system

Abstract

The system according to this embodiment aims to automatically control household appliances and equipment according to the user's lifestyle and preferences. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a control unit, a learning unit, and an adjustment unit. The reception unit receives voice commands. The analysis unit analyzes the voice commands received by the reception unit. The control unit controls household appliances and equipment based on the commands analyzed by the analysis unit. The learning unit learns the user's lifestyle habits and preferences. The adjustment unit automatically adjusts the settings of household appliances based on the information learned by the learning unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the control of devices and equipment in the home is often performed manually, and there is a problem that it is difficult to perform optimal environmental settings according to the user's living habits and preferences.

[0005] The system according to the embodiment aims to automatically control devices and equipment in the home according to the user's living habits and preferences.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a control unit, a learning unit, and an adjustment unit. The reception unit receives voice commands. The analysis unit analyzes the voice commands received by the reception unit. The control unit controls household appliances and equipment based on the commands analyzed by the analysis unit. The learning unit learns the user's lifestyle and preferences. The adjustment unit automatically adjusts the settings of household appliances based on the information learned by the learning unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically control household appliances and equipment according to the user's lifestyle and preferences. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The smart home control support AI assistant according to an embodiment of the present invention is a system that controls household devices and equipment by voice. This system has the function of learning the user's lifestyle and preferences and automatically adjusting the optimal environment settings. It also monitors and optimizes security and power consumption. For example, there is a reception unit that receives voice commands, and when the user gives a voice command, this reception unit receives the command. Next, the voice command received by the reception unit is sent to an analysis unit, which analyzes the command. Based on the analyzed command, the control unit controls the household devices or equipment. Furthermore, there is a learning unit that learns the user's lifestyle and preferences, and this learning unit learns the user's behavior patterns and preferences. Based on the learned information, the adjustment unit automatically adjusts the settings of household devices. For example, if the user has a habit of turning off the lights at a specific time every night, the adjustment unit sets the lights to turn off automatically at that time. The adjustment unit also has the function of optimizing security settings and power consumption settings. For example, it can automatically turn on the security system when the user leaves the house, or turn off unnecessary devices to minimize power consumption. Furthermore, the reception unit has the function of estimating the user's emotions and adjusting how voice instructions are received based on those emotions. For example, if the user is feeling stressed, the reception unit will provide simpler instructions. The reception unit can also refer to the user's past instruction history to select an appropriate method of receiving instructions. For example, if the user has previously given instructions in a particular way, that method will be given priority. In addition, the reception unit can adjust the method of receiving instructions based on the user's current situation. For example, if the user is busy, the reception unit will provide simpler instructions. The reception unit can also estimate the user's emotions and determine the priority of voice instructions based on those emotions. For example, if the user gives an urgent instruction, that instruction will be processed with priority. Finally, the adjustment unit can also estimate the user's emotions and automatically adjust the settings of home devices based on those emotions. For example, if the user wants to relax, the adjustment unit will dim the lights and play music. The reception unit can also analyze the user's social media activity and receive relevant instructions.For example, if a user mentions a specific event on social media, the system will adjust settings related to that event. This allows the smart home control support AI assistant to control devices and equipment in the home by voice and automatically adjust the optimal environment settings based on the user's lifestyle and preferences.

[0029] The AI ​​assistant for smart home control according to this embodiment comprises a reception unit, an analysis unit, a control unit, a learning unit, and an adjustment unit. The reception unit receives voice instructions. For example, when a user gives a voice instruction, the reception unit receives that instruction. The reception unit can convert voice instructions into text data using speech recognition technology. For example, the reception unit recognizes voice commands and performs specific operations. The reception unit can also understand natural language questions and generate appropriate responses. The analysis unit analyzes the voice instructions received by the reception unit. For example, the analysis unit analyzes voice instructions using speech recognition technology and understands their content. The analysis unit can analyze the intent of voice instructions using natural language processing technology. For example, the analysis unit extracts keywords from voice instructions and determines appropriate operations based on those keywords. The analysis unit can also understand the context of voice instructions and perform more advanced analysis. The control unit controls household appliances and equipment based on the instructions analyzed by the analysis unit. For example, the control unit controls household appliances such as lighting, air conditioners, and televisions. The control unit can monitor the status of household equipment and perform appropriate control. For example, it can adjust the brightness of lights or set the temperature of air conditioners. It can also control the household security system. The learning unit learns the user's lifestyle and preferences. For example, the learning unit learns the user's behavioral patterns using machine learning algorithms. The learning unit can analyze the user's past operation history and understand their preferences. For example, the learning unit learns the user's habit of performing a specific operation at a specific time and performs the operation automatically based on that habit. The learning unit can also optimize the settings of household devices based on the user's preferences. The adjustment unit automatically adjusts the settings of household devices based on the information learned by the learning unit. For example, if the user has a habit of turning off the lights at a specific time every night, the adjustment unit can set it to automatically turn off the lights at that time. The adjustment unit can also optimize security settings and power consumption settings. For example, the adjustment unit can automatically turn on the security system when the user leaves the house or turn off unnecessary devices to minimize power consumption.As a result, the AI ​​assistant for smart home control according to this embodiment can control household devices and equipment by voice and automatically adjust the optimal environmental settings based on the user's lifestyle and preferences.

[0030] The reception unit receives voice commands. For example, when a user gives a voice command, the reception unit receives that command. The reception unit can convert voice commands into text data using speech recognition technology. Specifically, the reception unit is equipped with a high-precision microphone to clearly capture the user's voice. The speech recognition engine uses noise cancellation technology to remove background noise and accurately recognize voice commands. For example, if the user says, "Turn on the living room lights," the reception unit converts this voice into text data and sends it to the analysis unit. The reception unit also has a multilingual speech recognition model to support multiple languages ​​and dialects, so it can handle commands given by the user in any language. Furthermore, the reception unit uses natural language processing technology to understand the context of the voice command and accurately grasp the user's intent. For example, if the user says, "It's cold today, so turn on the heater," the reception unit recognizes the keyword "cold" and sends it to the analysis unit as a command to turn on the heater. As a result, the reception unit can accurately and quickly receive the user's voice commands, improving the overall usability of the system.

[0031] The analysis unit analyzes the voice commands received by the reception unit. The analysis unit analyzes the voice commands using, for example, speech recognition technology and understands their content. Specifically, the analysis unit receives the text data of the voice command and analyzes its intent using natural language processing technology. The analysis unit extracts keywords from the voice command and determines the appropriate action based on those keywords. For example, if the user says, "Turn on the living room lights," the analysis unit extracts the keywords "living room," "lights," and "turn on," and determines the action to turn on the lights. The analysis unit can also understand the context of the voice command and perform more advanced analysis. For example, if the user says, "Use the same settings as yesterday," the analysis unit refers to past operation history and reproduces yesterday's settings. Furthermore, the analysis unit can use AI to deeply understand the intent of the voice command and handle complex commands. For example, if the user says, "I'm going to watch a movie, so dim the lights," the analysis unit understands the context of "watching a movie" and determines the action to dim the lights. As a result, the analysis unit can accurately analyze voice commands and quickly determine the appropriate action.

