System

Abstract

An object of a system according to an embodiment is to intuitively and quickly provide an operation method or troubleshooting information of a home appliance.SOLUTION: A system according to an embodiment includes an AR display unit, an AI support chatbot unit, and a user habit learning unit. The AR display visualizes the operation procedure of the product through the camera of the smartphone. The AI support chatbot component answers user questions quickly and accurately. The user habit learning unit learns habits and preferences of the user and provides an optimal operation and setting advice.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to provide intuitive and prompt operating instructions and troubleshooting information for home appliances.

[0005] The system according to the embodiment aims to provide intuitive and prompt operation methods and troubleshooting information for home appliances. [Means for solving the problem]

[0006] The system according to the embodiment includes an AR display unit, an AI support chatbot unit, and a user habit learning unit. The AR display unit visualizes product operation procedures through the smartphone camera. The AI ​​support chatbot unit responds to user questions quickly and accurately. The user habit learning unit learns the user's habits and preferences and provides optimal operation and setting advice. [Effects of the Invention]

[0007] The system according to the embodiment can provide operation methods and troubleshooting information for home appliances intuitively and quickly. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The home appliance instruction manual system according to an embodiment of the present invention utilizes generative AI and AR to instantly provide users with product operation methods and troubleshooting information. This allows users to intuitively understand how to operate the product, quickly obtain the necessary information, and receive optimal operation and setting advice tailored to each individual user.

[0029] According to an embodiment, a home appliance instruction manual system includes an AR display unit, an AI support chatbot unit, and a user habit learning unit. The AR display unit visualizes product operation procedures through a smartphone camera. For example, when a user points the smartphone camera at a home appliance, the operation procedures are displayed on the screen. The AR display unit can also visualize product operation procedures using 3D models and animations. For example, a 3D model can be displayed for a washing machine, allowing the user to intuitively understand how to operate it. The AR display unit can also update product operation procedures in real time. For example, changes to operation procedures due to product software updates can be immediately reflected. The AI ​​support chatbot unit responds to user questions quickly and accurately. For example, when a user asks, "How do I change the temperature setting on my refrigerator?" within an app, the chatbot will explain the specific steps using text and images. The AI ​​support chatbot unit can also provide answers to user questions using videos and animations. For example, a video explaining the steps for cleaning a washing machine filter can be used. The AI ​​support chatbot unit can also learn from the user's past question history to provide more personalized answers. For example, if a user has asked many questions about refrigerators in the past, the system provides detailed answers to those questions. The user habit learning unit learns the user's habits and preferences and provides optimal operation and setting advice. For example, it suggests optimal screen settings and usage methods based on the user's television viewing preferences. The user habit learning unit can also learn the user's daily rhythm and suggest optimal times to use home appliances. For example, if the user wakes up at 7:00 every morning, it can set the automatic startup time for the coffee maker. Furthermore, the user habit learning unit learns the user's energy consumption patterns and suggests energy-efficient usage methods. For example, it learns the time of day the user uses the air conditioner and suggests optimal settings. As a result, the home appliance instruction manual system according to the embodiment allows users to intuitively understand how to operate the product, quickly obtain necessary information, and receive optimal operation and setting advice tailored to each individual user.

[0030] The AR display unit can add audio guidance to the AR display, providing both visual and auditory guidance on operating procedures. For example, when a user points a smartphone camera at an appliance, the AR display unit plays audio guidance along with the AR display. For example, while visually displaying the operating procedures for a washing machine, the audio guidance may say, "Next, press the start button." The AR display unit can also combine audio guidance with the AR display to provide not only visual information but also auditory information. For example, while visually displaying how to set the temperature on a refrigerator, the audio guidance may say, "Press and hold the temperature setting button for three seconds." Furthermore, the AR display unit can provide audio guidance in multiple languages, providing operating procedures in the user's language of choice. For example, audio guidance is provided in the user's native language, such as English, Spanish, or Chinese. This allows the user to understand the operating procedures both visually and auditorily.

[0031] The AR display unit can add interactive elements and provide a simulation mode that allows the user to actually try out operating procedures. For example, when a user points a smartphone camera at an appliance, interactive buttons appear on the AR display, allowing the user to simulate operating procedures by tapping the buttons. For example, a user can virtually try out an operation by tapping the operation panel of a washing machine. The AR display unit can also use a simulation mode to allow the user to actually try out operating procedures. For example, the AR display can simulate operating an air conditioner remote control, allowing the user to tap the buttons on the remote control to confirm the operation. Furthermore, by adding interactive elements, the AR display unit can allow the user to learn by actually trying out operating procedures. For example, the AR display can simulate how to set an oven, allowing the user to try out temperature and timer settings. This allows the user to learn by actually trying out operating procedures.

[0032] The AR display unit is compatible with smart glasses or a headset, and can enable hands-free confirmation of operation procedures. The AR display unit enables hands-free confirmation of operation procedures, for example, using smart glasses or a headset. For example, when a user wears smart glasses and points them at a home appliance, the operation procedures are visualized. The AR display unit also enables hands-free confirmation of operation procedures using smart glasses or a headset. For example, the operation procedures are displayed on the headset display, and the user confirms the operations with their line of sight. Furthermore, the AR display unit provides AR display compatible with smart glasses or a headset to enable hands-free confirmation of operation procedures. For example, when a user wears smart glasses and points them at a home appliance, the operation procedures are visualized. This allows the user to confirm operation procedures without using their hands.

[0033] The AR display unit can also be applied to fields other than home appliances, such as visualizing operating procedures in automobile maintenance and DIY projects. For example, the AR display unit applies AR-based visualization of operating procedures to automobile maintenance. For example, by pointing a smartphone camera at a car's engine compartment, the procedures for changing the oil or replacing the battery are visualized. The AR display unit can also apply AR-based visualization of operating procedures to DIY projects. For example, furniture assembly procedures can be visualized using AR, and when a user points a smartphone camera at a furniture part, the assembly procedures are displayed. The AR display unit can also apply AR-based visualization of operating procedures to fields other than home appliances. For example, gardening procedures can be visualized using AR, and when a user points a smartphone camera at a plant, planting and pruning procedures are displayed. This makes it possible to visualize operating procedures in fields other than home appliances.

