system

Abstract

The system according to this embodiment aims to allow for easy operation and setting changes of home appliances using voice commands. [Solution] The system according to this embodiment comprises a reception unit, an operation unit, an inquiry unit, and a scene setting unit. The reception unit receives voice commands. The operation unit operates the home appliance based on the voice commands received by the reception unit. The inquiry unit inquires about the status of the home appliance based on the voice commands received by the reception unit. The scene setting unit changes the settings of the home appliance based on the voice commands received by the reception unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is necessary to manually operate and change the settings of home appliances, lacking convenience. <*

[0005] The system according to the embodiment aims to easily operate and change the settings of home appliances using voice commands.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an operation unit, an inquiry unit, and a scene setting unit. The reception unit receives voice commands. The operation unit operates the home appliance based on the voice commands received by the reception unit. The inquiry unit inquires about the status of the home appliance based on the voice commands received by the reception unit. The scene setting unit changes the settings of the home appliance based on the voice commands received by the reception unit. [Effects of the Invention]

[0007] The system according to this embodiment allows for easy operation and setting changes of home appliances using voice commands. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable 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 voice control system for home appliances according to an embodiment of the present invention is a system that operates home appliances using voice commands. This system can use an AI assistant to inquire about and change the status and settings of appliances by voice. It also has a function to support setting changes for each scene by linking multiple appliances. For example, the user issues a voice command such as "Turn on the TV" or "Set the air conditioner to 25 degrees." This voice command is received by the reception unit. The reception unit analyzes the voice command and extracts the information necessary to operate the appliance. Next, the operation unit operates the appliance based on the voice command received by the reception unit. For example, based on the command "Turn on the TV," the operation unit turns on the power to the TV. Also, based on the command "Set the air conditioner to 25 degrees," the operation unit changes the set temperature of the air conditioner to 25 degrees. Furthermore, based on the voice command received by the reception unit, the inquiry unit can inquire about the status of the appliance. For example, based on the command "What is the temperature of the air conditioner?", the inquiry unit obtains the current set temperature of the air conditioner and notifies the user by voice. In addition, the scene setting unit can change the settings of multiple appliances based on the voice command received by the reception unit. For example, based on a command such as "Switch to movie mode," the scene setting unit changes the TV's picture quality setting to movie mode and adjusts the air conditioner's temperature to an appropriate level. Furthermore, it has a storage unit that stores the settings of multiple home appliances for each scene, and the scene setting unit can change the settings of multiple home appliances to correspond to the scene indicated by the voice command received by the reception unit. For example, based on a command such as "Switch to relaxation mode," the scene setting unit retrieves the relaxation mode settings stored in the storage unit and changes the settings of multiple home appliances. In addition, the operation unit can estimate the user's emotions and operate home appliances based on the estimated emotions of the user. For example, if the user is estimated to be tired, the operation unit dims the lights and changes the air conditioner's temperature to a comfortable level. Furthermore, the scene setting unit can estimate the user's emotions, identify the scene corresponding to the estimated emotions of the user, and change the settings of multiple home appliances to correspond to the identified scene.For example, if the system estimates that the user wants to relax, the scene setting unit will recall the relaxation mode settings and change the settings of multiple home appliances. Finally, the reception unit can filter out the user's current ambient noise when receiving a voice command. This improves the accuracy of voice command recognition, enabling precise operation of home appliances. As a result, the voice control system for home appliances allows users to operate appliances, inquire about their status, and change settings for each scene using voice commands.

[0029] The voice control system for home appliances according to this embodiment comprises a reception unit, an operation unit, an inquiry unit, and a scene setting unit. The reception unit receives voice commands. For example, when a user makes a voice command such as "Turn on the TV" or "Set the air conditioner to 25 degrees," the reception unit receives the voice command. The reception unit analyzes the voice command and extracts the information necessary to operate the appliance. For example, the reception unit uses voice recognition technology to convert the voice command into text data and analyzes the text data. The operation unit operates the appliance based on the voice command received by the reception unit. For example, based on the command "Turn on the TV," the operation unit turns on the power to the TV. Also, based on the command "Set the air conditioner to 25 degrees," the operation unit changes the set temperature of the air conditioner to 25 degrees. The operation unit can operate the appliance by, for example, transmitting a remote control signal. The operation unit can also operate the appliance via a smart home device. The inquiry unit inquires about the status of the appliance based on the voice command received by the reception unit. The inquiry unit, for example, based on a command such as "What is the air conditioner temperature?", retrieves the current set temperature of the air conditioner and notifies the user by voice. The inquiry unit can receive feedback signals from appliances to obtain the status of the appliances. The inquiry unit can also use sensors to obtain the status of the appliances. The scene setting unit changes the settings of multiple appliances based on voice commands received by the reception unit. For example, based on a command such as "Set to movie mode", the scene setting unit changes the picture quality setting of the TV to movie mode and changes the set temperature of the air conditioner to an appropriate temperature. The scene setting unit can change the settings of multiple appliances at once. The scene setting unit can also apply customized scene settings according to the user's preferences. As a result, the voice control system for home appliances allows for the operation of appliances, inquiries about their status, and setting changes for each scene using voice commands.

[0030] The reception unit receives voice commands. For example, when a user makes a voice command such as "Turn on the TV" or "Set the air conditioner to 25 degrees," the reception unit receives that command. The reception unit analyzes the voice command and extracts the information necessary to operate the appliance. Specifically, the reception unit uses high-precision speech recognition technology to convert the voice command into text data and then analyzes that text data. The speech recognition technology incorporates noise cancellation and speech filtering technologies, enabling clear recognition of the user's voice. Furthermore, the speech recognition engine uses natural language processing (NLP) technology to accurately understand the user's intent. For example, if the command "Turn on the TV" is uttered, the speech recognition engine extracts the keywords "TV" and "turn on" and uses this to generate an instruction to turn on the TV. In addition, the reception unit can learn from the user's voice commands and adapt to each user's pronunciation and phrasing. As a result, the system's accuracy improves over time, allowing it to recognize the user's voice commands more quickly and accurately. Furthermore, the reception unit supports multiple languages ​​and dialects, ensuring smooth operation even in different language environments. This allows the reception unit to reliably receive and analyze user voice commands in a variety of situations and environments within the home.

[0031] The control unit operates home appliances based on voice commands received by the reception unit. For example, based on the command "Turn on the TV," the control unit will turn on the TV. Also, based on the command "Set the air conditioner to 25 degrees," the control unit will change the air conditioner's temperature setting to 25 degrees. The control unit can also operate home appliances by transmitting remote control signals. Specifically, the control unit uses infrared (IR) signals or radio frequency (RF) signals to send operation instructions to home appliances. This allows for operation similar to that of a conventional remote control. Furthermore, the control unit can operate home appliances via smart home devices. For example, it can use Wi-Fi or Bluetooth® to control smart plugs and smart switches, turning home appliances on and off. In addition, the control unit can be linked with cloud services to enable remote operation. When a user issues a voice command from their smartphone while away from home, the command is transmitted to the control unit via the cloud, and the home appliance is operated. This allows the user to control home appliances from anywhere in the house. The control unit can operate multiple home appliances simultaneously, and can even handle complex commands such as "turn off the living room lights and turn on the TV." This allows the control unit to provide flexible home appliance control that meets the diverse needs of users.