[0032] The control unit controls household appliances and equipment based on instructions analyzed by the analysis unit. For example, the control unit controls household appliances such as lighting, air conditioners, and televisions. Specifically, the control unit has protocols for communicating with each household appliance and controls them using wireless communication technologies such as Wi-Fi, Bluetooth®, and Zigbee®. For example, upon receiving the instruction "Turn on the living room lights" from the analysis unit, the control unit sends an ON signal to the living room lights. The control unit can also monitor the status of household equipment and perform appropriate control. For example, it can obtain the current room temperature from the air conditioner's temperature sensor and turn the air conditioner on or off based on the set temperature. Furthermore, the control unit can also control the household security system. For example, if the user instructs "Turn on the security system" when leaving the house, the control unit activates door locks and security cameras. This allows the control unit to efficiently control household appliances and equipment, creating a comfortable living environment for the user.

[0033] The learning unit learns the user's lifestyle and preferences. For example, it learns the user's behavioral patterns using machine learning algorithms. Specifically, the learning unit collects the user's past operation history and stores it in a database. Based on this data, it analyzes the user's behavioral patterns and preferences and predicts future operations. For example, if the user has a habit of turning on the air conditioner at 7 AM every morning, the learning unit learns this pattern and automatically turns on the air conditioner before 7 AM. The learning unit can also optimize the settings of household appliances based on the user's preferences. For example, if the user prefers a specific temperature setting, it will automatically adjust to that temperature. Furthermore, the learning unit can continuously learn new user behavioral patterns using AI to improve the system's accuracy. As a result, the learning unit can provide optimal environmental settings based on the user's lifestyle and preferences, supporting the user's comfortable life.

[0034] The adjustment unit automatically adjusts the settings of household appliances based on information learned by the learning unit. For example, if the user has a habit of turning off the lights at a specific time every night, the adjustment unit will set the lights to turn off automatically at that time. Specifically, the adjustment unit optimizes the settings of each household appliance based on the information provided by the learning unit. For example, it can set the security system to turn on automatically when the user leaves the house. The adjustment unit can also turn off unnecessary appliances to minimize power consumption. For example, if the user has a habit of turning off the TV before going to bed, the adjustment unit will turn off the TV automatically at that time. Furthermore, the adjustment unit can adjust appliance settings in real time based on data from household environmental sensors. For example, if the room temperature exceeds the set temperature, it will automatically turn on the air conditioner. In this way, the adjustment unit can automatically adjust the settings of household appliances based on the user's lifestyle and preferences, providing a comfortable and efficient living environment.

[0035] The adjustment unit can optimally adjust security settings and power consumption settings. For example, the adjustment unit can automatically turn on the security system. The adjustment unit can also turn off unnecessary devices to minimize power consumption. For example, the adjustment unit can automatically turn on the security system and turn off lights and air conditioners to reduce power consumption when the user leaves the house. This enables optimization of security settings and power consumption. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can make optimal settings using an AI model that turns the security system on and off and optimizes power consumption.

[0036] The reception unit can select the optimal reception method by referring to the user's past instruction history. For example, if the user has previously given instructions using a specific method, the reception unit will prioritize that method. For example, the reception unit can analyze past instruction history to identify the user's preferred instruction method. The reception unit can also select the method that allows the user to give instructions most efficiently based on past instruction history. This allows the reception unit to provide the optimal reception method based on past instruction history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input past instruction history into an AI model to select the optimal reception method.

[0037] The reception unit can adjust its reception method based on the user's current state. For example, if the user is busy, the reception unit can provide simple instructions. For instance, if the reception unit determines that the user is busy, it will prioritize short voice commands. Conversely, if the user is relaxed, the reception unit can also accept detailed instructions. For example, if the reception unit determines that the user is relaxed, it will accept natural language questions. This allows for a voice instruction reception method tailored to the user's situation. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's current state into an AI model and adjust the optimal reception method.

[0038] The reception unit can analyze the user's social media activity and receive relevant voice instructions. For example, if the user mentions a specific event on social media, the reception unit will make settings related to that event. For example, if the user posts "having a party" on social media, the reception unit will change the lighting and music settings to party mode. The reception unit can also analyze the content of social media posts to understand the user's preferences and interests. This allows it to receive instructions based on social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the content of social media posts into an AI model and receive relevant voice instructions.

[0039] The analysis unit can optimally adjust the analysis algorithm based on the content of the voice instruction. For example, the analysis unit analyzes the content of the voice instruction and selects the optimal analysis algorithm. For example, if the voice instruction is a simple command, the analysis unit uses a simple analysis algorithm. Alternatively, if the voice instruction is a complex question, the analysis unit can use an advanced natural language processing algorithm. This allows the analysis unit to provide the optimal analysis algorithm according to the content of the voice instruction. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the content of the voice instruction into an AI model and select the optimal analysis algorithm.

[0040] The analysis unit can improve the accuracy of its analysis by referring to the user's past instruction history. For example, the analysis unit can analyze past instruction history to identify the user's preferred instruction method. For example, based on past instruction history, the analysis unit can identify the method by which the user can issue instructions most efficiently. The analysis unit can also understand the user's preferences and tendencies based on past instruction history and improve the accuracy of its analysis. This allows the accuracy of the analysis to be improved based on past instruction history. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past instruction history into an AI model to improve the accuracy of its analysis.

[0041] The analysis unit can determine the priority of analysis based on the content of the voice instructions. For example, the analysis unit analyzes the content of the voice instructions and determines the priority. For example, if the voice instructions are urgent, the analysis unit will prioritize the analysis of those instructions. Alternatively, if the voice instructions are general, the analysis unit can analyze them with normal priority. This allows for the provision of analysis priorities according to the content of the voice instructions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the content of the voice instructions into an AI model and determine the priority.

[0042] The analysis unit can optimally adjust the analysis results based on the user's geographical location information. For example, the analysis unit adjusts the analysis results considering the user's geographical location information. For example, the analysis unit provides analysis results in different formats depending on whether the user is at home or out. The analysis unit can also prioritize providing information related to a specific location if the user is in that location. This enables the provision of optimal analysis results based on geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into an AI model to provide optimal analysis results.

[0043] The control unit can monitor the status of household appliances and equipment in real time and perform optimal control. For example, the control unit can monitor the status of household appliances and equipment in real time using sensors. For example, the control unit can monitor the brightness of lights and the temperature of air conditioners and adjust them as needed. The control unit can also monitor the household security system and issue an alert if an anomaly is detected. This enables optimal control in real time. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input data from sensors into an AI model and perform optimal control.

