperature exceeds 30 degrees, the set temperature of the air conditioner can be set to 26 degrees. Also, when the outside temperature exceeds 35 degrees, the set temperature of the air conditioner can be set to 24 degrees. In this way, a comfortable indoor environment can be provided by automatically adjusting the set temperature of the air conditioner according to the outside temperature.

[0031] The environmental information collecting unit can operate the dehumidifier when the humidity is rising. For example, the environmental information collecting unit operates the dehumidifier when the humidity is high. For example, when the humidity exceeds 70%, the dehumidifier is operated. Also, when the humidity exceeds 80%, the operating time of the dehumidifier can be extended. In this way, a comfortable indoor environment can be provided by operating the dehumidifier according to the humidity.

[0032] The environmental information collecting unit can refrain from using the air conditioner during times when electricity rates are rising. For example, the environmental information collecting unit refrains from using the air conditioner during times when electricity rates are high. For example, the unit refrains from using the air conditioner during times when electricity rates are peak, between 6:00 PM and 9:00 PM. The air conditioner's set temperature can also be increased during times when electricity rates are high. This allows costs to be reduced by refraining from using the air conditioner during times when electricity rates are high.

[0033] The pattern learning unit can automatically adjust the air conditioner or water heater to suit the user's lifestyle pattern. The pattern learning unit automatically adjusts the air conditioner to suit the user's lifestyle pattern, for example. For example, if the user returns home at 6 p.m. every day, the air conditioner can be turned on at 6 p.m. Also, if the user wakes up at 7 a.m. every morning, the air conditioner can be turned on at 7 a.m. The pattern learning unit also automatically adjusts the water heater to suit the user's lifestyle pattern. For example, if the user takes a bath at 10 p.m. every day, the water heater can be turned on at 10 p.m. Also, if the user takes a shower at 6 a.m. every morning, the water heater can be turned on at 6 a.m. In this way, a comfortable environment can be provided by automatically adjusting the air conditioner or water heater to suit the user's lifestyle pattern.

[0034] The compatibility assurance unit can control devices from different manufacturers in a single system. For example, the compatibility assurance unit controls air conditioners and lighting from different manufacturers in a single system. For example, by supporting Matter, it is possible to control air conditioners and lighting from different manufacturers in a single system. The compatibility assurance unit can also exchange data between devices from different manufacturers in real time. For example, data from air conditioners and lighting can be shared to set optimal environmental settings. This allows devices from different manufacturers to be controlled in a single system, improving the convenience of smart homes.

[0035] The environmental impact mitigation unit can reduce carbon dioxide emissions by optimizing energy consumption. The environmental impact mitigation unit reduces carbon dioxide emissions by optimizing energy consumption, for example. For example, optimizing the use of air conditioners and water heaters reduces energy consumption and decreases carbon dioxide emissions. The environmental impact mitigation unit can also analyze energy consumption data in detail and propose optimal settings to minimize carbon dioxide emissions. For example, optimizing the usage time of an air conditioner reduces carbon dioxide emissions. In this way, optimizing energy consumption reduces carbon dioxide emissions and contributes to environmental protection.

[0036] The environmental information collection unit collects data from sensors in real time, and the generative AI analysis unit analyzes the data on the spot, enabling it to instantly propose optimal settings. The environmental information collection unit, for example, collects data from each sensor in real time. For example, it uses temperature sensors, humidity sensors, light sensors, etc. to collect data such as outside temperature and humidity, electricity bills, and gas bills in real time. The generative AI analysis unit analyzes the collected data on the spot and instantly proposes optimal settings. For example, the generative AI analyzes data using text generation AI (e.g., LLM) or multimodal generation AI and proposes optimal settings for the living environment. This enables rapid response by collecting data in real time and instantly proposing optimal settings.

[0037] The environmental information collection unit collects external weather data or traffic information, and the generation AI analysis unit can perform comprehensive environmental analysis based on the weather data or traffic information. The environmental information collection unit, for example, collects external weather data. For example, it collects data on outside temperature and humidity based on weather forecasts. It can also collect traffic information. For example, it adjusts the timing of going out based on traffic congestion information. The generation AI analysis unit performs comprehensive environmental analysis based on the collected weather data and traffic information. For example, the generation AI analyzes the data using text generation AI (e.g., LLM) or multimodal generation AI and suggests optimal settings for the living environment. This enables more accurate suggestions by performing comprehensive environmental analysis based on external weather data and traffic information.

[0038] The environmental information collection unit can use a drone to collect a wide range of outdoor data, and the generation AI analysis unit can analyze that data. The environmental information collection unit, for example, uses a drone to collect a wide range of outdoor data. For example, the drone collects data such as outside temperature, humidity, and wind speed. The generation AI analysis unit analyzes the collected data. For example, the generation AI analyzes the data using a text generation AI (e.g., LLM) or a multimodal generation AI to propose optimal settings for the living environment. In this way, collecting and analyzing a wide range of data using a drone enables more accurate environmental settings.

[0039] The environmental information collection unit monitors the operating status of devices in a smart home and can automatically notify if an abnormality is detected. The environmental information collection unit, for example, monitors the operating status of devices in a smart home. For example, it monitors the operating status of air conditioners and lights in real time. If an abnormality is detected, it automatically notifies. For example, it can detect abnormal operation of an air conditioner and notify the user. It can also detect abnormal operation of lights and notify the user. This makes it possible to monitor the operating status of devices and automatically notify if an abnormality is detected, enabling a quick response.

[0040] The pattern learning unit can learn the user's lifestyle patterns in detail and automatically adjust the optimal settings for each season or time of day. The pattern learning unit, for example, learns the user's lifestyle patterns in detail and automatically adjusts the optimal settings for each season or time of day. For example, the generation AI learns the user's lifestyle patterns in detail and automatically adjusts the optimal settings for each season or time of day. For example, the air conditioner's set temperature can be lowered in the summer and raised in the winter. The air conditioner's set temperature can also be adjusted for each time of day. For example, the air conditioner's set temperature can be lowered at night and raised during the day. This makes it possible to provide a comfortable environment by automatically adjusting the optimal settings for each season and time of day based on the user's lifestyle patterns.

