system

Abstract

The system according to this embodiment aims to enable users to make efficient use of the multi-functionality of home appliances. [Solution] The system according to the embodiment comprises a monitoring unit, an analysis unit, a generation unit, a suggestion unit, and a feedback unit. The monitoring unit monitors the usage status of the home appliance. The analysis unit analyzes the data collected by the monitoring unit and identifies unused functions. The generation unit scans the instruction manual for the home appliance and generates a summary and video explanation. The suggestion unit suggests the summary and video explanation generated by the generation unit to the user. The feedback unit provides feedback to the home appliance manufacturer regarding the information suggested by the suggestion unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that the multifunctionality of home appliances cannot be fully utilized, and it is troublesome for users to read the instruction manuals.

[0005] The system according to the embodiment aims to enable users to utilize the multifunctionality of home appliances without waste.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a monitoring unit, an analysis unit, a generation unit, a suggestion unit, and a feedback unit. The monitoring unit monitors the usage status of home appliances. The analysis unit analyzes the data collected by the monitoring unit and identifies unused functions. The generation unit scans the instruction manual for the home appliance and generates a summary and video explanation. The suggestion unit suggests the summary and video explanation generated by the generation unit to the user. The feedback unit provides feedback to the home appliance manufacturer regarding the information suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to this embodiment can enable users to make efficient use of the multi-functionality of home appliances. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The smart home appliance coach and leader system according to an embodiment of the present invention is a system that monitors the usage status of home appliances, identifies unused functions, and makes suggestions to the user. The smart home appliance coach and leader system monitors the usage status of home appliances and identifies unused functions. Next, it scans the appliance's instruction manual, and the generating AI automatically generates a summary and video explanation. This allows the user to receive easy-to-understand suggestions and guidance through a smartphone app or the appliance's display. For example, the smart home appliance coach and leader system analyzes the usage patterns of home appliances in detail and identifies which functions are not being used. For example, if a specific washing mode of a washing machine is not being used, the smart home appliance coach and leader system notifies the user of that function. Next, it scans the appliance's instruction manual, and the generating AI automatically generates a summary and video explanation. For example, it scans the refrigerator's instruction manual, and the generating AI summarizes the key points and explains them in a video. This saves the user the trouble of reading the instruction manual and allows them to intuitively understand how to use the appliance. Furthermore, the smart home appliance coach and leader system provides easy-to-understand suggestions and guidance to the user. For example, it explains how to use unused functions through videos and audio via a smartphone app or the appliance's display. This allows the user to make full use of all the functions of the appliance and improve their quality of life. This system allows individuals, especially those unfamiliar with technology and the elderly, to easily utilize the multi-functionality of home appliances. Furthermore, by analyzing appliance usage data, it can provide feedback to appliance manufacturers for product development, thereby promoting technological innovation. In this way, the smart home appliance coach and leader system can efficiently utilize all the functions of home appliances, improving the quality of life.

[0029] The smart home appliance coach and leader system according to this embodiment comprises a monitoring unit, an analysis unit, a generation unit, a suggestion unit, and a feedback unit. The monitoring unit monitors the usage status of home appliances. For example, the monitoring unit monitors the frequency of use, usage time, and usage method of home appliances. The monitoring unit analyzes the usage patterns of home appliances in detail and identifies which functions are not being used. For example, if a specific washing mode of a washing machine is not being used, the monitoring unit notifies the user of that function. The monitoring unit can also store the usage history of home appliances over a long period and analyze seasonal usage patterns. For example, the monitoring unit compares winter and summer usage patterns to analyze seasonal usage trends. The analysis unit analyzes the data collected by the monitoring unit and identifies unused functions. For example, the analysis unit analyzes the usage patterns of home appliances in detail and identifies unused functions. The analysis unit can also store home appliance usage data in the cloud and analyze it in comparison with data from other users. For example, the analysis unit compares it with data from other users to analyze home appliance usage patterns. The generation unit scans the instruction manuals for home appliances and generates summaries and video explanations. The generation unit, for example, scans an appliance manual, summarizes the key points, and provides a video explanation. The generation unit can also generate summaries and video explanations in multiple languages ​​when scanning the appliance manual. For example, the generation unit scans the manual and generates summaries in multiple languages. The suggestion unit proposes the summary and video explanation generated by the generation unit to the user. For example, the suggestion unit explains how to use unused functions through videos and audio via a smartphone app or the appliance's display. The suggestion unit can also estimate the user's emotions and adjust the presentation of the suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit will provide more detailed suggestions. The feedback unit provides feedback to the appliance manufacturer based on the information suggested by the suggestion unit. For example, the feedback unit analyzes appliance usage data and provides feedback to the appliance manufacturer for product development. The feedback unit can also collect appliance usage data over a long period and perform trend analysis.For example, the feedback unit collects usage data for home appliances over a long period and analyzes usage trends. As a result, the smart home appliance coach and leader system according to this embodiment can make efficient use of all the functions of home appliances and improve the quality of life.