[0044] The control unit can optimize the control method by referring to the user's past operation history. For example, the control unit can analyze past operation history to identify the user's preferred control method. For example, based on past operation history, the control unit can identify the method that allows the user to operate most efficiently. The control unit can also understand the user's preferences and tendencies based on past operation history and optimize the control method accordingly. This allows the control unit to provide the optimal control method based on past operation history. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input past operation history into an AI model and select the optimal control method.

[0045] The control unit can perform optimal control based on the geographical location information of household appliances and equipment. For example, the control unit can take into account the geographical location information of household appliances and equipment to perform optimal control. For example, if the user is in a specific room, the control unit can adjust the lighting and air conditioning settings in that room. The control unit can also turn on the security system if the user is outside the house. This enables optimal control based on geographical location information. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the geographical location information of household appliances and equipment into an AI model to perform optimal control.

[0046] The control unit can analyze the user's social media activity and perform related controls. For example, if the user mentions a specific event on social media, the control unit can make settings related to that event. For instance, if the user posts "having a party" on social media, the control unit can change the lighting and music settings to party mode. The control unit can also analyze the content of social media posts to understand the user's preferences and interests. This enables optimal control based on social media activity. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the content of social media posts into an AI model and perform related controls.

[0047] The learning unit can optimally adjust the learning algorithm by referring to past learning data. For example, the learning unit can analyze past learning data and select the optimal learning algorithm. For example, the learning unit can understand the user's preferences and tendencies based on past learning data and optimize the learning algorithm. The learning unit can also improve the accuracy of learning based on past learning data. This allows the learning unit to provide the optimal learning algorithm based on past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into an AI model and select the optimal learning algorithm.

[0048] The learning unit can continuously learn the user's lifestyle and preferences to improve accuracy. For example, the learning unit can continuously learn the user's behavioral patterns using machine learning algorithms. For example, the learning unit can learn the user's habit of performing a specific operation at a specific time and automatically perform the operation based on that habit. The learning unit can also optimize the settings of household devices based on the user's preferences. This enables optimal learning based on the user's lifestyle and preferences. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's behavioral patterns into an AI model and continuously learn from it.

[0049] The learning unit can optimally adjust the training data based on the user's geographical location information. For example, the learning unit adjusts the training data considering the user's geographical location information. For example, the learning unit provides training data in different formats depending on whether the user is at home or out. The learning unit can also prioritize providing information relevant to a specific location if the user is in that location. This enables the provision of optimal training data based on geographical location information. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's geographical location information into an AI model to provide optimal training data.

[0050] The learning unit can analyze the user's social media activity and collect relevant learning data. For example, if the user mentions a specific event on social media, the learning unit will collect data related to that event. For example, if the user posts "I'm having a party" on social media, the learning unit will collect information related to the party. The learning unit can also analyze the content of social media posts to understand the user's preferences and interests. This allows the learning unit to provide optimal learning data based on social media activity. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the content of social media posts into an AI model and collect relevant learning data.

[0051] The adjustment unit can optimally adjust security settings and power consumption settings. For example, the adjustment unit can automatically turn on the security system. The adjustment unit can also turn off unnecessary devices to minimize power consumption. For example, the adjustment unit can automatically turn on the security system and turn off lights and air conditioners to reduce power consumption when the user leaves the house. This enables optimization of security settings and power consumption. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can make optimal settings using an AI model that turns the security system on and off and optimizes power consumption.

[0052] The adjustment unit can customize the settings of household appliances based on the user's lifestyle and preferences. For example, if the user has a habit of turning off the lights at a specific time every night, the adjustment unit can set the lights to turn off automatically at that time. The adjustment unit can also adjust the brightness of the lights and the temperature of the air conditioner based on the user's preferences. This makes it possible to achieve optimal settings based on the user's lifestyle and preferences. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input the user's lifestyle and preferences into an AI model to make optimal settings.

[0053] The adjustment unit can optimally adjust the settings of household appliances based on the user's geographical location information. For example, the adjustment unit adjusts the settings of household appliances taking the user's geographical location information into consideration. For example, if the user is in a specific room, the adjustment unit adjusts the lighting and air conditioning settings in that room. The adjustment unit can also turn on the security system if the user is outside the house. This enables optimal settings based on geographical location information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's geographical location information into an AI model to perform optimal settings.

[0054] The adjustment unit can analyze the user's social media activity and make relevant settings. For example, if the user mentions a specific event on social media, the adjustment unit will make settings related to that event. For example, if the user posts "having a party" on social media, the adjustment unit will change the lighting and music settings to party mode. The adjustment unit can also analyze the content of social media posts to understand the user's preferences and interests. This enables optimal settings based on social media activity. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input the content of social media posts into an AI model and make relevant settings.

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

[0056] Smart home AI assistants can also be equipped with a health management unit. This unit monitors the user's health status and provides appropriate advice. For example, it can monitor the user's heart rate and sleep patterns and issue alerts if abnormalities are detected. It can also manage the user's diet and exercise records and provide advice to support healthy lifestyle habits. Furthermore, it can analyze the user's health data and propose a personalized health plan. This allows smart home AI assistants to support the user's health management and enable a healthier lifestyle.

[0057] The AI ​​assistant for smart home control can also be equipped with an entertainment section. This section provides optimal entertainment content based on the user's preferences. For example, it can learn the user's music preferences and automatically generate appropriate playlists. It can also analyze the user's movie and TV show viewing history and recommend content to watch next. Furthermore, it can suggest new games based on the user's gaming preferences. This allows the AI ​​assistant for smart home control to enhance the user's entertainment experience.

[0058] A smart home AI assistant can also be equipped with a communication unit. This unit supports the user's communication and facilitates smooth contact with family and friends. For example, it can manage the user's schedule and remind them of important events and appointments. It can also automatically organize the user's messages and emails, prioritizing important messages. Furthermore, it can send messages to family and friends based on the user's voice commands. This allows the smart home AI assistant to support user communication and achieve smoother interactions.

[0059] The AI ​​assistant for smart home control can also be equipped with an energy management unit. This unit optimizes household energy consumption and improves energy efficiency. For example, it can monitor household electricity consumption in real time and provide advice to reduce wasteful energy consumption. It can also suggest schedules to optimize energy consumption based on the user's lifestyle. Furthermore, it can promote the use of renewable energy and achieve environmentally friendly energy management. In this way, the AI ​​assistant for smart home control can improve energy efficiency and support an environmentally conscious lifestyle.

[0060] The AI ​​assistant for smart home control can also be equipped with an educational support unit. This unit supports the user's learning and provides an effective learning environment. For example, it can monitor the user's learning progress and suggest appropriate learning plans. It can also provide optimal learning methods based on the user's learning style. Furthermore, it can provide resources and learning materials related to the user's learning, thereby improving learning effectiveness. In this way, the AI ​​assistant for smart home control can support the user's learning and create a more effective learning environment.