[0041] The pattern learning unit can learn the lifestyle patterns of all family members and provide an optimal environment tailored to each individual's preferences. The pattern learning unit, for example, learns the lifestyle patterns of all family members and provides an optimal environment tailored to each individual's preferences. For example, the generative AI learns the lifestyle patterns of all family members and provides an optimal environment tailored to each individual's preferences. For example, it learns the preferred temperature and humidity of each family member and sets the optimal settings. It can also adjust the air conditioner and lighting settings based on the lifestyle patterns of all family members. For example, during times when all family members gather in the living room, the air conditioner's set temperature is adjusted and the lights are brightened. This makes it possible to provide a comfortable environment by providing an optimal environment tailored to each individual's preferences based on the lifestyle patterns of all family members.

[0042] The pattern learning unit can collect the user's health data and suggest optimal environmental settings based on that data. The pattern learning unit, for example, collects the user's health data and suggests optimal environmental settings based on that data. For example, health data such as the user's heart rate and sleep patterns is collected, and the generation AI analyzes that data to suggest optimal environmental settings. For example, if the heart rate is high, the air conditioner's set temperature can be lowered. The air conditioner's set temperature can also be adjusted based on the sleep pattern. This makes it possible to provide a healthy environment by suggesting optimal environmental settings based on the user's health data.

[0043] The pattern learning unit can also work with devices outside the smart home to learn the user's behavioral patterns when they are out and about. The pattern learning unit, for example, works with devices outside the smart home to learn the user's behavioral patterns when they are out and about. For example, it works with a smartphone or a wearable device to learn the user's behavioral patterns when they are out and about. For example, it can collect activity data when they are out and set the optimal environment when they return home. It can also adjust the settings of the air conditioner and lighting based on the user's behavioral patterns when they are out and about. For example, if the user exercises while they are out, the air conditioner's set temperature can be lowered when they return home. In this way, by working with devices outside the smart home and learning the user's behavioral patterns when they are out and about, it becomes possible to set the environment more accurately.

[0044] The compatibility assurance unit can exchange data in real time between devices from different manufacturers, achieving seamless collaboration. The compatibility assurance unit can, for example, exchange data in real time between devices from different manufacturers, achieving seamless collaboration. For example, data on air conditioners and lighting can be shared to achieve optimal environmental settings. For example, the brightness of lighting can be adjusted according to the temperature setting of the air conditioner. The temperature setting of the air conditioner can also be adjusted according to the brightness of lighting. This allows data to be exchanged in real time between devices from different manufacturers, achieving seamless collaboration, thereby improving the convenience of smart homes.

[0045] The compatibility assurance unit can automatically check the compatibility of devices from each manufacturer and display a warning when an incompatible device is used. For example, the compatibility assurance unit can automatically check the compatibility of devices from each manufacturer and display a warning when an incompatible device is used. For example, the compatibility assurance unit can check the compatibility of an air conditioner and a light, and display a warning when an incompatible device is used. The compatibility assurance unit can also check device compatibility in real time and display a warning when an incompatible device is used. This can improve system safety by automatically checking the compatibility of devices from each manufacturer and displaying a warning when an incompatible device is used.

[0046] The compatibility assurance unit can develop an integrated platform for centrally managing devices from different manufacturers, allowing users to easily add and remove devices. For example, the compatibility assurance unit can develop an integrated platform for centrally managing devices from different manufacturers, allowing users to easily add and remove devices. For example, air conditioners and lighting can be managed on a single platform. The compatibility assurance unit can also provide an interface that allows users to easily add and remove devices. For example, adding and removing devices using a smartphone app. This allows the development of an integrated platform for centrally managing devices from different manufacturers, allowing users to easily add and remove devices, thereby improving the convenience of smart homes.

[0047] The compatibility assurance department can launch an open source project to ensure device compatibility between manufacturers and promote standardization across the industry. The compatibility assurance department can, for example, launch an open source project to ensure device compatibility between manufacturers and promote standardization across the industry. For example, it can launch an open source project to ensure compatibility between air conditioners and lighting. It can also provide guidelines to promote standardization across the industry. For example, it can standardize protocols to ensure device compatibility. This can promote the spread of smart homes by launching an open source project to ensure device compatibility between manufacturers and promoting standardization across the industry.

[0048] The comfortable environment providing unit can predict peak power consumption times and optimize device operation to suit those times. For example, the generating AI can predict peak power consumption times and optimize device operation to suit those times. For example, the comfortable environment providing unit can refrain from using the air conditioner during times of high power consumption. It can also recommend using the air conditioner during times of low power consumption. In this way, energy costs can be reduced by predicting peak power consumption times and optimizing device operation to suit those times.

[0049] The comfortable environment provision unit can analyze the energy consumption data of each device in detail and suggest the most efficient way to use it. The comfortable environment provision unit can, for example, analyze the energy consumption data of each device in detail and suggest the most efficient way to use it. For example, the generation AI can analyze the energy consumption data of each device in detail and suggest the most efficient way to use it. For example, it can optimize the usage time of the air conditioner. It can also optimize the usage of lighting. In this way, by analyzing the energy consumption data of each device in detail and suggesting the most efficient way to use it, it is possible to reduce energy costs.

[0050] The comfortable environment providing unit can visualize energy consumption within the smart home, allowing the user to check their own energy consumption in real time. The comfortable environment providing unit, for example, visualizes energy consumption within the smart home, allowing the user to check their own energy consumption in real time. For example, the unit can display energy consumption of air conditioners. It can also display energy consumption of lighting. In this way, energy consumption within the smart home can be visualized, allowing the user to check their own energy consumption in real time, making energy management easier.

