System

Abstract

An object of a system according to an embodiment is to provide an environment in which anyone can easily operate a highly functional home appliance.SOLUTION: A system includes an operation method analysis unit, an operation instruction providing unit, and an automatic setting unit. The operation method analysis unit analyzes an operation method of the home appliance. The operation instruction providing unit provides an operation instruction to the user based on the operation method analyzed by the operation method analysis unit. The automatic setting unit automatically adjusts the setting of the home appliance based on the operation method analyzed by the operation method analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology requires the skills to operate highly functional home appliances, and there is a problem in that an environment that allows anyone to operate them easily is not yet in place.

[0005] The system according to the embodiment aims to provide an environment in which anyone can easily operate highly functional home appliances. [Means for solving the problem]

[0006] A system according to an embodiment includes an operation method analysis unit, an operation instruction providing unit, and an automatic setting unit. The operation method analysis unit analyzes an operation method of a home appliance. The operation instruction providing unit provides operation instructions to a user based on the operation method analyzed by the operation method analysis unit. The automatic setting unit automatically adjusts settings of the home appliance based on the operation method analyzed by the operation method analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an environment in which anyone can easily operate highly functional home appliances. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The home appliance operation assistance system according to the embodiment of the present invention is a system that analyzes the operation method of a home appliance, provides appropriate instructions and support to the user, and automatically adjusts the settings of the home appliance, thereby enabling anyone to easily master the use of the home appliance.

[0029] A home appliance operation assistance system according to an embodiment includes an operation method analysis unit, an operation instruction providing unit, and an automatic setting unit. The operation method analysis unit analyzes an operation method of a home appliance. For example, the operation method analysis unit uses a generation AI to analyze the operation method of the home appliance and understand how the user should operate it. The operation method analysis unit can also analyze the operation method of a washing machine and suggest optimal settings based on the amount and type of laundry. The operation method analysis unit can also analyze how to change the temperature setting of a refrigerator or the operation mode of an air conditioner. For example, the generation AI receives prompts containing instructions on how to operate the home appliance and analyzes the operation method based on the prompts. The operation instruction providing unit provides operation instructions to the user based on the operation method analyzed by the operation method analysis unit. For example, the operation instruction providing unit instructs the user on how to change the temperature setting of a refrigerator or the operation mode of an air conditioner. The operation instruction providing unit can also present specific operation procedures in response to questions or requests from the user. The operation instruction providing unit can also use the generation AI to provide appropriate operation instructions to the user. For example, the generation AI presents specific operation procedures in response to questions from the user. The automatic setting unit automatically adjusts the settings of the home appliance based on the operation method analyzed by the operation method analysis unit. For example, the automatic setting unit can make an air conditioner sense the room temperature and humidity and automatically select the optimal operation mode. The automatic setting unit can also automatically adjust lighting according to the time of day and brightness. Furthermore, the automatic setting unit can learn a user's usage patterns and suggest optimal settings. For example, the generation AI automatically adjusts the settings of the home appliance according to the user's usage situation and environment. As a result, the home appliance operation assistance system according to the embodiment can make it easy for anyone to use home appliances. For example, even elderly people or those unfamiliar with technology can use home appliances effectively with the support of AI. Furthermore, when a problem with a home appliance occurs, it can be dealt with quickly.

[0030] The operation method analysis unit uses the generation AI to analyze the user's past operation history and identify the most frequently used operation patterns. For example, the operation method analysis unit collects the user's past operation history, and the generation AI analyzes that data. For example, based on the operation history of a washing machine, it identifies the washing mode the user uses most frequently. The operation method analysis unit also analyzes the user's operation history and extracts operation patterns performed during specific time periods or days of the week. For example, it can identify the settings of home appliances that are often used on weekends. Furthermore, based on the operation history, the generation AI learns the user's operation patterns and suggests the most efficient operation methods. For example, it can optimize the temperature setting and operation mode of an air conditioner. This allows the unit to analyze the user's past operation history and identify the most frequently used operation patterns, thereby providing the user with the optimal operation method.

[0031] The operation method analysis unit uses the generation AI to recognize the user's voice and gestures and suggest operation methods based on them. For example, the operation method analysis unit recognizes the user's voice and suggests operation methods for home appliances based on voice commands. For example, if the user says, "Start the washing machine," the appropriate wash mode will be suggested. The operation method analysis unit also uses gesture recognition technology to analyze the user's hand movements and suggest operation methods for home appliances based on that. For example, a hand wave can suggest turning lights on and off. Furthermore, the operation method analysis unit combines both voice and gestures to allow the generation AI to understand the user's intentions and suggest the optimal operation method. For example, the user can set the air conditioner temperature by voice and adjust the airflow by gesture. This allows the system to recognize the user's voice and gestures and suggest operation methods based on them, enabling intuitive operation for the user.