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

[0062] Step 1: The reception unit receives voice instructions. For example, when a user gives a voice instruction, the reception unit receives that instruction. The reception unit can use voice recognition technology to convert voice instructions into text data. It recognizes voice commands and performs specific operations. It can also understand natural language questions and generate appropriate responses. Step 2: The analysis unit analyzes the voice instructions received by the reception unit. For example, it analyzes the voice instructions using speech recognition technology and understands their content. It analyzes the intent of the voice instructions using natural language processing technology, extracts keywords, and determines the appropriate operation based on those keywords. It can also understand the context of the voice instructions and perform more advanced analysis. Step 3: The control unit controls household appliances and equipment based on the instructions analyzed by the analysis unit. For example, it controls household appliances such as lighting, air conditioners, and televisions. It can monitor the status of household equipment and perform appropriate control. This includes adjusting the brightness of lights and setting the temperature of air conditioners. It can also control household security systems. Step 4: The learning unit learns the user's lifestyle and preferences. For example, it uses machine learning algorithms to learn the user's behavioral patterns. It analyzes the user's past operation history to understand their preferences. It learns habits of performing specific operations at specific times and automatically performs operations based on those habits. It can also optimize the settings of home devices based on the user's preferences. Step 5: The adjustment unit automatically adjusts the settings of household devices based on the information learned by the learning unit. For example, if the user has a habit of turning off the lights at a specific time every night, the unit will set the lights to turn off automatically at that time. It can also optimize security settings and power consumption settings. The security system can be automatically turned on when the user leaves the house, or unnecessary devices can be turned off to minimize power consumption.

[0063] (Example of form 2) The smart home control support AI assistant according to an embodiment of the present invention is a system that controls household devices and equipment by voice. This system has the function of learning the user's lifestyle and preferences and automatically adjusting the optimal environment settings. It also monitors and optimizes security and power consumption. For example, there is a reception unit that receives voice commands, and when the user gives a voice command, this reception unit receives the command. Next, the voice command received by the reception unit is sent to an analysis unit, which analyzes the command. Based on the analyzed command, the control unit controls the household devices or equipment. Furthermore, there is a learning unit that learns the user's lifestyle and preferences, and this learning unit learns the user's behavior patterns and preferences. Based on the learned information, the adjustment unit automatically adjusts the settings of household devices. For example, if the user has a habit of turning off the lights at a specific time every night, the adjustment unit sets the lights to turn off automatically at that time. The adjustment unit also has the function of optimizing security settings and power consumption settings. For example, it can automatically turn on the security system when the user leaves the house, or turn off unnecessary devices to minimize power consumption. Furthermore, the reception unit has the function of estimating the user's emotions and adjusting how voice instructions are received based on those emotions. For example, if the user is feeling stressed, the reception unit will provide simpler instructions. The reception unit can also refer to the user's past instruction history to select an appropriate method of receiving instructions. For example, if the user has previously given instructions in a particular way, that method will be given priority. In addition, the reception unit can adjust the method of receiving instructions based on the user's current situation. For example, if the user is busy, the reception unit will provide simpler instructions. The reception unit can also estimate the user's emotions and determine the priority of voice instructions based on those emotions. For example, if the user gives an urgent instruction, that instruction will be processed with priority. Finally, the adjustment unit can also estimate the user's emotions and automatically adjust the settings of home devices based on those emotions. For example, if the user wants to relax, the adjustment unit will dim the lights and play music. The reception unit can also analyze the user's social media activity and receive relevant instructions.For example, if a user mentions a specific event on social media, the system will adjust settings related to that event. This allows the smart home control support AI assistant to control devices and equipment in the home by voice and automatically adjust the optimal environment settings based on the user's lifestyle and preferences.

[0064] The AI ​​assistant for smart home control according to this embodiment comprises a reception unit, an analysis unit, a control unit, a learning unit, and an adjustment unit. The reception unit receives voice instructions. For example, when a user gives a voice instruction, the reception unit receives that instruction. The reception unit can convert voice instructions into text data using speech recognition technology. For example, the reception unit recognizes voice commands and performs specific operations. The reception unit can also understand natural language questions and generate appropriate responses. The analysis unit analyzes the voice instructions received by the reception unit. For example, the analysis unit analyzes voice instructions using speech recognition technology and understands their content. The analysis unit can analyze the intent of voice instructions using natural language processing technology. For example, the analysis unit extracts keywords from voice instructions and determines appropriate operations based on those keywords. The analysis unit can also understand the context of voice instructions and perform more advanced analysis. The control unit controls household appliances and equipment based on the instructions analyzed by the analysis unit. For example, the control unit controls household appliances such as lighting, air conditioners, and televisions. The control unit can monitor the status of household equipment and perform appropriate control. For example, it can adjust the brightness of lights or set the temperature of air conditioners. It can also control the household security system. The learning unit learns the user's lifestyle and preferences. For example, the learning unit learns the user's behavioral patterns using machine learning algorithms. The learning unit can analyze the user's past operation history and understand their preferences. For example, the learning unit learns the user's habit of performing a specific operation at a specific time and performs the operation automatically based on that habit. The learning unit can also optimize the settings of household devices based on the user's preferences. The adjustment unit automatically adjusts the settings of household devices based on the information learned by the learning unit. For example, if the user has a habit of turning off the lights at a specific time every night, the adjustment unit can set it to automatically turn off the lights at that time. The adjustment unit can also optimize security settings and power consumption settings. For example, the adjustment unit can automatically turn on the security system when the user leaves the house or turn off unnecessary devices to minimize power consumption.As a result, the AI ​​assistant for smart home control according to this embodiment can control household devices and equipment by voice and automatically adjust the optimal environmental settings based on the user's lifestyle and preferences.

[0065] The reception unit receives voice commands. For example, when a user gives a voice command, the reception unit receives that command. The reception unit can convert voice commands into text data using speech recognition technology. Specifically, the reception unit is equipped with a high-precision microphone to clearly capture the user's voice. The speech recognition engine uses noise cancellation technology to remove background noise and accurately recognize voice commands. For example, if the user says, "Turn on the living room lights," the reception unit converts this voice into text data and sends it to the analysis unit. The reception unit also has a multilingual speech recognition model to support multiple languages ​​and dialects, so it can handle commands given by the user in any language. Furthermore, the reception unit uses natural language processing technology to understand the context of the voice command and accurately grasp the user's intent. For example, if the user says, "It's cold today, so turn on the heater," the reception unit recognizes the keyword "cold" and sends it to the analysis unit as a command to turn on the heater. As a result, the reception unit can accurately and quickly receive the user's voice commands, improving the overall usability of the system.