[0030] The monitoring unit monitors the usage status of home appliances. For example, it monitors the frequency, duration, and method of use of each appliance. Specifically, the monitoring unit uses sensors built into the appliances and internet connectivity to monitor the operating status of each appliance in real time. For example, it records in detail the number of times the refrigerator is opened and closed and the temperature setting, the number of times the washing machine is used and the selected washing mode, and the operating time and set temperature of the air conditioner. This data is sent to a cloud server and stored for a long period of time. The monitoring unit analyzes the usage patterns of the appliances in detail and identifies which functions are not being used. For example, if a specific washing mode of the washing machine is not being used, the monitoring unit will notify the user of that function. Furthermore, the monitoring unit can store the usage history of appliances for a long period of time and analyze seasonal usage patterns. For example, the monitoring unit can compare winter and summer usage patterns and analyze seasonal usage trends. This allows users to learn the optimal way to use appliances according to the season. The monitoring unit also has a function to detect abnormal operation or signs of malfunction of appliances early and notify the user. For example, if the refrigerator temperature rises abnormally or the washing machine vibrates more than usual, the monitoring unit immediately issues a warning to the user. This allows the user to respond quickly and prevent appliance malfunctions. The monitoring unit centrally manages appliance usage data and can also collaborate with other systems and departments. For instance, collected data can be stored on a cloud server and accessed by the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the monitoring unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes data collected by the monitoring department to identify unused functions. Specifically, the analysis department analyzes appliance usage patterns in detail to identify unused functions. For example, if a particular washing mode on a washing machine is rarely used, the analysis department analyzes the reasons and explains the benefits of that function to the user. The analysis department can also store appliance usage data in the cloud and analyze it in comparison to data from other users. For example, the analysis department analyzes appliance usage patterns by comparing them with data from other users. This allows them to identify cases where certain functions are not generally used, or where they tend to be used in specific regions or seasons. Furthermore, the analysis department uses AI to analyze data and provide personalized advice based on user usage patterns. For example, the AI ​​suggests optimal usage methods and ways to utilize unused functions based on the user's past usage data. The analysis department can also use anomaly detection algorithms to detect unusual patterns and abnormal data and issue early warnings. This allows the analysis department to not only understand the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system. In addition, the analysis department can collect user feedback and continuously improve the accuracy and effectiveness of the analysis results. For example, the analysis algorithm is adjusted based on user feedback to provide more accurate analysis results. This allows the analysis unit to provide users with optimal advice and improve the efficiency of using home appliances.

[0032] The generation unit scans appliance manuals and generates summaries and video explanations. Specifically, it scans appliance manuals, summarizes key points, and provides video explanations. For example, it scans a washing machine manual, summarizes how to use the washing mode and maintenance methods, and provides video explanations. When scanning appliance manuals, the generation unit can also generate summaries and video explanations in multiple languages. For example, it scans the manual and generates summaries in multiple languages. This allows it to accommodate users who speak different languages. Furthermore, the generation unit uses AI to analyze the content of the manual and provide information in a format that is easy for users to understand. For example, the AI ​​analyzes the text of the manual, extracts important information, and generates a concise summary. The generation unit can also provide customized explanations according to the user's usage. For example, if a user frequently uses a particular function, it can provide a detailed explanation related to that function. This allows the generation unit to provide users with optimal information and improve the efficiency of using appliances. In addition, the generation unit can save the generated summaries and video explanations to the cloud and share them with other users. This allows users to learn how to use home appliances by leveraging the experiences and knowledge of other users. The generation unit can also collect user feedback and continuously improve the accuracy and effectiveness of the generated summaries and video explanations. For example, it can adjust the content of summaries and video explanations based on user feedback and provide information in a more easily understandable format. In this way, the generation unit can provide users with optimal information and improve the efficiency of using home appliances.

[0033] The suggestion unit proposes summaries and video explanations generated by the generation unit to the user. Specifically, the suggestion unit explains how to use unused functions through videos and audio via smartphone apps or home appliance displays. For example, if a user hasn't used a particular washing mode on their washing machine, the suggestion unit will provide a video explanation of the benefits and usage of that mode and propose it to the user. The suggestion unit can also estimate the user's emotions and adjust the way suggestions are presented based on the estimated emotions. For example, if the suggestion unit is relaxed, it will provide detailed suggestions. Conversely, if the user is busy, it will provide concise suggestions. The suggestion unit can use AI to analyze the user's emotions and make suggestions at the optimal time. For example, the AI ​​can analyze the user's facial expressions and tone of voice to determine whether the user is relaxed. Furthermore, the suggestion unit can customize the content of suggestions based on the user's usage history and feedback. For example, if a user has accepted suggestions previously, it will provide new suggestions based on those suggestions. This allows the suggestion unit to provide the user with the most suitable suggestions and improve the efficiency of using home appliances. The suggestion unit can also collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, based on user feedback, the proposals can be adjusted to make them more effective. This allows the proposal department to provide users with optimal suggestions and improve the efficiency of using home appliances.

[0034] The Feedback Department provides information proposed by the Proposal Department to home appliance manufacturers. Specifically, the Feedback Department analyzes home appliance usage data and provides feedback to home appliance manufacturers for product development. For example, if the Feedback Department analyzes home appliance usage data and finds that a particular function is rarely used, it will propose improvements to that function to the home appliance manufacturer. The Feedback Department can also collect home appliance usage data over a long period and conduct trend analysis. For example, the Feedback Department collects home appliance usage data over a long period and analyzes usage trends. This allows home appliance manufacturers to develop products that meet user needs. Furthermore, the Feedback Department can also collect user feedback and provide it to home appliance manufacturers. For example, based on user feedback, it proposes improvements to the functions and design of home appliances. This allows home appliance manufacturers to develop products that meet user needs. The Feedback Department can strengthen collaboration with home appliance manufacturers and support product development that meets user needs. For example, the Feedback Department holds regular meetings with home appliance manufacturers to share the latest usage data and feedback. This allows home appliance manufacturers to respond quickly to user needs and improve product quality. The Feedback Department can also collect user feedback and continuously improve the accuracy and effectiveness of its proposals. For example, based on user feedback, the system adjusts its suggestions to make them more effective. This allows the feedback unit to provide users with optimal suggestions and improve the efficiency of their home appliance usage.