[0032] The inquiry unit inquires about the status of home appliances based on voice commands received by the reception unit. For example, based on a command such as "What is the temperature of the air conditioner?", the inquiry unit obtains the current set temperature of the air conditioner and notifies the user by voice. The inquiry unit can receive feedback signals from home appliances in order to obtain their status. Specifically, the inquiry unit receives status signals transmitted by home appliances and analyzes that information. For example, if an air conditioner transmits the current set temperature or operating mode as a feedback signal, the inquiry unit receives that signal, analyzes it, and notifies the user. The inquiry unit can also use sensors to obtain the status of home appliances. For example, it can use temperature sensors and humidity sensors to obtain environmental information around the air conditioner and notify the user based on that information. Furthermore, the inquiry unit can link with cloud services to check the status of home appliances remotely. When a user inquires about the status of home appliances using a smartphone while away from home, that information is transmitted to the inquiry unit via the cloud, and the status of the home appliances is obtained. This allows users to check the status of their home appliances no matter where they are. The inquiry unit can simultaneously inquire about the status of multiple home appliances, supporting multiple commands such as "What is the status of the living room lights?" and "What is the temperature of the air conditioner?". This allows the inquiry unit to flexibly check the status of home appliances to meet the diverse needs of users.

[0033] The scene setting unit changes the settings of multiple home appliances based on voice commands received by the reception unit. For example, based on a command such as "set to movie mode," the scene setting unit changes the TV's picture quality setting to movie mode and adjusts the air conditioner's temperature to an appropriate temperature. The scene setting unit can change the settings of multiple home appliances at once. Specifically, the scene setting unit changes the settings of multiple home appliances simultaneously based on a pre-configured scene profile. For example, setting it to "relax mode" will lower the brightness of the lights, set the air conditioner temperature to a comfortable temperature, and play music. The scene setting unit can also apply customized scene settings according to the user's preferences. Users can create scene profiles according to their preferences and recall those profiles with specific voice commands. Furthermore, the scene setting unit can also set automatic scenes according to the time of day and day of the week. For example, "sleep mode" can be automatically applied at a specific time every night, the lights will turn off, and the air conditioner will switch to energy-saving mode. The scene setting unit can also link with cloud services to enable scene settings from remote locations. When a user changes scene settings using their smartphone while away from home, that information is transmitted to the scene setting unit via the cloud, and the settings of the home appliances are changed. This allows users to manage the scene settings of their home appliances from anywhere. By coordinating multiple home appliances, the scene setting unit can make the user's life more comfortable and enable more efficient operation of home appliances.

[0034] The system also includes a storage unit that saves settings for appliances for each scene. The scene setting unit can change the settings of appliances to correspond to the scene indicated by the voice command received by the reception unit. The storage unit saves scene settings such as relaxation mode and movie mode. The storage unit can also save scene settings customized by the user. For example, if the user issues the command "set to relaxation mode," the scene setting unit will retrieve the relaxation mode settings saved in the storage unit and change the settings of multiple appliances. The storage unit can also save scene settings to the cloud. In addition, the storage unit can save scene settings to a local device. This makes it possible to save settings for each scene and change the settings of multiple appliances based on voice commands.

[0035] The reception unit can filter out ambient noise and remove noise when receiving voice commands. For example, when receiving a voice command, the reception unit filters out ambient noise and removes noise. The reception unit can filter ambient noise using a noise reduction algorithm. For example, the reception unit can analyze ambient background noise in real time to improve the accuracy of voice command recognition. This improves the accuracy of voice command recognition and enables precise operation of home appliances. 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 ambient sound data into a generating AI and have the generating AI perform noise reduction.

[0036] The reception unit can select the optimal command analysis method by referring to the user's past command history when receiving a voice command. For example, the reception unit may prioritize analyzing commands that the user has frequently used in the past. The reception unit can analyze past command history and select the most efficient analysis method. This makes it possible to select the optimal command analysis method by referring to past command 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 command history data into a generating AI and have the generating AI perform the selection of the optimal command analysis method.

[0037] The reception unit can determine the priority of voice commands based on the user's current activity status when it receives a voice command. For example, if the user is doing housework, the reception unit will prioritize commands to operate home appliances. The reception unit can analyze the user's activity status in real time and determine the optimal command priority. This allows for a more appropriate response by determining the command priority based on the current activity status. 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 user activity status data into a generating AI and have the generating AI perform the command priority determination.

[0038] The reception unit can prioritize receiving voice commands by considering the user's geographical location information. For example, if the user is at home, the reception unit will prioritize commands to operate home appliances. The reception unit can analyze the user's geographical location information in real time and determine the optimal command priority. This makes it possible to prioritize receiving commands that are highly relevant by considering geographical location information. 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 geographical location information data into a generating AI and have the generating AI determine the command priority.

[0039] The reception unit can analyze the user's social media activity when receiving a voice command and prioritize relevant commands. For example, if the user mentions a specific home appliance on social media, the reception unit will prioritize commands to operate that appliance. The reception unit can analyze the user's social media activity in real time and determine the optimal command priority. This makes it possible to prioritize relevant commands by analyzing 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 user's social media activity data into a generating AI and have the generating AI determine the command priority.

[0040] The control unit can select the optimal operating method when operating an appliance by referring to the user's past operating history. For example, the control unit can prioritize the operating method that the user has frequently used in the past. The control unit can analyze the user's past operating history in real time and select the optimal operating method. This makes it possible to select the optimal operating method by referring to past operating 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 the user's past operating history data into a generating AI and have the generating AI perform the selection of the optimal operating method.

[0041] The control unit can determine the priority of operations based on the user's current living situation when operating home appliances. For example, the control unit will prioritize operating home appliances when the user is doing housework. The control unit can analyze the user's living situation in real time and determine the optimal priority of operations. This allows for more appropriate operation by determining the priority of operations based on the current living situation. 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 user's living situation data into a generating AI and have the generating AI perform the determination of the priority of operations.

[0042] The control unit can select the optimal operating method when operating home appliances, taking into account the user's geographical location information. For example, the control unit prioritizes operating home appliances when the user is at home. The control unit can analyze the user's geographical location information in real time and select the optimal operating method. This makes it possible to select the optimal operating method by considering 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 user's geographical location information data into a generating AI and have the generating AI select the optimal operating method.

[0043] The control unit can analyze the user's social media activity when operating home appliances and prioritize relevant operations. For example, if the user mentions a specific home appliance on social media, the control unit will prioritize operating that appliance. The control unit can analyze the user's social media activity in real time and determine the optimal operation priority. This makes it possible to prioritize relevant operations by analyzing 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 user's social media activity data into a generating AI and have the generating AI determine the operation priority.

[0044] The inquiry unit can select the optimal inquiry method by referring to the user's past inquiry history when inquiring about the status of home appliances. For example, the inquiry unit may prioritize selecting inquiry methods that the user has frequently used in the past. The inquiry unit can also analyze the user's past inquiry history in real time to select the optimal inquiry method. This makes it possible to select the optimal inquiry method by referring to past inquiry history. Some or all of the above processing in the inquiry unit may be performed using AI, for example, or without AI. For example, the inquiry unit can input the user's past inquiry history data into a generating AI and have the generating AI perform the selection of the optimal inquiry method.

[0045] The inquiry unit can prioritize inquiries about the status of home appliances based on the user's current living situation. For example, if the user is doing housework, the inquiry unit will prioritize inquiries about the status of home appliances. The inquiry unit can analyze the user's living situation in real time and determine the optimal inquiry priority. This allows for more appropriate inquiries by prioritizing inquiries based on the user's current living situation. Some or all of the above processing in the inquiry unit may be performed using AI, for example, or without AI. For example, the inquiry unit can input user living situation data into a generating AI and have the generating AI determine the inquiry priority.

[0046] The inquiry unit can select the optimal inquiry method when inquiring about the status of home appliances, taking into account the user's geographical location information. For example, if the user is at home, the inquiry unit will prioritize inquiring about the status of home appliances. The inquiry unit can analyze the user's geographical location information in real time and select the optimal inquiry method. This makes it possible to select the optimal inquiry method by considering geographical location information. Some or all of the above processing in the inquiry unit may be performed using AI, for example, or without AI. For example, the inquiry unit can input the user's geographical location information data into a generating AI and have the generating AI select the optimal inquiry method.