[0051] The comfortable environment providing unit can provide guidelines for optimizing energy consumption and enable the user to adjust settings by themselves. The comfortable environment providing unit can, for example, provide guidelines for optimizing energy consumption and enable the user to adjust settings by themselves. For example, the comfortable environment providing unit can provide guidelines for adjusting the set temperature of an air conditioner. It can also provide guidelines for adjusting lighting usage. In this way, by providing guidelines for optimizing energy consumption and enabling the user to adjust settings by themselves, energy management can be made easier.

[0052] The environmental impact mitigation unit can analyze energy consumption data in detail and propose optimal settings to minimize carbon dioxide emissions. The environmental impact mitigation unit can, for example, analyze energy consumption data in detail and propose optimal settings to minimize carbon dioxide emissions. For example, the generative AI can analyze energy consumption data in detail and propose optimal settings to minimize carbon dioxide emissions. For example, it can optimize the usage time of air conditioners. It can also optimize lighting usage. This makes it possible to contribute to environmental protection by analyzing energy consumption data in detail and proposing optimal settings to minimize carbon dioxide emissions.

[0053] The environmental impact mitigation unit can monitor energy consumption within the smart home in real time and make adjustments to minimize the impact on the environment. For example, the generation AI can monitor energy consumption within the smart home in real time and make adjustments to minimize the impact on the environment. For example, it can optimize the usage time of the air conditioner. It can also optimize the usage of lighting. In this way, it is possible to contribute to environmental protection by monitoring energy consumption within the smart home in real time and making adjustments to minimize the impact on the environment.

[0054] The Environmental Impact Mitigation Department can develop a comprehensive energy management system that includes energy consumption outside the smart home. For example, the generation AI develops a comprehensive energy management system that includes energy consumption outside the smart home and performs optimal energy management. For example, it can manage the charging of electric vehicles. It can also manage energy consumption of external devices. By developing a comprehensive energy management system that includes energy consumption outside the smart home, it is possible to centralize energy management and achieve efficient energy use.

[0055] The environmental impact mitigation unit can introduce environmentally friendly energy sources into the smart home and optimize energy consumption. The environmental impact mitigation unit, for example, introduces environmentally friendly energy sources into the smart home and optimizes energy consumption. For example, the generation AI introduces environmentally friendly energy sources into the smart home and optimizes energy consumption. For example, solar power generation can be used. Wind power generation can also be used. In this way, introducing environmentally friendly energy sources into the smart home and optimizing energy consumption can contribute to environmental protection.

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

[0057] The smart home system may further include a voice recognition unit. The voice recognition unit can recognize a user's voice commands and operate a specific device. For example, if a user says, "Turn on the air conditioner," the voice recognition unit recognizes the command and starts the air conditioner. Also, if a user says, "Dim the lights," the voice recognition unit can adjust the brightness of the lights. This allows users to operate devices using voice commands, improving convenience for the user.

[0058] The environmental information collection unit can further include an air quality sensor. The air quality sensor can monitor the indoor air quality and activate an air purifier as needed. For example, if the indoor PM2.5 concentration is high, the air purifier can be automatically activated. Also, if the carbon dioxide concentration is high, the ventilation system can be activated. This allows for optimizing indoor air quality and providing a healthy environment.

[0059] The environmental information collection unit can further include a sound sensor. The sound sensor can monitor the noise level in the room and take soundproofing measures as necessary. For example, if the noise level is high, the soundproof curtains can be automatically closed. If the noise level is low, the curtains can be opened. This makes it possible to optimize the noise level in the room and provide a comfortable environment.

[0060] The environmental information collection unit may further include a light sensor. The light sensor can monitor the illuminance in the room and adjust the lighting as needed. For example, if the room is dark, the light can be automatically turned on. Also, if the room is bright, the light can be turned off. This allows the illuminance in the room to be optimized, providing a comfortable environment.

[0061] The environmental information collection unit can further include a water leak sensor. The water leak sensor can detect water leaks in the room and issue a warning as necessary. For example, if a water leak occurs in the kitchen or bathroom, it can automatically issue a warning. It can also stop the water supply when a water leak is detected. This makes it possible to minimize damage caused by water leaks.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The environmental information collection unit collects data from various sensors. For example, it uses temperature sensors, humidity sensors, light sensors, etc. to collect data such as outside temperature, humidity, electricity charges, and gas charges. Step 2: The Generative AI Analysis Unit analyzes the data collected by the Environmental Information Collection Unit. For example, the Generative AI may analyze the data using a text generation AI (e.g., LLM) or a multimodal generation AI, and propose optimal settings for the living environment. Step 3: The pattern learning unit learns the user's lifestyle patterns based on the data analyzed by the generation AI analysis unit. For example, the generation AI can learn that the user tends to use the air conditioner at certain times of the day and automatically adjust the air conditioner to suit those times. Step 4: The compatibility assurance unit ensures compatibility between devices from different manufacturers. For example, by supporting the Matter global standard, it becomes possible to control air conditioners and lighting from different manufacturers with a single system. Step 5: The comfortable environment provider provides a comfortable and low-cost home environment. For example, it reduces the use of the air conditioner during times when electricity rates are high and operates it during times when electricity rates are low. Step 6: The Environmental Impact Mitigation Department optimizes energy consumption, for example by optimizing the use of air conditioners and water heaters, thereby reducing energy consumption and carbon dioxide emissions.

[0064] (Example 2) The smart home system according to the embodiment of the present invention utilizes generative AI to analyze various environmental information and learn patterns, thereby providing a comfortable, low-cost, and environmentally friendly home environment.