[0034] The AI ​​support chatbot unit can provide answers to user questions using videos or animations. For example, when a user inputs a question within the app, the chatbot provides an answer using videos or animations. For example, for a question such as "How do I change the temperature setting on my refrigerator?", specific steps are explained using videos. The AI ​​support chatbot unit can also provide answers to user questions using animations. For example, for a question such as "How do I clean the filter in my washing machine?", steps are explained using animations. Furthermore, when a user inputs a question within the app, the chatbot provides an answer using videos or animations. For example, for a question such as "How do I use the remote control for my air conditioner?", specific steps are explained using animations. This allows the user to obtain an answer that is visually easy to understand.

[0035] The AI ​​support chatbot unit can learn the user's past question history and provide more personalized answers. For example, the AI ​​support chatbot unit learns the user's past question history and provides more personalized answers. For example, if the user has asked many questions about refrigerators in the past, it will provide detailed answers to questions about refrigerators. The AI ​​support chatbot unit also provides personalized answers based on the user's past question history. For example, if the user has asked questions about air conditioners in the past, it will provide specific procedures for questions about air conditioners. The AI ​​support chatbot unit also learns the user's past question history and provides more personalized answers. For example, if the user has asked questions about washing machines in the past, it will provide detailed answers to questions about washing machines. This allows the user to obtain more personalized answers.

[0036] The AI ​​support chatbot unit can support multiple languages ​​and accommodate international users. The AI ​​support chatbot unit, for example, allows the chatbot to support multiple languages ​​and accommodate international users. For example, it answers questions in the user's native language, such as English, Spanish, or Chinese. The AI ​​support chatbot unit can also develop chatbots that support multiple languages ​​and accommodate international users. For example, it answers questions in a language selected by the user. The AI ​​support chatbot unit can also support multiple languages ​​and accommodate international users. For example, when a user inputs a question in their native language, it provides an answer in that language. This allows it to accommodate international users.

[0037] The AI ​​support chatbot unit can also respond to user voice input, enabling questions to be asked and answered via voice. For example, the AI ​​support chatbot unit allows the chatbot to respond to user voice input, enabling questions to be asked and answered via voice. For example, if a user asks by voice, "Please tell me how to change the temperature setting on my refrigerator," the answer will be provided via voice. The AI ​​support chatbot unit also develops a chatbot that responds to voice input, enabling users to ask and answer questions via voice. For example, if a user asks by voice, "Please tell me how to clean the filter in my washing machine," the answer will be provided via voice. The AI ​​support chatbot unit also responds to voice input, enabling users to ask and answer questions via voice. For example, if a user asks by voice, "Please tell me how to use the air conditioner remote control," the answer will be provided via voice. This allows users to ask and answer questions via voice.

[0038] The user habit learning unit can learn the user's lifestyle rhythm and suggest the optimal timing for using home appliances. For example, the user habit learning unit uses AI to learn the user's lifestyle rhythm and suggest the optimal timing for using home appliances. For example, if the user wakes up at 7am every morning, the automatic start time for the coffee maker is set. The user habit learning unit also suggests the optimal timing for using home appliances based on the user's lifestyle rhythm. For example, if the user has a habit of using the air conditioner at night, the optimal temperature setting is suggested. Furthermore, the user habit learning unit uses AI to learn the user's lifestyle rhythm and suggest the optimal timing for using home appliances. For example, if the user uses a vacuum cleaner every weekend, the optimal cleaning time is suggested. In this way, the user is suggested the optimal timing for using home appliances that matches their lifestyle rhythm.

[0039] The user habit learning unit can suggest new uses and recipes for home appliances based on the user's preferences. In the user habit learning unit, for example, the AI ​​suggests new uses and recipes for home appliances based on the user's preferences. For example, if the user is health-conscious, healthy cooking recipes are suggested. The user habit learning unit also learns the user's preferences and the AI ​​suggests new uses and recipes for home appliances. For example, if the user likes sweets, dessert recipes are suggested. In addition, the user habit learning unit suggests new uses and recipes for home appliances based on the user's preferences. For example, if the user is vegetarian, vegetable dish recipes are suggested. In this way, the user is suggested new uses and recipes based on their preferences.

[0040] The user habit learning unit can learn the user's health data and suggest health-conscious ways of using home appliances. For example, the user habit learning unit uses AI to learn the user's health data and suggest health-conscious ways of using home appliances. For example, the unit suggests optimal bedroom temperature settings based on the user's sleep data. The user habit learning unit also suggests health-conscious ways of using home appliances based on the user's health data. For example, the unit suggests optimal air conditioner settings based on the user's exercise data. Furthermore, the user habit learning unit uses AI to learn the user's health data and suggest health-conscious ways of using home appliances. For example, the unit suggests healthy cooking recipes based on the user's dietary data. In this way, the user is suggested health-conscious ways of using home appliances.

[0041] The user habit learning unit can learn the user's energy consumption patterns and suggest energy-efficient usage methods. For example, the user habit learning unit uses AI to learn the user's energy consumption patterns and suggest energy-efficient usage methods. For example, it learns the time of day the user uses the air conditioner and suggests the optimal settings. The user habit learning unit also suggests energy-efficient usage methods based on the user's energy consumption patterns. For example, it learns the time of day the user uses the washing machine and suggests the optimal timing for use. The user habit learning unit also uses AI to learn the user's energy consumption patterns and suggest energy-efficient usage methods. For example, it learns the time of day the user uses the lights and suggests the optimal settings. In this way, the user is suggested energy-efficient usage methods.

[0042] The troubleshooting unit can identify the cause of a problem by referring to the user's past usage history. In the troubleshooting unit, for example, AI refers to the user's past usage history to identify the cause of the problem. For example, if the washing machine won't start, it will identify a clogged filter based on the past usage history. In addition, the troubleshooting unit can identify the cause of the problem by AI based on the user's past usage history. For example, if the refrigerator won't heat up, it will identify the cause by referring to the past setting history. In addition, the troubleshooting unit can identify the cause of the problem by AI based on the user's past usage history. For example, if the air conditioner won't cool, it will identify the need to clean the filter based on the past usage history. This allows the user to identify the cause of the problem based on the past usage history.

[0043] The troubleshooting unit can provide troubleshooting procedures in the form of videos or animations to make them visually easier to understand. For example, the troubleshooting unit may use AI to provide troubleshooting procedures in the form of videos to make them visually easier to understand. For example, a video may explain the procedure for cleaning a washing machine filter. The troubleshooting unit may also provide troubleshooting procedures in the form of animations to make them visually easier to understand. For example, an animation may explain the procedure for changing the temperature setting of a refrigerator. The troubleshooting unit may also provide troubleshooting procedures in the form of videos or animations to make them visually easier to understand. For example, an animation may explain the procedure for cleaning an air conditioner filter. This allows the user to obtain troubleshooting procedures that are visually easier to understand.