[0047] The inquiry unit can analyze a user's social media activity when they inquire about the status of an appliance and prioritize relevant inquiries. For example, if a user mentions a specific appliance on social media, the inquiry unit will prioritize inquiries about the status of that appliance. The inquiry unit can analyze a user's social media activity in real time and determine the optimal inquiry priority. This makes it possible to prioritize relevant inquiries by analyzing social media activity. Some or all of the above processing in the inquiry unit may be performed using AI, for example, or not using AI. For example, the inquiry unit can input user social media activity data into a generating AI and have the generating AI determine the inquiry priority.

[0048] The scene setting unit can select the optimal setting method by referring to the user's past scene setting history when setting a scene. For example, the scene setting unit can prioritize selecting scene settings that the user has frequently used in the past. The scene setting unit can analyze the user's past scene setting history in real time and select the optimal setting method. This makes it possible to select the optimal setting method by referring to past scene setting history. Some or all of the above processing in the scene setting unit may be performed using AI, for example, or without using AI. For example, the scene setting unit can input the user's past scene setting history data into a generating AI and have the generating AI perform the selection of the optimal setting method.

[0049] The scene setting unit can determine the priority of settings based on the user's current living situation when setting a scene. For example, if the user is doing housework, the scene setting unit will prioritize the operation of home appliances. The scene setting unit can analyze the user's living situation in real time and determine the optimal priority of settings. This makes it possible to set more appropriate scenes by determining the priority of settings based on the current living situation. Some or all of the above processing in the scene setting unit may be performed using AI, for example, or without using AI. For example, the scene setting unit can input the user's living situation data into a generating AI and have the generating AI perform the determination of the setting priority.

[0050] The scene setting unit can select the optimal setting method when setting a scene, taking into account the user's geographical location information. For example, if the user is at home, the scene setting unit will set a scene that prioritizes the operation of home appliances. The scene setting unit can analyze the user's geographical location information in real time and select the optimal setting method. This makes it possible to select the optimal setting method by taking geographical location information into consideration. Some or all of the above processing in the scene setting unit may be performed using AI, for example, or without using AI. For example, the scene setting unit can input the user's geographical location information data into a generating AI and have the generating AI perform the selection of the optimal setting method.

[0051] The scene setting unit can analyze the user's social media activity during scene setting and prioritize relevant settings. For example, if the user mentions a specific home appliance on social media, the scene setting unit will prioritize the settings for that appliance. The scene setting unit can analyze the user's social media activity in real time and determine the optimal setting priority. This makes it possible to prioritize relevant settings by analyzing social media activity. Some or all of the above processing in the scene setting unit may be performed using AI, for example, or without AI. For example, the scene setting unit can input the user's social media activity data into a generating AI and have the generating AI determine the setting priority.

[0052] The storage unit can select the optimal storage method by referring to the user's past storage history when saving scene settings. For example, the storage unit may prioritize selecting storage methods that the user has frequently used in the past. The storage unit can also analyze the user's past storage history in real time and select the optimal storage method. This makes it possible to select the optimal storage method by referring to past storage history. Some or all of the above processing in the storage unit may be performed using AI, for example, or without using AI. For example, the storage unit can input the user's past storage history data into a generating AI and have the generating AI perform the selection of the optimal storage method.

[0053] The storage unit can select the optimal storage method when saving scene settings, taking into account the user's geographical location information. For example, if the user is at home, the storage unit will save a scene setting that prioritizes the operation of home appliances. The storage unit can analyze the user's geographical location information in real time and select the optimal storage method. This makes it possible to select the optimal storage method by taking geographical location information into consideration. Some or all of the above processing in the storage unit may be performed using AI, for example, or without using AI. For example, the storage unit can input the user's geographical location information data into a generating AI and have the generating AI perform the selection of the optimal storage method.

[0054] The storage unit can analyze the user's social media activity when saving scene settings and prioritize relevant saves. For example, if the user mentions a specific home appliance on social media, the storage unit will prioritize saving the settings for that appliance. The storage unit can analyze the user's social media activity in real time and determine the optimal saving priority. This makes it possible to prioritize relevant saves by analyzing social media activity. Some or all of the above processing in the storage unit may be performed using AI, for example, or not using AI. For example, the storage unit can input the user's social media activity data into a generating AI and have the generating AI perform the determination of saving priorities.

[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] Voice control systems for home appliances can also be equipped with a learning unit. This learning unit can learn the user's voice command usage patterns and improve the accuracy of subsequent voice command recognition. For example, if a user tends to operate a specific appliance at a particular time, the learning unit can learn this pattern and incorporate it into the analysis of subsequent voice commands. The learning unit can also learn the characteristics of the user's voice to improve voice recognition accuracy. Furthermore, the learning unit can learn the user's preferences and habits to provide more personalized appliance operation. This enables voice control systems for home appliances to achieve more accurate voice recognition and personalized operation based on the user's usage patterns and preferences.

[0057] Voice control systems for home appliances can also include a predictive unit. This unit can predict the next operation needed based on the user's past voice commands and appliance usage history. For example, if a user uses a coffee maker at the same time every morning, the predictive unit can learn this pattern and prepare the coffee maker before the user issues a voice command. The predictive unit can also refer to the user's schedule and calendar information to predict appliance operations tailored to specific events. Furthermore, it can refer to weather information and external environmental data to suggest appropriate appliance operations. This allows voice control systems for home appliances to provide more efficient and convenient appliance operation based on the user's lifestyle and external environment.

[0058] Voice control systems for home appliances can also be equipped with a notification unit. This notification unit can inform the user of the appliance's status and operation results. For example, if the user asks, "What time will the washing machine finish?", the notification unit can voice-notify the user of the estimated completion time. The notification unit can also send alerts to the user if it detects an abnormality or error in the appliance. Furthermore, the notification unit can notify the user of the timing for regular maintenance or filter replacement. As a result, voice control systems for home appliances can appropriately inform the user of the appliance's status and necessary actions, supporting smoother appliance management.

[0059] Voice control systems for home appliances can also be equipped with an energy management unit. This unit can monitor the energy consumption of appliances and suggest efficient energy use. For example, it can monitor the power consumption of each appliance in real time and notify the user if energy consumption is high. It can also suggest appliance operation schedules to avoid peak energy consumption times. Furthermore, it can suggest appliance operations to maximize the use of renewable energy. This enables voice control systems for home appliances to improve energy efficiency and achieve environmentally friendly appliance operation.

[0060] Voice control systems for home appliances can also be equipped with a health management unit. This unit can monitor the user's health status and suggest appropriate appliance operations. For example, it can monitor the user's sleep patterns and suggest appropriate lighting and temperature settings. It can also refer to the user's exercise and dietary records and suggest appropriate appliance operations. Furthermore, it can monitor the user's stress level and suggest appliance operations to provide a relaxing environment. This allows voice control systems for home appliances to provide a healthier and more comfortable living environment based on the user's health status.