[0035] The monitoring unit can analyze the usage patterns of home appliances in detail and identify which functions are not being used. For example, the monitoring unit can analyze the frequency of use, duration of use, and method of use of home appliances in detail. The monitoring unit can analyze the usage patterns of home appliances and identify unused functions. For example, if a specific washing mode of a washing machine is not being used, the monitoring unit will notify the user of that function. The monitoring unit can also store the usage history of home appliances over a long period and analyze seasonal usage patterns. For example, the monitoring unit can compare winter and summer usage patterns to analyze seasonal usage trends. By doing so, it can identify unused functions and notify the user by analyzing the usage patterns of home appliances in detail. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the usage patterns of home appliances into AI and have the AI ​​identify unused functions.

[0036] The generation unit can scan appliance manuals, summarize key points, and provide video explanations. For example, the generation unit can scan an appliance manual and summarize the key points. Based on the summarized information, the generation unit can generate video explanations. For example, the generation unit can scan a refrigerator manual, summarize the key points, and provide video explanations. When scanning appliance manuals, the generation unit can also generate summaries and video explanations in multiple languages. For example, the generation unit can scan a manual and generate summaries in multiple languages. This allows users to intuitively understand how to use appliances by scanning appliance manuals, summarizing key points, and providing video explanations. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input an appliance manual into a generation AI and have the generation AI perform the generation of summaries and video explanations.

[0037] The suggestion unit can explain how to use unused functions through videos and audio via smartphone apps or home appliance displays. For example, the suggestion unit can explain how to use unused functions through a smartphone app using a video. The suggestion unit can also explain how to use unused functions through audio via home appliance displays. For example, the suggestion unit can explain how to use unused functions through home appliance displays using a video. The suggestion unit can also estimate the user's emotions and adjust the way the suggestions are presented based on the estimated emotions. For example, if the user is relaxed, the suggestion unit will provide more detailed suggestions. This allows users to make full use of all the functions of their home appliances by explaining how to use unused functions through videos and audio via smartphone apps or home appliance displays. Some or all of the above processing in the suggestion unit may be performed using, for example, generative AI, or without generative AI. For example, the suggestion unit can input instructions on how to use unused functions into a generative AI and have the generative AI generate videos and audio.

[0038] The feedback unit can analyze appliance usage data and provide feedback to appliance manufacturers for product development. For example, the feedback unit can analyze appliance usage data and propose improvements to appliance manufacturers. The feedback unit can also analyze appliance usage patterns and provide feedback to appliance manufacturers for product development. For example, the feedback unit can propose improvements to appliance manufacturers based on appliance usage data. The feedback unit can also collect appliance usage data over a long period and perform trend analysis. For example, the feedback unit can collect appliance usage data over a long period and analyze usage trends. This allows for the promotion of technological innovation by analyzing appliance usage data and providing feedback to appliance manufacturers for product development. Some or all of the above-described processes in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit can input appliance usage data into a generative AI and have the generative AI generate feedback.

[0039] The monitoring unit can monitor the usage patterns of home appliances in real time and detect abnormal usage conditions. For example, if a home appliance operates differently from its normal usage pattern, the monitoring unit can detect the abnormality and notify the user. The monitoring unit can also detect if a home appliance has not been used for a long period of time as an abnormality and prompt the user to check. The monitoring unit can also detect if a home appliance is being used excessively as an abnormality and warn of a decrease in energy efficiency. This enables a rapid response by monitoring the usage patterns of home appliances in real time and detecting abnormal usage conditions. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input the usage patterns of home appliances into a generative AI and have the generative AI perform the detection of abnormal usage conditions.

[0040] The monitoring unit can store the usage history of home appliances over a long period and analyze seasonal usage patterns. For example, the monitoring unit can compare winter and summer usage patterns to analyze seasonal usage trends. The monitoring unit can also analyze changes in usage frequency in a specific season based on the annual usage history. The monitoring unit can also analyze seasonal energy consumption and suggest efficient usage methods. This allows for the suggestion of efficient usage methods by storing the usage history of home appliances over a long period and analyzing seasonal usage patterns. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input the usage history of home appliances into a generative AI and have the generative AI perform an analysis of seasonal usage patterns.

[0041] The monitoring unit can monitor the usage of home appliances, collaborate with other smart devices, and grasp the overall usage situation within the home. For example, the monitoring unit can collaborate with smart lighting to adjust the brightness of the lights according to the usage of the appliances. The monitoring unit can also collaborate with a smart thermostat to adjust the room temperature according to the usage of the appliances. The monitoring unit can also collaborate with a smart security system to adjust security settings according to the usage of the appliances. In this way, by collaborating with other smart devices, the overall usage situation within the home can be grasped. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the monitoring unit can input the usage status of home appliances into a generative AI and have the generative AI perform the collaboration with other smart devices.

[0042] The monitoring unit can improve energy efficiency by simultaneously recording energy consumption when monitoring the usage of home appliances. For example, the monitoring unit can record energy consumption according to the usage of home appliances and suggest efficient usage methods. The monitoring unit can also monitor energy consumption in real time and warn of excessive consumption. The monitoring unit can also make suggestions to improve energy efficiency based on the usage history of home appliances. In this way, energy efficiency can be improved by simultaneously recording energy consumption. Some or all of the above processing in the monitoring unit may be performed using, for example, a generating AI, or without a generating AI. For example, the monitoring unit can input the usage status of home appliances and energy consumption into a generating AI and have the generating AI execute suggestions to improve energy efficiency.