[0066] The analysis unit analyzes the voice commands received by the reception unit. The analysis unit analyzes the voice commands using, for example, speech recognition technology and understands their content. Specifically, the analysis unit receives the text data of the voice command and analyzes its intent using natural language processing technology. The analysis unit extracts keywords from the voice command and determines the appropriate action based on those keywords. For example, if the user says, "Turn on the living room lights," the analysis unit extracts the keywords "living room," "lights," and "turn on," and determines the action to turn on the lights. The analysis unit can also understand the context of the voice command and perform more advanced analysis. For example, if the user says, "Use the same settings as yesterday," the analysis unit refers to past operation history and reproduces yesterday's settings. Furthermore, the analysis unit can use AI to deeply understand the intent of the voice command and handle complex commands. For example, if the user says, "I'm going to watch a movie, so dim the lights," the analysis unit understands the context of "watching a movie" and determines the action to dim the lights. As a result, the analysis unit can accurately analyze voice commands and quickly determine the appropriate action.

[0067] The control unit controls household appliances and equipment based on instructions analyzed by the analysis unit. For example, the control unit controls household appliances such as lighting, air conditioners, and televisions. Specifically, the control unit has protocols for communicating with each household appliance and controls them using wireless communication technologies such as Wi-Fi, Bluetooth, and Zigbee. For example, upon receiving the instruction "Turn on the living room lights" from the analysis unit, the control unit sends an "on" signal to the living room lights. The control unit can also monitor the status of household equipment and perform appropriate control. For example, it can obtain the current room temperature from the air conditioner's temperature sensor and turn the air conditioner on or off based on the set temperature. Furthermore, the control unit can also control the household security system. For example, if the user instructs "Turn on the security system" when leaving the house, the control unit activates door locks and security cameras. This allows the control unit to efficiently control household appliances and equipment, creating a comfortable living environment for the user.

[0068] The learning unit learns the user's lifestyle and preferences. For example, it learns the user's behavioral patterns using machine learning algorithms. Specifically, the learning unit collects the user's past operation history and stores it in a database. Based on this data, it analyzes the user's behavioral patterns and preferences and predicts future operations. For example, if the user has a habit of turning on the air conditioner at 7 AM every morning, the learning unit learns this pattern and automatically turns on the air conditioner before 7 AM. The learning unit can also optimize the settings of household appliances based on the user's preferences. For example, if the user prefers a specific temperature setting, it will automatically adjust to that temperature. Furthermore, the learning unit can continuously learn new user behavioral patterns using AI to improve the system's accuracy. As a result, the learning unit can provide optimal environmental settings based on the user's lifestyle and preferences, supporting the user's comfortable life.

[0069] The adjustment unit automatically adjusts the settings of household appliances based on information learned by the learning unit. For example, if the user has a habit of turning off the lights at a specific time every night, the adjustment unit will set the lights to turn off automatically at that time. Specifically, the adjustment unit optimizes the settings of each household appliance based on the information provided by the learning unit. For example, it can set the security system to turn on automatically when the user leaves the house. The adjustment unit can also turn off unnecessary appliances to minimize power consumption. For example, if the user has a habit of turning off the TV before going to bed, the adjustment unit will turn off the TV automatically at that time. Furthermore, the adjustment unit can adjust appliance settings in real time based on data from household environmental sensors. For example, if the room temperature exceeds the set temperature, it will automatically turn on the air conditioner. In this way, the adjustment unit can automatically adjust the settings of household appliances based on the user's lifestyle and preferences, providing a comfortable and efficient living environment.

[0070] The adjustment unit can optimally adjust security settings and power consumption settings. For example, the adjustment unit can automatically turn on the security system. The adjustment unit can also turn off unnecessary devices to minimize power consumption. For example, the adjustment unit can automatically turn on the security system and turn off lights and air conditioners to reduce power consumption when the user leaves the house. This enables optimization of security settings and power consumption. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can make optimal settings using an AI model that turns the security system on and off and optimizes power consumption.

[0071] The reception unit can estimate the user's emotions and adjust the method of receiving voice instructions based on the estimated emotions. For example, the reception unit can estimate the user's emotions using voice tone analysis. For example, the reception unit can analyze the tone and speed of the voice to determine whether the user is feeling stressed. The reception unit can also estimate the user's emotions using facial recognition technology. For example, the reception unit can analyze the user's facial expressions captured by a camera and estimate their emotions. This allows for the provision of a method of receiving voice instructions that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can estimate emotions using voice tone analysis or facial recognition technology and adjust the method of receiving voice instructions based on the results.

[0072] The reception unit can select the optimal reception method by referring to the user's past instruction history. For example, if the user has previously given instructions using a specific method, the reception unit will prioritize that method. For example, the reception unit can analyze past instruction history to identify the user's preferred instruction method. The reception unit can also select the method that allows the user to give instructions most efficiently based on past instruction history. This allows the reception unit to provide the optimal reception method based on past instruction history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input past instruction history into an AI model to select the optimal reception method.

[0073] The reception unit can adjust its reception method based on the user's current state. For example, if the user is busy, the reception unit can provide simple instructions. For instance, if the reception unit determines that the user is busy, it will prioritize short voice commands. Conversely, if the user is relaxed, the reception unit can also accept detailed instructions. For example, if the reception unit determines that the user is relaxed, it will accept natural language questions. This allows for a voice instruction reception method tailored to the user's situation. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's current state into an AI model and adjust the optimal reception method.

[0074] The reception unit can estimate the user's emotions and determine the priority of voice instructions based on the estimated emotions. For example, the reception unit can estimate the user's emotions using voice tone analysis. For example, the reception unit can analyze the tone and speed of the voice to determine whether the user is giving an urgent instruction. The reception unit can also estimate the user's emotions using facial recognition technology. For example, the reception unit can analyze the user's facial expressions captured by a camera and estimate their emotions. This allows the reception unit to provide a priority of voice instructions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can estimate emotions using voice tone analysis or facial recognition technology and determine the priority of voice instructions based on the results.

[0075] The reception unit can analyze the user's social media activity and receive relevant voice instructions. For example, if the user mentions a specific event on social media, the reception unit will make settings related to that event. For example, if the user posts "having a party" on social media, the reception unit will change the lighting and music settings to party mode. The reception unit can also analyze the content of social media posts to understand the user's preferences and interests. This allows it to receive instructions based on social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the content of social media posts into an AI model and receive relevant voice instructions.

[0076] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, the analysis unit can estimate the user's emotions using voice tone analysis. For example, the analysis unit can analyze the tone and speed of the voice to determine whether the user is feeling stressed. The analysis unit can also estimate the user's emotions using facial recognition technology. For example, the analysis unit can analyze the user's facial expressions captured by a camera and estimate their emotions. This allows for analysis accuracy tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can estimate emotions using voice tone analysis or facial recognition technology and adjust the accuracy of the analysis based on the results.

