system

Abstract

The system according to this embodiment aims to enable intuitive understanding of the operating procedures for home appliances. [Solution] The system according to the embodiment comprises a visualization unit, an answering unit, and an optimization unit. The visualization unit visualizes the operation procedures of home appliances using the camera of a smartphone. The answering unit answers the user's questions. The optimization unit learns the user's habits and preferences and provides individually optimized operation and setting advice.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that the operation procedures of home appliances are complex and difficult for users to intuitively understand.

[0005] The system according to the embodiment aims to enable users to intuitively understand the operation procedures of home appliances.

Means for Solving the Problems

[0006] The system according to the embodiment includes a visualization unit, a response unit, and an optimization unit. The visualization unit visualizes the operation procedures of home appliances using the camera of a smartphone. The response unit answers user questions. The optimization unit learns the user's habits and preferences and provides individually optimized operation and setting advice.

Effects of the Invention

[0007] The system according to this embodiment can make the operating procedures of home appliances intuitively understandable. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The home appliance instruction system according to an embodiment of the present invention is a system that provides instruction manuals for home appliances using generative AI and augmented reality. This system visualizes the operation procedures of home appliances using a smartphone camera and conveys intuitive operating methods to the user. Furthermore, an AI support chatbot answers user questions quickly and accurately, providing necessary information at any time. The AI ​​also learns the user's habits and preferences and provides individually optimized operation and setting advice. For example, it suggests optimal screen settings and usage methods based on TV viewing preferences, allowing the user to find the optimal operating method that suits them. As a result, the user can operate home appliances more smoothly and have a more satisfying experience. Thus, the home appliance instruction system can make the user's use of home appliances smoother and more satisfying.

[0029] The instruction system for home appliances according to the embodiment comprises a visualization unit, an answering unit, and an optimization unit. The visualization unit visualizes the operation procedure of the home appliance using the camera of a smartphone. For example, when the smartphone camera is pointed at the home appliance, the visualization unit displays the operation procedure on the screen. The visualization unit visually indicates which button to press and which part to operate. The visualization unit can visually indicate the operation procedure of the home appliance using, for example, a generative AI. The visualization unit visualizes the operation procedure when the generative AI receives a prompt such as "Press this button." The visualization unit displays the operation procedure of the home appliance visually after the generative AI analyzes it. The answering unit answers the user's questions quickly and accurately. For example, the answering unit answers the user's questions using an AI support chatbot. For example, when the user asks "What is this button used for?", the AI ​​chatbot immediately answers and provides a detailed explanation. The answering unit uses AI to answer the user's questions quickly and accurately. The optimization unit learns the user's habits and preferences and provides individually optimized operation and setting advice. For example, the optimization unit analyzes the user's past operation and viewing history and proposes the optimal settings. The optimization unit proposes the optimal screen settings and usage methods based on the user's TV viewing preferences. The optimization unit's AI learns the user's habits and preferences and provides individually optimized operation and setting advice. As a result, the home appliance instruction system according to the embodiment allows the user to intuitively operate the home appliance and receive individually optimized advice.

[0030] The visualization unit visualizes the operating procedures for home appliances using the smartphone's camera. Specifically, when a user points their smartphone camera at a home appliance, the visualization unit analyzes the camera image in real time and overlays the operating procedures on the screen. For example, when the camera is pointed at the control panel of a washing machine, it visually shows which buttons to press and which dials to turn. The visualization unit can visually display the operating procedures for home appliances using generative AI. When the user inputs a prompt such as "Press this button," the generative AI analyzes the operating procedures based on that instruction and displays them visually. For example, the generative AI analyzes an image of the washing machine's control panel and highlights the "Start" button. The generative AI can also learn the user's operation history and past questions to provide more appropriate operating procedures. Because the visualization unit uses generative AI to analyze and visually display the operating procedures for home appliances, users can intuitively understand the procedures. Furthermore, the visualization unit can display not only operating procedures but also precautions and maintenance information. For example, when displaying the procedure for cleaning an air conditioner filter, it visually shows how to remove the filter and how to clean it. This allows the visualization unit to provide support to users in correctly operating and maintaining home appliances.

[0031] The answering function provides quick and accurate responses to user inquiries. Specifically, it uses an AI support chatbot to answer user questions. When a user asks, "What is this button used for?", the AI ​​chatbot immediately answers and provides a detailed explanation. For example, if a user asks about the "defrost" button on a microwave, the AI ​​chatbot provides a detailed explanation such as, "This button is used to defrost frozen food. In defrost mode, the food is defrosted for the appropriate time and power depending on the type and weight of the food." Because the answering function uses AI to answer user questions quickly and accurately, users can resolve their doubts immediately. Furthermore, the answering function can learn from the user's past question history and provide more appropriate answers. For example, if a user has asked the same question many times in the past, the AI ​​chatbot will improve its answers to those questions and provide more detailed information. The answering function can also present relevant operating procedures and precautions depending on the content of the user's question. For example, if a user asks, "How do I set the temperature of my oven?", the AI ​​chatbot will provide not only the procedure for setting the temperature but also advice and precautions for cooking at the appropriate temperature. This allows the answering unit to provide support to help users correctly understand and effectively use home appliances.