[0044] The troubleshooting unit can share troubleshooting information with other users and provide a community-based solution. For example, the AI ​​in the troubleshooting unit shares troubleshooting information with other users and provides a community-based solution. For example, a solution to a washing machine problem is shared with other users to collect opinions in the community. The troubleshooting unit also shares troubleshooting information with other users and provides a community-based solution. For example, a solution to a refrigerator problem is shared with other users to obtain feedback in the community. The troubleshooting unit also shares troubleshooting information with other users and provides a community-based solution. For example, a solution to an air conditioner problem is shared with other users to obtain advice in the community. This allows the user to obtain a community-based solution.

[0045] The troubleshooting unit can periodically update the troubleshooting information and add new problems and solutions. For example, the troubleshooting unit may use AI to periodically update the troubleshooting information and add new problems and solutions. For example, the troubleshooting unit may add problem information for a new home appliance and provide a solution. The troubleshooting unit may also periodically update the troubleshooting information and add new problems and solutions. For example, the troubleshooting unit may add problem information corresponding to the latest technology and provide a solution. The troubleshooting unit may also periodically update the troubleshooting information and add new problems and solutions. For example, the troubleshooting unit may add problem information corresponding to a new model of home appliance and provide a solution. This allows the user to obtain the latest troubleshooting information.

[0046] The user habit learning unit can suggest optimal operating methods in real time based on the user's usage status. In the user habit learning unit, for example, AI suggests optimal operating methods in real time based on the user's usage status. For example, it suggests optimal temperature settings and operation modes based on air conditioner usage status. The user habit learning unit also analyzes the user's usage status in real time, and AI suggests optimal operating methods. For example, it suggests optimal washing courses and settings based on washing machine usage status. Furthermore, the user habit learning unit also suggests optimal operating methods in real time based on the user's usage status. For example, it suggests optimal temperature settings and storage methods based on refrigerator usage status. In this way, the user is suggested optimal operating methods in real time based on their usage status.

[0047] The user habit learning unit can learn user feedback and continuously improve the content of suggestions. In the user habit learning unit, for example, the AI ​​learns user feedback and continuously improves the content of suggestions. For example, when a user provides feedback on air conditioner settings, the AI ​​improves the content of suggestions based on that feedback. In addition, the user habit learning unit continuously improves the content of suggestions based on user feedback. For example, when a user provides feedback on washing machine settings, the AI ​​improves the content of suggestions based on that feedback. In addition, the user habit learning unit allows the AI ​​to learn user feedback and continuously improve the content of suggestions. For example, when a user provides feedback on refrigerator settings, the AI ​​improves the content of suggestions based on that feedback. In this way, the user's suggestions are continuously improved based on their feedback.

[0048] The user habit learning unit can suggest methods for coordinating operation between different home appliances, supporting the realization of a smart home. For example, the user habit learning unit uses AI to suggest methods for coordinating operation between different home appliances, supporting the realization of a smart home. For example, coordinating air conditioners and lighting to suggest the optimal indoor environment. The user habit learning unit also supports the realization of a smart home by suggesting methods for coordinating operation between different home appliances. For example, coordinating a washing machine and dryer to suggest the optimal washing and drying process. The user habit learning unit also supports the realization of a smart home by suggesting methods for coordinating operation between different home appliances using AI. For example, coordinating a refrigerator and oven to suggest the optimal cooking process. In this way, users are suggested methods for coordinating operation between different home appliances, supporting the realization of a smart home.

[0049] The user habit learning unit can suggest operation methods that suit the user's lifestyle, improving the efficiency of their entire life. For example, the AI ​​in the user habit learning unit suggests operation methods that suit the user's lifestyle, improving the efficiency of their entire life. For example, the AI ​​suggests the best way for the user to use home appliances on busy mornings. The user habit learning unit also learns the user's lifestyle and the AI ​​suggests the best operation methods. For example, the AI ​​suggests the best lighting settings for the user to relax at night. The user habit learning unit can also suggest operation methods that suit the user's lifestyle, improving the efficiency of their entire life. For example, the AI ​​suggests the best way for the user to use home appliances on weekends. This allows the user to receive suggestions for operation methods that suit their lifestyle, improving the efficiency of their entire life.

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

[0051] The home appliance instruction manual system can further include a health management unit that learns the user's health data and suggests health-conscious ways of using home appliances. For example, it can suggest optimal bedroom temperature settings based on the user's sleep data. It can also suggest optimal air conditioner settings based on the user's exercise data. It can also suggest healthy cooking recipes based on the user's dietary data. In this way, the user can be suggested health-conscious ways of using the appliance.

[0052] The home appliance instruction manual system can further include an energy management unit that learns the user's energy consumption patterns and suggests energy-efficient usage methods. For example, it can learn the time periods when the user uses the air conditioner and suggest optimal settings. It can also learn the time periods when the user uses the washing machine and suggest optimal usage timing. It can also learn the time periods when the user uses the lights and suggest optimal settings. In this way, the user can be suggested energy-efficient usage methods.

[0053] The home appliance instruction manual system further includes a troubleshooting section that can identify the cause of a problem by referring to the user's past usage history. For example, if a washing machine won't start, it can identify a clogged filter based on the past usage history. Also, if a refrigerator won't heat up, it can identify the cause by referring to the past setting history. Furthermore, if an air conditioner won't cool, it can identify the need to clean the filter based on the past usage history. This allows the user to identify the cause of a problem based on past usage history.

[0054] The home appliance instruction manual system may further include a troubleshooting section that provides troubleshooting procedures in the form of videos or animations, making them visually easier to understand. For example, the procedure for cleaning a washing machine filter may be explained using a video. The procedure for changing the temperature setting of a refrigerator may also be explained using an animation. Furthermore, the procedure for cleaning an air conditioner filter may also be explained using an animation. This allows the user to obtain troubleshooting procedures that are visually easy to understand.

[0055] The home appliance instruction manual system further includes a troubleshooting section, which allows users to share troubleshooting information with other users and provide community-based solutions. For example, a solution to a washing machine problem can be shared with other users to gather opinions from the community. A solution to a refrigerator problem can also be shared with other users to obtain feedback from the community. Furthermore, a solution to an air conditioner problem can also be shared with other users to obtain advice from the community. This allows users to obtain community-based solutions.