[0043] The analysis unit can analyze appliance usage patterns in detail and propose optimal usage methods. For example, the analysis unit can propose optimal usage methods based on appliance usage history. The analysis unit can also analyze appliance usage patterns and propose energy-efficient usage methods. The analysis unit can also propose optimal usage methods tailored to the user's lifestyle based on appliance usage data. In this way, by analyzing appliance usage patterns in detail, it is possible to propose optimal usage methods. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input appliance usage patterns into a generative AI and have the generative AI propose optimal usage methods.

[0044] The analysis unit can store appliance usage data in the cloud and analyze it by comparing it with data from other users. For example, the analysis unit can analyze appliance usage patterns by comparing them with data from other users. The analysis unit can also analyze appliance usage trends based on the data stored in the cloud. The analysis unit can also suggest optimal usage methods by comparing them with data from other users. This allows for the suggestion of optimal usage methods by storing appliance usage data in the cloud and analyzing it by comparing it with data from other users. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input appliance usage data into generative AI and have the generative AI perform an analysis by comparing it with data from other users.

[0045] The analysis unit can perform analysis of appliance usage patterns while taking into account the user's lifestyle data. For example, the analysis unit can analyze appliance usage patterns based on the user's lifestyle data. The analysis unit can also propose the optimal usage method tailored to the user's daily rhythm. The analysis unit can also propose energy-efficient usage methods while taking the user's lifestyle data into consideration. In this way, by performing analysis while taking the user's lifestyle data into consideration, the optimal usage method can be proposed. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the user's lifestyle data into a generative AI and have the generative AI perform the analysis of usage patterns.

[0046] The analysis unit can perform its analysis of appliance usage patterns while taking appliance maintenance information into consideration. For example, the analysis unit can analyze usage patterns based on appliance maintenance information. The analysis unit can also propose optimal usage methods while considering maintenance information. The analysis unit can also propose energy-efficient usage methods based on appliance maintenance information. In this way, by performing the analysis while considering appliance maintenance information, it is possible to propose optimal usage methods. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input appliance maintenance information into a generative AI and have the generative AI perform the usage pattern analysis.

[0047] The generation unit can add a function to highlight important points when scanning appliance instruction manuals. For example, the generation unit can automatically detect and highlight important points in the instruction manual. The generation unit can also color-code important points to improve visibility. The generation unit can also display important points as pop-ups to draw the user's attention. This ensures that users understand the important parts of the instruction manual without missing anything by highlighting the important points. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input an appliance instruction manual into a generation AI and have the generation AI perform the highlighting of important points.

[0048] The generation unit can generate summaries and video explanations in multiple languages ​​when scanning appliance instruction manuals. For example, the generation unit scans the instruction manual and generates summaries in multiple languages. The generation unit can also generate video explanations in multiple languages ​​and provide them to the user. The generation unit can also provide summaries and video explanations in the appropriate language based on the user's language settings. This allows the unit to accommodate users who speak different languages ​​by generating summaries and video explanations in multiple languages. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input an appliance instruction manual into a generation AI and have the generation AI generate summaries and video explanations in multiple languages.

[0049] The generation unit can generate customized summaries and video explanations by scanning home appliance instruction manuals, taking into account the user's past usage history. For example, the generation unit can generate a customized summary based on the user's past usage history. The generation unit can also generate the most optimal video explanation by considering the user's usage patterns. The generation unit can also analyze the user's past usage history and generate the most efficient summary and video explanation. This allows the generation unit to provide the user with the most suitable explanation by generating customized summaries and video explanations that take into account the user's past usage history. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's past usage history into a generation AI and have the generation AI perform the generation of customized summaries and video explanations.

[0050] The generation unit can automatically add relevant FAQs and troubleshooting information when scanning appliance instruction manuals. For example, the generation unit can scan the manual and automatically add relevant FAQs. The generation unit can also automatically add troubleshooting information and provide it to the user. The generation unit can also automatically add relevant information based on the content of the manual. This allows users to quickly obtain information that helps them solve problems by automatically adding relevant FAQs and troubleshooting information. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the appliance instruction manual into a generation AI and have the generation AI perform the addition of relevant FAQs and troubleshooting information.

[0051] The suggestion unit can make optimal suggestions by referring to the user's past usage history when making suggestions. For example, the suggestion unit can make optimal suggestions based on the user's past usage history. The suggestion unit can also make optimal suggestions by considering the user's usage patterns. The suggestion unit can also analyze the user's past usage history and make the most efficient suggestions. In this way, by referring to the user's past usage history and making optimal suggestions, the suggestion unit can provide the user with the most suitable suggestions. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's past usage history into a generative AI and have the generative AI generate optimal suggestions.

[0052] The suggestion unit can make suggestions at the optimal time based on the usage status of home appliances. For example, the suggestion unit can make suggestions at the optimal time based on the usage status of home appliances. The suggestion unit can also analyze the usage patterns of home appliances and make suggestions at the optimal time. The suggestion unit can also make suggestions at the most efficient time based on the usage data of home appliances. As a result, by making suggestions at the optimal time based on the usage status of home appliances, suggestions can be provided to the user at the optimal time. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the usage status of home appliances into a generative AI and have the generative AI execute a suggestion at the optimal time.