[0032] The optimization unit learns the user's habits and preferences and provides individually optimized operation and setting advice. Specifically, it analyzes the user's past operation and viewing history and proposes optimal settings. For example, it suggests optimal screen settings and usage methods based on the user's TV viewing preferences. The optimization unit uses AI to learn the user's habits and preferences and provide individually optimized operation and setting advice. For example, if a user often watches TV at night, the optimization unit will suggest screen brightness and volume settings suitable for nighttime viewing. Also, if a user prefers to watch a specific genre of program, it can suggest the optimal screen mode and sound settings for that genre. Furthermore, the optimization unit learns the user's operation patterns and suggests efficient operation methods. For example, it suggests frequently used functions and settings as shortcuts to reduce the effort required for operation. In addition, the optimization unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, after a user adopts a suggested setting, it collects feedback on whether the setting was satisfactory and incorporates it into future suggestions. In this way, the optimization unit can always provide the user with optimal operation and setting advice, improving the user experience of home appliances.

[0033] The visualization unit can display operating instructions on the screen when a smartphone camera is pointed at an appliance. For example, when a smartphone camera is pointed at an appliance, the visualization unit displays the operating instructions on the screen. The visualization unit can visually show the operating instructions of an appliance using generative AI. The visualization unit's generative AI receives a prompt such as "Press this button" and visualizes the operating instructions. The visualization unit's generative AI analyzes the operating instructions of the appliance and displays them visually. This allows the user to intuitively understand the operating instructions.

[0034] The answering function can answer user questions quickly and accurately and provide detailed explanations. For example, the answering function uses an AI support chatbot to answer user questions. When a user asks, "What is this button for?", the AI ​​chatbot will immediately answer and provide a detailed explanation. The answering function uses AI to answer user questions quickly and accurately, allowing users to resolve their doubts immediately.

[0035] The optimization unit can analyze the user's past operation and viewing history and suggest the optimal settings. For example, the optimization unit analyzes the user's past operation and viewing history and suggests the optimal settings. The optimization unit's AI learns the user's habits and preferences and provides individually optimized operation and setting advice. This allows the user to receive the most suitable settings.

[0036] The optimization unit can suggest optimal screen settings and usage methods based on the user's TV viewing preferences. For example, the optimization unit uses AI to learn the user's habits and preferences, providing individually optimized operation and setting advice. This allows the user to experience the best possible TV viewing experience.

[0037] The visualization unit can visually indicate which button to press or which part to operate. For example, the visualization unit can visually show which button to press or which part to operate. The visualization unit can use generative AI to visually display the operating procedures for home appliances. The visualization unit receives a prompt such as "Press this button" from the generative AI and visualizes the operating procedure. The visualization unit uses generative AI to analyze the operating procedure of the home appliance and displays it visually. This allows users to operate the appliance without confusion.

[0038] The visualization unit can apply different visualization algorithms depending on the type of home appliance. For example, in the case of a television, the visualization unit applies an algorithm that visually shows the operation of the remote control buttons. In the case of a washing machine, the visualization unit applies an algorithm that visually shows how to add detergent and select the washing course. In the case of a refrigerator, the visualization unit applies an algorithm that visually shows the temperature setting and storage method. The visualization unit uses generative AI to apply different visualization algorithms depending on the type of home appliance. This allows it to provide optimal visualization tailored to each home appliance.

[0039] The visualization unit can prioritize displaying the most frequently used operation procedures by referring to the user's past operation history during visualization. For example, the visualization unit prioritizes displaying operation procedures that the user has frequently used in the past. The visualization unit predicts and prioritizes displaying operation procedures used during a specific time period based on the user's past operation history. The visualization unit analyzes the user's past operation history and prioritizes displaying the most efficient operation procedures. The visualization unit uses generative AI to prioritize displaying the most frequently used operation procedures by referring to the user's past operation history during visualization. This allows the system to prioritize displaying operation procedures that the user frequently uses.

[0040] The visualization unit can display region-specific operating procedures based on the user's geographical location information during visualization. For example, if the user is in Japan, the visualization unit will display operating procedures in Japanese. If the user is in the United States, the visualization unit will display operating procedures in English. If the user is in a specific region, the visualization unit will display operating procedures specific to that region. The visualization unit uses generative AI to consider the user's geographical location information during visualization and displays region-specific operating procedures. This enables the provision of region-specific operating procedures.

[0041] The visualization unit can analyze the user's social media activity during visualization and display relevant operating procedures. For example, the visualization unit can display relevant operating procedures based on operating procedures shared by the user on social media. The visualization unit can display operating procedures for accounts followed by the user on social media. The visualization unit can display relevant operating procedures based on operating procedures that the user "liked" on social media. The visualization unit uses generative AI to analyze the user's social media activity during visualization and display relevant operating procedures. This enables the provision of operating procedures based on social media activity.

[0042] The answering function can prioritize providing the most relevant answers by referring to the user's past question history when answering. For example, the answering function prioritizes providing relevant answers based on the content of questions the user has asked in the past. The answering function predicts and prioritizes answers to specific questions based on the user's past question history. The answering function analyzes the user's past question history and prioritizes providing the most efficient answers. The answering function uses AI to refer to the user's past question history when answering and prioritizes providing the most relevant answers. This allows it to provide answers based on the user's past question history.

