system

Abstract

The system according to the embodiment aims to integrate and connect home devices and automatically adjust the environment based on the user's behavioral patterns. [Solution] A system according to an embodiment includes a connection unit, an adapter unit, a data collection unit, an analysis unit, and an adjustment unit. The connection unit wirelessly connects household devices. The adapter unit connects non-compatible home appliances. The data collection unit collects data from each device. The analysis unit analyzes the data collected by the data collection unit and learns the user's behavioral patterns and preferences. The adjustment unit automatically adjusts lighting, temperature, music, etc. based on the analysis results obtained by the analysis unit.

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, home devices are connected and controlled individually, making it difficult to achieve overall optimization.

[0005] The system according to the embodiment aims to integrate and connect home devices and automatically adjust the environment based on the user's behavioral patterns. [Means for solving the problem]

[0006] The system according to the embodiment includes a connection unit, an adapter unit, a data collection unit, an analysis unit, and an adjustment unit. The connection unit wirelessly connects household devices. The adapter unit connects non-compatible home appliances. The data collection unit collects data from each device. The analysis unit analyzes the data collected by the data collection unit and learns the user's behavioral patterns and preferences. The adjustment unit automatically adjusts lighting, temperature, music, etc. based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can integrate and connect home devices and automatically adjust the environment based on the user's behavioral patterns. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. 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) A home device management system according to an embodiment of the present invention wirelessly connects all home devices in a provided residence, using adapters to connect non-compatible home appliances. This system uses cloud-based AI to analyze and learn about the user's daily life and optimize the home environment (lighting, temperature, music). This optimization is not simply control but also predictive and generative environmental maintenance. For example, the system can automatically turn on the lights, adjust the room temperature to a comfortable level, and play relaxing music when the user arrives home. The cloud-based AI constantly collects and analyzes data to learn the user's behavioral patterns and preferences and provide an optimal environment. This allows the home device management system to predictively and generatively adjust lighting, temperature, music, and other settings based on the user's behavioral patterns and preferences.

[0029] A home device management system according to an embodiment includes a connection unit, an adapter unit, a data collection unit, an analysis unit, and an optimization unit. The connection unit wirelessly connects home devices. Examples of home devices include, but are not limited to, smartphones, smart speakers, and smart lighting. The connection unit connects home devices using wireless connection technologies such as Wi-Fi, Bluetooth (registered trademark), and Zigbee (registered trademark). The connection unit can also use a combination of multiple wireless connection technologies. For example, the connection unit can connect to the Internet using Wi-Fi and connect to short-range devices using Bluetooth. The adapter unit connects non-compatible home appliances. Examples of non-compatible home appliances include, but are not limited to, older models of home appliances and home appliances from a specific manufacturer. The adapter unit can provide, for example, a dedicated adapter for connecting to the non-compatible home appliances. The adapter unit can also provide a general-purpose adapter compatible with multiple non-compatible home appliances. For example, the adapter unit can provide a dedicated adapter for connecting to older models of home appliances and a general-purpose adapter for connecting to home appliances from a specific manufacturer. The data collection unit collects data from each device and transmits it to the cloud. Examples of the collected data include, but are not limited to, usage time, power consumption, and operation history. The data collection unit uses protocols such as HTTP, MQTT, and WebSocket to transmit the data collected from each device to the cloud. The data collection unit can also transmit the collected data to the cloud in real time. For example, the data collection unit transmits the data collected from each device to the cloud in real time so that the cloud AI can analyze the data immediately. The analysis unit analyzes the collected data using the cloud AI to learn the user's behavioral patterns and preferences. Examples of cloud AI include, but are not limited to, technologies and services such as AWS (registered trademark) AI, Google (registered trademark) Cloud AI, and Microsoft Azure (registered trademark) AI.The analysis unit, for example, analyzes data collected using cloud AI and uses machine learning algorithms and statistical analysis to learn the user's behavioral patterns and preferences. The analysis unit can also update the analysis results based on user feedback. For example, the analysis unit updates the analysis results based on user feedback to perform more accurate analysis. The optimization unit predictively and generatively adjusts lighting, temperature, music, etc. based on the analysis results. The optimization unit optimizes settings based on the analysis results, for example, to adjust lighting brightness, temperature settings, music selection, etc. The optimization unit can also automatically adjust lighting, temperature, music, etc. based on the user's behavioral patterns and preferences. For example, the optimization unit can automatically turn on the lights, adjust the room temperature to a comfortable temperature, or play relaxing music according to the time the user returns home. As a result, the home device management system according to the embodiment can predictively and generatively adjust lighting, temperature, music, etc. based on the user's behavioral patterns and preferences.

[0030] The connection unit can wirelessly connect home devices. The connection unit connects home devices using wireless connection technologies such as Wi-Fi, Bluetooth, and Zigbee. For example, the connection unit can connect to the Internet using Wi-Fi and connect to nearby devices using Bluetooth. The connection unit can also use a combination of multiple wireless connection technologies. For example, the connection unit can use a combination of Wi-Fi and Bluetooth to efficiently connect home devices. This improves the overall system integration by wirelessly connecting home devices.

[0031] The adapter unit can connect non-compatible home appliances. The adapter unit, for example, provides a dedicated adapter for connecting to non-compatible home appliances. For example, the adapter unit can provide a dedicated adapter for connecting to an older model home appliance. The adapter unit can also provide a general-purpose adapter that is compatible with multiple non-compatible home appliances. For example, the adapter unit can provide a general-purpose adapter for connecting to home appliances from a specific manufacturer. This expands the scope of application of the system by connecting non-compatible home appliances.

[0032] The data collection unit can collect data from each device and send it to the cloud. The data collection unit uses protocols such as HTTP, MQTT, and WebSocket to send the data collected from each device to the cloud. For example, the data collection unit can send data to the cloud using HTTP. The data collection unit can also send the collected data to the cloud in real time. For example, the data collection unit can send the data collected from each device to the cloud in real time so that the cloud AI can analyze the data immediately. In this way, by sending the data to the cloud, the analysis unit can efficiently analyze the data.

[0033] The analysis unit can analyze the data collected using cloud AI and learn the user's behavioral patterns and preferences. The analysis unit, for example, uses machine learning algorithms and statistical analysis to analyze the data collected using cloud AI and learn the user's behavioral patterns and preferences. For example, the analysis unit can analyze the collected data using machine learning algorithms and learn the user's behavioral patterns and preferences. The analysis unit can also update the analysis results based on user feedback. For example, the analysis unit updates the analysis results based on user feedback and performs more accurate analysis. As a result, the use of cloud AI improves the accuracy of the analysis.

[0034] The optimization unit can automatically adjust lighting, temperature, music, etc. based on the analysis results. The optimization unit performs optimal settings based on the analysis results, for example, to adjust lighting brightness, temperature settings, music selection, etc. For example, the optimization unit can adjust lighting brightness to optimize the brightness in a room. The optimization unit can also adjust temperature settings to maintain a comfortable room temperature. The optimization unit can also adjust music selection to play music that matches the user's preferences. This makes it possible to provide an optimal environment for the user by predictively and generatively adjusting the environment.

[0035] The connection unit can adjust the connection order based on the frequency of use of the devices when connecting them. For example, the connection unit can prioritize connecting devices that are used frequently (e.g., lighting and air conditioners). For example, the connection unit can prioritize connecting lighting that is used frequently to ensure brightness in a room. The connection unit can also connect devices that are used less frequently (e.g., seasonal home appliances) later. For example, the connection unit can connect seasonal home appliances that are used less frequently later and prioritize connection of other devices. The connection unit can also connect devices that are used moderately (e.g., music playback devices) in the middle. For example, the connection unit can connect a music playback device that is used moderately in the middle to provide music. In this way, adjusting the connection order based on the frequency of use of the devices enables efficient device connection.

[0036] When connecting devices, the connection unit can select the optimal connection method by taking into account the power consumption of the devices. For example, the connection unit can connect devices with high power consumption (e.g., air conditioners and heaters) first and devices with low power consumption (e.g., LED lighting) last. For example, the connection unit can connect an air conditioner with high power consumption first to maintain a comfortable room temperature. The connection unit can also adjust the number of devices to be connected simultaneously by taking into account the balance of power consumption. For example, the connection unit can adjust the number of devices to be connected simultaneously by taking into account the balance of power consumption, thereby optimizing energy efficiency. The connection unit can also connect devices sequentially, avoiding peak power consumption hours. For example, the connection unit can connect devices sequentially, avoiding peak power consumption hours, to reduce energy consumption. In this way, energy efficiency is improved by selecting a connection method by taking into account the power consumption of the devices.