[0053] The proposal unit can make customized proposals by taking into account the user's lifestyle data. For example, the proposal unit can make customized proposals based on the user's lifestyle data. The proposal unit can also make optimal proposals that match the user's daily rhythm. The proposal unit can also make energy-efficient proposals by taking into account the user's lifestyle data. In this way, by making customized proposals that take into account the user's lifestyle data, the optimal proposal can be provided to the user. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the proposal unit can input the user's lifestyle data into a generative AI and have the generative AI execute the generation of customized proposals.

[0054] The proposal unit can make proposals while considering appliance maintenance information. For example, the proposal unit can make the optimal proposal based on appliance maintenance information. The proposal unit can also make the optimal proposal by considering maintenance information. The proposal unit can also make energy-efficient proposals based on appliance maintenance information. In this way, by making proposals while considering appliance maintenance information, the optimal proposal can be provided to the user. Some or all of the above processing in the proposal unit may be performed using, for example, a generation AI, or without a generation AI. For example, the proposal unit can input appliance maintenance information into a generation AI and have the generation AI execute the generation of the optimal proposal.

[0055] The feedback unit can analyze appliance usage data and propose specific improvements to appliance manufacturers. For example, the feedback unit can propose specific improvements based on appliance usage data. The feedback unit can also analyze appliance usage patterns and propose improvements to appliance manufacturers. The feedback unit can also propose improvements to improve energy efficiency based on appliance usage data. In this way, technological innovation can be promoted by analyzing appliance usage data and proposing specific improvements to appliance manufacturers. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit can input appliance usage data into a generative AI and have the generative AI execute the proposal of specific improvements.

[0056] The feedback unit can collect appliance usage data over a long period and perform trend analysis. For example, the feedback unit can collect appliance usage data over a long period and analyze usage trends. Based on the trend analysis, the feedback unit can also predict appliance usage patterns. Based on the long-term data, the feedback unit can also make suggestions to improve energy efficiency. In this way, by collecting appliance usage data over a long period and performing trend analysis, usage trends can be understood and optimal suggestions can be made. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit can input appliance usage data into a generative AI and have the generative AI perform trend analysis.

[0057] The feedback unit can provide comprehensive feedback by considering data from other smart devices when analyzing home appliance usage data. For example, the feedback unit can provide comprehensive feedback based on data from other smart devices. The feedback unit can also compare home appliance usage data with data from other devices to provide optimal feedback. The feedback unit can also consider data from other smart devices to provide feedback aimed at improving energy efficiency. By providing comprehensive feedback that also considers data from other smart devices, the feedback unit can provide the user with the most optimal feedback. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit can input home appliance usage data and data from other smart devices into a generative AI and have the generative AI generate comprehensive feedback.

[0058] The feedback unit can provide feedback by considering energy consumption and maintenance information when analyzing appliance usage data. For example, the feedback unit can suggest efficient usage methods based on energy consumption. The feedback unit can also suggest optimal usage methods by considering maintenance information. The feedback unit can also provide feedback to improve energy efficiency based on energy consumption and maintenance information. In this way, by providing feedback while considering energy consumption and maintenance information, the feedback unit can provide the most optimal feedback for the user. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit can input appliance usage data, energy consumption, and maintenance information into a generative AI and have the generative AI generate the feedback.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The smart home appliance coach and leader system can acquire user health data and suggest how to use home appliances according to their health condition. For example, it can acquire user sleep data and suggest how to use appliances to improve sleep quality. It can also acquire user exercise data and suggest how to use appliances suitable for post-exercise recovery. Furthermore, it can acquire user dietary data and suggest how to use appliances to support a healthy diet. In this way, by suggesting how to use home appliances according to the user's health condition, it can improve their quality of life.

[0061] The smart home appliance coach and leader system can monitor the usage of home appliances and display energy consumption in real time. For example, it can monitor the usage of a refrigerator and display energy consumption in real time. It can also monitor the usage of a washing machine and display energy consumption in real time. Furthermore, it can monitor the usage of an air conditioner and display energy consumption in real time. This allows users to understand the energy consumption of their appliances in real time and choose efficient usage methods.

[0062] The smart home appliance coach and leader system monitors appliance usage and integrates with other smart devices to provide a comprehensive understanding of household usage. For example, it can integrate with smart lighting to adjust brightness based on appliance usage. It can also integrate with smart thermostats to adjust room temperature based on appliance usage. Furthermore, it can integrate with smart security systems to adjust security settings based on appliance usage. This integration with other smart devices allows for a comprehensive understanding of household usage.

[0063] The smart home appliance coach and leader system can monitor the usage of home appliances, detect abnormal usage, and notify the user. For example, it can monitor the usage of a refrigerator and notify the user if it detects abnormal temperature changes. It can also monitor the usage of a washing machine and notify the user if it detects abnormal vibrations. Furthermore, it can monitor the usage of an air conditioner and notify the user if it detects abnormal operation. This allows for the detection of abnormal usage of home appliances and prompt response.