[0056] The home appliance instruction manual system further includes a troubleshooting section, which periodically updates the troubleshooting information and adds new problems and solutions. For example, it can add trouble information for new home appliances and provide solutions. It can also add trouble information for the latest technology and provide solutions. It can also add trouble information for new models of home appliances and provide solutions. This allows the user to obtain the latest troubleshooting information.

[0057] The home appliance instruction manual system can further include a feedback learning unit that learns from user feedback and continuously improves the suggestions. For example, when a user provides feedback on air conditioner settings, the AI ​​can improve the suggestions based on that feedback. Also, when a user provides feedback on washing machine settings, the AI ​​can improve the suggestions based on that feedback. Furthermore, when a user provides feedback on refrigerator settings, the AI ​​can improve the suggestions based on that feedback. In this way, users can continuously improve the suggestions based on their feedback.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The AR display unit visualizes the product's operating procedures through the smartphone camera. For example, when a user points the smartphone camera at a home appliance, the operating procedures are displayed on the screen. The AR display unit can also visualize the product's operating procedures using 3D models or animations. Furthermore, the AR display unit can update the product's operating procedures in real time. Step 2: The AI ​​support chatbot responds to the user's questions quickly and accurately. For example, if a user asks within the app, "How do I change the temperature setting on my refrigerator?", the chatbot will explain the specific steps using text and images. The AI ​​support chatbot can also provide answers to the user's questions using videos and animations. Furthermore, the AI ​​support chatbot learns the user's past question history to provide more personalized answers. Step 3: The user habit learning unit learns the user's habits and preferences and provides optimal operation and setting advice. For example, it suggests optimal screen settings and usage methods based on the user's television viewing preferences. The user habit learning unit can also learn the user's daily rhythm and suggest optimal times to use home appliances. Furthermore, the user habit learning unit learns the user's energy consumption patterns and suggests energy-efficient usage methods.

[0060] (Example 2) The home appliance instruction manual system according to an embodiment of the present invention utilizes generative AI and AR to instantly provide users with product operation methods and troubleshooting information. This allows users to intuitively understand how to operate the product, quickly obtain the necessary information, and receive optimal operation and setting advice tailored to each individual user.

[0061] According to an embodiment, a home appliance instruction manual system includes an AR display unit, an AI support chatbot unit, and a user habit learning unit. The AR display unit visualizes product operation procedures through a smartphone camera. For example, when a user points the smartphone camera at a home appliance, the operation procedures are displayed on the screen. The AR display unit can also visualize product operation procedures using 3D models and animations. For example, a 3D model can be displayed for a washing machine, allowing the user to intuitively understand how to operate it. The AR display unit can also update product operation procedures in real time. For example, changes to operation procedures due to product software updates can be immediately reflected. The AI ​​support chatbot unit responds to user questions quickly and accurately. For example, when a user asks, "How do I change the temperature setting on my refrigerator?" within an app, the chatbot will explain the specific steps using text and images. The AI ​​support chatbot unit can also provide answers to user questions using videos and animations. For example, a video explaining the steps for cleaning a washing machine filter can be used. The AI ​​support chatbot unit can also learn from the user's past question history to provide more personalized answers. For example, if a user has asked many questions about refrigerators in the past, the system provides detailed answers to those questions. The user habit learning unit learns the user's habits and preferences and provides optimal operation and setting advice. For example, it suggests optimal screen settings and usage methods based on the user's television viewing preferences. The user habit learning unit can also learn the user's daily rhythm and suggest optimal times to use home appliances. For example, if the user wakes up at 7:00 every morning, it can set the automatic startup time for the coffee maker. Furthermore, the user habit learning unit learns the user's energy consumption patterns and suggests energy-efficient usage methods. For example, it learns the time of day the user uses the air conditioner and suggests optimal settings. As a result, the home appliance instruction manual system according to the embodiment allows users to intuitively understand how to operate the product, quickly obtain necessary information, and receive optimal operation and setting advice tailored to each individual user.

[0062] The AR display unit can add audio guidance to the AR display, providing both visual and auditory guidance on operating procedures. For example, when a user points a smartphone camera at an appliance, the AR display unit plays audio guidance along with the AR display. For example, while visually displaying the operating procedures for a washing machine, the audio guidance may say, "Next, press the start button." The AR display unit can also combine audio guidance with the AR display to provide not only visual information but also auditory information. For example, while visually displaying how to set the temperature on a refrigerator, the audio guidance may say, "Press and hold the temperature setting button for three seconds." Furthermore, the AR display unit can provide audio guidance in multiple languages, providing operating procedures in the user's language of choice. For example, audio guidance is provided in the user's native language, such as English, Spanish, or Chinese. This allows the user to understand the operating procedures both visually and auditorily.

[0063] The AR display unit can add interactive elements and provide a simulation mode that allows the user to actually try out operating procedures. For example, when a user points a smartphone camera at an appliance, interactive buttons appear on the AR display, allowing the user to simulate operating procedures by tapping the buttons. For example, a user can virtually try out an operation by tapping the operation panel of a washing machine. The AR display unit can also use a simulation mode to allow the user to actually try out operating procedures. For example, the AR display can simulate operating an air conditioner remote control, allowing the user to tap the buttons on the remote control to confirm the operation. Furthermore, by adding interactive elements, the AR display unit can allow the user to learn by actually trying out operating procedures. For example, the AR display can simulate how to set an oven, allowing the user to try out temperature and timer settings. This allows the user to learn by actually trying out operating procedures.

[0064] The AR display unit can use its emotion estimation function to detect in real time the stress or anxiety a user feels as they operate a home appliance and provide appropriate support. For example, when a user points a smartphone camera at a home appliance to check operating procedures, the AR display unit uses its emotion estimation function to analyze the user's facial expressions and voice to detect stress or anxiety. For example, if the user shows a confused expression, additional support information is displayed. The AR display unit can also use its emotion estimation function to detect in real time the stress or anxiety a user feels as they operate the appliance and provide appropriate support. For example, if the user is confused about an operation, detailed explanations or video guides are provided. Furthermore, the AR display unit monitors the user's emotional state in real time and provides encouraging messages or additional support if the user feels stress or anxiety. For example, it displays a message such as "It's okay, let's try again." This reduces the user's stress and anxiety and allows for smoother operation.

[0065] The AR display unit is compatible with smart glasses or a headset, and can enable hands-free confirmation of operation procedures. The AR display unit enables hands-free confirmation of operation procedures, for example, using smart glasses or a headset. For example, when a user wears smart glasses and points them at a home appliance, the operation procedures are visualized. The AR display unit also enables hands-free confirmation of operation procedures using smart glasses or a headset. For example, the operation procedures are displayed on the headset display, and the user confirms the operations with their line of sight. Furthermore, the AR display unit provides AR display compatible with smart glasses or a headset to enable hands-free confirmation of operation procedures. For example, when a user wears smart glasses and points them at a home appliance, the operation procedures are visualized. This allows the user to confirm operation procedures without using their hands.