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

[0062] Step 1: The reception unit receives voice commands. For example, when a user makes a voice command such as "Turn on the TV" or "Set the air conditioner to 25 degrees," the reception unit receives that voice command. The reception unit analyzes the voice command and extracts the information necessary to operate the appliance. Using voice recognition technology, the voice command is converted into text data, and that text data is then analyzed. Step 2: The control unit operates home appliances based on voice commands received by the reception unit. For example, it turns on the TV based on the command "Turn on the TV." It also changes the air conditioner's temperature setting to 25 degrees based on the command "Set the air conditioner to 25 degrees." The control unit can operate home appliances by transmitting remote control signals, and can also operate home appliances via smart home devices. Step 3: The inquiry unit inquires about the status of the home appliance based on the voice command received by the reception unit. For example, based on the command "What is the temperature of the air conditioner?", it retrieves the current set temperature of the air conditioner and notifies the user by voice. The inquiry unit can receive feedback signals from the home appliance and can also use sensors to obtain the status of the home appliance. Step 4: The scene setting unit changes the settings of multiple home appliances based on the voice commands received by the reception unit. For example, based on the command "set to movie mode," it changes the TV's picture quality setting to movie mode and adjusts the air conditioner's temperature setting to an appropriate temperature. The scene setting unit can change the settings of multiple home appliances at once and can also apply a customized scene setting according to the user's preferences.

[0063] (Example of form 2) The voice control system for home appliances according to an embodiment of the present invention is a system that operates home appliances using voice commands. This system can use an AI assistant to inquire about and change the status and settings of appliances by voice. It also has a function to support setting changes for each scene by linking multiple appliances. For example, the user issues a voice command such as "Turn on the TV" or "Set the air conditioner to 25 degrees." This voice command is received by the reception unit. The reception unit analyzes the voice command and extracts the information necessary to operate the appliance. Next, the operation unit operates the appliance based on the voice command received by the reception unit. For example, based on the command "Turn on the TV," the operation unit turns on the power to the TV. Also, based on the command "Set the air conditioner to 25 degrees," the operation unit changes the set temperature of the air conditioner to 25 degrees. Furthermore, based on the voice command received by the reception unit, the inquiry unit can inquire about the status of the appliance. For example, based on the command "What is the temperature of the air conditioner?", the inquiry unit obtains the current set temperature of the air conditioner and notifies the user by voice. In addition, the scene setting unit can change the settings of multiple appliances based on the voice command received by the reception unit. For example, based on a command such as "Switch to movie mode," the scene setting unit changes the TV's picture quality setting to movie mode and adjusts the air conditioner's temperature to an appropriate level. Furthermore, it has a storage unit that stores the settings of multiple home appliances for each scene, and the scene setting unit can change the settings of multiple home appliances to correspond to the scene indicated by the voice command received by the reception unit. For example, based on a command such as "Switch to relaxation mode," the scene setting unit retrieves the relaxation mode settings stored in the storage unit and changes the settings of multiple home appliances. In addition, the operation unit can estimate the user's emotions and operate home appliances based on the estimated emotions of the user. For example, if the user is estimated to be tired, the operation unit dims the lights and changes the air conditioner's temperature to a comfortable level. Furthermore, the scene setting unit can estimate the user's emotions, identify the scene corresponding to the estimated emotions of the user, and change the settings of multiple home appliances to correspond to the identified scene.For example, if the system estimates that the user wants to relax, the scene setting unit will recall the relaxation mode settings and change the settings of multiple home appliances. Finally, the reception unit can filter out the user's current ambient noise when receiving a voice command. This improves the accuracy of voice command recognition, enabling precise operation of home appliances. As a result, the voice control system for home appliances allows users to operate appliances, inquire about their status, and change settings for each scene using voice commands.

[0064] The voice control system for home appliances according to this embodiment comprises a reception unit, an operation unit, an inquiry unit, and a scene setting unit. The reception unit receives voice commands. For example, when a user makes a voice command such as "Turn on the TV" or "Set the air conditioner to 25 degrees," the reception unit receives the voice command. The reception unit analyzes the voice command and extracts the information necessary to operate the appliance. For example, the reception unit uses voice recognition technology to convert the voice command into text data and analyzes the text data. The operation unit operates the appliance based on the voice command received by the reception unit. For example, based on the command "Turn on the TV," the operation unit turns on the power to the TV. Also, based on the command "Set the air conditioner to 25 degrees," the operation unit changes the set temperature of the air conditioner to 25 degrees. The operation unit can operate the appliance by, for example, transmitting a remote control signal. The operation unit can also operate the appliance via a smart home device. The inquiry unit inquires about the status of the appliance based on the voice command received by the reception unit. The inquiry unit, for example, based on a command such as "What is the air conditioner temperature?", retrieves the current set temperature of the air conditioner and notifies the user by voice. The inquiry unit can receive feedback signals from appliances to obtain the status of the appliances. The inquiry unit can also use sensors to obtain the status of the appliances. The scene setting unit changes the settings of multiple appliances based on voice commands received by the reception unit. For example, based on a command such as "Set to movie mode", the scene setting unit changes the picture quality setting of the TV to movie mode and changes the set temperature of the air conditioner to an appropriate temperature. The scene setting unit can change the settings of multiple appliances at once. The scene setting unit can also apply customized scene settings according to the user's preferences. As a result, the voice control system for home appliances allows for the operation of appliances, inquiries about their status, and setting changes for each scene using voice commands.

[0065] The reception unit receives voice commands. For example, when a user makes a voice command such as "Turn on the TV" or "Set the air conditioner to 25 degrees," the reception unit receives that command. The reception unit analyzes the voice command and extracts the information necessary to operate the appliance. Specifically, the reception unit uses high-precision speech recognition technology to convert the voice command into text data and then analyzes that text data. The speech recognition technology incorporates noise cancellation and speech filtering technologies, enabling clear recognition of the user's voice. Furthermore, the speech recognition engine uses natural language processing (NLP) technology to accurately understand the user's intent. For example, if the command "Turn on the TV" is uttered, the speech recognition engine extracts the keywords "TV" and "turn on" and uses this to generate an instruction to turn on the TV. In addition, the reception unit can learn from the user's voice commands and adapt to each user's pronunciation and phrasing. As a result, the system's accuracy improves over time, allowing it to recognize the user's voice commands more quickly and accurately. Furthermore, the reception unit supports multiple languages ​​and dialects, ensuring smooth operation even in different language environments. This allows the reception unit to reliably receive and analyze user voice commands in a variety of situations and environments within the home.

[0066] The control unit operates home appliances based on voice commands received by the reception unit. For example, based on the command "Turn on the TV," the control unit will turn on the TV. Similarly, based on the command "Set the air conditioner to 25 degrees," the control unit will change the air conditioner's temperature setting to 25 degrees. The control unit can also operate home appliances by transmitting remote control signals. Specifically, it uses infrared (IR) signals and radio frequency (RF) signals to send operation instructions to home appliances. This allows for operation similar to that of a conventional remote control. Furthermore, the control unit can operate home appliances via smart home devices. For example, it can use Wi-Fi or Bluetooth to control smart plugs and smart switches, turning home appliances on and off. In addition, the control unit can integrate with cloud services for remote operation. When a user issues a voice command from their smartphone while away from home, the command is transmitted to the control unit via the cloud, and the home appliance is operated. This allows the user to control home appliances from anywhere. The control unit can also operate multiple home appliances simultaneously, supporting complex commands such as "Turn off the living room lights and turn on the TV." This allows the control unit to provide flexible operation of home appliances to meet the diverse needs of users.

[0067] The inquiry unit inquires about the status of home appliances based on voice commands received by the reception unit. For example, based on a command such as "What is the temperature of the air conditioner?", the inquiry unit obtains the current set temperature of the air conditioner and notifies the user by voice. The inquiry unit can receive feedback signals from home appliances in order to obtain their status. Specifically, the inquiry unit receives status signals transmitted by home appliances and analyzes that information. For example, if an air conditioner transmits the current set temperature or operating mode as a feedback signal, the inquiry unit receives that signal, analyzes it, and notifies the user. The inquiry unit can also use sensors to obtain the status of home appliances. For example, it can use temperature sensors and humidity sensors to obtain environmental information around the air conditioner and notify the user based on that information. Furthermore, the inquiry unit can link with cloud services to check the status of home appliances remotely. When a user inquires about the status of home appliances using a smartphone while away from home, that information is transmitted to the inquiry unit via the cloud, and the status of the home appliances is obtained. This allows users to check the status of their home appliances no matter where they are. The inquiry unit can simultaneously inquire about the status of multiple home appliances, supporting multiple commands such as "What is the status of the living room lights?" and "What is the temperature of the air conditioner?". This allows the inquiry unit to flexibly check the status of home appliances to meet the diverse needs of users.