[0077] The analysis unit can optimally adjust the analysis algorithm based on the content of the voice instruction. For example, the analysis unit analyzes the content of the voice instruction and selects the optimal analysis algorithm. For example, if the voice instruction is a simple command, the analysis unit uses a simple analysis algorithm. Alternatively, if the voice instruction is a complex question, the analysis unit can use an advanced natural language processing algorithm. This allows the analysis unit to provide the optimal analysis algorithm according to the content of the voice instruction. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the content of the voice instruction into an AI model and select the optimal analysis algorithm.

[0078] The analysis unit can improve the accuracy of its analysis by referring to the user's past instruction history. For example, the analysis unit can analyze past instruction history to identify the user's preferred instruction method. For example, based on past instruction history, the analysis unit can identify the method by which the user can issue instructions most efficiently. The analysis unit can also understand the user's preferences and tendencies based on past instruction history and improve the accuracy of its analysis. This allows the accuracy of the analysis to be improved based on past instruction history. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past instruction history into an AI model to improve the accuracy of its analysis.

[0079] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, the analysis unit can estimate the user's emotions using voice tone analysis. For example, the analysis unit can analyze the tone and speed of the voice to determine whether the user is feeling stressed. The analysis unit can also estimate the user's emotions using facial recognition technology. For example, the analysis unit can analyze the user's facial expressions captured by a camera and estimate their emotions. This allows for the provision of a display method of the analysis results that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can estimate emotions using voice tone analysis or facial recognition technology and adjust the display method of the analysis results based on the results.

[0080] The analysis unit can determine the priority of analysis based on the content of the voice instructions. For example, the analysis unit analyzes the content of the voice instructions and determines the priority. For example, if the voice instructions are urgent, the analysis unit will prioritize the analysis of those instructions. Alternatively, if the voice instructions are general, the analysis unit can analyze them with normal priority. This allows for the provision of analysis priorities according to the content of the voice instructions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the content of the voice instructions into an AI model and determine the priority.

[0081] The analysis unit can optimally adjust the analysis results based on the user's geographical location information. For example, the analysis unit adjusts the analysis results considering the user's geographical location information. For example, the analysis unit provides analysis results in different formats depending on whether the user is at home or out. The analysis unit can also prioritize providing information related to a specific location if the user is in that location. This enables the provision of optimal analysis results based on geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into an AI model to provide optimal analysis results.

[0082] The control unit can estimate the user's emotions and adjust the control method based on the estimated emotions. For example, the control unit can estimate the user's emotions using voice tone analysis. For example, the control unit can analyze the tone and speed of the voice to determine whether the user is relaxed. The control unit can also estimate the user's emotions using facial recognition technology. For example, the control unit can analyze the user's facial expressions captured by a camera and estimate their emotions. This allows for the provision of a control method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can estimate emotions using voice tone analysis or facial recognition technology and adjust the control method based on the results.

[0083] The control unit can monitor the status of household appliances and equipment in real time and perform optimal control. For example, the control unit can monitor the status of household appliances and equipment in real time using sensors. For example, the control unit can monitor the brightness of lights and the temperature of air conditioners and adjust them as needed. The control unit can also monitor the household security system and issue an alert if an anomaly is detected. This enables optimal control in real time. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input data from sensors into an AI model and perform optimal control.

[0084] The control unit can optimize the control method by referring to the user's past operation history. For example, the control unit can analyze past operation history to identify the user's preferred control method. For example, based on past operation history, the control unit can identify the method that allows the user to operate most efficiently. The control unit can also understand the user's preferences and tendencies based on past operation history and optimize the control method accordingly. This allows the control unit to provide the optimal control method based on past operation history. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input past operation history into an AI model and select the optimal control method.

[0085] The control unit can estimate the user's emotions and determine control priorities based on the estimated emotions. For example, the control unit can estimate the user's emotions using voice tone analysis. For example, the control unit can analyze the tone and speed of the voice to determine whether the user is giving an urgent instruction. The control unit can also estimate the user's emotions using facial recognition technology. For example, the control unit can analyze the user's facial expressions captured by a camera and estimate their emotions. This allows for the provision of control priorities that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can estimate emotions using voice tone analysis or facial recognition technology and determine control priorities based on the results.

[0086] The control unit can perform optimal control based on the geographical location information of household appliances and equipment. For example, the control unit can take into account the geographical location information of household appliances and equipment to perform optimal control. For example, if the user is in a specific room, the control unit can adjust the lighting and air conditioning settings in that room. The control unit can also turn on the security system if the user is outside the house. This enables optimal control based on geographical location information. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the geographical location information of household appliances and equipment into an AI model to perform optimal control.

[0087] The control unit can analyze the user's social media activity and perform related controls. For example, if the user mentions a specific event on social media, the control unit can make settings related to that event. For instance, if the user posts "having a party" on social media, the control unit can change the lighting and music settings to party mode. The control unit can also analyze the content of social media posts to understand the user's preferences and interests. This enables optimal control based on social media activity. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the content of social media posts into an AI model and perform related controls.

[0088] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, the learning unit can estimate the user's emotions using speech tone analysis. For example, the learning unit can analyze the tone and speed of the speech to determine whether the user is relaxed. The learning unit can also estimate the user's emotions using facial recognition technology. For example, the learning unit can analyze the user's facial expressions captured by a camera and estimate their emotions. This makes it possible to select training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can estimate emotions using speech tone analysis or facial recognition technology and select training data based on the results.

[0089] The learning unit can optimally adjust the learning algorithm by referring to past learning data. For example, the learning unit can analyze past learning data and select the optimal learning algorithm. For example, the learning unit can understand the user's preferences and tendencies based on past learning data and optimize the learning algorithm. The learning unit can also improve the accuracy of learning based on past learning data. This allows the learning unit to provide the optimal learning algorithm based on past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into an AI model and select the optimal learning algorithm.

[0090] The learning unit can continuously learn the user's lifestyle and preferences to improve accuracy. For example, the learning unit can continuously learn the user's behavioral patterns using machine learning algorithms. For example, the learning unit can learn the user's habit of performing a specific operation at a specific time and automatically perform the operation based on that habit. The learning unit can also optimize the settings of household devices based on the user's preferences. This enables optimal learning based on the user's lifestyle and preferences. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's behavioral patterns into an AI model and continuously learn from it.

[0091] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, the learning unit can estimate the user's emotions using voice tone analysis. For example, the learning unit can analyze the tone and speed of the voice to determine whether the user is relaxed. The learning unit can also estimate the user's emotions using facial recognition technology. For example, the learning unit can analyze the user's facial expressions captured by a camera and estimate their emotions. This allows the learning frequency to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can estimate emotions using voice tone analysis or facial recognition technology and adjust the learning frequency based on the results.