[0032] The operation method analysis unit uses the generative AI to find commonalities in the operation methods of different home appliances and propose a unified operation interface. For example, the operation method analysis unit analyzes the operation methods of different home appliances using the generative AI and extracts common operation patterns. For example, the operation methods of washing machines and dryers can be unified. The operation method analysis unit also proposes a common operation interface between different home appliances. For example, the operation panels of refrigerators and air conditioners can be unified, allowing users to have a consistent operation experience. Furthermore, the operation method analysis unit uses the generative AI to learn the operation methods of home appliances and design a common operation interface. For example, it can unify operation methods for voice commands and touch panels. This allows the operation method analysis unit to find commonalities in the operation methods of different home appliances and propose a unified operation interface, allowing users to have a consistent operation experience.

[0033] The operation method analysis unit uses the generative AI to learn the user's lifestyle habits and behavioral patterns and customize the operation method based on that. For example, the operation method analysis unit uses the generative AI to learn the user's lifestyle habits and customize the operation method of a home appliance based on that. For example, it can automatically operate a coffee maker to match the user's morning routine. The operation method analysis unit also analyzes the user's daily behavioral patterns and suggests the optimal operation method. For example, it can automatically adjust the air conditioner temperature at night. Furthermore, the operation method analysis unit personalizes the operation method of a home appliance based on the user's lifestyle data. For example, it can automatically adjust the brightness of lights at specific times of the day. In this way, the system can learn the user's lifestyle habits and behavioral patterns and customize the operation method based on that, thereby providing the user with the optimal operation method.

[0034] The operation instruction providing unit can use the generation AI to propose the most efficient operation procedure in real time based on the user's operation history. For example, the generation AI analyzes the user's past operation history and proposes the most efficient operation procedure in real time. For example, it optimizes the operation procedure for a washing machine. The operation instruction providing unit also builds a system in which the generation AI proposes efficient operation procedures based on the user's operation history. For example, it can optimize the temperature setting of a refrigerator. Furthermore, the operation instruction providing unit learns the user's operation history and proposes the optimal operation procedure in real time. For example, it can automatically adjust the operation mode of an air conditioner. This improves the user's operation efficiency by proposing the most efficient operation procedure in real time based on the user's operation history.

[0035] The operation instruction providing unit can use the generation AI to recognize the user's voice and gestures and provide corresponding operation instructions. For example, the generation AI recognizes the user's voice and provides operation instructions based on voice commands. For example, if the user says, "Turn on the air conditioner," the unit will teach them how to operate the air conditioner. The operation instruction providing unit can also use gesture recognition technology to analyze the user's hand movements and provide corresponding operation instructions. For example, a user can turn the lights on and off with a wave of their hand. Furthermore, the operation instruction providing unit combines both voice and gestures so that the generation AI can understand the user's intentions and provide optimal operation instructions. For example, a user can set the washing machine settings by voice and start it with a gesture. This allows the system to recognize the user's voice and gestures and provide corresponding operation instructions, enabling intuitive operation for the user.

[0036] The operation instruction providing unit uses a generation AI to provide operation instructions in different languages, making it possible to accommodate international users. The operation instruction providing unit, for example, builds a system in which a generation AI provides operation instructions in different languages. For example, it supports multiple languages ​​such as English, French, and Chinese. In addition, the operation instruction providing unit automatically translates and provides operation instructions using the generation AI according to the user's language setting. For example, it can translate operation instructions in Japanese into English. Furthermore, in order to provide operation instructions in different languages, the generation AI uses multilingual voice recognition technology. For example, it can provide operation instructions in the user's native language. This makes it possible to accommodate international users by providing operation instructions in different languages.

[0037] The operation instruction providing unit can use the generation AI to develop a multimodal interface that provides appropriate operation instructions even to users with visual or hearing impairments. For example, the operation instruction providing unit develops a multimodal interface that allows the generation AI to provide appropriate operation instructions to users with visual or hearing impairments. For example, it combines voice instructions and tactile feedback. The operation instruction providing unit also provides voice guidance and explanations of operation methods for visually impaired users. For example, it can provide voice guidance on how to set the refrigerator temperature. Furthermore, the operation instruction providing unit provides visual operation instructions for hearing impaired users. For example, it can display instructions on how to operate an air conditioner on a screen. This allows appropriate operation instructions to be provided to users with visual or hearing impairments, enabling everyone to use home appliances.

[0038] The automatic setting unit can use the generating AI to learn the user's past usage patterns and automatically optimize the settings of the home appliances based on that. For example, the automatic setting unit uses the generating AI to learn the user's past usage patterns and automatically optimize the settings of the home appliances based on that. For example, it automatically adjusts the temperature setting of an air conditioner. The automatic setting unit also analyzes the user's usage patterns and builds a system in which the generating AI suggests optimal home appliance settings. For example, it can automatically adjust the brightness of lights. Furthermore, the automatic setting unit personalizes the settings of the home appliances based on the user's usage data. For example, it can automatically select the wash mode of a washing machine. In this way, the system can learn the user's past usage patterns and automatically optimize the settings of the home appliances based on that, thereby providing the user with optimal settings.