[0037] The connection unit can select an optimal connection method by taking into account device location information when connecting. For example, when a user is in the living room, the connection unit can prioritize connecting devices in the living room (e.g., a television or air conditioner). For example, when a user is in the living room, the connection unit can prioritize connecting a television to provide entertainment. Furthermore, when a user is in the bedroom, the connection unit can prioritize connecting devices in the bedroom (e.g., a bedside lamp or a humidifier). For example, when a user is in the bedroom, the connection unit can prioritize connecting a bedside lamp to provide comfortable lighting. Furthermore, when a user is in the kitchen, the connection unit can prioritize connecting devices in the kitchen (e.g., a microwave or a coffee maker). For example, when a user is in the kitchen, the connection unit can prioritize connecting a microwave to support cooking. As a result, efficient device connection is possible by selecting a connection method by taking into account device location information.

[0038] The connection unit can check the compatibility of the connection by taking into account manufacturer information of the devices when connecting. For example, the connection unit can preferentially connect devices from the same manufacturer to avoid compatibility issues. For example, the connection unit can preferentially connect devices from the same manufacturer to avoid compatibility issues. The connection unit can also use a compatible protocol when connecting devices from different manufacturers. For example, the connection unit can use a compatible protocol when connecting devices from different manufacturers to ensure connection stability. The connection unit can also use an adapter to ensure connection when connecting incompatible devices. For example, the connection unit can use an adapter to ensure connection when connecting incompatible devices, thereby improving cooperation throughout the system. As a result, checking the compatibility of the connection by taking into account manufacturer information of the devices improves connection stability.

[0039] When the adapter is in use, the adapter unit can analyze the usage history of the home appliance and select the optimal adapter. The adapter unit, for example, selects the most suitable adapter for a frequently used home appliance. For example, the adapter unit can select the most suitable adapter for a frequently used home appliance and achieve an efficient connection. The adapter unit can also select the optimal adapter for a home appliance used during a specific time period based on the usage history. For example, the adapter unit can select the optimal adapter for a home appliance used during a specific time period based on the usage history and achieve an efficient connection. The adapter unit can also select a general-purpose adapter that can be used with multiple home appliances based on the usage history. For example, the adapter unit can select a general-purpose adapter that can be used with multiple home appliances based on the usage history and achieve an efficient connection. In this way, efficient device connection is possible by analyzing the usage history of the home appliance and selecting the optimal adapter.

[0040] When using the adapter, the adapter unit can select the optimal adapter taking into account the power consumption of the home appliance. For example, the adapter unit selects a corresponding high-output adapter for a home appliance with high power consumption. For example, the adapter unit can select a corresponding high-output adapter for a home appliance with high power consumption, thereby realizing a stable connection. The adapter unit can also select a corresponding low-output adapter for a home appliance with low power consumption. For example, the adapter unit can select a corresponding low-output adapter for a home appliance with low power consumption, thereby realizing an efficient connection. The adapter unit can also select an adapter that can be used with multiple home appliances by taking into account the balance of power consumption. For example, the adapter unit can select an adapter that can be used with multiple home appliances by taking into account the balance of power consumption, thereby realizing an efficient connection. In this way, energy efficiency is improved by selecting the optimal adapter taking into account the power consumption of the home appliances.

[0041] When using the adapter, the adapter unit can select the optimal adapter by taking into consideration the location information of the home appliance. For example, if the home appliance is in the living room, the adapter unit selects the optimal adapter for the living room. For example, if the home appliance is in the living room, the adapter unit can select the optimal adapter for the living room to achieve an efficient connection. Furthermore, if the home appliance is in the bedroom, the adapter unit can select the optimal adapter for the bedroom. For example, if the home appliance is in the bedroom, the adapter unit can select the optimal adapter for the bedroom to achieve an efficient connection. Furthermore, if the home appliance is in the kitchen, the adapter unit can select the optimal adapter for the kitchen. For example, if the home appliance is in the kitchen, the adapter unit can select the optimal adapter for the kitchen to achieve an efficient connection. As a result, efficient device connection is possible by selecting the optimal adapter by taking into consideration the location information of the home appliance.

[0042] When using an adapter, the adapter unit can check the compatibility of the adapter by taking into account manufacturer information of the home appliance. The adapter unit, for example, selects a compatible adapter for home appliances from the same manufacturer. For example, the adapter unit can select a compatible adapter for home appliances from the same manufacturer and achieve a stable connection. The adapter unit can also select an adapter using a compatible protocol for home appliances from different manufacturers. For example, the adapter unit can select an adapter using a compatible protocol for home appliances from different manufacturers and achieve a stable connection. The adapter unit can also ensure a connection using a generic adapter for incompatible home appliances. For example, the adapter unit can ensure a connection using a generic adapter for incompatible home appliances, thereby improving cooperation throughout the system. In this way, checking the compatibility of the adapter by taking into account manufacturer information of the home appliances improves the stability of the connection.

[0043] When collecting data, the data collection unit can analyze the device usage history and select the optimal data collection method. The data collection unit, for example, prioritizes collecting data from devices that are used frequently. For example, the data collection unit can prioritize collecting data from devices that are used frequently, thereby achieving efficient data collection. The data collection unit can also collect data from devices that are used during a specific time period based on the usage history. For example, the data collection unit can collect data from devices that are used during a specific time period based on the usage history, thereby achieving efficient data collection. The data collection unit can also efficiently collect data from multiple devices based on the usage history. For example, the data collection unit can efficiently collect data from multiple devices based on the usage history, thereby achieving efficient data collection. This enables efficient data collection by analyzing the device usage history and selecting the optimal data collection method.

[0044] When collecting data, the data collection unit can select an optimal data collection method by taking into account the power consumption of the devices. The data collection unit, for example, can prioritize collecting data from devices with high power consumption to optimize energy efficiency. For example, the data collection unit can prioritize collecting data from devices with high power consumption to optimize energy efficiency. The data collection unit can also collect data from devices with low power consumption to reduce energy consumption. For example, the data collection unit can collect data from devices with low power consumption to reduce energy consumption. The data collection unit can also efficiently collect data from multiple devices by taking into account the balance of power consumption. For example, the data collection unit can efficiently collect data from multiple devices by taking into account the balance of power consumption to optimize energy efficiency. As a result, energy efficiency is improved by selecting an optimal data collection method by taking into account the power consumption of the devices.

[0045] When collecting data, the data collection unit can select the optimal data collection method by taking into account the location information of the device. For example, if the device is in the living room, the data collection unit prioritizes collecting data from the device in the living room. For example, if the device is in the living room, the data collection unit can prioritize collecting data from the device in the living room, thereby achieving efficient data collection. Furthermore, if the device is in the bedroom, the data collection unit can prioritize collecting data from the device in the bedroom. For example, if the device is in the bedroom, the data collection unit can prioritize collecting data from the device in the bedroom, thereby achieving efficient data collection. Furthermore, if the device is in the kitchen, the data collection unit can prioritize collecting data from the device in the kitchen. For example, if the device is in the kitchen, the data collection unit can prioritize collecting data from the device in the kitchen, thereby achieving efficient data collection. This enables efficient data collection by selecting the optimal data collection method by taking into account the location information of the device.

[0046] The data collection unit can check compatibility of data collection by taking into account device manufacturer information when collecting data. For example, the data collection unit can preferentially collect data from devices of the same manufacturer to avoid compatibility issues. For example, the data collection unit can preferentially collect data from devices of the same manufacturer to avoid compatibility issues. The data collection unit can also use a compatible protocol when collecting data from devices of different manufacturers. For example, the data collection unit can use a compatible protocol when collecting data from devices of different manufacturers to ensure stability of data collection. The data collection unit can also use an adapter to ensure data collection when collecting data from incompatible devices. For example, the data collection unit can use an adapter to ensure data collection when collecting data from incompatible devices, thereby improving cooperation throughout the system. As a result, checking compatibility of data collection by taking into account device manufacturer information improves stability of data collection.