[0066] The AR display unit can also be applied to fields other than home appliances, such as visualizing operating procedures in automobile maintenance and DIY projects. For example, the AR display unit applies AR-based visualization of operating procedures to automobile maintenance. For example, by pointing a smartphone camera at a car's engine compartment, the procedures for changing the oil or replacing the battery are visualized. The AR display unit can also apply AR-based visualization of operating procedures to DIY projects. For example, furniture assembly procedures can be visualized using AR, and when a user points a smartphone camera at a furniture part, the assembly procedures are displayed. The AR display unit can also apply AR-based visualization of operating procedures to fields other than home appliances. For example, gardening procedures can be visualized using AR, and when a user points a smartphone camera at a plant, planting and pruning procedures are displayed. This makes it possible to visualize operating procedures in fields other than home appliances.

[0067] The AR display unit can use the emotion estimation function to detect the joy and sense of accomplishment the user feels when they understand an operation procedure and provide feedback that reinforces that emotion. For example, the AR display unit can use the emotion estimation function to detect the joy and sense of accomplishment the user feels when they understand an operation procedure and provide feedback that reinforces that emotion. For example, when the user completes an operation procedure, it displays a message such as "Well done!". The AR display unit can also monitor the user's emotional state in real time and detect the joy and sense of accomplishment the user feels when they understand an operation procedure. For example, it can provide positive feedback when the user smiles. The AR display unit can also use the emotion estimation function to provide feedback that reinforces the joy and sense of accomplishment the user feels when they understand an operation procedure. For example, when the user completes an operation procedure, it can provide feedback such as "Great!" using animation or audio. This can reinforce the user's joy and sense of accomplishment and provide a positive experience.

[0068] The AI ​​support chatbot unit can provide answers to user questions using videos or animations. For example, when a user inputs a question within the app, the chatbot provides an answer using videos or animations. For example, for a question such as "How do I change the temperature setting on my refrigerator?", specific steps are explained using videos. The AI ​​support chatbot unit can also provide answers to user questions using animations. For example, for a question such as "How do I clean the filter in my washing machine?", steps are explained using animations. Furthermore, when a user inputs a question within the app, the chatbot provides an answer using videos or animations. For example, for a question such as "How do I use the remote control for my air conditioner?", specific steps are explained using animations. This allows the user to obtain an answer that is visually easy to understand.

[0069] The AI ​​support chatbot unit can learn the user's past question history and provide more personalized answers. For example, the AI ​​support chatbot unit learns the user's past question history and provides more personalized answers. For example, if the user has asked many questions about refrigerators in the past, it will provide detailed answers to questions about refrigerators. The AI ​​support chatbot unit also provides personalized answers based on the user's past question history. For example, if the user has asked questions about air conditioners in the past, it will provide specific procedures for questions about air conditioners. The AI ​​support chatbot unit also learns the user's past question history and provides more personalized answers. For example, if the user has asked questions about washing machines in the past, it will provide detailed answers to questions about washing machines. This allows the user to obtain more personalized answers.

[0070] The AI ​​support chatbot unit can use the emotion estimation function to analyze the emotion of the user's question and provide an answer in an appropriate tone. The AI ​​support chatbot unit, for example, uses the emotion estimation function to analyze the emotion of the user's question and provide an answer in an appropriate tone. For example, if the user is asking a question that makes them anxious, the answer will be provided in a gentle tone. The AI ​​support chatbot unit also analyzes the emotion of the user's question in real time and provides an answer in an appropriate tone. For example, if the user is feeling angry, the answer will be provided in a calm and polite tone. The AI ​​support chatbot unit also uses the emotion estimation function to analyze the emotion of the user's question and provide an answer in an appropriate tone. For example, if the user is feeling happy, the answer will be provided in a bright tone. This makes it possible to provide an answer in an appropriate tone according to the user's emotions.

[0071] The AI ​​support chatbot unit can support multiple languages ​​and accommodate international users. The AI ​​support chatbot unit, for example, allows the chatbot to support multiple languages ​​and accommodate international users. For example, it answers questions in the user's native language, such as English, Spanish, or Chinese. The AI ​​support chatbot unit can also develop chatbots that support multiple languages ​​and accommodate international users. For example, it answers questions in a language selected by the user. The AI ​​support chatbot unit can also support multiple languages ​​and accommodate international users. For example, when a user inputs a question in their native language, it provides an answer in that language. This allows it to accommodate international users.

[0072] The AI ​​support chatbot unit can also respond to user voice input, enabling questions to be asked and answered via voice. For example, the AI ​​support chatbot unit allows the chatbot to respond to user voice input, enabling questions to be asked and answered via voice. For example, if a user asks by voice, "Please tell me how to change the temperature setting on my refrigerator," the answer will be provided via voice. The AI ​​support chatbot unit also develops a chatbot that responds to voice input, enabling users to ask and answer questions via voice. For example, if a user asks by voice, "Please tell me how to clean the filter in my washing machine," the answer will be provided via voice. The AI ​​support chatbot unit also responds to voice input, enabling users to ask and answer questions via voice. For example, if a user asks by voice, "Please tell me how to use the air conditioner remote control," the answer will be provided via voice. This allows users to ask and answer questions via voice.

[0073] The AI ​​support chatbot unit uses the emotion estimation function to analyze the emotion a user has when asking a question in real time and provide an answer that elicits positive emotions. For example, the AI ​​support chatbot unit uses the emotion estimation function to analyze the emotion a user has when asking a question in real time and provide an answer that elicits positive emotions. For example, if a user is asking a question that makes them anxious, an answer that gives a sense of security is provided. The AI ​​support chatbot unit also analyzes the emotion a user has in response to a question in real time and provides an answer that elicits positive emotions. For example, if a user is confused, an answer is provided that includes words of encouragement. The AI ​​support chatbot unit also uses the emotion estimation function to analyze the emotion a user has when asking a question in real time and provide an answer that elicits positive emotions. For example, if a user is feeling angry, an answer is provided in a calm and gentle tone. This makes it possible to provide an answer that elicits positive emotions from the user.