[0039] The automatic setting unit can use the generation AI to recognize the user's voice and gestures and automatically adjust the settings of home appliances accordingly. For example, the generation AI recognizes the user's voice and automatically adjusts the settings of home appliances based on voice commands. For example, if the user says, "Turn down the air conditioner temperature," the air conditioner's temperature setting will be automatically adjusted. The automatic setting unit also uses gesture recognition technology to analyze the user's hand movements and automatically adjust the settings of home appliances accordingly. For example, a wave of the hand can adjust the brightness of the lights. Furthermore, the automatic setting unit combines both voice and gestures to allow the generation AI to understand the user's intentions and automatically adjust the optimal settings for the home appliance. For example, the air conditioner's operation mode can be set by voice and the air volume can be adjusted by gesture. This allows the user to operate the appliance intuitively by recognizing the user's voice and gestures and automatically adjusting the settings of the appliance accordingly.

[0040] The automatic setting unit uses a generative AI to coordinate settings between different home appliances, providing a seamless operating experience. For example, the automatic setting unit uses a generative AI to coordinate settings between different home appliances, building a system that provides a seamless operating experience. For example, the automatic setting unit coordinates the settings of air conditioners and lights. The automatic setting unit also integrates settings between different home appliances, allowing the generative AI to provide a consistent operating experience. For example, the settings of a refrigerator and oven can be coordinated to optimize the cooking process. Furthermore, the automatic setting unit uses a generative AI to automatically adjust settings between home appliances, allowing the user to have a consistent operating experience. For example, the settings of a washing machine and dryer can be coordinated. This enables the coordination of settings between different home appliances, providing a seamless operating experience and improving user convenience.

[0041] The automatic setting unit can use the generating AI to monitor the user's living environment (e.g., weather and indoor conditions) in real time and automatically adjust the settings of home appliances based on that. For example, the generating AI collects weather data in real time and automatically adjusts the settings of home appliances based on that data. For example, if the outside temperature is high, the air conditioner's set temperature is lowered. The automatic setting unit also monitors indoor conditions and the generating AI automatically adjusts the optimal home appliance settings. For example, if the indoor humidity is high, the generating AI can automatically activate a dehumidifier. Furthermore, the automatic setting unit builds a system in which the generating AI adjusts the settings of home appliances in real time based on the user's living environment data. For example, it can adjust the brightness of lights depending on the brightness during the day. In this way, the system can provide the user with an optimal environment by monitoring the user's living environment in real time and automatically adjusting the settings of home appliances based on that information.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The home appliance operation assistance system can further include a health management unit that monitors the user's health condition and adjusts the settings of the home appliance based on the health condition. For example, the health management unit can monitor the user's heart rate and body temperature and adjust the temperature of the air conditioner. The health management unit can also analyze the user's sleep patterns and adjust the brightness of the lights and the music playback. Furthermore, the health management unit can optimize the temperature and storage mode of the refrigerator based on the user's dietary records. This allows for a more comfortable and healthy living environment by providing home appliance settings that correspond to the user's health condition.

[0044] The home appliance operation support system can further include an energy management unit that optimizes energy consumption. For example, the energy management unit can monitor the usage of home appliances and suggest settings to minimize energy consumption. The energy management unit can also adjust the usage schedule of home appliances to avoid peak electricity rates. Furthermore, the energy management unit can optimize the operation of home appliances in cooperation with a solar power generation system to maximize the use of renewable energy. This allows for the optimization of energy consumption and the realization of an environmentally friendly lifestyle.

[0045] The home appliance operation support system can further include a security management unit to ensure user safety. For example, the security management unit can detect abnormal behavior of home appliances and issue a warning to the user. The security management unit can also monitor the usage history of home appliances and issue an alert if unauthorized operation is attempted. Furthermore, the security management unit can automatically manage software updates for home appliances and minimize security risks. This ensures user safety and allows users to use home appliances with peace of mind.

[0046] The home appliance operation assistance system may further include a preference setting unit that customizes the settings of the home appliance based on the user's preferences. For example, the preference setting unit may learn the user's preferred temperature and humidity and adjust the air conditioner settings accordingly. The preference setting unit may also learn the user's preferred lighting brightness and color and adjust the lighting settings accordingly. The preference setting unit may also learn the user's preferred music and volume and adjust the audio system settings accordingly. This allows for the creation of a more comfortable living environment by providing home appliance settings based on the user's preferences.