[0047] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit analyzes data of high importance (e.g., security-related data) in detail. For example, the analysis unit can analyze security-related data of high importance in detail to strengthen security. The analysis unit can also analyze data of medium importance (e.g., energy consumption data) at a medium level of detail. For example, the analysis unit can analyze energy consumption data of medium importance at a medium level of detail to improve energy efficiency. The analysis unit can also analyze data of low importance (e.g., entertainment-related data) in a simplified manner. For example, the analysis unit can analyze entertainment-related data of low importance in a simplified manner to provide entertainment. Thus, by adjusting the level of detail of the analysis based on the importance of the data, efficient data analysis is possible.

[0048] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies an algorithm that analyzes lighting usage patterns to lighting data. For example, the analysis unit can apply an algorithm that analyzes lighting usage patterns to lighting data to optimize lighting. The analysis unit can also apply an algorithm that analyzes temperature fluctuation patterns to temperature data. For example, the analysis unit can apply an algorithm that analyzes temperature fluctuation patterns to temperature data to optimize temperature. The analysis unit can also apply an algorithm that analyzes a user's music preferences to music data. For example, the analysis unit can apply an algorithm that analyzes a user's music preferences to music data to optimize music. This improves the accuracy of analysis by applying the optimal analysis algorithm depending on the category of data.

[0049] During analysis, the analysis unit can determine the priority of analysis based on the time of data collection. For example, the analysis unit can prioritize analyzing the latest data and provide real-time information. For example, the analysis unit can prioritize analyzing the latest data and provide real-time information. The analysis unit can also analyze past data and grasp long-term trends. For example, the analysis unit can analyze past data and grasp long-term trends. The analysis unit can also prioritize analyzing data from a specific time period and grasp patterns for each time period. For example, the analysis unit can prioritize analyzing data from a specific time period and grasp patterns for each time period. In this way, by determining the priority of analysis based on the time of data collection, it becomes possible to provide real-time information.

[0050] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize analyzing highly relevant data to clarify correlations. For example, the analysis unit can prioritize analyzing highly relevant data to clarify correlations. The analysis unit can also analyze data with a medium degree of relevance first to grasp partial correlations. For example, the analysis unit can analyze data with a medium degree of relevance first to grasp partial correlations. The analysis unit can also analyze data with a low degree of relevance later to grasp overall trends. For example, the analysis unit can analyze data with a low degree of relevance later to grasp overall trends. As a result, by adjusting the order of analysis based on the relevance of the data, efficient data analysis is possible.

[0051] During optimization, the optimization unit can analyze the user's past behavioral patterns and select the optimal optimization method. The optimization unit provides an optimal environment based on, for example, environmental settings that the user has previously preferred. For example, the optimization unit can provide an optimal environment based on environmental settings that the user has previously preferred. The optimization unit can also provide an optimal environment for a specific time period based on the user's past behavioral patterns. For example, the optimization unit can provide an optimal environment for a specific time period based on the user's past behavioral patterns. The optimization unit can also analyze the user's past behavioral patterns and select a long-term optimization method. For example, the optimization unit can analyze the user's past behavioral patterns and select a long-term optimization method to provide an efficient environment. As a result, by analyzing the user's past behavioral patterns and selecting the optimal optimization method, an efficient environment can be provided.

[0052] The optimization unit can customize the optimization means based on the user's current living situation during optimization. For example, when the user is at home, the optimization unit provides an optimal environment for the user at home. For example, when the user is at home, the optimization unit can provide an optimal environment for the user at home. Furthermore, when the user is out, the optimization unit can provide an optimal environment for the user when he or she is out. For example, when the user is out, the optimization unit can provide an optimal environment for the user when he or she is out. Furthermore, when the user is sleeping, the optimization unit can provide an optimal environment for the user when he or she is sleeping. For example, when the user is sleeping, the optimization unit can provide an optimal environment for the user when he or she is sleeping. This makes it possible to provide a more appropriate environment by customizing the optimization means based on the user's current living situation.

[0053] During optimization, the optimization unit can select the optimal optimization method by taking into account the user's geographical location information. For example, when the user is at home, the optimization unit provides an optimal environment for the home. For example, when the user is at home, the optimization unit can provide an optimal environment for the home. Furthermore, when the user is in the office, the optimization unit can provide an optimal environment for the office. For example, when the user is in the office, the optimization unit can provide an optimal environment for the office. Furthermore, when the user is out, the optimization unit can provide an optimal environment for the out-of-office location. For example, when the user is out, the optimization unit can provide an optimal environment for the out-of-office location. This makes it possible to provide an efficient environment by selecting the optimal optimization method by taking into account the user's geographical location information.

[0054] During optimization, the optimization unit can analyze the user's social media activities and suggest optimization measures. For example, if the user seeks relaxation on social media, the optimization unit can provide an environment with a relaxing effect. For example, if the user seeks relaxation on social media, the optimization unit can provide an environment with a relaxing effect. Furthermore, if the user seeks concentration on social media, the optimization unit can provide an environment that increases concentration. For example, if the user seeks concentration on social media, the optimization unit can provide an environment that increases concentration. Furthermore, if the user seeks enjoyment on social media, the optimization unit can provide an environment with an entertaining effect. For example, if the user seeks enjoyment on social media, the optimization unit can provide an environment with an entertaining effect. In this way, by analyzing the user's social media activities and suggesting optimization measures, it is possible to provide a more appropriate environment.

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

[0056] The home device management system may further include an energy management unit that optimizes energy consumption. The energy management unit monitors the power consumption of each device in real time and makes adjustments to maximize energy efficiency. For example, the energy management unit may temporarily restrict the use of specific devices to avoid peak power consumption. The energy management unit may also work with solar power generation systems and storage batteries to optimize the use of renewable energy. Furthermore, the energy management unit may provide the user with energy consumption reports and advice on how to improve energy efficiency. This allows for efficient management of energy consumption throughout the home and reduces environmental impact.

[0057] The connection unit can be equipped with a security management unit to further strengthen security functions. The security management unit authenticates connected devices and prevents unauthorized access. For example, the security management unit can generate a different authentication key for each device and perform authentication at the time of connection. The security management unit can also detect abnormal connection patterns and issue alerts. Furthermore, the security management unit can visualize the security status to the user and suggest necessary countermeasures. This improves the security of the home device management system and allows it to be used with peace of mind.

[0058] The adapter unit may further include a health monitoring unit that monitors the health status of the device. The health monitoring unit monitors the operating status of the connected device and the status of consumable parts, and notifies the user if an abnormality is detected. For example, the health monitoring unit can monitor the device's temperature and voltage and issue an alert if an abnormal value is detected. The health monitoring unit can also predict when consumable parts will need to be replaced and notify the user. Furthermore, the health monitoring unit can analyze the device's operating history and detect signs of failure. This allows the device's health status to be constantly monitored and failures to be prevented.

[0059] The data collection unit may further include a privacy management unit to further strengthen data privacy protection. The privacy management unit anonymizes and encrypts collected data to protect user privacy. For example, the privacy management unit can anonymize data by deleting personally identifiable information when collecting data. The privacy management unit can also encrypt data when transmitting it to prevent unauthorized access by third parties. Furthermore, the privacy management unit can visualize data usage status for users and customize privacy protection settings. This allows data collection while protecting user privacy.

[0060] The analysis unit can further include a data verification unit to improve the reliability of the data. The data verification unit checks the consistency and integrity of the collected data and uses only highly reliable data for analysis. For example, the data verification unit can detect and correct duplicate or missing data. The data verification unit can also detect and filter out abnormal values. Furthermore, the data verification unit can record the source and method of data collection and evaluate the reliability of the data. This allows analysis to be performed using highly reliable data, resulting in more accurate analysis results.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The connection unit wirelessly connects home devices. Home devices include smartphones, smart speakers, smart lighting, etc. The connection unit connects home devices using wireless connection technologies such as Wi-Fi, Bluetooth, and Zigbee. Multiple wireless connection technologies can also be used in combination. For example, Wi-Fi can be used to connect to the Internet and Bluetooth can be used to connect to nearby devices. Step 2: The adapter unit connects the non-compatible home appliance. Non-compatible home appliances include older model home appliances and home appliances from a specific manufacturer. The adapter unit provides a dedicated adapter or a general-purpose adapter for connecting to the non-compatible home appliance. For example, it is possible to provide a dedicated adapter for connecting to older model home appliances and a general-purpose adapter for connecting to home appliances from a specific manufacturer. Step 3: The data collection unit collects data from each device and sends it to the cloud. Collected data includes usage time, power consumption, operation history, etc. The data collection unit sends the data to the cloud using protocols such as HTTP, MQTT, and WebSocket. It can also send collected data to the cloud in real time. For example, data collected from each device can be sent to the cloud in real time so that cloud AI can analyze the data immediately. Step 4: The analysis unit uses cloud AI to analyze the collected data and learn the user's behavioral patterns and preferences. Cloud AI includes technologies and services such as AWS AI, Google Cloud AI, and Microsoft Azure AI. The analysis unit uses machine learning algorithms and statistical analysis to analyze the data and learn the user's behavioral patterns and preferences. It can also update the analysis results based on user feedback. For example, the analysis results can be updated based on user feedback to perform more accurate analysis. Step 5: The adjustment unit automatically adjusts lighting, temperature, music, etc. based on the analysis results. The adjustment unit uses the analysis results to optimize settings such as lighting brightness, temperature settings, and music selection. For example, it can automatically turn on the lights when the user returns home, adjust the room temperature to a comfortable level, or play relaxing music.