[0074] The user habit learning unit can learn the user's lifestyle rhythm and suggest the optimal timing for using home appliances. For example, the user habit learning unit uses AI to learn the user's lifestyle rhythm and suggest the optimal timing for using home appliances. For example, if the user wakes up at 7am every morning, the automatic start time for the coffee maker is set. The user habit learning unit also suggests the optimal timing for using home appliances based on the user's lifestyle rhythm. For example, if the user has a habit of using the air conditioner at night, the optimal temperature setting is suggested. Furthermore, the user habit learning unit uses AI to learn the user's lifestyle rhythm and suggest the optimal timing for using home appliances. For example, if the user uses a vacuum cleaner every weekend, the optimal cleaning time is suggested. In this way, the user is suggested the optimal timing for using home appliances that matches their lifestyle rhythm.

[0075] The user habit learning unit can suggest new uses and recipes for home appliances based on the user's preferences. In the user habit learning unit, for example, the AI ​​suggests new uses and recipes for home appliances based on the user's preferences. For example, if the user is health-conscious, healthy cooking recipes are suggested. The user habit learning unit also learns the user's preferences and the AI ​​suggests new uses and recipes for home appliances. For example, if the user likes sweets, dessert recipes are suggested. In addition, the user habit learning unit suggests new uses and recipes for home appliances based on the user's preferences. For example, if the user is vegetarian, vegetable dish recipes are suggested. In this way, the user is suggested new uses and recipes based on their preferences.

[0076] The user habit learning unit can use the emotion estimation function to analyze the emotions of the user when using a home appliance and perform optimization to provide a positive experience. The user habit learning unit, for example, uses the emotion estimation function to analyze the emotions of the user when using a home appliance and perform optimization to provide a positive experience. For example, if the user wants to relax, the unit suggests optimal lighting settings. The user habit learning unit also analyzes the user's emotions in real time and performs optimization to provide a positive experience. For example, if the user is feeling stressed, the unit plays relaxing music. The user habit learning unit also uses the emotion estimation function to analyze the emotions of the user when using a home appliance and perform optimization to provide a positive experience. For example, if the user wants to have fun, the unit suggests an entertainment function. This provides the user with a positive experience.

[0077] The user habit learning unit can learn the user's health data and suggest health-conscious ways of using home appliances. For example, the user habit learning unit uses AI to learn the user's health data and suggest health-conscious ways of using home appliances. For example, the unit suggests optimal bedroom temperature settings based on the user's sleep data. The user habit learning unit also suggests health-conscious ways of using home appliances based on the user's health data. For example, the unit suggests optimal air conditioner settings based on the user's exercise data. Furthermore, the user habit learning unit uses AI to learn the user's health data and suggest health-conscious ways of using home appliances. For example, the unit suggests healthy cooking recipes based on the user's dietary data. In this way, the user is suggested health-conscious ways of using home appliances.

[0078] The user habit learning unit can learn the user's energy consumption patterns and suggest energy-efficient usage methods. For example, the user habit learning unit uses AI to learn the user's energy consumption patterns and suggest energy-efficient usage methods. For example, it learns the time of day the user uses the air conditioner and suggests the optimal settings. The user habit learning unit also suggests energy-efficient usage methods based on the user's energy consumption patterns. For example, it learns the time of day the user uses the washing machine and suggests the optimal timing for use. The user habit learning unit also uses AI to learn the user's energy consumption patterns and suggest energy-efficient usage methods. For example, it learns the time of day the user uses the lights and suggests the optimal settings. In this way, the user is suggested energy-efficient usage methods.

[0079] The user habit learning unit can use the emotion estimation function to monitor the emotions of the user when using a home appliance in real time and provide advice to reduce negative emotions. The user habit learning unit, for example, uses the emotion estimation function to monitor the emotions of the user when using a home appliance in real time and provide advice to reduce negative emotions. For example, if the user is feeling stressed, the unit suggests settings that will help the user relax. The user habit learning unit also monitors the emotions of the user in real time and provides advice to reduce negative emotions. For example, if the user is feeling anxious, the unit suggests settings that will give the user a sense of security. The user habit learning unit also uses the emotion estimation function to monitor the emotions of the user when using a home appliance in real time and provide advice to reduce negative emotions. For example, if the user is feeling angry, the unit suggests settings that will help the user stay calm. This allows the user to receive advice to reduce negative emotions.

[0080] The troubleshooting unit can identify the cause of a problem by referring to the user's past usage history. In the troubleshooting unit, for example, AI refers to the user's past usage history to identify the cause of the problem. For example, if the washing machine won't start, it will identify a clogged filter based on the past usage history. In addition, the troubleshooting unit can identify the cause of the problem by AI based on the user's past usage history. For example, if the refrigerator won't heat up, it will identify the cause by referring to the past setting history. In addition, the troubleshooting unit can identify the cause of the problem by AI based on the user's past usage history. For example, if the air conditioner won't cool, it will identify the need to clean the filter based on the past usage history. This allows the user to identify the cause of the problem based on the past usage history.

[0081] The troubleshooting unit can provide troubleshooting procedures in the form of videos or animations to make them visually easier to understand. For example, the troubleshooting unit may use AI to provide troubleshooting procedures in the form of videos to make them visually easier to understand. For example, a video may explain the procedure for cleaning a washing machine filter. The troubleshooting unit may also provide troubleshooting procedures in the form of animations to make them visually easier to understand. For example, an animation may explain the procedure for changing the temperature setting of a refrigerator. The troubleshooting unit may also provide troubleshooting procedures in the form of videos or animations to make them visually easier to understand. For example, an animation may explain the procedure for cleaning an air conditioner filter. This allows the user to obtain troubleshooting procedures that are visually easier to understand.

[0082] The troubleshooting unit can use the emotion estimation function to detect the stress the user feels about a problem and provide support to reduce the stress. For example, the troubleshooting unit uses the emotion estimation function to detect the stress the user feels about a problem and provide support to reduce the stress. For example, if the user is confused, the troubleshooting unit provides a detailed explanation or an encouraging message. The troubleshooting unit also monitors the user's emotions in real time and provides support to reduce the stress caused by the problem. For example, if the user feels anxious, the troubleshooting unit provides support that gives a sense of security. Furthermore, the troubleshooting unit uses the emotion estimation function to detect the stress the user feels about a problem and provides support to reduce the stress. For example, if the user feels angry, the troubleshooting unit provides support to help the user stay calm. This allows the user to receive support to reduce the stress caused by the problem.