[0047] The home appliance operation assistance system may further include a schedule management unit that automatically adjusts the settings of home appliances based on the user's schedule. For example, the schedule management unit may acquire the user's calendar information and adjust the operation schedule of an air conditioner. The schedule management unit may also automatically set the on / off of lights according to the user's schedule. Furthermore, the schedule management unit may adjust the playback schedule of an audio system based on the user's schedule. This allows for a more efficient living environment by providing home appliance settings based on the user's schedule.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The operation method analysis unit analyzes how to operate the home appliance. For example, the generation AI can be used to analyze how to operate the appliance and understand how the user should operate it. It can also analyze how to operate a washing machine and suggest optimal settings based on the amount and type of laundry. It can also analyze how to change the temperature setting of a refrigerator or the operating mode of an air conditioner. For example, the generation AI receives prompts containing instructions on how to operate the appliance and analyzes the operation method based on those prompts. Step 2: The operation instruction providing unit provides operation instructions to the user based on the operation method analyzed by the operation method analysis unit. For example, it instructs the user on how to change the refrigerator temperature setting or the air conditioner operation mode. It can also present specific operation procedures in response to questions or requests from the user. Furthermore, it can use the generation AI to provide appropriate operation instructions to the user. For example, the generation AI presents specific operation procedures in response to questions from the user. Step 3: The automatic setting unit automatically adjusts the settings of the home appliances based on the operation method analyzed by the operation method analysis unit. For example, an air conditioner can sense the temperature and humidity in the room and automatically select the optimal operating mode. Lighting can also be automatically adjusted according to the time of day and brightness. Furthermore, it can learn the user's usage patterns and suggest optimal settings. For example, the generative AI automatically adjusts the settings of home appliances according to the user's usage situation and environment.

[0050] (Example 2) The home appliance operation assistance system according to the embodiment of the present invention is a system that analyzes the operation method of a home appliance, provides appropriate instructions and support to the user, and automatically adjusts the settings of the home appliance, thereby enabling anyone to easily master the use of the home appliance.

[0051] A home appliance operation assistance system according to an embodiment includes an operation method analysis unit, an operation instruction providing unit, and an automatic setting unit. The operation method analysis unit analyzes an operation method of a home appliance. For example, the operation method analysis unit uses a generation AI to analyze the operation method of the home appliance and understand how the user should operate it. The operation method analysis unit can also analyze the operation method of a washing machine and suggest optimal settings based on the amount and type of laundry. The operation method analysis unit can also analyze how to change the temperature setting of a refrigerator or the operation mode of an air conditioner. For example, the generation AI receives prompts containing instructions on how to operate the home appliance and analyzes the operation method based on the prompts. The operation instruction providing unit provides operation instructions to the user based on the operation method analyzed by the operation method analysis unit. For example, the operation instruction providing unit instructs the user on how to change the temperature setting of a refrigerator or the operation mode of an air conditioner. The operation instruction providing unit can also present specific operation procedures in response to questions or requests from the user. The operation instruction providing unit can also use the generation AI to provide appropriate operation instructions to the user. For example, the generation AI presents specific operation procedures in response to questions from the user. The automatic setting unit automatically adjusts the settings of the home appliance based on the operation method analyzed by the operation method analysis unit. For example, the automatic setting unit can make an air conditioner sense the room temperature and humidity and automatically select the optimal operation mode. The automatic setting unit can also automatically adjust lighting according to the time of day and brightness. Furthermore, the automatic setting unit can learn a user's usage patterns and suggest optimal settings. For example, the generation AI automatically adjusts the settings of the home appliance according to the user's usage situation and environment. As a result, the home appliance operation assistance system according to the embodiment can make it easy for anyone to use home appliances. For example, even elderly people or those unfamiliar with technology can use home appliances effectively with the support of AI. Furthermore, when a problem with a home appliance occurs, it can be dealt with quickly.

[0052] The operation method analysis unit uses the generation AI to analyze the user's past operation history and identify the most frequently used operation patterns. For example, the operation method analysis unit collects the user's past operation history, and the generation AI analyzes that data. For example, based on the operation history of a washing machine, it identifies the washing mode the user uses most frequently. The operation method analysis unit also analyzes the user's operation history and extracts operation patterns performed during specific time periods or days of the week. For example, it can identify the settings of home appliances that are often used on weekends. Furthermore, based on the operation history, the generation AI learns the user's operation patterns and suggests the most efficient operation methods. For example, it can optimize the temperature setting and operation mode of an air conditioner. This allows the unit to analyze the user's past operation history and identify the most frequently used operation patterns, thereby providing the user with the optimal operation method.

[0053] The operation method analysis unit uses the generation AI to recognize the user's voice and gestures and suggest operation methods based on them. For example, the operation method analysis unit recognizes the user's voice and suggests operation methods for home appliances based on voice commands. For example, if the user says, "Start the washing machine," the appropriate wash mode will be suggested. The operation method analysis unit also uses gesture recognition technology to analyze the user's hand movements and suggest operation methods for home appliances based on that. For example, a hand wave can suggest turning lights on and off. Furthermore, the operation method analysis unit combines both voice and gestures to allow the generation AI to understand the user's intentions and suggest the optimal operation method. For example, the user can set the air conditioner temperature by voice and adjust the airflow by gesture. This allows the system to recognize the user's voice and gestures and suggest operation methods based on them, enabling intuitive operation for the user.