[0068] The scene setting unit changes the settings of multiple home appliances based on voice commands received by the reception unit. For example, based on a command such as "set to movie mode," the scene setting unit changes the TV's picture quality setting to movie mode and adjusts the air conditioner's temperature to an appropriate temperature. The scene setting unit can change the settings of multiple home appliances at once. Specifically, the scene setting unit changes the settings of multiple home appliances simultaneously based on a pre-configured scene profile. For example, setting it to "relax mode" will lower the brightness of the lights, set the air conditioner temperature to a comfortable temperature, and play music. The scene setting unit can also apply customized scene settings according to the user's preferences. Users can create scene profiles according to their preferences and recall those profiles with specific voice commands. Furthermore, the scene setting unit can also set automatic scenes according to the time of day and day of the week. For example, "sleep mode" can be automatically applied at a specific time every night, the lights will turn off, and the air conditioner will switch to energy-saving mode. The scene setting unit can also link with cloud services to enable scene settings from remote locations. When a user changes scene settings using their smartphone while away from home, that information is transmitted to the scene setting unit via the cloud, and the settings of the home appliances are changed. This allows users to manage the scene settings of their home appliances from anywhere. By coordinating multiple home appliances, the scene setting unit can make the user's life more comfortable and enable more efficient operation of home appliances.

[0069] The system also includes a storage unit that saves settings for appliances for each scene. The scene setting unit can change the settings of appliances to correspond to the scene indicated by the voice command received by the reception unit. The storage unit saves scene settings such as relaxation mode and movie mode. The storage unit can also save scene settings customized by the user. For example, if the user issues the command "set to relaxation mode," the scene setting unit will retrieve the relaxation mode settings saved in the storage unit and change the settings of multiple appliances. The storage unit can also save scene settings to the cloud. In addition, the storage unit can save scene settings to a local device. This makes it possible to save settings for each scene and change the settings of multiple appliances based on voice commands.

[0070] The control unit can estimate the user's emotions and operate home appliances based on those estimated emotions. For example, if the control unit estimates that the user is tired, it may dim the lights and change the air conditioner's temperature setting to a comfortable level. The control unit can estimate the user's emotions using an emotion estimation algorithm. For example, it can estimate emotions from the user's facial expressions and voice. It can also estimate emotions using the user's biometric data (heart rate and skin electrical activity). This makes it possible to adjust the operation of home appliances based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 control unit may be performed using AI, for example, or without AI. For example, the control unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0071] The scene setting unit can estimate the user's emotions, identify a scene corresponding to the estimated user's emotions, and change the settings of multiple home appliances to correspond to the identified scene. For example, if the scene setting unit estimates that the user wants to relax, it will recall the relaxation mode setting and change the settings of multiple home appliances. The scene setting unit can estimate the user's emotions using an emotion estimation algorithm. For example, the scene setting unit can estimate emotions from the user's facial expressions and voice. The scene setting unit can also estimate emotions using the user's biometric data (heart rate and skin electrical activity). This makes it possible to adjust the scene settings based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the scene setting unit may be performed using AI, or not using AI. For example, the scene setting unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0072] The reception unit can filter out ambient noise and remove noise when receiving voice commands. For example, when receiving a voice command, the reception unit filters out ambient noise and removes noise. The reception unit can filter ambient noise using a noise reduction algorithm. For example, the reception unit can analyze ambient background noise in real time to improve the accuracy of voice command recognition. This improves the accuracy of voice command recognition and enables precise operation of home appliances. 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 ambient sound data into a generating AI and have the generating AI perform noise reduction.

[0073] The reception unit can estimate the user's emotions and adjust the timing of voice command reception based on the estimated emotions. For example, if the user is stressed, the reception unit will shorten the voice command reception time and respond quickly. The reception unit can estimate the user's emotions using an emotion estimation algorithm. For example, the reception unit can estimate emotions from the user's facial expressions and voice. The reception unit can also estimate emotions using the user's biometric data (heart rate and skin electrical activity). This allows for a more appropriate response by adjusting the timing of voice command reception 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, or not using AI. For example, the reception unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0074] The reception unit can select the optimal command analysis method by referring to the user's past command history when receiving a voice command. For example, the reception unit may prioritize analyzing commands that the user has frequently used in the past. The reception unit can analyze past command history and select the most efficient analysis method. This makes it possible to select the optimal command analysis method by referring to past command 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 command history data into a generating AI and have the generating AI perform the selection of the optimal command analysis method.

[0075] The reception unit can determine the priority of voice commands based on the user's current activity status when it receives a voice command. For example, if the user is doing housework, the reception unit will prioritize commands to operate home appliances. The reception unit can analyze the user's activity status in real time and determine the optimal command priority. This allows for a more appropriate response by determining the command priority based on the current activity status. 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 user activity status data into a generating AI and have the generating AI perform the command priority determination.

[0076] The reception unit can estimate the user's emotions and filter voice commands based on the estimated emotions. For example, if the user is stressed, the reception unit will filter out unnecessary commands and accept only important ones. The reception unit can estimate the user's emotions using an emotion estimation algorithm. For example, the reception unit can estimate emotions from the user's facial expressions and voice. The reception unit can also estimate emotions using the user's biometric data (heart rate and skin electrical activity). This allows for more appropriate commands to be accepted by filtering voice commands according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not using AI. For example, the reception unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0077] The reception unit can prioritize receiving voice commands by considering the user's geographical location information. For example, if the user is at home, the reception unit will prioritize commands to operate home appliances. The reception unit can analyze the user's geographical location information in real time and determine the optimal command priority. This makes it possible to prioritize receiving commands that are highly relevant by considering geographical location information. 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 geographical location information data into a generating AI and have the generating AI determine the command priority.

[0078] The reception unit can analyze the user's social media activity when receiving a voice command and prioritize relevant commands. For example, if the user mentions a specific home appliance on social media, the reception unit will prioritize commands to operate that appliance. The reception unit can analyze the user's social media activity in real time and determine the optimal command priority. This makes it possible to prioritize relevant commands by analyzing 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 user's social media activity data into a generating AI and have the generating AI determine the command priority.

[0079] The control unit can estimate the user's emotions and adjust the operation of the home appliance based on the estimated emotions. For example, if the user is tired, the control unit may dim the lights and change the air conditioner's temperature setting to a comfortable level. The control unit can estimate the user's emotions using an emotion estimation algorithm. For example, the control unit can estimate emotions from the user's facial expressions and voice. The control unit can also estimate emotions using the user's biometric data (heart rate and skin electrical activity). This allows for more appropriate operation by adjusting the operation of the home appliance based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the control unit may be performed using AI, or not using AI. For example, the control unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0080] The control unit can select the optimal operating method when operating an appliance by referring to the user's past operating history. For example, the control unit can prioritize the operating method that the user has frequently used in the past. The control unit can analyze the user's past operating history in real time and select the optimal operating method. This makes it possible to select the optimal operating method by referring to past operating 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 the user's past operating history data into a generating AI and have the generating AI perform the selection of the optimal operating method.