[0043] The answering unit can apply different answering algorithms depending on the type of home appliance when answering. For example, in the case of a television, the answering unit applies an answering algorithm related to remote control button operation. In the case of a washing machine, the answering unit applies an answering algorithm related to detergent dispensing method and wash cycle selection. In the case of a refrigerator, the answering unit applies an answering algorithm related to temperature settings and storage methods. The answering unit uses AI to apply different answering algorithms depending on the type of home appliance when answering. This allows it to provide the optimal answer for each home appliance.

[0044] The response unit can provide region-specific information based on the user's geographical location when responding. For example, if the user is in Japan, the response unit will provide an answer in Japanese. If the user is in the United States, the response unit will provide an answer in English. If the user is in a specific region, the response unit will provide region-specific information. The response unit uses AI to consider the user's geographical location when responding and provides region-specific information. This enables the provision of region-specific information.

[0045] The response unit can analyze the user's social media activity and provide relevant information when they respond. For example, the response unit can provide relevant answers based on information the user has shared on social media. The response unit can provide relevant answers based on information the user follows on social media. The response unit can provide relevant answers based on information the user has "liked" on social media. The response unit uses AI to analyze the user's social media activity and provide relevant information when they respond. This allows the system to provide information based on social media activity.

[0046] The optimization unit can suggest the most effective settings by referring to the user's past operation history during optimization. For example, the optimization unit suggests the optimal settings based on the settings the user has used in the past. The optimization unit predicts and suggests settings to be used during a specific time period based on the user's past operation history. The optimization unit analyzes the user's past operation history and suggests the most efficient settings. The optimization unit uses AI to suggest the most effective settings by referring to the user's past operation history during optimization. This allows it to provide optimal settings based on the user's past operation history.

[0047] The optimization unit can apply different optimization algorithms depending on the type of home appliance during optimization. For example, in the case of a television, the optimization unit applies an optimization algorithm related to screen settings. In the case of a washing machine, the optimization unit applies an optimization algorithm related to the selection of the washing course. In the case of a refrigerator, the optimization unit applies an optimization algorithm related to temperature settings. The optimization unit uses AI to apply different optimization algorithms depending on the type of home appliance during optimization. This allows it to provide optimal settings tailored to each home appliance.

[0048] The optimization unit can suggest region-specific settings based on the user's geographical location during optimization. For example, if the user is in Japan, the optimization unit will suggest settings suited to Japan's climate. If the user is in the United States, the optimization unit will suggest settings suited to the US climate. If the user is in a specific region, the optimization unit will suggest region-specific settings for that region. The optimization unit uses AI to suggest region-specific settings during optimization, taking into account the user's geographical location. This allows it to provide region-specific settings.

[0049] The optimization unit can analyze the user's social media activity during optimization and suggest relevant settings. For example, the optimization unit suggests relevant settings based on settings the user has shared on social media. The optimization unit suggests relevant settings based on settings the user follows on social media. The optimization unit suggests relevant settings based on settings the user has "liked" on social media. The optimization unit uses AI to analyze the user's social media activity during optimization and suggest relevant settings. This allows it to provide settings based on social media activity.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The visualization unit can apply different visualization algorithms depending on the type of home appliance. For example, in the case of a television, an algorithm can be applied that visually shows the operation of the remote control buttons. In the case of a washing machine, an algorithm can be applied that visually shows how to add detergent and select a washing course. In the case of a refrigerator, an algorithm can be applied that visually shows the temperature setting and storage method. This allows for the provision of optimal visualization tailored to each home appliance.

[0052] The visualization unit can, during visualization, refer to the user's past operation history and prioritize displaying the most frequently used operation procedures. For example, it can prioritize displaying operation procedures that the user has frequently used in the past. It can predict operation procedures used during specific time periods based on the user's past operation history and prioritize their display. It can analyze the user's past operation history and prioritize displaying the most efficient operation procedures. This allows for the prioritization of operation procedures that the user frequently uses.

[0053] The answering function can prioritize providing the most relevant answers by referring to the user's past question history when answering. For example, it can prioritize providing relevant answers based on the content of questions the user has asked in the past. It can predict and prioritize answers to specific questions based on the user's past question history. It can analyze the user's past question history and prioritize providing the most efficient answers. This allows it to provide answers based on the user's past question history.

[0054] The answering function can apply different answering algorithms depending on the type of home appliance. For example, in the case of a television, an answering algorithm related to remote control button operation can be applied. In the case of a washing machine, an answering algorithm related to detergent dispensing method and wash cycle selection can be applied. In the case of a refrigerator, an answering algorithm related to temperature settings and storage methods can be applied. This allows the system to provide the most appropriate answer for each home appliance.

[0055] The optimization unit can suggest the most effective settings by referring to the user's past operation history during optimization. For example, it can suggest the optimal settings based on the settings the user has used in the past. It can predict and suggest settings to be used during a specific time period based on the user's past operation history. It can analyze the user's past operation history and suggest the most efficient settings. This allows it to provide optimal settings based on the user's past operation history.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The visualization unit uses the smartphone's camera to visualize the operating procedures for home appliances. For example, when the smartphone camera is pointed at a home appliance, the operating procedures are displayed on the screen. The visualization unit visually indicates which buttons to press and which parts to operate. It is also possible to analyze the operating procedures of home appliances using generative AI and display them visually. Step 2: The response section answers user questions quickly and accurately. For example, an AI support chatbot is used to answer user questions. When a user asks, "What is this button for?", the AI ​​chatbot immediately answers and provides a detailed explanation. Step 3: The optimization unit learns the user's habits and preferences and provides individually optimized operation and setting advice. For example, it analyzes the user's past operation and viewing history and suggests optimal settings. It suggests optimal screen settings and usage methods based on the user's TV viewing preferences.