[0054] The operation method analysis unit uses the emotion estimation function to detect stress or confusion felt by the user during operation and can improve the operation method based on that. For example, the operation method analysis unit uses the emotion estimation function to analyze the facial expressions and voice of the user when operating a home appliance to detect stress or confusion. For example, if a confused expression is detected during operation, the generation AI suggests a simple operation method. The operation method analysis unit also improves the operation method based on the user's emotional data. For example, it can simplify stressful operation procedures to allow the user to operate comfortably. Furthermore, the operation method analysis unit uses the emotion estimation function to suggest operation methods that reinforce the positive emotions felt by the user during operation. For example, it can provide guidance to increase successful experiences. This improves the user's operation experience by detecting stress or confusion felt by the user during operation and improving the operation method based on that information.

[0055] The operation method analysis unit uses the generative AI to find commonalities in the operation methods of different home appliances and propose a unified operation interface. For example, the operation method analysis unit analyzes the operation methods of different home appliances using the generative AI and extracts common operation patterns. For example, the operation methods of washing machines and dryers can be unified. The operation method analysis unit also proposes a common operation interface between different home appliances. For example, the operation panels of refrigerators and air conditioners can be unified, allowing users to have a consistent operation experience. Furthermore, the operation method analysis unit uses the generative AI to learn the operation methods of home appliances and design a common operation interface. For example, it can unify operation methods for voice commands and touch panels. This allows the operation method analysis unit to find commonalities in the operation methods of different home appliances and propose a unified operation interface, allowing users to have a consistent operation experience.

[0056] The operation method analysis unit uses the generative AI to learn the user's lifestyle habits and behavioral patterns and customize the operation method based on that. For example, the operation method analysis unit uses the generative AI to learn the user's lifestyle habits and customize the operation method of a home appliance based on that. For example, it can automatically operate a coffee maker to match the user's morning routine. The operation method analysis unit also analyzes the user's daily behavioral patterns and suggests the optimal operation method. For example, it can automatically adjust the air conditioner temperature at night. Furthermore, the operation method analysis unit personalizes the operation method of a home appliance based on the user's lifestyle data. For example, it can automatically adjust the brightness of lights at specific times of the day. In this way, the system can learn the user's lifestyle habits and behavioral patterns and customize the operation method based on that, thereby providing the user with the optimal operation method.

[0057] The operation method analysis unit can use the emotion estimation function to provide an interactive tutorial to increase the user's motivation when learning operation methods. For example, the operation method analysis unit uses the emotion estimation function to analyze the user's emotional state when learning operation methods and provide an interactive tutorial to increase motivation. For example, positive feedback can be displayed in real time. The operation method analysis unit also designs a tutorial to increase learning effectiveness based on the user's emotional data. For example, additional explanations can be provided when the user is confused. Furthermore, the operation method analysis unit uses the emotion estimation function to provide a guide to maintain the user's motivation when learning operation methods. For example, interactive elements that emphasize successful experiences can be added. This improves the user's learning effectiveness by providing an interactive tutorial to increase the user's motivation when learning operation methods.

[0058] The operation instruction providing unit can use the generation AI to propose the most efficient operation procedure in real time based on the user's operation history. For example, the generation AI analyzes the user's past operation history and proposes the most efficient operation procedure in real time. For example, it optimizes the operation procedure for a washing machine. The operation instruction providing unit also builds a system in which the generation AI proposes efficient operation procedures based on the user's operation history. For example, it can optimize the temperature setting of a refrigerator. Furthermore, the operation instruction providing unit learns the user's operation history and proposes the optimal operation procedure in real time. For example, it can automatically adjust the operation mode of an air conditioner. This improves the user's operation efficiency by proposing the most efficient operation procedure in real time based on the user's operation history.

[0059] The operation instruction providing unit can use the generation AI to recognize the user's voice and gestures and provide corresponding operation instructions. For example, the generation AI recognizes the user's voice and provides operation instructions based on voice commands. For example, if the user says, "Turn on the air conditioner," the unit will teach them how to operate the air conditioner. The operation instruction providing unit can also use gesture recognition technology to analyze the user's hand movements and provide corresponding operation instructions. For example, a user can turn the lights on and off with a wave of their hand. Furthermore, the operation instruction providing unit combines both voice and gestures so that the generation AI can understand the user's intentions and provide optimal operation instructions. For example, a user can set the washing machine settings by voice and start it with a gesture. This allows the system to recognize the user's voice and gestures and provide corresponding operation instructions, enabling intuitive operation for the user.

[0060] The operation instruction providing unit can use the emotion estimation function to analyze the emotions of the user when receiving operation instructions and provide an instruction method that elicits positive emotions. For example, the operation instruction providing unit can use the emotion estimation function to analyze the emotions of the user when receiving operation instructions in real time and provide an instruction method that elicits positive emotions. For example, the operation instruction providing unit can add words of encouragement. The operation instruction providing unit can also design operation instructions that the generation AI uses based on the user's emotion data to elicit positive emotions. For example, the operation instruction providing unit can provide instructions that emphasize successful experiences. Furthermore, the operation instruction providing unit can use the emotion estimation function to monitor the emotions of the user when receiving operation instructions and provide feedback that elicits positive emotions. For example, the operation instruction providing unit can praise the user when the operation is successful. This improves the user's operation experience by analyzing the user's emotions when receiving operation instructions and providing an instruction method that elicits positive emotions.