[0065] A smart home system according to an embodiment includes an environmental information collection unit, a generation AI analysis unit, a pattern learning unit, a compatibility assurance unit, a comfortable environment provision unit, and an environmental impact reduction unit. The environmental information collection unit collects data from various sensors. For example, it uses temperature sensors, humidity sensors, light sensors, etc. to collect data such as outside temperature and humidity, electricity bills, and gas bills. The generation AI analysis unit analyzes the collected data. For example, the generation AI analyzes the data using a text generation AI (e.g., LLM) or a multimodal generation AI and proposes optimal settings for the living environment. The pattern learning unit learns the user's lifestyle patterns based on the analyzed data. For example, the generation AI learns a user's tendency to use the air conditioner during specific time periods and automatically adjusts the air conditioner to suit those time periods. The compatibility assurance unit ensures compatibility between devices from different manufacturers. For example, by supporting the global standard Matter, air conditioners and lighting from different manufacturers can be controlled by a single system. The comfortable environment provision unit provides a comfortable and low-cost home environment. For example, it reduces the use of the air conditioner during times when electricity rates are high and operates it during times when electricity rates are low. The environmental impact reduction unit optimizes energy consumption. For example, by optimizing the use of air conditioners and water heaters, energy consumption is reduced and carbon dioxide emissions are reduced. As a result, the smart home system according to the embodiment can provide a comfortable, low-cost, and environmentally friendly home environment.

[0066] The environmental information collecting unit can automatically adjust the set temperature of the air conditioner when the outside temperature is rising. For example, the environmental information collecting unit automatically adjusts the set temperature of the air conditioner when the outside temperature is high. For example, when the outside temperature exceeds 30 degrees, the set temperature of the air conditioner can be set to 26 degrees. Also, when the outside temperature exceeds 35 degrees, the set temperature of the air conditioner can be set to 24 degrees. In this way, a comfortable indoor environment can be provided by automatically adjusting the set temperature of the air conditioner according to the outside temperature.

[0067] The environmental information collecting unit can operate the dehumidifier when the humidity is rising. For example, the environmental information collecting unit operates the dehumidifier when the humidity is high. For example, when the humidity exceeds 70%, the dehumidifier is operated. Also, when the humidity exceeds 80%, the operating time of the dehumidifier can be extended. In this way, a comfortable indoor environment can be provided by operating the dehumidifier according to the humidity.

[0068] The environmental information collecting unit can refrain from using the air conditioner during times when electricity rates are rising. For example, the environmental information collecting unit refrains from using the air conditioner during times when electricity rates are high. For example, the unit refrains from using the air conditioner during times when electricity rates are peak, between 6:00 PM and 9:00 PM. The air conditioner's set temperature can also be increased during times when electricity rates are high. This allows costs to be reduced by refraining from using the air conditioner during times when electricity rates are high.

[0069] The pattern learning unit can automatically adjust the air conditioner or water heater to suit the user's lifestyle pattern. The pattern learning unit automatically adjusts the air conditioner to suit the user's lifestyle pattern, for example. For example, if the user returns home at 6 p.m. every day, the air conditioner can be turned on at 6 p.m. Also, if the user wakes up at 7 a.m. every morning, the air conditioner can be turned on at 7 a.m. The pattern learning unit also automatically adjusts the water heater to suit the user's lifestyle pattern. For example, if the user takes a bath at 10 p.m. every day, the water heater can be turned on at 10 p.m. Also, if the user takes a shower at 6 a.m. every morning, the water heater can be turned on at 6 a.m. In this way, a comfortable environment can be provided by automatically adjusting the air conditioner or water heater to suit the user's lifestyle pattern.

[0070] The compatibility assurance unit can control devices from different manufacturers in a single system. For example, the compatibility assurance unit controls air conditioners and lighting from different manufacturers in a single system. For example, by supporting Matter, it is possible to control air conditioners and lighting from different manufacturers in a single system. The compatibility assurance unit can also exchange data between devices from different manufacturers in real time. For example, data from air conditioners and lighting can be shared to set optimal environmental settings. This allows devices from different manufacturers to be controlled in a single system, improving the convenience of smart homes.

[0071] The environmental impact mitigation unit can reduce carbon dioxide emissions by optimizing energy consumption. The environmental impact mitigation unit reduces carbon dioxide emissions by optimizing energy consumption, for example. For example, optimizing the use of air conditioners and water heaters reduces energy consumption and decreases carbon dioxide emissions. The environmental impact mitigation unit can also analyze energy consumption data in detail and propose optimal settings to minimize carbon dioxide emissions. For example, optimizing the usage time of an air conditioner reduces carbon dioxide emissions. In this way, optimizing energy consumption reduces carbon dioxide emissions and contributes to environmental protection.

[0072] The environmental information collection unit collects data from sensors in real time, and the generative AI analysis unit analyzes the data on the spot, enabling it to instantly propose optimal settings. The environmental information collection unit, for example, collects data from each sensor in real time. For example, it uses temperature sensors, humidity sensors, light sensors, etc. to collect data such as outside temperature and humidity, electricity bills, and gas bills in real time. The generative AI analysis unit analyzes the collected data on the spot and instantly proposes optimal settings. For example, the generative AI analyzes data using text generation AI (e.g., LLM) or multimodal generation AI and proposes optimal settings for the living environment. This enables rapid response by collecting data in real time and instantly proposing optimal settings.

[0073] The environmental information collection unit can estimate the user's emotions using the emotion engine and adjust environmental settings that the user finds comfortable in real time. The environmental information collection unit, for example, estimates the user's emotions using the emotion engine and adjusts environmental settings that the user finds comfortable in real time. For example, the environmental information collection unit estimates the user's emotions from the user's facial expressions and voice and adjusts environmental settings that the user finds comfortable in real time. For example, if the user is relaxed, the air conditioner's set temperature can be slightly lowered. Also, if the user is feeling stressed, the air conditioner's set temperature can be slightly raised. In this way, a comfortable environment can be provided by adjusting environmental settings in real time based on the user's emotions.