[0058] (Example of form 2) The home appliance instruction system according to an embodiment of the present invention is a system that provides instruction manuals for home appliances using generative AI and augmented reality. This system visualizes the operation procedures of home appliances using a smartphone camera and conveys intuitive operating methods to the user. Furthermore, an AI support chatbot answers user questions quickly and accurately, providing necessary information at any time. The AI ​​also learns the user's habits and preferences and provides individually optimized operation and setting advice. For example, it suggests optimal screen settings and usage methods based on TV viewing preferences, allowing the user to find the optimal operating method that suits them. As a result, the user can operate home appliances more smoothly and have a more satisfying experience. Thus, the home appliance instruction system can make the user's use of home appliances smoother and more satisfying.

[0059] The instruction system for home appliances according to the embodiment comprises a visualization unit, an answering unit, and an optimization unit. The visualization unit visualizes the operation procedure of the home appliance using the camera of a smartphone. For example, when the smartphone camera is pointed at the home appliance, the visualization unit displays the operation procedure on the screen. The visualization unit visually indicates which button to press and which part to operate. The visualization unit can visually indicate the operation procedure of the home appliance using, for example, a generative AI. The visualization unit visualizes the operation procedure when the generative AI receives a prompt such as "Press this button." The visualization unit displays the operation procedure of the home appliance visually after the generative AI analyzes it. The answering unit answers the user's questions quickly and accurately. For example, the answering unit answers the user's questions using an AI support chatbot. For example, when the user asks "What is this button used for?", the AI ​​chatbot immediately answers and provides a detailed explanation. The answering unit uses AI to answer the user's questions quickly and accurately. The optimization unit learns the user's habits and preferences and provides individually optimized operation and setting advice. For example, the optimization unit analyzes the user's past operation and viewing history and proposes the optimal settings. The optimization unit proposes the optimal screen settings and usage methods based on the user's TV viewing preferences. The optimization unit's AI learns the user's habits and preferences and provides individually optimized operation and setting advice. As a result, the home appliance instruction system according to the embodiment allows the user to intuitively operate the home appliance and receive individually optimized advice.

[0060] The visualization unit visualizes the operating procedures for home appliances using the smartphone's camera. Specifically, when a user points their smartphone camera at a home appliance, the visualization unit analyzes the camera image in real time and overlays the operating procedures on the screen. For example, when the camera is pointed at the control panel of a washing machine, it visually shows which buttons to press and which dials to turn. The visualization unit can visually display the operating procedures for home appliances using generative AI. When the user inputs a prompt such as "Press this button," the generative AI analyzes the operating procedures based on that instruction and displays them visually. For example, the generative AI analyzes an image of the washing machine's control panel and highlights the "Start" button. The generative AI can also learn the user's operation history and past questions to provide more appropriate operating procedures. Because the visualization unit uses generative AI to analyze and visually display the operating procedures for home appliances, users can intuitively understand the procedures. Furthermore, the visualization unit can display not only operating procedures but also precautions and maintenance information. For example, when displaying the procedure for cleaning an air conditioner filter, it visually shows how to remove the filter and how to clean it. This allows the visualization unit to provide support to users in correctly operating and maintaining home appliances.

[0061] The answering function provides quick and accurate responses to user inquiries. Specifically, it uses an AI support chatbot to answer user questions. When a user asks, "What is this button used for?", the AI ​​chatbot immediately answers and provides a detailed explanation. For example, if a user asks about the "defrost" button on a microwave, the AI ​​chatbot provides a detailed explanation such as, "This button is used to defrost frozen food. In defrost mode, the food is defrosted for the appropriate time and power depending on the type and weight of the food." Because the answering function uses AI to answer user questions quickly and accurately, users can resolve their doubts immediately. Furthermore, the answering function can learn from the user's past question history and provide more appropriate answers. For example, if a user has asked the same question many times in the past, the AI ​​chatbot will improve its answers to those questions and provide more detailed information. The answering function can also present relevant operating procedures and precautions depending on the content of the user's question. For example, if a user asks, "How do I set the temperature of my oven?", the AI ​​chatbot will provide not only the procedure for setting the temperature but also advice and precautions for cooking at the appropriate temperature. This allows the answering unit to provide support to help users correctly understand and effectively use home appliances.