[0061] The operation instruction providing unit uses a generation AI to provide operation instructions in different languages, making it possible to accommodate international users. The operation instruction providing unit, for example, builds a system in which a generation AI provides operation instructions in different languages. For example, it supports multiple languages ​​such as English, French, and Chinese. In addition, the operation instruction providing unit automatically translates and provides operation instructions using the generation AI according to the user's language setting. For example, it can translate operation instructions in Japanese into English. Furthermore, in order to provide operation instructions in different languages, the generation AI uses multilingual voice recognition technology. For example, it can provide operation instructions in the user's native language. This makes it possible to accommodate international users by providing operation instructions in different languages.

[0062] The operation instruction providing unit can use the generation AI to develop a multimodal interface that provides appropriate operation instructions even to users with visual or hearing impairments. For example, the operation instruction providing unit develops a multimodal interface that allows the generation AI to provide appropriate operation instructions to users with visual or hearing impairments. For example, it combines voice instructions and tactile feedback. The operation instruction providing unit also provides voice guidance and explanations of operation methods for visually impaired users. For example, it can provide voice guidance on how to set the refrigerator temperature. Furthermore, the operation instruction providing unit provides visual operation instructions for hearing impaired users. For example, it can display instructions on how to operate an air conditioner on a screen. This allows appropriate operation instructions to be provided to users with visual or hearing impairments, enabling everyone to use home appliances.

[0063] The operation instruction providing unit can use the emotion estimation function to monitor the user's emotions when receiving operation instructions in real time and provide instructions at the optimal timing. The operation instruction providing unit can, for example, use the emotion estimation function to monitor the user's emotions when receiving operation instructions in real time and provide instructions at the optimal timing. For example, it can issue operation instructions when the user is relaxed. The operation instruction providing unit also builds a system in which a generation AI provides operation instructions at the optimal timing based on the user's emotion data. For example, it can issue instructions when the user is concentrating. Furthermore, the operation instruction providing unit can use the emotion estimation function to monitor the user's emotions when receiving operation instructions and provide instructions at a timing when stress is low. For example, it can teach the user how to operate the device when the user is calm. This improves the user's operation experience by monitoring the user's emotions when receiving operation instructions in real time and providing instructions at the optimal timing.

[0064] The automatic setting unit can use the generating AI to learn the user's past usage patterns and automatically optimize the settings of the home appliances based on that. For example, the automatic setting unit uses the generating AI to learn the user's past usage patterns and automatically optimize the settings of the home appliances based on that. For example, it automatically adjusts the temperature setting of an air conditioner. The automatic setting unit also analyzes the user's usage patterns and builds a system in which the generating AI suggests optimal home appliance settings. For example, it can automatically adjust the brightness of lights. Furthermore, the automatic setting unit personalizes the settings of the home appliances based on the user's usage data. For example, it can automatically select the wash mode of a washing machine. In this way, the system can learn the user's past usage patterns and automatically optimize the settings of the home appliances based on that, thereby providing the user with optimal settings.

[0065] The automatic setting unit can use the generation AI to recognize the user's voice and gestures and automatically adjust the settings of home appliances accordingly. For example, the generation AI recognizes the user's voice and automatically adjusts the settings of home appliances based on voice commands. For example, if the user says, "Turn down the air conditioner temperature," the air conditioner's temperature setting will be automatically adjusted. The automatic setting unit also uses gesture recognition technology to analyze the user's hand movements and automatically adjust the settings of home appliances accordingly. For example, a wave of the hand can adjust the brightness of the lights. Furthermore, the automatic setting unit combines both voice and gestures to allow the generation AI to understand the user's intentions and automatically adjust the optimal settings for the home appliance. For example, the air conditioner's operation mode can be set by voice and the air volume can be adjusted by gesture. This allows the user to operate the appliance intuitively by recognizing the user's voice and gestures and automatically adjusting the settings of the appliance accordingly.

[0066] The automatic setting unit can use the emotion estimation function to analyze the user's satisfaction with the home appliance settings and improve the settings based on that. For example, the automatic setting unit uses the emotion estimation function to analyze the user's satisfaction with the home appliance settings in real time and improve the settings. For example, it adjusts the air conditioner's temperature setting if it is not comfortable. The automatic setting unit also builds a system in which a generative AI improves the home appliance settings based on the user's emotion data. For example, it can automatically adjust the lighting brightness if it is not appropriate. Furthermore, the automatic setting unit uses the emotion estimation function to monitor the user's satisfaction with the home appliance settings and suggests optimal settings. For example, it can adjust the washing mode of a washing machine to suit the user's preferences. In this way, the user's satisfaction is improved by analyzing the user's satisfaction with the home appliance settings and improving the settings based on that.