[0074] The environmental information collection unit collects external weather data or traffic information, and the generation AI analysis unit can perform comprehensive environmental analysis based on the weather data or traffic information. The environmental information collection unit, for example, collects external weather data. For example, it collects data on outside temperature and humidity based on weather forecasts. It can also collect traffic information. For example, it adjusts the timing of going out based on traffic congestion information. The generation AI analysis unit performs comprehensive environmental analysis based on the collected weather data and traffic information. For example, the generation AI analyzes the data using text generation AI (e.g., LLM) or multimodal generation AI and suggests optimal settings for the living environment. This enables more accurate suggestions by performing comprehensive environmental analysis based on external weather data and traffic information.

[0075] The environmental information collection unit can use a drone to collect a wide range of outdoor data, and the generation AI analysis unit can analyze that data. The environmental information collection unit, for example, uses a drone to collect a wide range of outdoor data. For example, the drone collects data such as outside temperature, humidity, and wind speed. The generation AI analysis unit analyzes the collected data. For example, the generation AI analyzes the data using a text generation AI (e.g., LLM) or a multimodal generation AI to propose optimal settings for the living environment. In this way, collecting and analyzing a wide range of data using a drone enables more accurate environmental settings.

[0076] The environmental information collection unit monitors the operating status of devices in a smart home and can automatically notify if an abnormality is detected. The environmental information collection unit, for example, monitors the operating status of devices in a smart home. For example, it monitors the operating status of air conditioners and lights in real time. If an abnormality is detected, it automatically notifies. For example, it can detect abnormal operation of an air conditioner and notify the user. It can also detect abnormal operation of lights and notify the user. This makes it possible to monitor the operating status of devices and automatically notify if an abnormality is detected, enabling a quick response.

[0077] The environmental information collection unit can use the emotion estimation function to analyze how the user feels about a specific environmental setting and optimize the environmental setting based on the results. The environmental information collection unit can, for example, use the emotion estimation function to analyze how the user feels about a specific environmental setting. For example, the emotion can be estimated from the user's facial expressions and voice, and the emotion about the specific environmental setting can be analyzed. For example, the emotion about lowering the air conditioner's temperature setting can be analyzed, and a temperature that the user finds comfortable can be suggested. The environmental information collection unit can also learn how the user feels about a specific environmental setting and optimize the environmental setting based on the results. For example, if the user is relaxed, the air conditioner's temperature setting can be slightly lowered. Also, if the user is feeling stressed, the air conditioner's temperature setting can be slightly raised. In this way, a comfortable environment can be provided by optimizing the environmental setting based on the user's emotions.

[0078] The pattern learning unit can learn the user's lifestyle patterns in detail and automatically adjust the optimal settings for each season or time of day. The pattern learning unit, for example, learns the user's lifestyle patterns in detail and automatically adjusts the optimal settings for each season or time of day. For example, the generation AI learns the user's lifestyle patterns in detail and automatically adjusts the optimal settings for each season or time of day. For example, the air conditioner's set temperature can be lowered in the summer and raised in the winter. The air conditioner's set temperature can also be adjusted for each time of day. For example, the air conditioner's set temperature can be lowered at night and raised during the day. This makes it possible to provide a comfortable environment by automatically adjusting the optimal settings for each season and time of day based on the user's lifestyle patterns.

[0079] The pattern learning unit can use the emotion estimation function to monitor changes in the user's emotions in real time and adjust the environmental settings accordingly. The pattern learning unit, for example, uses the emotion estimation function to monitor changes in the user's emotions in real time and adjusts the environmental settings accordingly. For example, the emotion estimation function can be used to monitor changes in emotions in real time from the user's facial expressions and voice, and the air conditioner's set temperature can be adjusted accordingly. For example, if the user is relaxed, the air conditioner's set temperature can be slightly lowered. Also, if the user is feeling stressed, the air conditioner's set temperature can be slightly raised. In this way, a comfortable environment can be provided by adjusting the environmental settings according to changes in the user's emotions.

[0080] The pattern learning unit can learn the lifestyle patterns of all family members and provide an optimal environment tailored to each individual's preferences. The pattern learning unit, for example, learns the lifestyle patterns of all family members and provides an optimal environment tailored to each individual's preferences. For example, the generative AI learns the lifestyle patterns of all family members and provides an optimal environment tailored to each individual's preferences. For example, it learns the preferred temperature and humidity of each family member and sets the optimal settings. It can also adjust the air conditioner and lighting settings based on the lifestyle patterns of all family members. For example, during times when all family members gather in the living room, the air conditioner's set temperature is adjusted and the lights are brightened. This makes it possible to provide a comfortable environment by providing an optimal environment tailored to each individual's preferences based on the lifestyle patterns of all family members.

[0081] The pattern learning unit can collect the user's health data and suggest optimal environmental settings based on that data. The pattern learning unit, for example, collects the user's health data and suggests optimal environmental settings based on that data. For example, health data such as the user's heart rate and sleep patterns is collected, and the generation AI analyzes that data to suggest optimal environmental settings. For example, if the heart rate is high, the air conditioner's set temperature can be lowered. The air conditioner's set temperature can also be adjusted based on the sleep pattern. This makes it possible to provide a healthy environment by suggesting optimal environmental settings based on the user's health data.

[0082] The pattern learning unit can also work with devices outside the smart home to learn the user's behavioral patterns when they are out and about. The pattern learning unit, for example, works with devices outside the smart home to learn the user's behavioral patterns when they are out and about. For example, it works with a smartphone or a wearable device to learn the user's behavioral patterns when they are out and about. For example, it can collect activity data when they are out and set the optimal environment when they return home. It can also adjust the settings of the air conditioner and lighting based on the user's behavioral patterns when they are out and about. For example, if the user exercises while they are out, the air conditioner's set temperature can be lowered when they return home. In this way, by working with devices outside the smart home and learning the user's behavioral patterns when they are out and about, it becomes possible to set the environment more accurately.