[0064] The smart home appliance coach and leader system can monitor the usage of home appliances and display energy consumption in real time. For example, it can monitor the usage of a refrigerator and display energy consumption in real time. It can also monitor the usage of a washing machine and display energy consumption in real time. Furthermore, it can monitor the usage of an air conditioner and display energy consumption in real time. This allows users to understand the energy consumption of their appliances in real time and choose efficient usage methods.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The monitoring unit monitors the usage status of home appliances. For example, it monitors the frequency of use, usage time, and usage method of home appliances, and analyzes the usage patterns of home appliances in detail. This makes it possible to identify which functions are not being used. The monitoring unit can also store the usage history of home appliances over a long period of time and analyze seasonal usage patterns. Step 2: The analysis unit analyzes the data collected by the monitoring unit and identifies unused functions. For example, it analyzes the usage patterns of home appliances in detail to identify unused functions. The analysis unit can also store the home appliance usage data in the cloud and analyze it by comparing it with data from other users. Step 3: The generation unit scans the appliance manual and generates a summary and video explanation. For example, it scans the appliance manual, summarizes the key points, and provides a video explanation. The generation unit can also generate summaries and video explanations in multiple languages. Step 4: The suggestion unit proposes the summary and video explanation generated by the generation unit to the user. For example, it may explain how to use unused functions through videos and audio via a smartphone app or home appliance display. The suggestion unit can also estimate the user's emotions and adjust the way the suggestion is presented based on the estimated user emotions. Step 5: The Feedback Department provides feedback to the home appliance manufacturers based on the information proposed by the Proposal Department. For example, it analyzes home appliance usage data and provides feedback to home appliance manufacturers for product development. The Feedback Department can also collect home appliance usage data over a long period and conduct trend analysis.

[0067] (Example of form 2) The smart home appliance coach and leader system according to an embodiment of the present invention is a system that monitors the usage status of home appliances, identifies unused functions, and makes suggestions to the user. The smart home appliance coach and leader system monitors the usage status of home appliances and identifies unused functions. Next, it scans the appliance's instruction manual, and the generating AI automatically generates a summary and video explanation. This allows the user to receive easy-to-understand suggestions and guidance through a smartphone app or the appliance's display. For example, the smart home appliance coach and leader system analyzes the usage patterns of home appliances in detail and identifies which functions are not being used. For example, if a specific washing mode of a washing machine is not being used, the smart home appliance coach and leader system notifies the user of that function. Next, it scans the appliance's instruction manual, and the generating AI automatically generates a summary and video explanation. For example, it scans the refrigerator's instruction manual, and the generating AI summarizes the key points and explains them in a video. This saves the user the trouble of reading the instruction manual and allows them to intuitively understand how to use the appliance. Furthermore, the smart home appliance coach and leader system provides easy-to-understand suggestions and guidance to the user. For example, it explains how to use unused functions through videos and audio via a smartphone app or the appliance's display. This allows the user to make full use of all the functions of the appliance and improve their quality of life. This system allows individuals, especially those unfamiliar with technology and the elderly, to easily utilize the multi-functionality of home appliances. Furthermore, by analyzing appliance usage data, it can provide feedback to appliance manufacturers for product development, thereby promoting technological innovation. In this way, the smart home appliance coach and leader system can efficiently utilize all the functions of home appliances, improving the quality of life.

[0068] The smart home appliance coach and leader system according to this embodiment comprises a monitoring unit, an analysis unit, a generation unit, a suggestion unit, and a feedback unit. The monitoring unit monitors the usage status of home appliances. For example, the monitoring unit monitors the frequency of use, usage time, and usage method of home appliances. The monitoring unit analyzes the usage patterns of home appliances in detail and identifies which functions are not being used. For example, if a specific washing mode of a washing machine is not being used, the monitoring unit notifies the user of that function. The monitoring unit can also store the usage history of home appliances over a long period and analyze seasonal usage patterns. For example, the monitoring unit compares winter and summer usage patterns to analyze seasonal usage trends. The analysis unit analyzes the data collected by the monitoring unit and identifies unused functions. For example, the analysis unit analyzes the usage patterns of home appliances in detail and identifies unused functions. The analysis unit can also store home appliance usage data in the cloud and analyze it in comparison with data from other users. For example, the analysis unit compares it with data from other users to analyze home appliance usage patterns. The generation unit scans the instruction manuals for home appliances and generates summaries and video explanations. The generation unit, for example, scans an appliance manual, summarizes the key points, and provides a video explanation. The generation unit can also generate summaries and video explanations in multiple languages ​​when scanning the appliance manual. For example, the generation unit scans the manual and generates summaries in multiple languages. The suggestion unit proposes the summary and video explanation generated by the generation unit to the user. For example, the suggestion unit explains how to use unused functions through videos and audio via a smartphone app or the appliance's display. The suggestion unit can also estimate the user's emotions and adjust the presentation of the suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit will provide more detailed suggestions. The feedback unit provides feedback to the appliance manufacturer based on the information suggested by the suggestion unit. For example, the feedback unit analyzes appliance usage data and provides feedback to the appliance manufacturer for product development. The feedback unit can also collect appliance usage data over a long period and perform trend analysis.For example, the feedback unit collects usage data for home appliances over a long period and analyzes usage trends. As a result, the smart home appliance coach and leader system according to this embodiment can make efficient use of all the functions of home appliances and improve the quality of life.