[0067] The automatic setting unit uses a generative AI to coordinate settings between different home appliances, providing a seamless operating experience. For example, the automatic setting unit uses a generative AI to coordinate settings between different home appliances, building a system that provides a seamless operating experience. For example, the automatic setting unit coordinates the settings of air conditioners and lights. The automatic setting unit also integrates settings between different home appliances, allowing the generative AI to provide a consistent operating experience. For example, the settings of a refrigerator and oven can be coordinated to optimize the cooking process. Furthermore, the automatic setting unit uses a generative AI to automatically adjust settings between home appliances, allowing the user to have a consistent operating experience. For example, the settings of a washing machine and dryer can be coordinated. This enables the coordination of settings between different home appliances, providing a seamless operating experience and improving user convenience.

[0068] The automatic setting unit can use the generating AI to monitor the user's living environment (e.g., weather and indoor conditions) in real time and automatically adjust the settings of home appliances based on that. For example, the generating AI collects weather data in real time and automatically adjusts the settings of home appliances based on that data. For example, if the outside temperature is high, the air conditioner's set temperature is lowered. The automatic setting unit also monitors indoor conditions and the generating AI automatically adjusts the optimal home appliance settings. For example, if the indoor humidity is high, the generating AI can automatically activate a dehumidifier. Furthermore, the automatic setting unit builds a system in which the generating AI adjusts the settings of home appliances in real time based on the user's living environment data. For example, it can adjust the brightness of lights depending on the brightness during the day. In this way, the system can provide the user with an optimal environment by monitoring the user's living environment in real time and automatically adjusting the settings of home appliances based on that information.

[0069] The automatic setting unit can use the emotion estimation function to monitor the user's emotions in real time when changing home appliance settings and suggest optimal settings. For example, the automatic setting unit can use the emotion estimation function to monitor the user's emotions in real time when changing home appliance settings and suggest optimal settings. For example, it can adjust settings when the user is dissatisfied. The automatic setting unit also builds a system in which a generative AI suggests optimal home appliance settings based on user emotion data. For example, it can adjust the air conditioner's set temperature according to the user's emotions. Furthermore, the automatic setting unit can use the emotion estimation function to monitor the user's emotions when changing home appliance settings and suggest settings that will elicit positive emotions. For example, it can adjust the brightness of the lights. In this way, the automatic setting unit can monitor the user's emotions in real time when changing home appliance settings and suggest optimal settings, thereby improving user satisfaction.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The home appliance operation assistance system can further include a health management unit that monitors the user's health condition and adjusts the settings of the home appliance based on the health condition. For example, the health management unit can monitor the user's heart rate and body temperature and adjust the temperature of the air conditioner. The health management unit can also analyze the user's sleep patterns and adjust the brightness of the lights and the music playback. Furthermore, the health management unit can optimize the temperature and storage mode of the refrigerator based on the user's dietary records. This allows for a more comfortable and healthy living environment by providing home appliance settings that correspond to the user's health condition.

[0072] The home appliance operation support system can further include an energy management unit that optimizes energy consumption. For example, the energy management unit can monitor the usage of home appliances and suggest settings to minimize energy consumption. The energy management unit can also adjust the usage schedule of home appliances to avoid peak electricity rates. Furthermore, the energy management unit can optimize the operation of home appliances in cooperation with a solar power generation system to maximize the use of renewable energy. This allows for the optimization of energy consumption and the realization of an environmentally friendly lifestyle.

[0073] The home appliance operation support system can further include a security management unit to ensure user safety. For example, the security management unit can detect abnormal behavior of home appliances and issue a warning to the user. The security management unit can also monitor the usage history of home appliances and issue an alert if unauthorized operation is attempted. Furthermore, the security management unit can automatically manage software updates for home appliances and minimize security risks. This ensures user safety and allows users to use home appliances with peace of mind.

[0074] The home appliance operation assistance system may further include an emotion adjustment unit that estimates the user's emotion and adjusts the settings of the home appliance based on the estimated emotion. For example, the emotion adjustment unit may adjust the brightness of the lights when the user is relaxed. The emotion adjustment unit may also play relaxing music when the user is feeling stressed. The emotion adjustment unit may also adjust the temperature of the air conditioner to a comfortable setting when the user is happy. This allows for the realization of a more comfortable living environment by providing home appliance settings that correspond to the user's emotion.

[0075] The home appliance operation assistance system may further include an emotion suggestion unit that estimates the user's emotion and suggests an operation method based on the estimated emotion. For example, the emotion suggestion unit may suggest a simple operation method when the user is confused. The emotion suggestion unit may also suggest an operation method that has a relaxing effect when the user is feeling stressed. The emotion suggestion unit may also suggest a new operation method when the user is happy. This allows for a more comfortable operation experience by providing an operation method that corresponds to the user's emotion.