[0081] The control unit can determine the priority of operations based on the user's current living situation when operating home appliances. For example, the control unit will prioritize operating home appliances when the user is doing housework. The control unit can analyze the user's living situation in real time and determine the optimal priority of operations. This allows for more appropriate operation by determining the priority of operations based on the current living situation. 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 user's living situation data into a generating AI and have the generating AI perform the determination of the priority of operations.

[0082] The control unit can estimate the user's emotions and adjust the order of operation of the home appliances based on the estimated emotions. For example, if the user is feeling stressed, the control unit will prioritize important operations and postpone unnecessary ones. The control unit can estimate the user's emotions using an emotion estimation algorithm. For example, the control unit can estimate emotions from the user's facial expressions and voice. The control unit can also estimate emotions using the user's biometric data (heart rate and skin electrical activity). This allows for more appropriate operation by adjusting the order of operation of the home appliances based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0083] The control unit can select the optimal operating method when operating home appliances, taking into account the user's geographical location information. For example, the control unit prioritizes operating home appliances when the user is at home. The control unit can analyze the user's geographical location information in real time and select the optimal operating method. This makes it possible to select the optimal operating method by considering 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 user's geographical location information data into a generating AI and have the generating AI select the optimal operating method.

[0084] The control unit can analyze the user's social media activity when operating home appliances and prioritize relevant operations. For example, if the user mentions a specific home appliance on social media, the control unit will prioritize operating that appliance. The control unit can analyze the user's social media activity in real time and determine the optimal operation priority. This makes it possible to prioritize relevant operations by analyzing 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 user's social media activity data into a generating AI and have the generating AI determine the operation priority.

[0085] The inquiry unit can estimate the user's emotions and adjust the method of querying the status of the home appliance based on the estimated user emotions. For example, if the user is feeling stressed, the inquiry unit can provide a concise query. The inquiry unit can estimate the user's emotions using an emotion estimation algorithm. For example, the inquiry unit can estimate emotions from the user's facial expressions and voice. The inquiry unit can also estimate emotions using the user's biometric data (heart rate and skin electrical activity). This allows for more appropriate queries by adjusting the method of querying the status of the home appliance based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the inquiry unit may be performed using AI, or not using AI. For example, the inquiry unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0086] The inquiry unit can select the optimal inquiry method by referring to the user's past inquiry history when inquiring about the status of home appliances. For example, the inquiry unit may prioritize selecting inquiry methods that the user has frequently used in the past. The inquiry unit can also analyze the user's past inquiry history in real time to select the optimal inquiry method. This makes it possible to select the optimal inquiry method by referring to past inquiry history. Some or all of the above processing in the inquiry unit may be performed using AI, for example, or without AI. For example, the inquiry unit can input the user's past inquiry history data into a generating AI and have the generating AI perform the selection of the optimal inquiry method.

[0087] The inquiry unit can prioritize inquiries about the status of home appliances based on the user's current living situation. For example, if the user is doing housework, the inquiry unit will prioritize inquiries about the status of home appliances. The inquiry unit can analyze the user's living situation in real time and determine the optimal inquiry priority. This allows for more appropriate inquiries by prioritizing inquiries based on the user's current living situation. Some or all of the above processing in the inquiry unit may be performed using AI, for example, or without AI. For example, the inquiry unit can input user living situation data into a generating AI and have the generating AI determine the inquiry priority.

[0088] The inquiry unit can estimate the user's emotions and adjust the order of appliance status inquiries based on the estimated user emotions. For example, if the user is feeling stressed, the inquiry unit will prioritize inquiries about important states. The inquiry unit can estimate the user's emotions using an emotion estimation algorithm. For example, the inquiry unit can estimate emotions from the user's facial expressions and voice. The inquiry unit can also estimate emotions using the user's biometric data (heart rate and skin electrical activity). This allows for more appropriate inquiries by adjusting the order of appliance status inquiries based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the inquiry unit may be performed using AI, or not using AI. For example, the inquiry unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0089] The inquiry unit can select the optimal inquiry method when inquiring about the status of home appliances, taking into account the user's geographical location information. For example, if the user is at home, the inquiry unit will prioritize inquiring about the status of home appliances. The inquiry unit can analyze the user's geographical location information in real time and select the optimal inquiry method. This makes it possible to select the optimal inquiry method by considering geographical location information. Some or all of the above processing in the inquiry unit may be performed using AI, for example, or without AI. For example, the inquiry unit can input the user's geographical location information data into a generating AI and have the generating AI select the optimal inquiry method.

[0090] The inquiry unit can analyze a user's social media activity when they inquire about the status of an appliance and prioritize relevant inquiries. For example, if a user mentions a specific appliance on social media, the inquiry unit will prioritize inquiries about the status of that appliance. The inquiry unit can analyze a user's social media activity in real time and determine the optimal inquiry priority. This makes it possible to prioritize relevant inquiries by analyzing social media activity. Some or all of the above processing in the inquiry unit may be performed using AI, for example, or not using AI. For example, the inquiry unit can input user social media activity data into a generating AI and have the generating AI determine the inquiry priority.

[0091] The scene setting unit can estimate the user's emotions and adjust the scene setting method based on the estimated user emotions. For example, if the user is tired, the scene setting unit will prioritize a relaxed scene setting. The scene setting unit can estimate the user's emotions using an emotion estimation algorithm. For example, the scene setting unit can estimate emotions from the user's facial expressions and voice. The scene setting unit can also estimate emotions using the user's biometric data (heart rate and skin electrical activity). This allows for more appropriate scene setting by adjusting the scene setting method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the scene setting unit may be performed using AI, or not using AI. For example, the scene setting unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0092] The scene setting unit can select the optimal setting method by referring to the user's past scene setting history when setting a scene. For example, the scene setting unit can prioritize selecting scene settings that the user has frequently used in the past. The scene setting unit can analyze the user's past scene setting history in real time and select the optimal setting method. This makes it possible to select the optimal setting method by referring to past scene setting history. Some or all of the above processing in the scene setting unit may be performed using AI, for example, or without using AI. For example, the scene setting unit can input the user's past scene setting history data into a generating AI and have the generating AI perform the selection of the optimal setting method.

[0093] The scene setting unit can determine the priority of settings based on the user's current living situation when setting a scene. For example, if the user is doing housework, the scene setting unit will prioritize the operation of home appliances. The scene setting unit can analyze the user's living situation in real time and determine the optimal priority of settings. This makes it possible to set more appropriate scenes by determining the priority of settings based on the current living situation. Some or all of the above processing in the scene setting unit may be performed using AI, for example, or without using AI. For example, the scene setting unit can input the user's living situation data into a generating AI and have the generating AI perform the determination of the setting priority.

[0094] The scene setting unit can estimate the user's emotions and adjust the order of scene settings based on the estimated emotions. For example, if the user is stressed, the scene setting unit will prioritize important scene settings and postpone unnecessary ones. The scene setting unit can estimate the user's emotions using an emotion estimation algorithm. For example, the scene setting unit can estimate emotions from the user's facial expressions and voice. The scene setting unit can also estimate emotions using the user's biometric data (heart rate and skin electrical activity). This allows for more appropriate scene settings by adjusting the order of scene settings based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the scene setting unit may be performed using AI, or not using AI. For example, the scene setting unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0095] The scene setting unit can select the optimal setting method when setting a scene, taking into account the user's geographical location information. For example, if the user is at home, the scene setting unit will set a scene that prioritizes the operation of home appliances. The scene setting unit can analyze the user's geographical location information in real time and select the optimal setting method. This makes it possible to select the optimal setting method by taking geographical location information into consideration. Some or all of the above processing in the scene setting unit may be performed using AI, for example, or without using AI. For example, the scene setting unit can input the user's geographical location information data into a generating AI and have the generating AI perform the selection of the optimal setting method.