[0062] The optimization unit learns the user's habits and preferences and provides individually optimized operation and setting advice. Specifically, it analyzes the user's past operation and viewing history and proposes optimal settings. For example, it suggests optimal screen settings and usage methods based on the user's TV viewing preferences. The optimization unit uses AI to learn the user's habits and preferences and provide individually optimized operation and setting advice. For example, if a user often watches TV at night, the optimization unit will suggest screen brightness and volume settings suitable for nighttime viewing. Also, if a user prefers to watch a specific genre of program, it can suggest the optimal screen mode and sound settings for that genre. Furthermore, the optimization unit learns the user's operation patterns and suggests efficient operation methods. For example, it suggests frequently used functions and settings as shortcuts to reduce the effort required for operation. In addition, the optimization unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, after a user adopts a suggested setting, it collects feedback on whether the setting was satisfactory and incorporates it into future suggestions. In this way, the optimization unit can always provide the user with optimal operation and setting advice, improving the user experience of home appliances.

[0063] The visualization unit can display operating instructions on the screen when a smartphone camera is pointed at an appliance. For example, when a smartphone camera is pointed at an appliance, the visualization unit displays the operating instructions on the screen. The visualization unit can visually show the operating instructions of an appliance using generative AI. The visualization unit's generative AI receives a prompt such as "Press this button" and visualizes the operating instructions. The visualization unit's generative AI analyzes the operating instructions of the appliance and displays them visually. This allows the user to intuitively understand the operating instructions.

[0064] The answering function can answer user questions quickly and accurately and provide detailed explanations. For example, the answering function uses an AI support chatbot to answer user questions. When a user asks, "What is this button for?", the AI ​​chatbot will immediately answer and provide a detailed explanation. The answering function uses AI to answer user questions quickly and accurately, allowing users to resolve their doubts immediately.

[0065] The optimization unit can analyze the user's past operation and viewing history and suggest the optimal settings. For example, the optimization unit analyzes the user's past operation and viewing history and suggests the optimal settings. The optimization unit's AI learns the user's habits and preferences and provides individually optimized operation and setting advice. This allows the user to receive the most suitable settings.

[0066] The optimization unit can suggest optimal screen settings and usage methods based on the user's TV viewing preferences. For example, the optimization unit uses AI to learn the user's habits and preferences, providing individually optimized operation and setting advice. This allows the user to experience the best possible TV viewing experience.

[0067] The visualization unit can visually indicate which button to press or which part to operate. For example, the visualization unit can visually show which button to press or which part to operate. The visualization unit can use generative AI to visually display the operating procedures for home appliances. The visualization unit receives a prompt such as "Press this button" from the generative AI and visualizes the operating procedure. The visualization unit uses generative AI to analyze the operating procedure of the home appliance and displays it visually. This allows users to operate the appliance without confusion.

[0068] The visualization unit can estimate the user's emotions and adjust the display method of the operation procedures based on the estimated emotions. For example, if the user is stressed, the visualization unit displays simple and visually easy-to-understand operation procedures. If the user is relaxed, the visualization unit displays detailed operation procedures and also provides background information for the operations. If the user is in a hurry, the visualization unit highlights and displays only the most important operation procedures. The visualization unit uses generative AI to estimate the user's emotions and adjusts the display method of the operation procedures based on the estimated emotions. This allows it to provide operation procedures that are tailored to the user's emotions.

[0069] The visualization unit can apply different visualization algorithms depending on the type of home appliance. For example, in the case of a television, the visualization unit applies an algorithm that visually shows the operation of the remote control buttons. In the case of a washing machine, the visualization unit applies an algorithm that visually shows how to add detergent and select the washing course. In the case of a refrigerator, the visualization unit applies an algorithm that visually shows the temperature setting and storage method. The visualization unit uses generative AI to apply different visualization algorithms depending on the type of home appliance. This allows it to provide optimal visualization tailored to each home appliance.

[0070] The visualization unit can prioritize displaying the most frequently used operation procedures by referring to the user's past operation history during visualization. For example, the visualization unit prioritizes displaying operation procedures that the user has frequently used in the past. The visualization unit predicts and prioritizes displaying operation procedures used during a specific time period based on the user's past operation history. The visualization unit analyzes the user's past operation history and prioritizes displaying the most efficient operation procedures. The visualization unit uses generative AI to prioritize displaying the most frequently used operation procedures by referring to the user's past operation history during visualization. This allows the system to prioritize displaying operation procedures that the user frequently uses.

[0071] The visualization unit can estimate the user's emotions and adjust the display order of the operation steps based on the estimated emotions. For example, if the user is stressed, the visualization unit will display the simplest operation steps first. If the user is relaxed, the visualization unit will display the detailed operation steps in order. If the user is in a hurry, the visualization unit will display the most important operation steps first. The visualization unit uses generative AI to estimate the user's emotions and adjusts the display order of the operation steps based on the estimated emotions. This allows it to provide a display order that is appropriate to the user's emotions.

[0072] The visualization unit can display region-specific operating procedures based on the user's geographical location information during visualization. For example, if the user is in Japan, the visualization unit will display operating procedures in Japanese. If the user is in the United States, the visualization unit will display operating procedures in English. If the user is in a specific region, the visualization unit will display operating procedures specific to that region. The visualization unit uses generative AI to consider the user's geographical location information during visualization and displays region-specific operating procedures. This enables the provision of region-specific operating procedures.

[0073] The visualization unit can analyze the user's social media activity during visualization and display relevant operating procedures. For example, the visualization unit can display relevant operating procedures based on operating procedures shared by the user on social media. The visualization unit can display operating procedures for accounts followed by the user on social media. The visualization unit can display relevant operating procedures based on operating procedures that the user "liked" on social media. The visualization unit uses generative AI to analyze the user's social media activity during visualization and display relevant operating procedures. This enables the provision of operating procedures based on social media activity.