[0063] (Example 2) A home device management system according to an embodiment of the present invention wirelessly connects all home devices in a provided residence, using adapters to connect non-compatible home appliances. This system uses cloud-based AI to analyze and learn about the user's daily life and optimize the home environment (lighting, temperature, music). This optimization is not simply control but also predictive and generative environmental maintenance. For example, the system can automatically turn on the lights, adjust the room temperature to a comfortable level, and play relaxing music when the user arrives home. The cloud-based AI constantly collects and analyzes data to learn the user's behavioral patterns and preferences and provide an optimal environment. This allows the home device management system to predictively and generatively adjust lighting, temperature, music, and other settings based on the user's behavioral patterns and preferences.

[0064] A home device management system according to an embodiment includes a connection unit, an adapter unit, a data collection unit, an analysis unit, and an optimization unit. The connection unit wirelessly connects home devices. Examples of home devices include, but are not limited to, smartphones, smart speakers, and smart lighting. The connection unit connects home devices using wireless connection technologies such as Wi-Fi, Bluetooth, and Zigbee. The connection unit can also use a combination of multiple wireless connection technologies. For example, the connection unit can connect to the Internet using Wi-Fi and connect to short-range devices using Bluetooth. The adapter unit connects non-compatible home appliances. Examples of non-compatible home appliances include, but are not limited to, older models of home appliances and home appliances from a specific manufacturer. The adapter unit can provide, for example, a dedicated adapter for connecting to the non-compatible home appliances. The adapter unit can also provide a general-purpose adapter compatible with multiple non-compatible home appliances. For example, the adapter unit can provide a dedicated adapter for connecting to older models of home appliances and a general-purpose adapter for connecting to home appliances from a specific manufacturer. The data collection unit collects data from each device and transmits it to the cloud. Examples of the collected data include, but are not limited to, usage time, power consumption, and operation history. The data collection unit uses protocols such as HTTP, MQTT, and WebSocket to transmit the data collected from each device to the cloud. The data collection unit can also transmit the collected data to the cloud in real time. For example, the data collection unit transmits the data collected from each device to the cloud in real time so that the cloud AI can immediately analyze the data. The analysis unit analyzes the collected data using cloud AI to learn user behavior patterns and preferences. Examples of cloud AI include, but are not limited to, technologies and services such as AWS AI, Google Cloud AI, and Microsoft Azure AI. The analysis unit uses, for example, machine learning algorithms and statistical analysis to analyze the data collected using cloud AI and learn user behavior patterns and preferences.The analysis unit can also update the analysis results based on user feedback. For example, the analysis unit updates the analysis results based on user feedback to perform more accurate analysis. The optimization unit predictively and generatively adjusts lighting, temperature, music, and the like based on the analysis results. The optimization unit optimizes settings based on the analysis results, for example, to adjust lighting brightness, temperature settings, music selection, and the like. The optimization unit can also automatically adjust lighting, temperature, music, and the like based on the user's behavioral patterns and preferences. For example, the optimization unit can automatically turn on the lights, adjust the room temperature to a comfortable temperature, and play relaxing music based on the user's behavioral patterns and preferences. As a result, the home device management system according to the embodiment can predictively and generatively adjust lighting, temperature, music, and the like based on the user's behavioral patterns and preferences.

[0065] The connection unit can wirelessly connect home devices. The connection unit connects home devices using wireless connection technologies such as Wi-Fi, Bluetooth, and Zigbee. For example, the connection unit can connect to the Internet using Wi-Fi and connect to nearby devices using Bluetooth. The connection unit can also use a combination of multiple wireless connection technologies. For example, the connection unit can use a combination of Wi-Fi and Bluetooth to efficiently connect home devices. This improves the overall system integration by wirelessly connecting home devices.

[0066] The adapter unit can connect non-compatible home appliances. The adapter unit, for example, provides a dedicated adapter for connecting to non-compatible home appliances. For example, the adapter unit can provide a dedicated adapter for connecting to an older model home appliance. The adapter unit can also provide a general-purpose adapter that is compatible with multiple non-compatible home appliances. For example, the adapter unit can provide a general-purpose adapter for connecting to home appliances from a specific manufacturer. This expands the scope of application of the system by connecting non-compatible home appliances.

[0067] The data collection unit can collect data from each device and send it to the cloud. The data collection unit uses protocols such as HTTP, MQTT, and WebSocket to send the data collected from each device to the cloud. For example, the data collection unit can send data to the cloud using HTTP. The data collection unit can also send the collected data to the cloud in real time. For example, the data collection unit can send the data collected from each device to the cloud in real time so that the cloud AI can analyze the data immediately. In this way, by sending the data to the cloud, the analysis unit can efficiently analyze the data.

[0068] The analysis unit can analyze the data collected using cloud AI and learn the user's behavioral patterns and preferences. The analysis unit, for example, uses machine learning algorithms and statistical analysis to analyze the data collected using cloud AI and learn the user's behavioral patterns and preferences. For example, the analysis unit can analyze the collected data using machine learning algorithms and learn the user's behavioral patterns and preferences. The analysis unit can also update the analysis results based on user feedback. For example, the analysis unit updates the analysis results based on user feedback and performs more accurate analysis. As a result, the use of cloud AI improves the accuracy of the analysis.

[0069] The optimization unit can automatically adjust lighting, temperature, music, etc. based on the analysis results. The optimization unit performs optimal settings based on the analysis results, for example, to adjust lighting brightness, temperature settings, music selection, etc. For example, the optimization unit can adjust lighting brightness to optimize the brightness in a room. The optimization unit can also adjust temperature settings to maintain a comfortable room temperature. The optimization unit can also adjust music selection to play music that matches the user's preferences. This makes it possible to provide an optimal environment for the user by predictively and generatively adjusting the environment.

[0070] The connection unit can estimate the user's emotions and determine the priority of devices to be connected based on the estimated user's emotions. For example, when the user is feeling stressed, the connection unit can prioritize connecting a device with a relaxing effect (e.g., an aroma diffuser or a relaxing music playback device). For example, when the user is feeling stressed, the connection unit can prioritize connecting an aroma diffuser to provide a relaxing scent. Furthermore, when the user is tired, the connection unit can prioritize connecting a device that enhances comfort, such as lighting or an air conditioner. For example, when the user is tired, the connection unit can prioritize connecting an air conditioner to maintain a comfortable room temperature. Furthermore, when the user is having fun, the connection unit can prioritize connecting an entertainment device (e.g., a television or a game console). For example, when the user is having fun, the connection unit can prioritize connecting a television to provide entertainment. In this way, by determining the device connection priority based on the user's emotions, more appropriate device connection is possible.

[0071] The connection unit can adjust the connection order based on the frequency of use of the devices when connecting them. For example, the connection unit can prioritize connecting devices that are used frequently (e.g., lighting and air conditioners). For example, the connection unit can prioritize connecting lighting that is used frequently to ensure brightness in a room. The connection unit can also connect devices that are used less frequently (e.g., seasonal home appliances) later. For example, the connection unit can connect seasonal home appliances that are used less frequently later and prioritize connection of other devices. The connection unit can also connect devices that are used moderately (e.g., music playback devices) in the middle. For example, the connection unit can connect a music playback device that is used moderately in the middle to provide music. In this way, adjusting the connection order based on the frequency of use of the devices enables efficient device connection.