[0083] The pattern learning unit can use the emotion estimation function to learn what emotions the user feels in specific time periods and situations, and optimize environmental settings based on that. The pattern learning unit, for example, uses the emotion estimation function to learn what emotions the user feels in specific time periods and situations. For example, the emotion estimation function is used to estimate emotions from the user's facial expressions and voice, and learn emotions in specific time periods and situations. For example, if the user is relaxing at night, the set temperature of the air conditioner can be adjusted. Also, if the user is feeling stressed, the set temperature of the air conditioner can be adjusted. In this way, a comfortable environment can be provided by optimizing environmental settings for specific time periods and situations based on the user's emotions.

[0084] The compatibility assurance unit can exchange data in real time between devices from different manufacturers, achieving seamless collaboration. The compatibility assurance unit can, for example, exchange data in real time between devices from different manufacturers, achieving seamless collaboration. For example, data on air conditioners and lighting can be shared to achieve optimal environmental settings. For example, the brightness of lighting can be adjusted according to the temperature setting of the air conditioner. The temperature setting of the air conditioner can also be adjusted according to the brightness of lighting. This allows data to be exchanged in real time between devices from different manufacturers, achieving seamless collaboration, thereby improving the convenience of smart homes.

[0085] The compatibility ensuring unit can use the emotion estimation function to analyze the emotions of a user when using devices from different manufacturers and propose an optimal device combination. The compatibility ensuring unit, for example, uses the emotion estimation function to analyze the emotions of a user when using devices from different manufacturers and proposes an optimal device combination. For example, the emotion estimation function is used to estimate emotions from the user's facial expressions and voice, and analyze the emotions when using devices from different manufacturers. For example, it can propose a combination of air conditioners and lighting. It can also propose a device combination that the user finds comfortable. This makes it possible to provide a comfortable environment by proposing an optimal combination of devices from different manufacturers based on the user's emotions.

[0086] The compatibility assurance unit can automatically check the compatibility of devices from each manufacturer and display a warning when an incompatible device is used. For example, the compatibility assurance unit can automatically check the compatibility of devices from each manufacturer and display a warning when an incompatible device is used. For example, the compatibility assurance unit can check the compatibility of an air conditioner and a light, and display a warning when an incompatible device is used. The compatibility assurance unit can also check device compatibility in real time and display a warning when an incompatible device is used. This can improve system safety by automatically checking the compatibility of devices from each manufacturer and displaying a warning when an incompatible device is used.

[0087] The compatibility assurance unit can develop an integrated platform for centrally managing devices from different manufacturers, allowing users to easily add and remove devices. For example, the compatibility assurance unit can develop an integrated platform for centrally managing devices from different manufacturers, allowing users to easily add and remove devices. For example, air conditioners and lighting can be managed on a single platform. The compatibility assurance unit can also provide an interface that allows users to easily add and remove devices. For example, adding and removing devices using a smartphone app. This allows the development of an integrated platform for centrally managing devices from different manufacturers, allowing users to easily add and remove devices, thereby improving the convenience of smart homes.

[0088] The compatibility assurance department can launch an open source project to ensure device compatibility between manufacturers and promote standardization across the industry. The compatibility assurance department can, for example, launch an open source project to ensure device compatibility between manufacturers and promote standardization across the industry. For example, it can launch an open source project to ensure compatibility between air conditioners and lighting. It can also provide guidelines to promote standardization across the industry. For example, it can standardize protocols to ensure device compatibility. This can promote the spread of smart homes by launching an open source project to ensure device compatibility between manufacturers and promoting standardization across the industry.

[0089] The compatibility ensuring unit can use the emotion estimation function to monitor the emotions of a user when using devices from different manufacturers in real time and propose an optimal device combination. The compatibility ensuring unit, for example, uses the emotion estimation function to monitor the emotions of a user when using devices from different manufacturers in real time and proposes an optimal device combination. For example, the emotion estimation function is used to estimate emotions from the user's facial expressions and voice, and the emotions of a user when using devices from different manufacturers in real time are monitored. For example, the compatibility ensuring unit can propose a combination of air conditioners and lighting. It can also propose a device combination that the user finds comfortable. In this way, a comfortable environment can be provided by monitoring the user's emotions in real time and proposing an optimal combination of devices from different manufacturers.

[0090] The comfortable environment providing unit can predict peak power consumption times and optimize device operation to suit those times. For example, the generating AI can predict peak power consumption times and optimize device operation to suit those times. For example, the comfortable environment providing unit can refrain from using the air conditioner during times of high power consumption. It can also recommend using the air conditioner during times of low power consumption. In this way, energy costs can be reduced by predicting peak power consumption times and optimizing device operation to suit those times.

[0091] The comfortable environment providing unit can use the emotion estimation function to adjust environmental settings that the user finds comfortable in real time and minimize energy consumption. The comfortable environment providing unit can, for example, use the emotion estimation function to adjust environmental settings that the user finds comfortable in real time and minimize energy consumption. For example, the emotion estimation function is used to estimate the user's emotion from their facial expressions and voice, and adjust environmental settings that the user finds comfortable in real time. For example, if the user is relaxed, the air conditioner's set temperature can be slightly lowered. Also, if the user is feeling stressed, the air conditioner's set temperature can be slightly raised. In this way, a comfortable environment can be provided by adjusting environmental settings in real time based on the user's emotions and minimizing energy consumption.