[0074] The response unit can estimate the user's emotions and adjust the way it expresses its response based on those emotions. For example, if the user is stressed, the response unit will provide a concise and easy-to-understand response. If the user is relaxed, the response unit will provide a response that includes detailed explanations. If the user is in a hurry, the response unit will provide a quick and concise response. The response unit uses AI to estimate the user's emotions and adjust the way it expresses its response based on those emotions. This allows it to provide responses that are appropriate to the user's emotions.

[0075] The answering function can prioritize providing the most relevant answers by referring to the user's past question history when answering. For example, the answering function prioritizes providing relevant answers based on the content of questions the user has asked in the past. The answering function predicts and prioritizes answers to specific questions based on the user's past question history. The answering function analyzes the user's past question history and prioritizes providing the most efficient answers. The answering function uses AI to refer to the user's past question history when answering and prioritizes providing the most relevant answers. This allows it to provide answers based on the user's past question history.

[0076] The answering unit can apply different answering algorithms depending on the type of home appliance when answering. For example, in the case of a television, the answering unit applies an answering algorithm related to remote control button operation. In the case of a washing machine, the answering unit applies an answering algorithm related to detergent dispensing method and wash cycle selection. In the case of a refrigerator, the answering unit applies an answering algorithm related to temperature settings and storage methods. The answering unit uses AI to apply different answering algorithms depending on the type of home appliance when answering. This allows it to provide the optimal answer for each home appliance.

[0077] The response unit can estimate the user's emotions and adjust the level of detail in its responses based on those emotions. For example, if the user is stressed, the response unit will provide a concise and easy-to-understand response. If the user is relaxed, the response unit will provide a response that includes detailed explanations. If the user is in a hurry, the response unit will provide a quick and concise response. The response unit uses AI to estimate the user's emotions and adjust the level of detail in its responses based on those emotions. This allows it to provide responses with a level of detail that matches the user's emotions.

[0078] The response unit can provide region-specific information based on the user's geographical location when responding. For example, if the user is in Japan, the response unit will provide an answer in Japanese. If the user is in the United States, the response unit will provide an answer in English. If the user is in a specific region, the response unit will provide region-specific information. The response unit uses AI to consider the user's geographical location when responding and provides region-specific information. This enables the provision of region-specific information.

[0079] The response unit can analyze the user's social media activity and provide relevant information when they respond. For example, the response unit can provide relevant answers based on information the user has shared on social media. The response unit can provide relevant answers based on information the user follows on social media. The response unit can provide relevant answers based on information the user has "liked" on social media. The response unit uses AI to analyze the user's social media activity and provide relevant information when they respond. This allows the system to provide information based on social media activity.

[0080] The optimization unit can estimate the user's emotions and adjust the optimization method based on those emotions. For example, if the user is stressed, the optimization unit provides a simple and easy-to-understand optimization method. If the user is relaxed, the optimization unit provides a detailed optimization method. If the user is in a hurry, the optimization unit provides a quick and concise optimization method. The optimization unit uses AI to estimate the user's emotions and adjusts the optimization method based on those emotions. This allows it to provide an optimization method that is tailored to the user's emotions.

[0081] The optimization unit can suggest the most effective settings by referring to the user's past operation history during optimization. For example, the optimization unit suggests the optimal settings based on the settings the user has used in the past. The optimization unit predicts and suggests settings to be used during a specific time period based on the user's past operation history. The optimization unit analyzes the user's past operation history and suggests the most efficient settings. The optimization unit uses AI to suggest the most effective settings by referring to the user's past operation history during optimization. This allows it to provide optimal settings based on the user's past operation history.

[0082] The optimization unit can apply different optimization algorithms depending on the type of home appliance during optimization. For example, in the case of a television, the optimization unit applies an optimization algorithm related to screen settings. In the case of a washing machine, the optimization unit applies an optimization algorithm related to the selection of the washing course. In the case of a refrigerator, the optimization unit applies an optimization algorithm related to temperature settings. The optimization unit uses AI to apply different optimization algorithms depending on the type of home appliance during optimization. This allows it to provide optimal settings tailored to each home appliance.

[0083] The optimization unit can estimate the user's emotions and determine optimization priorities based on those emotions. For example, if the user is stressed, the optimization unit will prioritize optimizing the simplest settings first. If the user is relaxed, the optimization unit will prioritize optimizing the more detailed settings first. If the user is in a hurry, the optimization unit will prioritize optimizing the most important settings first. The optimization unit uses AI to estimate the user's emotions and determines optimization priorities based on those emotions. This allows for optimization with priorities that match the user's emotions.

[0084] The optimization unit can suggest region-specific settings based on the user's geographical location during optimization. For example, if the user is in Japan, the optimization unit will suggest settings suited to Japan's climate. If the user is in the United States, the optimization unit will suggest settings suited to the US climate. If the user is in a specific region, the optimization unit will suggest region-specific settings for that region. The optimization unit uses AI to suggest region-specific settings during optimization, taking into account the user's geographical location. This allows it to provide region-specific settings.