[0072] When connecting devices, the connection unit can select the optimal connection method by taking into account the power consumption of the devices. For example, the connection unit can connect devices with high power consumption (e.g., air conditioners and heaters) first and devices with low power consumption (e.g., LED lighting) last. For example, the connection unit can connect an air conditioner with high power consumption first to maintain a comfortable room temperature. The connection unit can also adjust the number of devices to be connected simultaneously by taking into account the balance of power consumption. For example, the connection unit can adjust the number of devices to be connected simultaneously by taking into account the balance of power consumption, thereby optimizing energy efficiency. The connection unit can also connect devices sequentially, avoiding peak power consumption hours. For example, the connection unit can connect devices sequentially, avoiding peak power consumption hours, to reduce energy consumption. In this way, energy efficiency is improved by selecting a connection method by taking into account the power consumption of the devices.

[0073] The connection unit can estimate the user's emotion and select the type of device to connect based on the estimated user's emotion. For example, when the user is relaxed, the connection unit selects a device with a relaxing effect (e.g., an aroma diffuser or a relaxing music playback device). For example, when the user is relaxed, the connection unit can select an aroma diffuser to provide a relaxing scent. Furthermore, when the user is concentrating, the connection unit can select a device that enhances concentration (e.g., a desk lamp or noise-canceling headphones). For example, when the user is concentrating, the connection unit can select a desk lamp to provide lighting that enhances concentration. Furthermore, when the user is having fun, the connection unit can select an entertainment device (e.g., a television or a game console). For example, when the user is having fun, the connection unit can select a television to provide entertainment. In this way, by selecting the type of device based on the user's emotion, more appropriate device connection is possible.

[0074] The connection unit can select an optimal connection method by taking into account device location information when connecting. For example, when a user is in the living room, the connection unit can prioritize connecting devices in the living room (e.g., a television or air conditioner). For example, when a user is in the living room, the connection unit can prioritize connecting a television to provide entertainment. Furthermore, when a user is in the bedroom, the connection unit can prioritize connecting devices in the bedroom (e.g., a bedside lamp or a humidifier). For example, when a user is in the bedroom, the connection unit can prioritize connecting a bedside lamp to provide comfortable lighting. Furthermore, when a user is in the kitchen, the connection unit can prioritize connecting devices in the kitchen (e.g., a microwave or a coffee maker). For example, when a user is in the kitchen, the connection unit can prioritize connecting a microwave to support cooking. As a result, efficient device connection is possible by selecting a connection method by taking into account device location information.

[0075] The connection unit can check the compatibility of the connection by taking into account manufacturer information of the devices when connecting. For example, the connection unit can preferentially connect devices from the same manufacturer to avoid compatibility issues. For example, the connection unit can preferentially connect devices from the same manufacturer to avoid compatibility issues. The connection unit can also use a compatible protocol when connecting devices from different manufacturers. For example, the connection unit can use a compatible protocol when connecting devices from different manufacturers to ensure connection stability. The connection unit can also use an adapter to ensure connection when connecting incompatible devices. For example, the connection unit can use an adapter to ensure connection when connecting incompatible devices, thereby improving cooperation throughout the system. As a result, checking the compatibility of the connection by taking into account manufacturer information of the devices improves connection stability.

[0076] The adapter unit can estimate the user's emotions and adjust the timing of adapter use based on the estimated user's emotions. For example, when the user is relaxed, the adapter unit can delay use of the adapter and prioritize connecting a device that has a relaxing effect. For example, when the user is relaxed, the adapter unit can delay use of the adapter and prioritize connecting a device that has a relaxing effect, thereby providing a relaxing effect. Furthermore, when the user is in a hurry, the adapter unit can quickly use the adapter to instantly connect a required device. For example, when the user is in a hurry, the adapter unit can quickly use the adapter to instantly connect a required device, thereby meeting the user's needs. Furthermore, when the user is tired, the adapter unit can minimize use of the adapter and prioritize connecting a device that enhances comfort. For example, when the user is tired, the adapter unit can minimize use of the adapter and prioritize connecting a device that enhances comfort, thereby reducing user fatigue. As a result, adjusting the timing of adapter use based on the user's emotions enables more appropriate device connection.

[0077] When the adapter is in use, the adapter unit can analyze the usage history of the home appliance and select the optimal adapter. The adapter unit, for example, selects the most suitable adapter for a frequently used home appliance. For example, the adapter unit can select the most suitable adapter for a frequently used home appliance and achieve an efficient connection. The adapter unit can also select the optimal adapter for a home appliance used during a specific time period based on the usage history. For example, the adapter unit can select the optimal adapter for a home appliance used during a specific time period based on the usage history and achieve an efficient connection. The adapter unit can also select a general-purpose adapter that can be used with multiple home appliances based on the usage history. For example, the adapter unit can select a general-purpose adapter that can be used with multiple home appliances based on the usage history and achieve an efficient connection. In this way, efficient device connection is possible by analyzing the usage history of the home appliance and selecting the optimal adapter.

[0078] When using the adapter, the adapter unit can select the optimal adapter taking into account the power consumption of the home appliance. For example, the adapter unit selects a corresponding high-output adapter for a home appliance with high power consumption. For example, the adapter unit can select a corresponding high-output adapter for a home appliance with high power consumption, thereby realizing a stable connection. The adapter unit can also select a corresponding low-output adapter for a home appliance with low power consumption. For example, the adapter unit can select a corresponding low-output adapter for a home appliance with low power consumption, thereby realizing an efficient connection. The adapter unit can also select an adapter that can be used with multiple home appliances by taking into account the balance of power consumption. For example, the adapter unit can select an adapter that can be used with multiple home appliances by taking into account the balance of power consumption, thereby realizing an efficient connection. In this way, energy efficiency is improved by selecting the optimal adapter taking into account the power consumption of the home appliances.

[0079] The adapter unit can estimate the user's emotions and adjust the frequency of adapter use based on the estimated user's emotions. For example, when the user is relaxed, the adapter unit can reduce the frequency of adapter use and prioritize connecting devices that have a relaxing effect. For example, when the user is relaxed, the adapter unit can reduce the frequency of adapter use and prioritize connecting devices that have a relaxing effect, thereby providing a relaxing effect. Furthermore, when the user is in a hurry, the adapter unit can increase the frequency of adapter use and quickly connect necessary devices. For example, when the user is in a hurry, the adapter unit can increase the frequency of adapter use and quickly connect necessary devices, thereby meeting the user's needs. Furthermore, when the user is tired, the adapter unit can minimize the frequency of adapter use and prioritize connecting devices that enhance comfort. For example, when the user is tired, the adapter unit can minimize the frequency of adapter use and prioritize connecting devices that enhance comfort, thereby reducing the user's fatigue. As a result, adjusting the frequency of adapter use based on the user's emotions enables more appropriate device connection.

[0080] When using the adapter, the adapter unit can select the optimal adapter by taking into consideration the location information of the home appliance. For example, if the home appliance is in the living room, the adapter unit selects the optimal adapter for the living room. For example, if the home appliance is in the living room, the adapter unit can select the optimal adapter for the living room to achieve an efficient connection. Furthermore, if the home appliance is in the bedroom, the adapter unit can select the optimal adapter for the bedroom. For example, if the home appliance is in the bedroom, the adapter unit can select the optimal adapter for the bedroom to achieve an efficient connection. Furthermore, if the home appliance is in the kitchen, the adapter unit can select the optimal adapter for the kitchen. For example, if the home appliance is in the kitchen, the adapter unit can select the optimal adapter for the kitchen to achieve an efficient connection. As a result, efficient device connection is possible by selecting the optimal adapter by taking into consideration the location information of the home appliance.

[0081] When using an adapter, the adapter unit can check the compatibility of the adapter by taking into account manufacturer information of the home appliance. The adapter unit, for example, selects a compatible adapter for home appliances from the same manufacturer. For example, the adapter unit can select a compatible adapter for home appliances from the same manufacturer and achieve a stable connection. The adapter unit can also select an adapter using a compatible protocol for home appliances from different manufacturers. For example, the adapter unit can select an adapter using a compatible protocol for home appliances from different manufacturers and achieve a stable connection. The adapter unit can also ensure a connection using a generic adapter for incompatible home appliances. For example, the adapter unit can ensure a connection using a generic adapter for incompatible home appliances, thereby improving cooperation throughout the system. In this way, checking the compatibility of the adapter by taking into account manufacturer information of the home appliances improves the stability of the connection.