[0083] The troubleshooting unit can share troubleshooting information with other users and provide a community-based solution. For example, the AI ​​in the troubleshooting unit shares troubleshooting information with other users and provides a community-based solution. For example, a solution to a washing machine problem is shared with other users to collect opinions in the community. The troubleshooting unit also shares troubleshooting information with other users and provides a community-based solution. For example, a solution to a refrigerator problem is shared with other users to obtain feedback in the community. The troubleshooting unit also shares troubleshooting information with other users and provides a community-based solution. For example, a solution to an air conditioner problem is shared with other users to obtain advice in the community. This allows the user to obtain a community-based solution.

[0084] The troubleshooting unit can periodically update the troubleshooting information and add new problems and solutions. For example, the troubleshooting unit may use AI to periodically update the troubleshooting information and add new problems and solutions. For example, the troubleshooting unit may add problem information for a new home appliance and provide a solution. The troubleshooting unit may also periodically update the troubleshooting information and add new problems and solutions. For example, the troubleshooting unit may add problem information corresponding to the latest technology and provide a solution. The troubleshooting unit may also periodically update the troubleshooting information and add new problems and solutions. For example, the troubleshooting unit may add problem information corresponding to a new model of home appliance and provide a solution. This allows the user to obtain the latest troubleshooting information.

[0085] The troubleshooting unit can use the emotion estimation function to provide feedback that reinforces the sense of accomplishment the user feels when solving a problem. For example, the troubleshooting unit uses the emotion estimation function to provide feedback that reinforces the sense of accomplishment the user feels when solving a problem. For example, a message such as "Great!" is displayed when the user solves a problem. The troubleshooting unit also monitors the user's emotions in real time and provides feedback that reinforces the sense of accomplishment the user feels when solving a problem. For example, when the user solves a problem, the troubleshooting unit provides feedback such as "Good job!" using animation or audio. Furthermore, the troubleshooting unit uses the emotion estimation function to provide feedback that reinforces the sense of accomplishment the user feels when solving a problem. For example, a positive message or effect is displayed when the user solves a problem. This allows the user to receive feedback that reinforces the sense of accomplishment the user feels when solving a problem.

[0086] The user habit learning unit can suggest optimal operating methods in real time based on the user's usage status. In the user habit learning unit, for example, AI suggests optimal operating methods in real time based on the user's usage status. For example, it suggests optimal temperature settings and operation modes based on air conditioner usage status. The user habit learning unit also analyzes the user's usage status in real time, and AI suggests optimal operating methods. For example, it suggests optimal washing courses and settings based on washing machine usage status. Furthermore, the user habit learning unit also suggests optimal operating methods in real time based on the user's usage status. For example, it suggests optimal temperature settings and storage methods based on refrigerator usage status. In this way, the user is suggested optimal operating methods in real time based on their usage status.

[0087] The user habit learning unit can learn user feedback and continuously improve the content of suggestions. In the user habit learning unit, for example, the AI ​​learns user feedback and continuously improves the content of suggestions. For example, when a user provides feedback on air conditioner settings, the AI ​​improves the content of suggestions based on that feedback. In addition, the user habit learning unit continuously improves the content of suggestions based on user feedback. For example, when a user provides feedback on washing machine settings, the AI ​​improves the content of suggestions based on that feedback. In addition, the user habit learning unit allows the AI ​​to learn user feedback and continuously improve the content of suggestions. For example, when a user provides feedback on refrigerator settings, the AI ​​improves the content of suggestions based on that feedback. In this way, the user's suggestions are continuously improved based on their feedback.

[0088] The user habit learning unit can use the emotion estimation function to analyze the user's level of satisfaction with the proposed operation methods and prioritize suggestions that provide high satisfaction. The user habit learning unit, for example, uses the emotion estimation function to analyze the user's level of satisfaction with the proposed operation methods and prioritize suggestions that provide high satisfaction. For example, if the user is satisfied with the air conditioner settings, the unit preferentially suggests those settings. The user habit learning unit also monitors the user's emotions in real time and analyzes the user's level of satisfaction with the proposed operation methods. For example, if the user is satisfied with the washing machine settings, the unit preferentially suggests those settings. The user habit learning unit also uses the emotion estimation function to analyze the user's level of satisfaction with the proposed operation methods and prioritize suggestions that provide high satisfaction. For example, if the user is satisfied with the refrigerator settings, the unit preferentially suggests those settings. This allows the user to preferentially receive suggestions that provide high satisfaction.

[0089] The user habit learning unit can suggest methods for coordinating operation between different home appliances, supporting the realization of a smart home. For example, the user habit learning unit uses AI to suggest methods for coordinating operation between different home appliances, supporting the realization of a smart home. For example, coordinating air conditioners and lighting to suggest the optimal indoor environment. The user habit learning unit also supports the realization of a smart home by suggesting methods for coordinating operation between different home appliances. For example, coordinating a washing machine and dryer to suggest the optimal washing and drying process. The user habit learning unit also supports the realization of a smart home by suggesting methods for coordinating operation between different home appliances using AI. For example, coordinating a refrigerator and oven to suggest the optimal cooking process. In this way, users are suggested methods for coordinating operation between different home appliances, supporting the realization of a smart home.

[0090] The user habit learning unit can suggest operation methods that suit the user's lifestyle, improving the efficiency of their entire life. For example, the AI ​​in the user habit learning unit suggests operation methods that suit the user's lifestyle, improving the efficiency of their entire life. For example, the AI ​​suggests the best way for the user to use home appliances on busy mornings. The user habit learning unit also learns the user's lifestyle and the AI ​​suggests the best operation methods. For example, the AI ​​suggests the best lighting settings for the user to relax at night. The user habit learning unit can also suggest operation methods that suit the user's lifestyle, improving the efficiency of their entire life. For example, the AI ​​suggests the best way for the user to use home appliances on weekends. This allows the user to receive suggestions for operation methods that suit their lifestyle, improving the efficiency of their entire life.

[0091] The user habit learning unit can use the emotion estimation function to monitor the emotion the user feels toward a proposed operation method in real time and provide suggestions that elicit positive emotions. The user habit learning unit, for example, uses the emotion estimation function to monitor the emotion the user feels toward a proposed operation method in real time and provide suggestions that elicit positive emotions. For example, if the user has positive emotions toward an air conditioner setting, those settings are preferentially suggested. The user habit learning unit also monitors the user's emotions in real time and analyzes their emotions toward a proposed operation method. For example, if the user has positive emotions toward a washing machine setting, those settings are preferentially suggested. The user habit learning unit also uses the emotion estimation function to monitor the emotion the user feels toward a proposed operation method in real time and provide suggestions that elicit positive emotions. For example, if the user has positive emotions toward a refrigerator setting, those settings are preferentially suggested. This allows the user to receive suggestions that elicit positive emotions.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The home appliance instruction manual system can further include a health management unit that learns the user's health data and suggests health-conscious ways of using home appliances. For example, it can suggest optimal bedroom temperature settings based on the user's sleep data. It can also suggest optimal air conditioner settings based on the user's exercise data. It can also suggest healthy cooking recipes based on the user's dietary data. In this way, the user can be suggested health-conscious ways of using the appliance.