[0092] The comfortable environment provision unit can analyze the energy consumption data of each device in detail and suggest the most efficient way to use it. The comfortable environment provision unit can, for example, analyze the energy consumption data of each device in detail and suggest the most efficient way to use it. For example, the generation AI can analyze the energy consumption data of each device in detail and suggest the most efficient way to use it. For example, it can optimize the usage time of the air conditioner. It can also optimize the usage of lighting. In this way, by analyzing the energy consumption data of each device in detail and suggesting the most efficient way to use it, it is possible to reduce energy costs.

[0093] The comfortable environment providing unit can visualize energy consumption within the smart home, allowing the user to check their own energy consumption in real time. The comfortable environment providing unit, for example, visualizes energy consumption within the smart home, allowing the user to check their own energy consumption in real time. For example, the unit can display energy consumption of air conditioners. It can also display energy consumption of lighting. In this way, energy consumption within the smart home can be visualized, allowing the user to check their own energy consumption in real time, making energy management easier.

[0094] The comfortable environment providing unit can provide guidelines for optimizing energy consumption and enable the user to adjust settings by themselves. The comfortable environment providing unit can, for example, provide guidelines for optimizing energy consumption and enable the user to adjust settings by themselves. For example, the comfortable environment providing unit can provide guidelines for adjusting the set temperature of an air conditioner. It can also provide guidelines for adjusting lighting usage. In this way, by providing guidelines for optimizing energy consumption and enabling the user to adjust settings by themselves, energy management can be made easier.

[0095] The comfortable environment providing unit can use the emotion estimation function to analyze how the user feels about the settings for reducing energy consumption and suggest optimal settings based on the results. The comfortable environment providing unit, for example, uses the emotion estimation function to analyze how the user feels about the settings for reducing energy consumption and suggests optimal settings based on the results. For example, the emotion estimation function can be used to estimate the user's emotion from their facial expressions and voice, and analyze their emotion about the settings for reducing energy consumption. For example, the emotion estimation function can be used to analyze their emotion about lowering the air conditioner's temperature setting and suggest a temperature that the user finds comfortable. It can also learn how the user feels about the settings for reducing energy consumption and suggest optimal settings based on the results. In this way, energy costs can be reduced by suggesting optimal settings for reducing energy consumption based on the user's emotions.

[0096] The environmental impact mitigation unit can analyze energy consumption data in detail and propose optimal settings to minimize carbon dioxide emissions. The environmental impact mitigation unit can, for example, analyze energy consumption data in detail and propose optimal settings to minimize carbon dioxide emissions. For example, the generative AI can analyze energy consumption data in detail and propose optimal settings to minimize carbon dioxide emissions. For example, it can optimize the usage time of air conditioners. It can also optimize lighting usage. This makes it possible to contribute to environmental protection by analyzing energy consumption data in detail and proposing optimal settings to minimize carbon dioxide emissions.

[0097] The environmental impact reduction unit can use the emotion estimation function to analyze how the user feels about environmentally friendly settings and propose optimal settings based on the results. The environmental impact reduction unit, for example, uses the emotion estimation function to analyze how the user feels about environmentally friendly settings and proposes optimal settings based on the results. For example, the emotion estimation function can be used to estimate emotions from the user's facial expressions and voice, and analyze emotions about environmentally friendly settings. For example, the emotion estimation function can analyze emotions about lowering the air conditioner's temperature setting and propose a temperature that the user finds comfortable. It can also learn how the user feels about environmentally friendly settings and propose optimal settings based on the results. In this way, by proposing optimal environmentally friendly settings based on the user's emotions, it is possible to contribute to environmental protection.

[0098] The environmental impact mitigation unit can monitor energy consumption within the smart home in real time and make adjustments to minimize the impact on the environment. For example, the generation AI can monitor energy consumption within the smart home in real time and make adjustments to minimize the impact on the environment. For example, it can optimize the usage time of the air conditioner. It can also optimize the usage of lighting. In this way, it is possible to contribute to environmental protection by monitoring energy consumption within the smart home in real time and making adjustments to minimize the impact on the environment.

[0099] The Environmental Impact Mitigation Department can develop a comprehensive energy management system that includes energy consumption outside the smart home. For example, the generation AI develops a comprehensive energy management system that includes energy consumption outside the smart home and performs optimal energy management. For example, it can manage the charging of electric vehicles. It can also manage energy consumption of external devices. By developing a comprehensive energy management system that includes energy consumption outside the smart home, it is possible to centralize energy management and achieve efficient energy use.

[0100] The environmental impact mitigation unit can introduce environmentally friendly energy sources into the smart home and optimize energy consumption. The environmental impact mitigation unit, for example, introduces environmentally friendly energy sources into the smart home and optimizes energy consumption. For example, the generation AI introduces environmentally friendly energy sources into the smart home and optimizes energy consumption. For example, solar power generation can be used. Wind power generation can also be used. In this way, introducing environmentally friendly energy sources into the smart home and optimizing energy consumption can contribute to environmental protection.

[0101] The environmental impact reduction unit can use the emotion estimation function to monitor in real time how the user feels about the environmentally friendly settings and suggest optimal settings based on the results. The environmental impact reduction unit can, for example, use the emotion estimation function to monitor in real time how the user feels about the environmentally friendly settings and suggest optimal settings based on the results. For example, the emotion estimation function can be used to estimate the user's emotion from their facial expressions and voice, and the emotion about the environmentally friendly settings can be monitored in real time. For example, the temperature setting of the air conditioner can be adjusted. It can also suggest environmental settings that the user finds comfortable. In this way, by monitoring the user's emotions in real time and suggesting optimal environmentally friendly settings, it is possible to contribute to environmental protection.

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

[0103] The smart home system may further include a voice recognition unit. The voice recognition unit can recognize a user's voice commands and operate a specific device. For example, if a user says, "Turn on the air conditioner," the voice recognition unit recognizes the command and starts the air conditioner. Also, if a user says, "Dim the lights," the voice recognition unit can adjust the brightness of the lights. This allows users to operate devices using voice commands, improving convenience for the user.