[0082] The data collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated user's emotions. For example, when the user is relaxed, the data collection unit can reduce the frequency of data collection to respect the user's privacy. For example, when the user is relaxed, the data collection unit can reduce the frequency of data collection to respect the user's privacy. The data collection unit can also increase the frequency of data collection when the user is in a hurry to quickly collect necessary information. For example, when the user is in a hurry, the data collection unit can increase the frequency of data collection to quickly collect necessary information. The data collection unit can also minimize the frequency of data collection when the user is tired to reduce the burden on the user. For example, when the user is tired, the data collection unit can minimize the frequency of data collection to reduce the burden on the user. This allows more appropriate data collection by adjusting the frequency of data collection based on the user's emotions.

[0083] When collecting data, the data collection unit can analyze the device usage history and select the optimal data collection method. The data collection unit, for example, prioritizes collecting data from devices that are used frequently. For example, the data collection unit can prioritize collecting data from devices that are used frequently, thereby achieving efficient data collection. The data collection unit can also collect data from devices that are used during a specific time period based on the usage history. For example, the data collection unit can collect data from devices that are used during a specific time period based on the usage history, thereby achieving efficient data collection. The data collection unit can also efficiently collect data from multiple devices based on the usage history. For example, the data collection unit can efficiently collect data from multiple devices based on the usage history, thereby achieving efficient data collection. This enables efficient data collection by analyzing the device usage history and selecting the optimal data collection method.

[0084] When collecting data, the data collection unit can select an optimal data collection method by taking into account the power consumption of the devices. The data collection unit, for example, can prioritize collecting data from devices with high power consumption to optimize energy efficiency. For example, the data collection unit can prioritize collecting data from devices with high power consumption to optimize energy efficiency. The data collection unit can also collect data from devices with low power consumption to reduce energy consumption. For example, the data collection unit can collect data from devices with low power consumption to reduce energy consumption. The data collection unit can also efficiently collect data from multiple devices by taking into account the balance of power consumption. For example, the data collection unit can efficiently collect data from multiple devices by taking into account the balance of power consumption to optimize energy efficiency. As a result, energy efficiency is improved by selecting an optimal data collection method by taking into account the power consumption of the devices.

[0085] The data collection unit can estimate the user's emotions and select the type of data to collect based on the estimated user's emotions. For example, when the user is relaxed, the data collection unit prioritizes collecting data from devices that have a relaxing effect. For example, when the user is relaxed, the data collection unit can prioritize collecting data from devices that have a relaxing effect, thereby providing a relaxing effect. Furthermore, when the user is concentrating, the data collection unit can prioritize collecting data from devices that enhance concentration. For example, when the user is concentrating, the data collection unit can prioritize collecting data from devices that enhance concentration, thereby providing an environment that enhances concentration. Furthermore, when the user is having fun, the data collection unit can prioritize collecting data from entertainment devices. For example, when the user is having fun, the data collection unit can prioritize collecting data from entertainment devices, thereby providing entertainment. This enables more appropriate data collection by selecting the type of data to collect based on the user's emotions.

[0086] When collecting data, the data collection unit can select the optimal data collection method by taking into account the location information of the device. For example, if the device is in the living room, the data collection unit prioritizes collecting data from the device in the living room. For example, if the device is in the living room, the data collection unit can prioritize collecting data from the device in the living room, thereby achieving efficient data collection. Furthermore, if the device is in the bedroom, the data collection unit can prioritize collecting data from the device in the bedroom. For example, if the device is in the bedroom, the data collection unit can prioritize collecting data from the device in the bedroom, thereby achieving efficient data collection. Furthermore, if the device is in the kitchen, the data collection unit can prioritize collecting data from the device in the kitchen. For example, if the device is in the kitchen, the data collection unit can prioritize collecting data from the device in the kitchen, thereby achieving efficient data collection. This enables efficient data collection by selecting the optimal data collection method by taking into account the location information of the device.

[0087] The data collection unit can check compatibility of data collection by taking into account device manufacturer information when collecting data. For example, the data collection unit can preferentially collect data from devices of the same manufacturer to avoid compatibility issues. For example, the data collection unit can preferentially collect data from devices of the same manufacturer to avoid compatibility issues. The data collection unit can also use a compatible protocol when collecting data from devices of different manufacturers. For example, the data collection unit can use a compatible protocol when collecting data from devices of different manufacturers to ensure stability of data collection. The data collection unit can also use an adapter to ensure data collection when collecting data from incompatible devices. For example, the data collection unit can use an adapter to ensure data collection when collecting data from incompatible devices, thereby improving cooperation throughout the system. As a result, checking compatibility of data collection by taking into account device manufacturer information improves stability of data collection.

[0088] The analysis unit can estimate the user's emotions and determine an analysis priority based on the estimated user's emotions. For example, when the user is relaxed, the analysis unit prioritizes analyzing data from devices that have a relaxing effect. For example, when the user is relaxed, the analysis unit can prioritize analyzing data from devices that have a relaxing effect and provide a relaxing effect. Furthermore, when the user is concentrating, the analysis unit can prioritize analyzing data from devices that enhance concentration. For example, when the user is concentrating, the analysis unit can prioritize analyzing data from devices that enhance concentration and provide an environment that enhances concentration. Furthermore, the analysis unit can prioritize analyzing data from entertainment devices when the user is having fun. For example, when the user is having fun, the analysis unit can prioritize analyzing data from entertainment devices and provide entertainment. This enables more appropriate data analysis by determining the analysis priority based on the user's emotions.

[0089] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit analyzes data of high importance (e.g., security-related data) in detail. For example, the analysis unit can analyze security-related data of high importance in detail to strengthen security. The analysis unit can also analyze data of medium importance (e.g., energy consumption data) at a medium level of detail. For example, the analysis unit can analyze energy consumption data of medium importance at a medium level of detail to improve energy efficiency. The analysis unit can also analyze data of low importance (e.g., entertainment-related data) in a simplified manner. For example, the analysis unit can analyze entertainment-related data of low importance in a simplified manner to provide entertainment. Thus, by adjusting the level of detail of the analysis based on the importance of the data, efficient data analysis is possible.

[0090] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies an algorithm that analyzes lighting usage patterns to lighting data. For example, the analysis unit can apply an algorithm that analyzes lighting usage patterns to lighting data to optimize lighting. The analysis unit can also apply an algorithm that analyzes temperature fluctuation patterns to temperature data. For example, the analysis unit can apply an algorithm that analyzes temperature fluctuation patterns to temperature data to optimize temperature. The analysis unit can also apply an algorithm that analyzes a user's music preferences to music data. For example, the analysis unit can apply an algorithm that analyzes a user's music preferences to music data to optimize music. This improves the accuracy of analysis by applying the optimal analysis algorithm depending on the category of data.

[0091] The analysis unit can estimate the user's emotions and adjust the order of analysis based on the estimated user's emotions. For example, when the user is relaxed, the analysis unit prioritizes analyzing data from devices that have a relaxing effect. For example, when the user is relaxed, the analysis unit can prioritize analyzing data from devices that have a relaxing effect, thereby providing a relaxing effect. Furthermore, when the user is concentrating, the analysis unit can prioritize analyzing data from devices that enhance concentration. For example, when the user is concentrating, the analysis unit can prioritize analyzing data from devices that enhance concentration, thereby providing an environment that enhances concentration. Furthermore, the analysis unit can prioritize analyzing data from entertainment devices, when the user is having fun. For example, when the user is having fun, the analysis unit can prioritize analyzing data from entertainment devices, thereby providing entertainment. This allows for more appropriate data analysis by adjusting the order of analysis based on the user's emotions.

[0092] During analysis, the analysis unit can determine the priority of analysis based on the time of data collection. For example, the analysis unit can prioritize analyzing the latest data and provide real-time information. For example, the analysis unit can prioritize analyzing the latest data and provide real-time information. The analysis unit can also analyze past data and grasp long-term trends. For example, the analysis unit can analyze past data and grasp long-term trends. The analysis unit can also prioritize analyzing data from a specific time period and grasp patterns for each time period. For example, the analysis unit can prioritize analyzing data from a specific time period and grasp patterns for each time period. In this way, by determining the priority of analysis based on the time of data collection, it becomes possible to provide real-time information.