[0069] The monitoring unit monitors the usage status of home appliances. For example, it monitors the frequency, duration, and method of use of each appliance. Specifically, the monitoring unit uses sensors built into the appliances and internet connectivity to monitor the operating status of each appliance in real time. For example, it records in detail the number of times the refrigerator is opened and closed and the temperature setting, the number of times the washing machine is used and the selected washing mode, and the operating time and set temperature of the air conditioner. This data is sent to a cloud server and stored for a long period of time. The monitoring unit analyzes the usage patterns of the appliances in detail and identifies which functions are not being used. For example, if a specific washing mode of the washing machine is not being used, the monitoring unit will notify the user of that function. Furthermore, the monitoring unit can store the usage history of appliances for a long period of time and analyze seasonal usage patterns. For example, the monitoring unit can compare winter and summer usage patterns and analyze seasonal usage trends. This allows users to learn the optimal way to use appliances according to the season. The monitoring unit also has a function to detect abnormal operation or signs of malfunction of appliances early and notify the user. For example, if the refrigerator temperature rises abnormally or the washing machine vibrates more than usual, the monitoring unit immediately issues a warning to the user. This allows the user to respond quickly and prevent appliance malfunctions. The monitoring unit centrally manages appliance usage data and can also collaborate with other systems and departments. For instance, collected data can be stored on a cloud server and accessed by the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the monitoring unit to collect data efficiently and effectively, improving the overall system performance.

[0070] The analysis department analyzes data collected by the monitoring department to identify unused functions. Specifically, the analysis department analyzes appliance usage patterns in detail to identify unused functions. For example, if a particular washing mode on a washing machine is rarely used, the analysis department analyzes the reasons and explains the benefits of that function to the user. The analysis department can also store appliance usage data in the cloud and analyze it in comparison to data from other users. For example, the analysis department analyzes appliance usage patterns by comparing them with data from other users. This allows them to identify cases where certain functions are not generally used, or where they tend to be used in specific regions or seasons. Furthermore, the analysis department uses AI to analyze data and provide personalized advice based on user usage patterns. For example, the AI ​​suggests optimal usage methods and ways to utilize unused functions based on the user's past usage data. The analysis department can also use anomaly detection algorithms to detect unusual patterns and abnormal data and issue early warnings. This allows the analysis department to not only understand the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system. In addition, the analysis department can collect user feedback and continuously improve the accuracy and effectiveness of the analysis results. For example, the analysis algorithm is adjusted based on user feedback to provide more accurate analysis results. This allows the analysis unit to provide users with optimal advice and improve the efficiency of using home appliances.

[0071] The generation unit scans appliance manuals and generates summaries and video explanations. Specifically, it scans appliance manuals, summarizes key points, and provides video explanations. For example, it scans a washing machine manual, summarizes how to use the washing mode and maintenance methods, and provides video explanations. When scanning appliance manuals, the generation unit can also generate summaries and video explanations in multiple languages. For example, it scans the manual and generates summaries in multiple languages. This allows it to accommodate users who speak different languages. Furthermore, the generation unit uses AI to analyze the content of the manual and provide information in a format that is easy for users to understand. For example, the AI ​​analyzes the text of the manual, extracts important information, and generates a concise summary. The generation unit can also provide customized explanations according to the user's usage. For example, if a user frequently uses a particular function, it can provide a detailed explanation related to that function. This allows the generation unit to provide users with optimal information and improve the efficiency of using appliances. In addition, the generation unit can save the generated summaries and video explanations to the cloud and share them with other users. This allows users to learn how to use home appliances by leveraging the experiences and knowledge of other users. The generation unit can also collect user feedback and continuously improve the accuracy and effectiveness of the generated summaries and video explanations. For example, it can adjust the content of summaries and video explanations based on user feedback and provide information in a more easily understandable format. In this way, the generation unit can provide users with optimal information and improve the efficiency of using home appliances.

[0072] The suggestion unit proposes summaries and video explanations generated by the generation unit to the user. Specifically, the suggestion unit explains how to use unused functions through videos and audio via smartphone apps or home appliance displays. For example, if a user hasn't used a particular washing mode on their washing machine, the suggestion unit will provide a video explanation of the benefits and usage of that mode and propose it to the user. The suggestion unit can also estimate the user's emotions and adjust the way suggestions are presented based on the estimated emotions. For example, if the suggestion unit is relaxed, it will provide detailed suggestions. Conversely, if the user is busy, it will provide concise suggestions. The suggestion unit can use AI to analyze the user's emotions and make suggestions at the optimal time. For example, the AI ​​can analyze the user's facial expressions and tone of voice to determine whether the user is relaxed. Furthermore, the suggestion unit can customize the content of suggestions based on the user's usage history and feedback. For example, if a user has accepted suggestions previously, it will provide new suggestions based on those suggestions. This allows the suggestion unit to provide the user with the most suitable suggestions and improve the efficiency of using home appliances. The suggestion unit can also collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, based on user feedback, the proposals can be adjusted to make them more effective. This allows the proposal department to provide users with optimal suggestions and improve the efficiency of using home appliances.

[0073] The Feedback Department provides information proposed by the Proposal Department to home appliance manufacturers. Specifically, the Feedback Department analyzes home appliance usage data and provides feedback to home appliance manufacturers for product development. For example, if the Feedback Department analyzes home appliance usage data and finds that a particular function is rarely used, it will propose improvements to that function to the home appliance manufacturer. The Feedback Department can also collect home appliance usage data over a long period and conduct trend analysis. For example, the Feedback Department collects home appliance usage data over a long period and analyzes usage trends. This allows home appliance manufacturers to develop products that meet user needs. Furthermore, the Feedback Department can also collect user feedback and provide it to home appliance manufacturers. For example, based on user feedback, it proposes improvements to the functions and design of home appliances. This allows home appliance manufacturers to develop products that meet user needs. The Feedback Department can strengthen collaboration with home appliance manufacturers and support product development that meets user needs. For example, the Feedback Department holds regular meetings with home appliance manufacturers to share the latest usage data and feedback. This allows home appliance manufacturers to respond quickly to user needs and improve product quality. The Feedback Department can also collect user feedback and continuously improve the accuracy and effectiveness of its proposals. For example, based on user feedback, the system adjusts its suggestions to make them more effective. This allows the feedback unit to provide users with optimal suggestions and improve the efficiency of their home appliance usage.