[0104] The environmental information collection unit can further include an air quality sensor. The air quality sensor can monitor the indoor air quality and activate an air purifier as needed. For example, if the indoor PM2.5 concentration is high, the air purifier can be automatically activated. Also, if the carbon dioxide concentration is high, the ventilation system can be activated. This allows for optimizing indoor air quality and providing a healthy environment.

[0105] The environmental information collection unit can further include a sound sensor. The sound sensor can monitor the noise level in the room and take soundproofing measures as necessary. For example, if the noise level is high, the soundproof curtains can be automatically closed. If the noise level is low, the curtains can be opened. This makes it possible to optimize the noise level in the room and provide a comfortable environment.

[0106] The environmental information collection unit may further include a light sensor. The light sensor can monitor the illuminance in the room and adjust the lighting as needed. For example, if the room is dark, the light can be automatically turned on. Also, if the room is bright, the light can be turned off. This allows the illuminance in the room to be optimized, providing a comfortable environment.

[0107] The environmental information collection unit can further include a water leak sensor. The water leak sensor can detect water leaks in the room and issue a warning as necessary. For example, if a water leak occurs in the kitchen or bathroom, it can automatically issue a warning. It can also stop the water supply when a water leak is detected. This makes it possible to minimize damage caused by water leaks.

[0108] The determination unit can estimate the user's emotion and adjust the color temperature of the lighting based on the estimated user's emotion. For example, if the user is relaxed, the lighting can be adjusted to a warm color. Alternatively, if the user is concentrating, the lighting can be adjusted to a white color. In this way, a comfortable environment can be provided by adjusting the color temperature of the lighting based on the user's emotion.

[0109] The determination unit can estimate the user's emotions and control the playback of music based on the estimated user emotions. For example, if the user is relaxed, it can play relaxing music. Also, if the user is feeling stressed, it can play music that reduces stress. In this way, by controlling the playback of music based on the user's emotions, it is possible to provide a comfortable environment.

[0110] The determination unit can estimate the user's emotions and control the scent diffuser based on the estimated user emotions. For example, if the user is relaxed, the unit can emit a relaxing scent. Alternatively, if the user is concentrating, the unit can emit a scent that enhances concentration. In this way, controlling the scent diffuser based on the user's emotions can provide a comfortable environment.

[0111] The determination unit can estimate the user's emotions and adjust the room temperature based on the estimated user's emotions. For example, if the user is relaxed, the room temperature can be slightly lowered. Alternatively, if the user is feeling stressed, the room temperature can be slightly raised. In this way, a comfortable environment can be provided by adjusting the room temperature based on the user's emotions.

[0112] The determination unit can estimate the user's emotion and control the opening and closing of the curtains based on the estimated user's emotion. For example, if the user is relaxed, the curtains can be opened slightly. Alternatively, if the user is concentrating, the curtains can be closed. In this way, by controlling the opening and closing of the curtains based on the user's emotion, a comfortable environment can be provided.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The environmental information collection unit collects data from various sensors. For example, it uses temperature sensors, humidity sensors, light sensors, etc. to collect data such as outside temperature, humidity, electricity charges, and gas charges. Step 2: The Generative AI Analysis Unit analyzes the data collected by the Environmental Information Collection Unit. For example, the Generative AI may analyze the data using a text generation AI (e.g., LLM) or a multimodal generation AI, and propose optimal settings for the living environment. Step 3: The pattern learning unit learns the user's lifestyle patterns based on the data analyzed by the generation AI analysis unit. For example, the generation AI can learn that the user tends to use the air conditioner at certain times of the day and automatically adjust the air conditioner to suit those times. Step 4: The compatibility assurance unit ensures compatibility between devices from different manufacturers. For example, by supporting the Matter global standard, it becomes possible to control air conditioners and lighting from different manufacturers with a single system. Step 5: The comfortable environment provider provides a comfortable and low-cost home environment. For example, it reduces the use of the air conditioner during times when electricity rates are high and operates it during times when electricity rates are low. Step 6: The Environmental Impact Mitigation Department optimizes energy consumption, for example by optimizing the use of air conditioners and water heaters, thereby reducing energy consumption and carbon dioxide emissions.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0143] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0152] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0154] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0155] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0156] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0157] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0159] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0160] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0161] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0162] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0165] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0167] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0168] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

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

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

[0171] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

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

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

[0174] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0175] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

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

[0177] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0180] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an environmental information collection unit that collects data from various sensors; a generation AI analysis unit that analyzes the data collected by the environmental information collection unit; a pattern learning unit that learns a user's lifestyle pattern based on the data analyzed by the generation AI analysis unit; a compatibility assurance unit that ensures compatibility between devices from different manufacturers; A comfortable environment provision department that provides a comfortable and cost-effective home environment; and an environmental impact reduction unit that optimizes energy consumption. A system characterized by:

2. The environmental information collection unit Estimating the user's emotion using an emotion engine; Adjusting the user's comfortable environment settings in real time 2. The system of claim 1.

3. The pattern learning unit Automatically adjust the air conditioner or water heater according to the user's lifestyle pattern 2. The system of claim 1.

4. The compatibility ensuring unit Controlling devices from different manufacturers with one system 2. The system of claim 1.

5. The comfortable environment providing unit adjusts the user's comfortable environment settings in real time; Minimize energy consumption 2. The system of claim 1.

6. The environmental impact reduction unit Analyzing how the user feels about environmentally friendly settings; Based on the results, we will propose the optimal settings.

2. The system of claim 1.

7. The compatibility ensuring unit exchanging data in real time between said devices of different manufacturers; Achieving seamless integration 2. The system of claim 1.

8. The pattern learning unit collecting health data of said user; Based on this, we will suggest the best environment settings 2. The system of claim 1.

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