[0092] The learning unit can optimally adjust the training data based on the user's geographical location information. For example, the learning unit adjusts the training data considering the user's geographical location information. For example, the learning unit provides training data in different formats depending on whether the user is at home or out. The learning unit can also prioritize providing information relevant to a specific location if the user is in that location. This enables the provision of optimal training data based on geographical location information. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's geographical location information into an AI model to provide optimal training data.

[0093] The learning unit can analyze the user's social media activity and collect relevant learning data. For example, if the user mentions a specific event on social media, the learning unit will collect data related to that event. For example, if the user posts "I'm having a party" on social media, the learning unit will collect information related to the party. The learning unit can also analyze the content of social media posts to understand the user's preferences and interests. This allows the learning unit to provide optimal learning data based on social media activity. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the content of social media posts into an AI model and collect relevant learning data.

[0094] The adjustment unit can estimate the user's emotions and automatically adjust the settings of household devices based on the estimated emotions. For example, the adjustment unit estimates the user's emotions using voice tone analysis. For example, the adjustment unit analyzes the tone and speed of the voice to determine whether the user is relaxed. The adjustment unit can also estimate the user's emotions using facial recognition technology. For example, the adjustment unit analyzes the user's facial expressions captured by a camera and estimates their emotions. This allows the adjustment unit to provide household device settings that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can estimate emotions using voice tone analysis or facial recognition technology and automatically adjust the settings of household devices based on the results.

[0095] The adjustment unit can optimally adjust security settings and power consumption settings. For example, the adjustment unit can automatically turn on the security system. The adjustment unit can also turn off unnecessary devices to minimize power consumption. For example, the adjustment unit can automatically turn on the security system and turn off lights and air conditioners to reduce power consumption when the user leaves the house. This enables optimization of security settings and power consumption. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can make optimal settings using an AI model that turns the security system on and off and optimizes power consumption.

[0096] The adjustment unit can customize the settings of household appliances based on the user's lifestyle and preferences. For example, if the user has a habit of turning off the lights at a specific time every night, the adjustment unit can set the lights to turn off automatically at that time. The adjustment unit can also adjust the brightness of the lights and the temperature of the air conditioner based on the user's preferences. This makes it possible to achieve optimal settings based on the user's lifestyle and preferences. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input the user's lifestyle and preferences into an AI model to make optimal settings.

[0097] The adjustment unit can estimate the user's emotions and determine the priority of adjustments based on the estimated emotions. For example, the adjustment unit can estimate the user's emotions using voice tone analysis. For example, the adjustment unit can analyze the tone and speed of the voice to determine whether the user is giving an urgent instruction. The adjustment unit can also estimate the user's emotions using facial recognition technology. For example, the adjustment unit can analyze the user's facial expressions captured by a camera and estimate their emotions. This allows the adjustment unit to provide a priority of adjustments according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can estimate emotions using voice tone analysis or facial recognition technology and determine the priority of adjustments based on the results.

[0098] The adjustment unit can optimally adjust the settings of household appliances based on the user's geographical location information. For example, the adjustment unit adjusts the settings of household appliances taking the user's geographical location information into consideration. For example, if the user is in a specific room, the adjustment unit adjusts the lighting and air conditioning settings in that room. The adjustment unit can also turn on the security system if the user is outside the house. This enables optimal settings based on geographical location information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's geographical location information into an AI model to perform optimal settings.

[0099] The adjustment unit can analyze the user's social media activity and make relevant settings. For example, if the user mentions a specific event on social media, the adjustment unit will make settings related to that event. For example, if the user posts "having a party" on social media, the adjustment unit will change the lighting and music settings to party mode. The adjustment unit can also analyze the content of social media posts to understand the user's preferences and interests. This enables optimal settings based on social media activity. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input the content of social media posts into an AI model and make relevant settings.

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

[0101] Smart home AI assistants can also be equipped with a health management unit. This unit monitors the user's health status and provides appropriate advice. For example, it can monitor the user's heart rate and sleep patterns and issue alerts if abnormalities are detected. It can also manage the user's diet and exercise records and provide advice to support healthy lifestyle habits. Furthermore, it can analyze the user's health data and propose a personalized health plan. This allows smart home AI assistants to support the user's health management and enable a healthier lifestyle.

[0102] The AI ​​assistant for smart home control can also be equipped with an entertainment section. This section provides optimal entertainment content based on the user's preferences. For example, it can learn the user's music preferences and automatically generate appropriate playlists. It can also analyze the user's movie and TV show viewing history and recommend content to watch next. Furthermore, it can suggest new games based on the user's gaming preferences. This allows the AI ​​assistant for smart home control to enhance the user's entertainment experience.

[0103] A smart home AI assistant can also be equipped with a communication unit. This unit supports the user's communication and facilitates smooth contact with family and friends. For example, it can manage the user's schedule and remind them of important events and appointments. It can also automatically organize the user's messages and emails, prioritizing important messages. Furthermore, it can send messages to family and friends based on the user's voice commands. This allows the smart home AI assistant to support user communication and achieve smoother interactions.

[0104] The AI ​​assistant for smart home control can also be equipped with an energy management unit. This unit optimizes household energy consumption and improves energy efficiency. For example, it can monitor household electricity consumption in real time and provide advice to reduce wasteful energy consumption. It can also suggest schedules to optimize energy consumption based on the user's lifestyle. Furthermore, it can promote the use of renewable energy and achieve environmentally friendly energy management. In this way, the AI ​​assistant for smart home control can improve energy efficiency and support an environmentally conscious lifestyle.

[0105] The AI ​​assistant for smart home control can also be equipped with an educational support unit. This unit supports the user's learning and provides an effective learning environment. For example, it can monitor the user's learning progress and suggest appropriate learning plans. It can also provide optimal learning methods based on the user's learning style. Furthermore, it can provide resources and learning materials related to the user's learning, thereby improving learning effectiveness. In this way, the AI ​​assistant for smart home control can support the user's learning and create a more effective learning environment.

[0106] A smart home AI assistant can estimate the user's emotions and provide entertainment content based on those emotions. For example, if the user is stressed, it can recommend relaxing music or movies. If the user is happy, it can recommend energetic music or action movies. Furthermore, if the user is sad, it can provide comedy movies or content containing positive messages to lift their spirits. In this way, a smart home AI assistant can provide an entertainment experience tailored to the user's emotions and improve their mood.

[0107] A smart home AI assistant can estimate the user's emotions and manage their health based on those emotions. For example, if the user is stressed, it can provide guidance on breathing exercises or meditation to help them relax. If the user is tired, it can also provide advice on appropriate rest and sleep. Furthermore, if the user is feeling energetic, it can suggest exercises and activities. In this way, a smart home AI assistant can support health management tailored to the user's emotions, enabling a healthier lifestyle.

[0108] A smart home AI assistant can estimate the user's emotions and adjust its communication style based on those emotions. For example, if the user is stressed, it can provide a simple and quick communication method. If the user is relaxed, it can provide a communication method that includes more detailed information. Furthermore, if the user is sad, it can provide encouraging messages and positive information. In this way, a smart home AI assistant can support communication that is tailored to the user's emotions, enabling smoother communication.