[0085] The optimization unit can analyze the user's social media activity during optimization and suggest relevant settings. For example, the optimization unit suggests relevant settings based on settings the user has shared on social media. The optimization unit suggests relevant settings based on settings the user follows on social media. The optimization unit suggests relevant settings based on settings the user has "liked" on social media. The optimization unit uses AI to analyze the user's social media activity during optimization and suggest relevant settings. This allows it to provide settings based on social media activity.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The visualization unit can estimate the user's emotions and adjust the display method of the operating procedures based on the estimated emotions. For example, if the user is stressed, it can display simple and visually easy-to-understand operating procedures. If the user is relaxed, it can display detailed operating procedures and provide background information for the procedures. If the user is in a hurry, it can highlight and display only the most important operating procedures. In this way, it can provide operating procedures that are tailored to the user's emotions.

[0088] The response unit can estimate the user's emotions and adjust the way the response is presented based on those emotions. For example, if the user is stressed, it can provide a concise and easy-to-understand response. If the user is relaxed, it can provide a response that includes detailed explanations. If the user is in a hurry, it can provide a quick and concise response. This allows the system to provide responses that are tailored to the user's emotions.

[0089] The optimization unit can estimate the user's emotions and adjust the optimization method based on those emotions. For example, if the user is stressed, it can provide a simple and easy-to-understand optimization method. If the user is relaxed, it can provide a detailed optimization method. If the user is in a hurry, it can provide a quick and concise optimization method. This allows the system to provide an optimization method that is tailored to the user's emotions.

[0090] The visualization unit can estimate the user's emotions and adjust the display order of the operating procedures based on the estimated emotions. For example, if the user is stressed, the simplest operating procedures can be displayed first. If the user is relaxed, the detailed operating procedures can be displayed sequentially. If the user is in a hurry, the most important operating procedures can be displayed first. This provides a display order that is appropriate to the user's emotions.

[0091] The optimization unit can estimate the user's emotions and determine optimization priorities based on those emotions. For example, if the user is stressed, the simplest settings can be prioritized for optimization. If the user is relaxed, more detailed settings can be prioritized for optimization. If the user is in a hurry, the most important settings can be prioritized for optimization. This allows for optimization to be performed with priorities that match the user's emotions.

[0092] The visualization unit can apply different visualization algorithms depending on the type of home appliance. For example, in the case of a television, an algorithm can be applied that visually shows the operation of the remote control buttons. In the case of a washing machine, an algorithm can be applied that visually shows how to add detergent and select a washing course. In the case of a refrigerator, an algorithm can be applied that visually shows the temperature setting and storage method. This allows for the provision of optimal visualization tailored to each home appliance.

[0093] The visualization unit can, during visualization, refer to the user's past operation history and prioritize displaying the most frequently used operation procedures. For example, it can prioritize displaying operation procedures that the user has frequently used in the past. It can predict operation procedures used during specific time periods based on the user's past operation history and prioritize their display. It can analyze the user's past operation history and prioritize displaying the most efficient operation procedures. This allows for the prioritization of operation procedures that the user frequently uses.

[0094] The answering function can prioritize providing the most relevant answers by referring to the user's past question history when answering. For example, it can prioritize providing relevant answers based on the content of questions the user has asked in the past. It can predict and prioritize answers to specific questions based on the user's past question history. It can analyze the user's past question history and prioritize providing the most efficient answers. This allows it to provide answers based on the user's past question history.

[0095] The answering function can apply different answering algorithms depending on the type of home appliance. For example, in the case of a television, an answering algorithm related to remote control button operation can be applied. In the case of a washing machine, an answering algorithm related to detergent dispensing method and wash cycle selection can be applied. In the case of a refrigerator, an answering algorithm related to temperature settings and storage methods can be applied. This allows the system to provide the most appropriate answer for each home appliance.

[0096] The optimization unit can suggest the most effective settings by referring to the user's past operation history during optimization. For example, it can suggest the optimal settings based on the settings the user has used in the past. It can predict and suggest settings to be used during a specific time period based on the user's past operation history. It can analyze the user's past operation history and suggest the most efficient settings. This allows it to provide optimal settings based on the user's past operation history.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The visualization unit uses the smartphone's camera to visualize the operating procedures for home appliances. For example, when the smartphone camera is pointed at a home appliance, the operating procedures are displayed on the screen. The visualization unit visually indicates which buttons to press and which parts to operate. It is also possible to analyze the operating procedures of home appliances using generative AI and display them visually. Step 2: The response section answers user questions quickly and accurately. For example, an AI support chatbot is used to answer user questions. When a user asks, "What is this button for?", the AI ​​chatbot immediately answers and provides a detailed explanation. Step 3: The optimization unit learns the user's habits and preferences and provides individually optimized operation and setting advice. For example, it analyzes the user's past operation and viewing history and suggests optimal settings. It suggests optimal screen settings and usage methods based on the user's TV viewing preferences.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements described above, including the visualization unit, response unit, and optimization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the visualization unit visualizes the operation procedure of a home appliance using the camera 42 of the smart device 14 and displays the operation procedure by the control unit 46A. The response unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides quick and accurate answers to user questions using an AI support chatbot. The optimization unit is implemented in the specific processing unit 290 of the data processing unit 12 and learns the user's habits and preferences and provides individually optimized operation and setting advice. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the visualization unit, response unit, and optimization unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the visualization unit visualizes the operation procedure of a home appliance using the camera 42 of the smart glasses 214 and displays the operation procedure by the control unit 46A. The response unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and answers the user's questions quickly and accurately using an AI support chatbot. The optimization unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and learns the user's habits and preferences and provides individually optimized operation and setting advice. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0122] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