[0093] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize analyzing highly relevant data to clarify correlations. For example, the analysis unit can prioritize analyzing highly relevant data to clarify correlations. The analysis unit can also analyze data with a medium degree of relevance first to grasp partial correlations. For example, the analysis unit can analyze data with a medium degree of relevance first to grasp partial correlations. The analysis unit can also analyze data with a low degree of relevance later to grasp overall trends. For example, the analysis unit can analyze data with a low degree of relevance later to grasp overall trends. As a result, by adjusting the order of analysis based on the relevance of the data, efficient data analysis is possible.

[0094] The optimization unit can estimate the user's emotions and adjust the optimization method based on the estimated user's emotions. For example, when the user is relaxed, the optimization unit provides an environment with a relaxing effect (for example, soft lighting and relaxing music). For example, when the user is relaxed, the optimization unit can provide soft lighting to provide a relaxing effect. Furthermore, when the user is concentrating, the optimization unit can also provide an environment that enhances concentration (for example, bright lighting and a quiet environment). For example, when the user is concentrating, the optimization unit can provide bright lighting to provide an environment that enhances concentration. Furthermore, when the user is having fun, the optimization unit can also provide an environment with an entertainment effect (for example, colorful lighting and fun music). For example, when the user is having fun, the optimization unit can provide colorful lighting to provide an entertainment effect. In this way, by adjusting the optimization method based on the user's emotions, it is possible to provide a more appropriate environment.

[0095] During optimization, the optimization unit can analyze the user's past behavioral patterns and select the optimal optimization method. The optimization unit provides an optimal environment based on, for example, environmental settings that the user has previously preferred. For example, the optimization unit can provide an optimal environment based on environmental settings that the user has previously preferred. The optimization unit can also provide an optimal environment for a specific time period based on the user's past behavioral patterns. For example, the optimization unit can provide an optimal environment for a specific time period based on the user's past behavioral patterns. The optimization unit can also analyze the user's past behavioral patterns and select a long-term optimization method. For example, the optimization unit can analyze the user's past behavioral patterns and select a long-term optimization method to provide an efficient environment. As a result, by analyzing the user's past behavioral patterns and selecting the optimal optimization method, an efficient environment can be provided.

[0096] The optimization unit can customize the optimization means based on the user's current living situation during optimization. For example, when the user is at home, the optimization unit provides an optimal environment for the user at home. For example, when the user is at home, the optimization unit can provide an optimal environment for the user at home. Furthermore, when the user is out, the optimization unit can provide an optimal environment for the user when he or she is out. For example, when the user is out, the optimization unit can provide an optimal environment for the user when he or she is out. Furthermore, when the user is sleeping, the optimization unit can provide an optimal environment for the user when he or she is sleeping. For example, when the user is sleeping, the optimization unit can provide an optimal environment for the user when he or she is sleeping. This makes it possible to provide a more appropriate environment by customizing the optimization means based on the user's current living situation.

[0097] The optimization unit can estimate the user's emotions and determine optimization priorities based on the estimated user's emotions. For example, when the user is relaxed, the optimization unit can prioritize providing an environment with a relaxing effect. For example, when the user is relaxed, the optimization unit can prioritize providing an environment with a relaxing effect, thereby providing a relaxing effect. Furthermore, when the user is concentrating, the optimization unit can prioritize providing an environment that enhances concentration. For example, when the user is concentrating, the optimization unit can prioritize providing an environment that enhances concentration, thereby providing an environment that enhances concentration. Furthermore, when the user is having fun, the optimization unit can prioritize providing an environment with an entertainment effect. For example, when the user is having fun, the optimization unit can prioritize providing an environment with an entertainment effect, thereby providing entertainment. In this way, by determining optimization priorities based on the user's emotions, it is possible to provide a more appropriate environment.

[0098] During optimization, the optimization unit can select the optimal optimization method by taking into account the user's geographical location information. For example, when the user is at home, the optimization unit provides an optimal environment for the home. For example, when the user is at home, the optimization unit can provide an optimal environment for the home. Furthermore, when the user is in the office, the optimization unit can provide an optimal environment for the office. For example, when the user is in the office, the optimization unit can provide an optimal environment for the office. Furthermore, when the user is out, the optimization unit can provide an optimal environment for the out-of-office location. For example, when the user is out, the optimization unit can provide an optimal environment for the out-of-office location. This makes it possible to provide an efficient environment by selecting the optimal optimization method by taking into account the user's geographical location information.

[0099] During optimization, the optimization unit can analyze the user's social media activities and suggest optimization measures. For example, if the user seeks relaxation on social media, the optimization unit can provide an environment with a relaxing effect. For example, if the user seeks relaxation on social media, the optimization unit can provide an environment with a relaxing effect. Furthermore, if the user seeks concentration on social media, the optimization unit can provide an environment that increases concentration. For example, if the user seeks concentration on social media, the optimization unit can provide an environment that increases concentration. Furthermore, if the user seeks enjoyment on social media, the optimization unit can provide an environment with an entertaining effect. For example, if the user seeks enjoyment on social media, the optimization unit can provide an environment with an entertaining effect. In this way, by analyzing the user's social media activities and suggesting optimization measures, it is possible to provide a more appropriate environment. === Hard Collateral 1-1 === Each of the multiple elements, including the connection unit, adapter unit, data collection unit, analysis unit, and optimization unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the connection unit wirelessly connects household devices using the communication I / F 44 of the smart device 14. The adapter unit connects non-compatible home appliances using the control unit 46A of the smart device 14. The data collection unit collects data from each device using the processor 46 of the smart device 14 and transmits it to the cloud. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 using cloud AI and learns the user's behavioral patterns and preferences. The optimization unit predictively and generatively adjusts lighting, temperature, music, etc. based on the analysis results of the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the connection unit, adapter unit, data collection unit, analysis unit, and optimization unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the connection unit wirelessly connects household devices using the communication I / F 44 of the smart glasses 214. The adapter unit connects non-compatible home appliances using the control unit 46A of the smart glasses 214. The data collection unit collects data from each device using the processor 46 of the smart glasses 214 and transmits it to the cloud. The analysis unit analyzes the data collected using cloud AI by the specific processing unit 290 of the data processing device 12 and learns the user's behavioral patterns and preferences. The optimization unit predictively or generatively adjusts lighting, temperature, music, etc. based on the analysis results by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements, including the connection unit, adapter unit, data collection unit, analysis unit, and optimization unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the connection unit wirelessly connects household devices using the communication I / F 44 of the headset-type terminal 314. The adapter unit connects non-compatible home appliances using the control unit 46A of the headset-type terminal 314. The data collection unit collects data from each device using the processor 46 of the headset-type terminal 314 and transmits it to the cloud. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 using cloud AI and learns the user's behavioral patterns and preferences. The optimization unit predictively and generatively adjusts lighting, temperature, music, etc. based on the analysis results of the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the connection unit, adapter unit, data collection unit, analysis unit, and optimization unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the connection unit wirelessly connects household devices using the communication I / F 44 of the robot 414. The adapter unit connects non-compatible home appliances using the control unit 46A of the robot 414. The data collection unit collects data from each device using the processor 46 of the robot 414 and transmits it to the cloud. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 using cloud AI and learns the user's behavioral patterns and preferences. The optimization unit predictively and generatively adjusts lighting, temperature, music, etc. based on the analysis results by the specific processing unit 290 of the data processing device 12.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The home device management system may further include an energy management unit that optimizes energy consumption. The energy management unit monitors the power consumption of each device in real time and makes adjustments to maximize energy efficiency. For example, the energy management unit may temporarily restrict the use of specific devices to avoid peak power consumption. The energy management unit may also work with solar power generation systems and storage batteries to optimize the use of renewable energy. Furthermore, the energy management unit may provide the user with energy consumption reports and advice on how to improve energy efficiency. This allows for efficient management of energy consumption throughout the home and reduces environmental impact.

[0102] The connection unit can be equipped with a security management unit to further strengthen security functions. The security management unit authenticates connected devices and prevents unauthorized access. For example, the security management unit can generate a different authentication key for each device and perform authentication at the time of connection. The security management unit can also detect abnormal connection patterns and issue alerts. Furthermore, the security management unit can visualize the security status to the user and suggest necessary countermeasures. This improves the security of the home device management system and allows it to be used with peace of mind.