[0096] The scene setting unit can analyze the user's social media activity during scene setting and prioritize relevant settings. For example, if the user mentions a specific home appliance on social media, the scene setting unit will prioritize the settings for that appliance. The scene setting unit can analyze the user's social media activity in real time and determine the optimal setting priority. This makes it possible to prioritize relevant settings by analyzing social media activity. Some or all of the above processing in the scene setting unit may be performed using AI, for example, or without AI. For example, the scene setting unit can input the user's social media activity data into a generating AI and have the generating AI determine the setting priority.

[0097] The storage unit can estimate the user's emotions and adjust the scene setting saving method based on the estimated user emotions. For example, if the user is feeling stressed, the storage unit can provide a simpler saving method. The storage unit can estimate the user's emotions using an emotion estimation algorithm. For example, the storage unit can estimate emotions from the user's facial expressions or voice. The storage unit can also estimate emotions using the user's biometric data (heart rate or skin electrical activity). This allows for more appropriate saving by adjusting the scene setting saving method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0098] The storage unit can select the optimal storage method by referring to the user's past storage history when saving scene settings. For example, the storage unit may prioritize selecting storage methods that the user has frequently used in the past. The storage unit can also analyze the user's past storage history in real time and select the optimal storage method. This makes it possible to select the optimal storage method by referring to past storage history. Some or all of the above processing in the storage unit may be performed using AI, for example, or without using AI. For example, the storage unit can input the user's past storage history data into a generating AI and have the generating AI perform the selection of the optimal storage method.

[0099] The storage unit can estimate the user's emotions and adjust the saving order of scene settings based on the estimated emotions. For example, if the user is stressed, the storage unit will prioritize saving important scene settings. The storage unit can estimate the user's emotions using an emotion estimation algorithm. For example, the storage unit can estimate emotions from the user's facial expressions and voice. The storage unit can also estimate emotions using the user's biometric data (heart rate and skin electrical activity). This allows for more appropriate saving by adjusting the saving order of scene settings based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0100] The storage unit can select the optimal storage method when saving scene settings, taking into account the user's geographical location information. For example, if the user is at home, the storage unit will save a scene setting that prioritizes the operation of home appliances. The storage unit can analyze the user's geographical location information in real time and select the optimal storage method. This makes it possible to select the optimal storage method by taking geographical location information into consideration. Some or all of the above processing in the storage unit may be performed using AI, for example, or without using AI. For example, the storage unit can input the user's geographical location information data into a generating AI and have the generating AI perform the selection of the optimal storage method.

[0101] The storage unit can analyze the user's social media activity when saving scene settings and prioritize relevant saves. For example, if the user mentions a specific home appliance on social media, the storage unit will prioritize saving the settings for that appliance. The storage unit can analyze the user's social media activity in real time and determine the optimal saving priority. This makes it possible to prioritize relevant saves by analyzing social media activity. Some or all of the above processing in the storage unit may be performed using AI, for example, or not using AI. For example, the storage unit can input the user's social media activity data into a generating AI and have the generating AI perform the determination of saving priorities.

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

[0103] Voice control systems for home appliances can also be equipped with a learning unit. This learning unit can learn the user's voice command usage patterns and improve the accuracy of subsequent voice command recognition. For example, if a user tends to operate a specific appliance at a particular time, the learning unit can learn this pattern and incorporate it into the analysis of subsequent voice commands. The learning unit can also learn the characteristics of the user's voice to improve voice recognition accuracy. Furthermore, the learning unit can learn the user's preferences and habits to provide more personalized appliance operation. This enables voice control systems for home appliances to achieve more accurate voice recognition and personalized operation based on the user's usage patterns and preferences.

[0104] Voice control systems for home appliances can also include a predictive unit. This unit can predict the next operation needed based on the user's past voice commands and appliance usage history. For example, if a user uses a coffee maker at the same time every morning, the predictive unit can learn this pattern and prepare the coffee maker before the user issues a voice command. The predictive unit can also refer to the user's schedule and calendar information to predict appliance operations tailored to specific events. Furthermore, it can refer to weather information and external environmental data to suggest appropriate appliance operations. This allows voice control systems for home appliances to provide more efficient and convenient appliance operation based on the user's lifestyle and external environment.

[0105] Voice control systems for home appliances can also be equipped with a notification unit. This notification unit can inform the user of the appliance's status and operation results. For example, if the user asks, "What time will the washing machine finish?", the notification unit can voice-notify the user of the estimated completion time. The notification unit can also send alerts to the user if it detects an abnormality or error in the appliance. Furthermore, the notification unit can notify the user of the timing for regular maintenance or filter replacement. As a result, voice control systems for home appliances can appropriately inform the user of the appliance's status and necessary actions, supporting smoother appliance management.

[0106] Voice control systems for home appliances can also be equipped with an energy management unit. This unit can monitor the energy consumption of appliances and suggest efficient energy use. For example, it can monitor the power consumption of each appliance in real time and notify the user if energy consumption is high. It can also suggest appliance operation schedules to avoid peak energy consumption times. Furthermore, it can suggest appliance operations to maximize the use of renewable energy. This enables voice control systems for home appliances to improve energy efficiency and achieve environmentally friendly appliance operation.

[0107] Voice control systems for home appliances can also be equipped with a health management unit. This unit can monitor the user's health status and suggest appropriate appliance operations. For example, it can monitor the user's sleep patterns and suggest appropriate lighting and temperature settings. It can also refer to the user's exercise and dietary records and suggest appropriate appliance operations. Furthermore, it can monitor the user's stress level and suggest appliance operations to provide a relaxing environment. This allows voice control systems for home appliances to provide a healthier and more comfortable living environment based on the user's health status.

[0108] Voice control systems for home appliances can also be equipped with an emotion estimation unit. This unit can estimate the user's emotions and adjust the operation of the appliances based on those emotions. For example, if the emotion estimation unit estimates that the user is stressed, it can play relaxing music and change the lighting to a warmer color. If the emotion estimation unit estimates that the user is happy, it can provide bright lighting and upbeat music. Furthermore, the emotion estimation unit can adjust the order of appliance operations according to the user's emotions, prioritizing important operations. This allows voice control systems for home appliances to provide more appropriate and comfortable appliance operation based on the user's emotions.

[0109] Voice control systems for home appliances can also be equipped with an emotional feedback unit. This emotional feedback unit can estimate the user's emotions and provide feedback based on those emotions. For example, if the emotional feedback unit estimates the user is tired, it can suggest ways to create a relaxing environment. Similarly, if the emotional feedback unit estimates the user is stressed, it can suggest activities to reduce stress. Furthermore, the emotional feedback unit can adjust the operation of the appliance according to the user's emotions, providing a more comfortable environment. This allows voice control systems for home appliances to provide more appropriate feedback and appliance operation based on the user's emotions.

[0110] Voice control systems for home appliances can also be equipped with an emotion history unit. This unit can record the user's emotional history and adjust appliance operation based on that history. For example, it can record times when the user previously felt relaxed and automatically set a relaxation mode during those times. It can also record situations in which the user previously felt stressed and suggest appliance operations to avoid those situations. Furthermore, it can analyze changes in the user's emotions and adjust appliance operation based on long-term emotional trends. This allows voice control systems for home appliances to provide more personalized appliance operation based on the user's emotional history.

[0111] Voice control systems for home appliances can also be equipped with an emotion prediction unit. This unit can predict future emotions based on the user's past emotional data and adjust appliance operation accordingly. For example, if a user tends to feel stressed on certain days or times, the emotion prediction unit can provide a relaxing environment during those times. Furthermore, the unit can refer to the user's schedule and event information to suggest appropriate appliance operation before and after specific events. Additionally, the emotion prediction unit can predict the user's emotions based on seasonal and weather changes and provide appropriate appliance operation. This enables voice control systems for home appliances to predict the user's future emotions and provide a more comfortable living environment.