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

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

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the visualization unit, response unit, and optimization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the visualization unit visualizes the operation procedure of a home appliance using the camera 42 of the headset terminal 314 and displays the operation procedure by the control unit 46A. The response unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides quick and accurate answers to user questions using an AI support chatbot. The optimization unit is implemented in the specific processing unit 290 of the data processing unit 12 and learns the user's habits and preferences and provides individually optimized operation and setting advice. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

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

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

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

[0142] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the visualization unit, response unit, and optimization unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the visualization unit uses the camera 42 of the robot 414 to visualize the operation procedure of a home appliance and displays the operation procedure by the control unit 46A. The response unit is implemented in the specific processing unit 290 of the data processing unit 12 and uses an AI support chatbot to answer user questions quickly and accurately. The optimization unit is implemented in the specific processing unit 290 of the data processing unit 12 and learns the user's habits and preferences to provide individually optimized operation and setting advice. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0152] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0161] 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.

[0162] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0164] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) A visualization unit that uses a smartphone camera to visualize the operating procedures of home appliances, A response section that answers user questions, It includes an optimization unit that learns the user's habits and preferences and provides individually optimized operation and setting advice. A system characterized by the following features. (Note 2) The visualization unit is, When you point your smartphone camera at a home appliance, the operating instructions will be displayed on the screen. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned response section is, To answer user questions quickly and accurately, and to provide detailed explanations. The system described in Appendix 1, characterized by the features described herein. (Note 4) The optimization unit, It analyzes the user's past operation and viewing history and suggests the optimal settings. The system described in Appendix 1, characterized by the features described herein. (Note 5) The optimization unit, We suggest optimal screen settings and usage methods based on your TV viewing preferences. The system described in Appendix 1, characterized by the features described herein. (Note 6) The visualization unit is, It visually shows which button to press and which part to operate. The system described in Appendix 1, characterized by the features described herein. (Note 7) The visualization unit is, The system estimates the user's emotions and adjusts the display of operating procedures based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The visualization unit is, Apply different visualization algorithms depending on the type of home appliance. The system described in Appendix 1, characterized by the features described herein. (Note 9) The visualization unit is, During visualization, the system prioritizes displaying the most frequently used steps by referencing the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 10) The visualization unit is, It estimates the user's emotions and adjusts the display order of the operating procedures based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The visualization unit is, When visualization is performed, region-specific instructions are displayed based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The visualization unit is, During visualization, the system analyzes the user's social media activity and displays relevant steps. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned response section is, It estimates the user's emotions and adjusts the way responses are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned response section is, When providing an answer, the system will refer to the user's past question history to prioritize providing the most relevant answer. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned response section is, When answering, different answer algorithms are applied depending on the type of home appliance. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned response section is, The system estimates the user's emotions and adjusts the level of detail in the responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned response section is, When responding, region-specific information will be provided based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned response section is, When users respond, their social media activity is analyzed and relevant information is provided. The system described in Appendix 1, characterized by the features described herein. (Note 19) The optimization unit, It estimates the user's emotions and adjusts the optimization method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The optimization unit, During optimization, the system refers to the user's past operation history to suggest the most effective settings. The system described in Appendix 1, characterized by the features described herein. (Note 21) The optimization unit, During optimization, different optimization algorithms are applied depending on the type of home appliance. The system described in Appendix 1, characterized by the features described herein. (Note 22) The optimization unit, It estimates user emotions and determines optimization priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The optimization unit, During optimization, region-specific settings are suggested based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The optimization unit, During optimization, the system analyzes the user's social media activity and suggests relevant settings. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A visualization unit that uses a smartphone camera to visualize the operating procedures of home appliances, A response section that answers user questions, It includes an optimization unit that learns the user's habits and preferences and provides individually optimized operation and setting advice. A system characterized by the following features.

2. The visualization unit is, When you point your smartphone camera at a home appliance, the operating instructions will be displayed on the screen. The system according to feature 1.

3. The aforementioned response section is, To answer user questions quickly and accurately, and to provide detailed explanations. The system according to feature 1.

4. The optimization unit, It analyzes the user's past operation and viewing history and suggests the optimal settings. The system according to feature 1.

5. The optimization unit, We suggest optimal screen settings and usage methods based on your TV viewing preferences. The system according to feature 1.

6. The visualization unit is, It visually shows which button to press and which part to operate. The system according to feature 1.

7. The visualization unit is, The system estimates the user's emotions and adjusts the display of operating procedures based on those emotions. The system according to feature 1.

8. The visualization unit is, Apply different visualization algorithms depending on the type of home appliance. The system according to feature 1.

9. The visualization unit is, During visualization, the system prioritizes displaying the most frequently used steps by referencing the user's past operation history. The system according to feature 1.

10. The visualization unit is, It estimates the user's emotions and adjusts the display order of the operating procedures based on the estimated user emotions. The system according to feature 1.

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