[0103] The adapter unit may further include a health monitoring unit that monitors the health status of the device. The health monitoring unit monitors the operating status of the connected device and the status of consumable parts, and notifies the user if an abnormality is detected. For example, the health monitoring unit can monitor the device's temperature and voltage and issue an alert if an abnormal value is detected. The health monitoring unit can also predict when consumable parts will need to be replaced and notify the user. Furthermore, the health monitoring unit can analyze the device's operating history and detect signs of failure. This allows the device's health status to be constantly monitored and failures to be prevented.

[0104] The data collection unit may further include a privacy management unit to further strengthen data privacy protection. The privacy management unit anonymizes and encrypts collected data to protect user privacy. For example, the privacy management unit can anonymize data by deleting personally identifiable information when collecting data. The privacy management unit can also encrypt data when transmitting it to prevent unauthorized access by third parties. Furthermore, the privacy management unit can visualize data usage status for users and customize privacy protection settings. This allows data collection while protecting user privacy.

[0105] The analysis unit can further include a data verification unit to improve the reliability of the data. The data verification unit checks the consistency and integrity of the collected data and uses only highly reliable data for analysis. For example, the data verification unit can detect and correct duplicate or missing data. The data verification unit can also detect and filter out abnormal values. Furthermore, the data verification unit can record the source and method of data collection and evaluate the reliability of the data. This allows analysis to be performed using highly reliable data, resulting in more accurate analysis results.

[0106] The optimization unit may further include a health management unit that adjusts the environment taking into account the user's health condition. The health management unit collects the user's health data (e.g., heart rate and sleep data) and provides an optimal environment. For example, if the user's heart rate is high, the health management unit may provide an environment that has a relaxing effect. The health management unit may also provide a comfortable sleeping environment based on the user's sleep data. Furthermore, the health management unit may suggest timing for exercise and rest depending on the user's health condition. This makes it possible to provide an optimal environment that takes into account the user's health condition.

[0107] The connection unit can estimate the user's emotions and determine the priority of devices to be connected based on the estimated user's emotions. For example, when the user is feeling stressed, the connection unit can prioritize connecting a device with a relaxing effect (e.g., an aroma diffuser or a relaxing music playback device). For example, when the user is feeling stressed, the connection unit can prioritize connecting an aroma diffuser to provide a relaxing scent. Furthermore, when the user is tired, the connection unit can prioritize connecting a device that enhances comfort, such as lighting or an air conditioner. For example, when the user is tired, the connection unit can prioritize connecting an air conditioner to maintain a comfortable room temperature. Furthermore, when the user is having fun, the connection unit can prioritize connecting an entertainment device (e.g., a television or a game console). For example, when the user is having fun, the connection unit can prioritize connecting a television to provide entertainment. In this way, by determining the device connection priority based on the user's emotions, more appropriate device connection is possible.

[0108] The adapter unit can estimate the user's emotions and adjust the timing of adapter use based on the estimated user's emotions. For example, when the user is relaxed, the adapter unit can delay use of the adapter and prioritize connecting a device that has a relaxing effect. For example, when the user is relaxed, the adapter unit can delay use of the adapter and prioritize connecting a device that has a relaxing effect, thereby providing a relaxing effect. Furthermore, when the user is in a hurry, the adapter unit can quickly use the adapter to instantly connect a required device. For example, when the user is in a hurry, the adapter unit can quickly use the adapter to instantly connect a required device, thereby meeting the user's needs. Furthermore, when the user is tired, the adapter unit can minimize use of the adapter and prioritize connecting a device that enhances comfort. For example, when the user is tired, the adapter unit can minimize use of the adapter and prioritize connecting a device that enhances comfort, thereby reducing user fatigue. As a result, adjusting the timing of adapter use based on the user's emotions enables more appropriate device connection.

[0109] The data collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated user's emotions. For example, when the user is relaxed, the data collection unit can reduce the frequency of data collection to respect the user's privacy. For example, when the user is relaxed, the data collection unit can reduce the frequency of data collection to respect the user's privacy. The data collection unit can also increase the frequency of data collection when the user is in a hurry to quickly collect necessary information. For example, when the user is in a hurry, the data collection unit can increase the frequency of data collection to quickly collect necessary information. The data collection unit can also minimize the frequency of data collection when the user is tired to reduce the burden on the user. For example, when the user is tired, the data collection unit can minimize the frequency of data collection to reduce the burden on the user. This allows more appropriate data collection by adjusting the frequency of data collection based on the user's emotions.

[0110] The optimization unit can estimate the user's emotions and adjust the optimization method based on the estimated user's emotions. For example, when the user is relaxed, the optimization unit provides an environment with a relaxing effect (for example, soft lighting and relaxing music). For example, when the user is relaxed, the optimization unit can provide soft lighting to provide a relaxing effect. Furthermore, when the user is concentrating, the optimization unit can also provide an environment that enhances concentration (for example, bright lighting and a quiet environment). For example, when the user is concentrating, the optimization unit can provide bright lighting to provide an environment that enhances concentration. Furthermore, when the user is having fun, the optimization unit can also provide an environment with an entertainment effect (for example, colorful lighting and fun music). For example, when the user is having fun, the optimization unit can provide colorful lighting to provide an entertainment effect. In this way, by adjusting the optimization method based on the user's emotions, it is possible to provide a more appropriate environment.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The connection unit wirelessly connects home devices. Home devices include smartphones, smart speakers, smart lighting, etc. The connection unit connects home devices using wireless connection technologies such as Wi-Fi, Bluetooth, and Zigbee. Multiple wireless connection technologies can also be used in combination. For example, Wi-Fi can be used to connect to the Internet and Bluetooth can be used to connect to nearby devices. Step 2: The adapter unit connects the non-compatible home appliance. Non-compatible home appliances include older model home appliances and home appliances from a specific manufacturer. The adapter unit provides a dedicated adapter or a general-purpose adapter for connecting to the non-compatible home appliance. For example, it is possible to provide a dedicated adapter for connecting to older model home appliances and a general-purpose adapter for connecting to home appliances from a specific manufacturer. Step 3: The data collection unit collects data from each device and sends it to the cloud. Collected data includes usage time, power consumption, operation history, etc. The data collection unit sends the data to the cloud using protocols such as HTTP, MQTT, and WebSocket. It can also send collected data to the cloud in real time. For example, data collected from each device can be sent to the cloud in real time so that cloud AI can analyze the data immediately. Step 4: The analysis unit uses cloud AI to analyze the collected data and learn the user's behavioral patterns and preferences. Cloud AI includes technologies and services such as AWS AI, Google Cloud AI, and Microsoft Azure AI. The analysis unit uses machine learning algorithms and statistical analysis to analyze the data and learn the user's behavioral patterns and preferences. It can also update the analysis results based on user feedback. For example, the analysis results can be updated based on user feedback to perform more accurate analysis. Step 5: The adjustment unit automatically adjusts lighting, temperature, music, etc. based on the analysis results. The adjustment unit uses the analysis results to optimize settings such as lighting brightness, temperature settings, and music selection. For example, it can automatically turn on the lights when the user returns home, adjust the room temperature to a comfortable level, or play relaxing music.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[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] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0160] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0161] 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.

[0162] 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.

[0163] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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, to avoid confusion and 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.

[0183] 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.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a connection section for wirelessly connecting home devices; An adapter part for connecting non-compatible home appliances, a data collection unit that collects data from each device; an analysis unit that analyzes the data collected by the data collection unit and learns user behavior patterns and preferences; and an adjustment unit that automatically adjusts lighting, temperature, music, etc. based on the analysis results obtained by the analysis unit. A system characterized by:

2. The connection portion is Connect your home devices wirelessly The system of claim 1 .

3. The adapter portion is Connecting non-compatible appliances The system of claim 1 .

4. The data collection unit Collect data from each device and send it to the cloud The system of claim 1 .

5. The analysis unit Cloud AI is used to analyze collected data and learn user behavior patterns and preferences. The system of claim 1 .

6. The adjustment unit Automatically adjust lighting, temperature, music, etc. based on analysis results The system of claim 1 .

7. The connection portion is Estimates user emotions and prioritizes devices to connect based on the estimated user emotions. The system of claim 1 .

8. The connection portion is When connecting, adjust the connection order based on how frequently you use the device The system of claim 1 .

Images