[0076] The home appliance operation assistance system may further include an emotion instruction unit that estimates the user's emotion and provides operation instructions based on the estimated emotion. For example, the emotion instruction unit provides operation instructions when the user is relaxed. The emotion instruction unit may also add words of encouragement when the user is feeling stressed. The emotion instruction unit may also provide additional explanations when the user is confused. This allows for a more comfortable operation experience by providing operation instructions according to the user's emotion.

[0077] The home appliance operation assistance system may further include an emotion troubleshooting unit that estimates the user's emotion and troubleshoots the home appliance based on the estimated emotion. For example, the emotion troubleshooting unit may suggest a simple solution when the user is confused. The emotion troubleshooting unit may also suggest a solution that has a relaxing effect when the user is stressed. The emotion troubleshooting unit may also suggest a new solution when the user is happy. This allows for a more comfortable operation experience by providing troubleshooting that is tailored to the user's emotion.

[0078] The home appliance operation assistance system may further include an emotion maintenance unit that estimates the user's emotion and performs maintenance on the home appliance based on the estimated emotion. For example, the emotion maintenance unit provides maintenance instructions when the user is relaxed. The emotion maintenance unit may also suggest a simple maintenance method when the user is feeling stressed. The emotion maintenance unit may also provide additional explanations when the user is confused. This allows for maintenance according to the user's emotion, thereby achieving a more comfortable operation experience.

[0079] The home appliance operation assistance system may further include a preference setting unit that customizes the settings of the home appliance based on the user's preferences. For example, the preference setting unit may learn the user's preferred temperature and humidity and adjust the air conditioner settings accordingly. The preference setting unit may also learn the user's preferred lighting brightness and color and adjust the lighting settings accordingly. The preference setting unit may also learn the user's preferred music and volume and adjust the audio system settings accordingly. This allows for the creation of a more comfortable living environment by providing home appliance settings based on the user's preferences.

[0080] The home appliance operation assistance system may further include a schedule management unit that automatically adjusts the settings of home appliances based on the user's schedule. For example, the schedule management unit may acquire the user's calendar information and adjust the operation schedule of an air conditioner. The schedule management unit may also automatically set the on / off of lights according to the user's schedule. Furthermore, the schedule management unit may adjust the playback schedule of an audio system based on the user's schedule. This allows for a more efficient living environment by providing home appliance settings based on the user's schedule.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The operation method analysis unit analyzes how to operate the home appliance. For example, the generation AI can be used to analyze how to operate the appliance and understand how the user should operate it. It can also analyze how to operate a washing machine and suggest optimal settings based on the amount and type of laundry. It can also analyze how to change the temperature setting of a refrigerator or the operating mode of an air conditioner. For example, the generation AI receives prompts containing instructions on how to operate the appliance and analyzes the operation method based on those prompts. Step 2: The operation instruction providing unit provides operation instructions to the user based on the operation method analyzed by the operation method analysis unit. For example, it instructs the user on how to change the refrigerator temperature setting or the air conditioner operation mode. It can also present specific operation procedures in response to questions or requests from the user. Furthermore, it can use the generation AI to provide appropriate operation instructions to the user. For example, the generation AI presents specific operation procedures in response to questions from the user. Step 3: The automatic setting unit automatically adjusts the settings of the home appliances based on the operation method analyzed by the operation method analysis unit. For example, an air conditioner can sense the temperature and humidity in the room and automatically select the optimal operating mode. Lighting can also be automatically adjusted according to the time of day and brightness. Furthermore, it can learn the user's usage patterns and suggest optimal settings. For example, the generative AI automatically adjusts the settings of home appliances according to the user's usage situation and environment.

[0083] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0124] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0135] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0138] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0139] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0140] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0141] 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.

[0142] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0145] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0146] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an operation method analysis unit that analyzes an operation method of a home appliance; an operation instruction providing unit that provides operation instructions to a user based on the operation method analyzed by the operation method analyzing unit; an automatic setting unit that automatically adjusts settings of the home appliance based on the operation method analyzed by the operation method analysis unit; A system characterized by:

2. The operation method analysis unit Generative AI is used to analyze the user's past operation history and identify the most frequently used operation patterns.

2. The system of claim 1.

3. The operation method analysis unit Using generative AI, we identify commonalities in the operation methods of different home appliances and propose a unified operation interface.

2. The system of claim 1.

4. The operation instruction providing unit Using generative AI, the most efficient operation procedure is suggested in real time based on the user's operation history.

2. The system of claim 1.

5. The automatic setting unit Using generative AI to learn the user's past usage patterns and automatically optimize appliance settings based on that.

2. The system of claim 1.

6. The operation method analysis unit Detecting stress or confusion felt by the user during operation and improving the operation method based on the detected stress or confusion 2. The system of claim 1.

7. The operation instruction providing unit The emotions of the user when receiving the operation instruction are analyzed, and an instruction method that elicits positive emotions is provided.

2. The system of claim 1.

8. The automatic setting unit Analyzing the satisfaction level that the user feels with the settings of the home appliance, and improving the settings based on the analysis.

2. The system of claim 1.

Images