[0094] The home appliance instruction manual system can further include an energy management unit that learns the user's energy consumption patterns and suggests energy-efficient usage methods. For example, it can learn the time periods when the user uses the air conditioner and suggest optimal settings. It can also learn the time periods when the user uses the washing machine and suggest optimal usage timing. It can also learn the time periods when the user uses the lights and suggest optimal settings. In this way, the user can be suggested energy-efficient usage methods.

[0095] The home appliance instruction manual system can further include an emotion management unit that uses the emotion estimation function to monitor the emotions of the user when using the home appliance in real time and provide advice to reduce negative emotions. For example, if the user is feeling stressed, it can suggest settings that will help them relax. Also, if the user is feeling anxious, it can suggest settings that will give them a sense of security. Furthermore, if the user is feeling angry, it can suggest settings that will help them calm down. In this way, the user can receive advice to reduce negative emotions.

[0096] The home appliance instruction manual system further includes a troubleshooting section that can identify the cause of a problem by referring to the user's past usage history. For example, if a washing machine won't start, it can identify a clogged filter based on the past usage history. Also, if a refrigerator won't heat up, it can identify the cause by referring to the past setting history. Furthermore, if an air conditioner won't cool, it can identify the need to clean the filter based on the past usage history. This allows the user to identify the cause of a problem based on past usage history.

[0097] The home appliance instruction manual system may further include a troubleshooting section that provides troubleshooting procedures in the form of videos or animations, making them visually easier to understand. For example, the procedure for cleaning a washing machine filter may be explained using a video. The procedure for changing the temperature setting of a refrigerator may also be explained using an animation. Furthermore, the procedure for cleaning an air conditioner filter may also be explained using an animation. This allows the user to obtain troubleshooting procedures that are visually easy to understand.

[0098] The home appliance instruction manual system can further include a trouble emotion management unit that uses the emotion estimation function to detect the stress the user feels when a problem occurs and provides support to reduce the stress. For example, if the user is confused, a detailed explanation or an encouraging message can be provided. Also, if the user is feeling anxious, support that gives a sense of security can be provided. Furthermore, if the user is feeling angry, support to help the user calm down can be provided. In this way, the user can receive support to reduce stress caused by the problem.

[0099] The home appliance instruction manual system further includes a troubleshooting section, which allows users to share troubleshooting information with other users and provide community-based solutions. For example, a solution to a washing machine problem can be shared with other users to gather opinions from the community. A solution to a refrigerator problem can also be shared with other users to obtain feedback from the community. Furthermore, a solution to an air conditioner problem can also be shared with other users to obtain advice from the community. This allows users to obtain community-based solutions.

[0100] The home appliance instruction manual system further includes a troubleshooting section, which periodically updates the troubleshooting information and adds new problems and solutions. For example, it can add trouble information for new home appliances and provide solutions. It can also add trouble information for the latest technology and provide solutions. It can also add trouble information for new models of home appliances and provide solutions. This allows the user to obtain the latest troubleshooting information.

[0101] The home appliance instruction manual system can further include a trouble-achievement reinforcement unit that uses the emotion estimation function to provide feedback that reinforces the sense of accomplishment the user feels when they solve a problem. For example, a message such as "Great!" can be displayed when the user solves a problem. The system can also monitor the user's emotions in real time and provide feedback that reinforces the sense of accomplishment when the user solves a problem. For example, when the user solves a problem, it can provide feedback such as "Good job!" using animation or audio. It can also display positive messages or effects. This allows the user to receive feedback that reinforces the sense of accomplishment when the user solves a problem.

[0102] The home appliance instruction manual system can further include a feedback learning unit that learns from user feedback and continuously improves the suggestions. For example, when a user provides feedback on air conditioner settings, the AI ​​can improve the suggestions based on that feedback. Also, when a user provides feedback on washing machine settings, the AI ​​can improve the suggestions based on that feedback. Furthermore, when a user provides feedback on refrigerator settings, the AI ​​can improve the suggestions based on that feedback. In this way, users can continuously improve the suggestions based on their feedback.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The AR display unit visualizes the product's operating procedures through the smartphone camera. For example, when a user points the smartphone camera at a home appliance, the operating procedures are displayed on the screen. The AR display unit can also visualize the product's operating procedures using 3D models or animations. Furthermore, the AR display unit can update the product's operating procedures in real time. Step 2: The AI ​​support chatbot responds to the user's questions quickly and accurately. For example, if a user asks within the app, "How do I change the temperature setting on my refrigerator?", the chatbot will explain the specific steps using text and images. The AI ​​support chatbot can also provide answers to the user's questions using videos and animations. Furthermore, the AI ​​support chatbot learns the user's past question history to provide more personalized answers. Step 3: The user habit learning unit learns the user's habits and preferences and provides optimal operation and setting advice. For example, it suggests optimal screen settings and usage methods based on the user's television viewing preferences. The user habit learning unit can also learn the user's daily rhythm and suggest optimal times to use home appliances. Furthermore, the user habit learning unit learns the user's energy consumption patterns and suggests energy-efficient usage methods.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0146] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0156] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0157] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0162] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0163] 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.

[0164] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0165] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0166] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0167] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0169] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An AR display that visualizes product operation procedures through the smartphone camera, An AI support chatbot that answers user questions quickly and accurately, A user habit learning unit that learns the habits and preferences of the user and provides optimal operation and setting advice. A system characterized by:

2. The AR display unit Add audio guidance to the AR display to guide you through the operation procedures both visually and audibly.

2. The system of claim 1.

3. The AR display unit Add interactivity and provide a simulation mode where users can try out procedures.

2. The system of claim 1.

4. The AR display unit Detects stress and anxiety felt by users in real time and provides appropriate support 2. The system of claim 1.

5. The AR display unit Compatible with smart glasses and headsets, allowing users to check operating procedures hands-free 2. The system of claim 1.

6. The AR display unit It can also be used in fields other than home appliances, such as visualizing operation procedures for car maintenance and DIY projects.

2. The system of claim 1.

Images