[0112] Voice control systems for home appliances can also incorporate an emotion-sharing unit. This unit can share the user's emotions with other family members and devices, and adjust the operation of appliances based on those shared emotions. For example, if the whole family wants to relax, the emotion-sharing unit can change the living room lighting to a warmer color and play relaxing music. It can also adjust the environment in a specific family member's room if that member is feeling stressed. Furthermore, the emotion-sharing unit can store emotional data in the cloud and work with other devices to enable emotion-based operation. This makes it possible for voice control systems for home appliances to provide a more comfortable and harmonious living environment based on the emotions of the entire family.

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

[0114] Step 1: The reception unit receives voice commands. For example, when a user makes a voice command such as "Turn on the TV" or "Set the air conditioner to 25 degrees," the reception unit receives that voice command. The reception unit analyzes the voice command and extracts the information necessary to operate the appliance. Using voice recognition technology, the voice command is converted into text data, and that text data is then analyzed. Step 2: The control unit operates home appliances based on voice commands received by the reception unit. For example, it turns on the TV based on the command "Turn on the TV." It also changes the air conditioner's temperature setting to 25 degrees based on the command "Set the air conditioner to 25 degrees." The control unit can operate home appliances by transmitting remote control signals, and can also operate home appliances via smart home devices. Step 3: The inquiry unit inquires about the status of the home appliance based on the voice command received by the reception unit. For example, based on the command "What is the temperature of the air conditioner?", it retrieves the current set temperature of the air conditioner and notifies the user by voice. The inquiry unit can receive feedback signals from the home appliance and can also use sensors to obtain the status of the home appliance. Step 4: The scene setting unit changes the settings of multiple home appliances based on the voice commands received by the reception unit. For example, based on the command "set to movie mode," it changes the TV's picture quality setting to movie mode and adjusts the air conditioner's temperature setting to an appropriate temperature. The scene setting unit can change the settings of multiple home appliances at once and can also apply a customized scene setting according to the user's preferences.

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

[0116] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

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

[0118] For example, the reception 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. For example, the operation 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. For example, the inquiry 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. For example, the scene setting 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 correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

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

[0120] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] For example, the reception 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. For example, the operation 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. For example, the inquiry 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. For example, the scene setting 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 correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

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

[0136] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0145] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] For example, the reception 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. For example, the operation 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. For example, the inquiry 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. For example, the scene setting 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 correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

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

[0152] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0159] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] For example, the reception unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the operation unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the inquiry unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the scene setting unit is implemented by the control unit 46A of the robot 414 or 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 examples described above and can be changed in various ways.

[0168] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0170] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0171] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0172] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0176] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0177] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0178] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0179] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0180] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0181] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0183] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0184] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0185] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0186] (Note 1) A reception area that accepts voice commands, Based on the voice commands received by the aforementioned reception unit, an operating unit operates the home appliance, Based on the voice command received by the aforementioned reception unit, there is a unit that inquires about the status of the home appliance, The system includes a scene setting unit that changes the settings of a home appliance based on a voice command received by the reception unit. A system characterized by the following features. (Note 2) It also includes a storage unit to save the settings for each appliance in different scenes. The aforementioned scene setting unit is The settings of the home appliance are changed to correspond to the scene indicated by the voice command received by the aforementioned reception unit. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned operating unit is To estimate the user's emotions, Operate home appliances based on estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned scene setting unit is To estimate the user's emotions, Identify scenes that correspond to the estimated user's emotions, Change the settings of multiple home appliances to correspond to the specified scene. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is When receiving a voice command, Filters out user ambient sounds to remove noise. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is To estimate the user's emotions, The timing of voice command acceptance is adjusted based on the estimated user's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When receiving a voice command, The system selects the optimal command analysis method by referring to the user's past command history. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving a voice command, Command priorities are determined based on the user's current activity. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is To estimate the user's emotions, Filter voice commands based on estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving a voice command, Prioritize accepting relevant commands based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving a voice command, Analyzes users' social media activity and prioritizes accepting relevant commands. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned operating unit is To estimate the user's emotions, Adjust the operation of home appliances based on estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned operating unit is When operating home appliances, The operation method is selected based on the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned operating unit is When operating home appliances, Prioritize operations based on the user's current life circumstances. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned operating unit is To estimate the user's emotions, The system adjusts the sequence of operations for home appliances based on the estimated user's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned operating unit is When operating home appliances, The optimal operating method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned operating unit is When operating home appliances, Analyze users' social media activity and prioritize relevant actions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned inquiry section is, To estimate the user's emotions, Adjust the way you inquire about the status of your home appliances based on your estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned inquiry section is, When inquiring about the condition of home appliances, Select the inquiry method based on the user's past inquiry history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned inquiry section is, When inquiring about the condition of home appliances, Prioritize inquiries based on the user's current life circumstances. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned inquiry section is, To estimate the user's emotions, The order of appliance status queries is adjusted based on the estimated user's sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned inquiry section is, When inquiring about the condition of home appliances, The optimal inquiry method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned inquiry section is, When inquiring about the condition of home appliances, Analyze users' social media activity and prioritize relevant inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned scene setting unit is To estimate the user's emotions, Adjust the scene settings based on estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned scene setting unit is When setting up the scene, The configuration method is selected based on the user's past scene configuration history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned scene setting unit is When setting up the scene, Prioritize settings based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned scene setting unit is To estimate the user's emotions, Adjust the order of scene settings based on estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned scene setting unit is When setting up the scene, The optimal configuration method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned scene setting unit is When setting up the scene, Analyze users' social media activity and prioritize relevant settings. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned storage unit is To estimate the user's emotions, Adjust how scene settings are saved based on estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned storage unit is When saving scene settings, The storage method is selected based on the user's past saving history. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned storage unit is To estimate the user's emotions, Adjust the save order of scene settings based on estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned storage unit is When saving scene settings, The optimal storage method is selected considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned storage unit is When saving scene settings, Analyze users' social media activity and prioritize saving relevant content. The system described in Appendix 2, characterized by the features described herein. [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that accepts voice commands, Based on the voice commands received by the aforementioned reception unit, an operating unit operates the home appliance, Based on the voice command received by the aforementioned reception unit, there is a unit that inquires about the status of the home appliance, The system includes a scene setting unit that changes the settings of a home appliance based on a voice command received by the reception unit. A system characterized by the following features.

2. It also includes a storage unit to save the settings for each appliance in different scenes. The aforementioned scene setting unit is The settings of the home appliance are changed to correspond to the scene indicated by the voice command received by the aforementioned reception unit. The system according to feature 1.

3. The aforementioned operating unit is To estimate the user's emotions, Operate home appliances based on estimated user emotions. The system according to feature 1.

4. The aforementioned scene setting unit is To estimate the user's emotions, Identify scenes that correspond to the estimated user's emotions, Change the settings of multiple home appliances to correspond to the specified scene. The system according to feature 1.

5. The aforementioned reception unit is When receiving a voice command, Filters out user ambient sounds to remove noise. The system according to feature 1.

6. The aforementioned reception unit is To estimate the user's emotions, The timing of voice command acceptance is adjusted based on the estimated user's emotions. The system according to feature 1.

7. The aforementioned reception unit is When receiving a voice command, The system selects the optimal command analysis method by referring to the user's past command history. The system according to feature 1.

8. The aforementioned reception unit is When receiving a voice command, Command priorities are determined based on the user's current activity. The system according to feature 1.

9. The aforementioned reception unit is Estimate the user's emotions, Filter voice commands based on estimated user sentiment. The system according to feature 1.

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