[0109] A smart home AI assistant can estimate the user's emotions and optimize energy consumption based on those emotions. For example, if the user is relaxed, it can change the lighting to a warmer color and adjust the air conditioner temperature to a comfortable setting. If the user is stressed, it can turn off unnecessary appliances to minimize energy consumption. Furthermore, if the user is energetic, it can suggest settings to maximize energy efficiency. In this way, a smart home AI assistant can support energy management that responds to the user's emotions, leading to more efficient energy consumption.

[0110] A smart home AI assistant can estimate the user's emotions and provide educational support based on those emotions. For example, if the user is stressed, it can suggest learning methods or breaks to help them relax. If the user is focused, it can also provide efficient learning methods or additional learning resources. Furthermore, if the user is tired, it can offer advice on appropriate rest and refreshment. In this way, a smart home AI assistant can support educational support tailored to the user's emotions, creating a more effective learning environment.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The reception unit receives voice instructions. For example, when a user gives a voice instruction, the reception unit receives that instruction. The reception unit can use voice recognition technology to convert voice instructions into text data. It recognizes voice commands and performs specific operations. It can also understand natural language questions and generate appropriate responses. Step 2: The analysis unit analyzes the voice instructions received by the reception unit. For example, it analyzes the voice instructions using speech recognition technology and understands their content. It analyzes the intent of the voice instructions using natural language processing technology, extracts keywords, and determines the appropriate operation based on those keywords. It can also understand the context of the voice instructions and perform more advanced analysis. Step 3: The control unit controls household appliances and equipment based on the instructions analyzed by the analysis unit. For example, it controls household appliances such as lighting, air conditioners, and televisions. It can monitor the status of household equipment and perform appropriate control. This includes adjusting the brightness of lights and setting the temperature of air conditioners. It can also control household security systems. Step 4: The learning unit learns the user's lifestyle and preferences. For example, it uses machine learning algorithms to learn the user's behavioral patterns. It analyzes the user's past operation history to understand their preferences. It learns habits of performing specific operations at specific times and automatically performs operations based on those habits. It can also optimize the settings of home devices based on the user's preferences. Step 5: The adjustment unit automatically adjusts the settings of household devices based on the information learned by the learning unit. For example, if the user has a habit of turning off the lights at a specific time every night, the unit will set the lights to turn off automatically at that time. It can also optimize security settings and power consumption settings. The security system can be automatically turned on when the user leaves the house, or unnecessary devices can be turned off to minimize power consumption.

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

[0114] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0115] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] For example, the reception unit is implemented by the microphone 38B and control unit 46A of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12. The control unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The learning unit is implemented by the specific processing unit 290 of the data processing device 12. The adjustment unit is implemented by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

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

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] For example, the reception unit is implemented by the microphone 238 and control unit 46A of the smart glasses 214. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12. The control unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The learning unit is implemented by the specific processing unit 290 of the data processing device 12. The adjustment unit is implemented by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

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

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

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

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

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

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] For example, the reception unit is implemented by the microphone 238 and control unit 46A of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12. The control unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The learning unit is implemented by the specific processing unit 290 of the data processing device 12. The adjustment unit is implemented by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

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

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] For example, the reception unit is implemented by the microphone 238 and control unit 46A of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12. The control unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0166] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0168] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0169] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0170] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

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

[0177] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0178] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0179] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0183] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0184] (Note 1) A reception desk that accepts voice commands, An analysis unit that analyzes voice instructions received by the reception unit, A control unit that controls household appliances and equipment based on instructions analyzed by the aforementioned analysis unit, A learning unit that learns the user's lifestyle and preferences, The system includes an adjustment unit that automatically adjusts the settings of household devices based on the information learned by the learning unit. A system characterized by the following features. (Note 2) The adjustment unit is, Optimize security settings and power consumption settings. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is The system estimates the user's emotions and adjusts how voice commands are received based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is The optimal reception method is selected by referring to the user's past instruction history. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is We adjust the acceptance method based on the user's current status. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and determines the priority of voice commands based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyzes the user's social media activity and accepts relevant voice instructions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, The analysis algorithm is optimally adjusted based on the content of the voice command. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, Improve the accuracy of the analysis by referring to the user's past instruction history. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The analysis priority is determined based on the content of the voice commands. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The analysis results are optimally adjusted based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The control unit, It estimates the user's emotions and adjusts the control method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The control unit, It monitors the status of household appliances and equipment in real time and controls them optimally. The system described in Appendix 1, characterized by the features described herein. (Note 16) The control unit, The control method is optimized by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The control unit, It estimates the user's emotions and determines control priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The control unit, Based on the geographical location information of appliances and equipment within the home, optimal control is performed. The system described in Appendix 1, characterized by the features described herein. (Note 19) The control unit, Analyze users' social media activity and implement relevant controls. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned learning unit, The learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned learning unit, The system continuously learns the user's lifestyle and preferences to improve accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned learning unit, It estimates the user's emotions and adjusts the frequency of learning based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned learning unit, The training data is optimally adjusted based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned learning unit, Analyze users' social media activity and collect relevant learning data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The adjustment unit is, It estimates the user's emotions and automatically adjusts the settings of home devices based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The adjustment unit is, Optimize security settings and power consumption settings. The system described in Appendix 1, characterized by the features described herein. (Note 28) The adjustment unit is, Customize the settings of home devices based on the user's lifestyle and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 29) The adjustment unit is, It estimates the user's emotions and determines the priority of adjustments based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The adjustment unit is, Based on the user's geographical location, the settings of devices within the home are optimally adjusted. The system described in Appendix 1, characterized by the features described herein. (Note 31) The adjustment unit is, Analyze the user's social media activity and make relevant settings. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

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

Claims

1. A reception desk that accepts voice commands, An analysis unit that analyzes voice instructions received by the reception unit, A control unit that controls household appliances and equipment based on instructions analyzed by the aforementioned analysis unit, A learning unit that learns the user's lifestyle and preferences, The system includes an adjustment unit that automatically adjusts the settings of household devices based on the information learned by the learning unit. A system characterized by the following features.

2. The adjustment unit is, Optimize security settings and power consumption settings. The system according to feature 1.

3. The aforementioned reception unit is The system estimates the user's emotions and adjusts how voice commands are received based on those estimated emotions. The system according to feature 1.

4. The aforementioned reception unit is The optimal reception method is selected by referring to the user's past instruction history. The system according to feature 1.

5. The aforementioned reception unit is We adjust the acceptance method based on the user's current status. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and determines the priority of voice commands based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyzes the user's social media activity and accepts relevant voice instructions. The system according to feature 1.

8. The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on those estimated emotions. The system according to feature 1.

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