[0074] The monitoring unit can analyze the usage patterns of home appliances in detail and identify which functions are not being used. For example, the monitoring unit can analyze the frequency of use, duration of use, and method of use of home appliances in detail. The monitoring unit can analyze the usage patterns of home appliances and identify unused functions. For example, if a specific washing mode of a washing machine is not being used, the monitoring unit will notify the user of that function. The monitoring unit can also store the usage history of home appliances over a long period and analyze seasonal usage patterns. For example, the monitoring unit can compare winter and summer usage patterns to analyze seasonal usage trends. By doing so, it can identify unused functions and notify the user by analyzing the usage patterns of home appliances in detail. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the usage patterns of home appliances into AI and have the AI ​​identify unused functions.

[0075] The generation unit can scan appliance manuals, summarize key points, and provide video explanations. For example, the generation unit can scan an appliance manual and summarize the key points. Based on the summarized information, the generation unit can generate video explanations. For example, the generation unit can scan a refrigerator manual, summarize the key points, and provide video explanations. When scanning appliance manuals, the generation unit can also generate summaries and video explanations in multiple languages. For example, the generation unit can scan a manual and generate summaries in multiple languages. This allows users to intuitively understand how to use appliances by scanning appliance manuals, summarizing key points, and providing video explanations. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input an appliance manual into a generation AI and have the generation AI perform the generation of summaries and video explanations.

[0076] The suggestion unit can explain how to use unused functions through videos and audio via smartphone apps or home appliance displays. For example, the suggestion unit can explain how to use unused functions through a smartphone app using a video. The suggestion unit can also explain how to use unused functions through audio via home appliance displays. For example, the suggestion unit can explain how to use unused functions through home appliance displays using a video. The suggestion unit can also estimate the user's emotions and adjust the way the suggestions are presented based on the estimated emotions. For example, if the user is relaxed, the suggestion unit will provide more detailed suggestions. This allows users to make full use of all the functions of their home appliances by explaining how to use unused functions through videos and audio via smartphone apps or home appliance displays. Some or all of the above processing in the suggestion unit may be performed using, for example, generative AI, or without generative AI. For example, the suggestion unit can input instructions on how to use unused functions into a generative AI and have the generative AI generate videos and audio.

[0077] The feedback unit can analyze appliance usage data and provide feedback to appliance manufacturers for product development. For example, the feedback unit can analyze appliance usage data and propose improvements to appliance manufacturers. The feedback unit can also analyze appliance usage patterns and provide feedback to appliance manufacturers for product development. For example, the feedback unit can propose improvements to appliance manufacturers based on appliance usage data. The feedback unit can also collect appliance usage data over a long period and perform trend analysis. For example, the feedback unit can collect appliance usage data over a long period and analyze usage trends. This allows for the promotion of technological innovation by analyzing appliance usage data and providing feedback to appliance manufacturers for product development. Some or all of the above-described processes in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit can input appliance usage data into a generative AI and have the generative AI generate feedback.

[0078] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency of appliance usage based on the estimated emotions. For example, if the user is stressed, the monitoring unit can reduce the monitoring frequency and provide less frequent notifications. If the user is relaxed, the monitoring unit can increase the monitoring frequency and provide more detailed usage information. If the user is in a hurry, the monitoring unit can minimize the monitoring frequency and provide only important notifications. This allows for optimal monitoring for the user by adjusting the monitoring frequency based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI adjust the monitoring frequency.

[0079] The monitoring unit can monitor the usage patterns of home appliances in real time and detect abnormal usage conditions. For example, if a home appliance operates differently from its normal usage pattern, the monitoring unit can detect the abnormality and notify the user. The monitoring unit can also detect if a home appliance has not been used for a long period of time as an abnormality and prompt the user to check. The monitoring unit can also detect if a home appliance is being used excessively as an abnormality and warn of a decrease in energy efficiency. This enables a rapid response by monitoring the usage patterns of home appliances in real time and detecting abnormal usage conditions. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input the usage patterns of home appliances into a generative AI and have the generative AI perform the detection of abnormal usage conditions.

[0080] The monitoring unit can store the usage history of home appliances over a long period and analyze seasonal usage patterns. For example, the monitoring unit can compare winter and summer usage patterns to analyze seasonal usage trends. The monitoring unit can also analyze changes in usage frequency in a specific season based on the annual usage history. The monitoring unit can also analyze seasonal energy consumption and suggest efficient usage methods. This allows for the suggestion of efficient usage methods by storing the usage history of home appliances over a long period and analyzing seasonal usage patterns. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input the usage history of home appliances into a generative AI and have the generative AI perform an analysis of seasonal usage patterns.

[0081] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated user emotions. For example, if the user is tense, the monitoring unit can provide a simple and highly visible display method. If the user is relaxed, the monitoring unit can also provide a display method that includes detailed information. If the user is in a hurry, the monitoring unit can also provide a display method that gets straight to the point. By adjusting the display method of the monitoring results based on the user's emotions, it is possible to provide the optimal display for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the monitoring results.

[0082] The monitoring unit can monitor the usage of home appliances, collaborate with other smart devices, and grasp the overall usage situation within the home. For example, the monitoring unit can collaborate with smart lighting to adjust the brightness of the lights according to the usage of the appliances. The monitoring unit can also collaborate with a smart thermostat to adjust the room temperature according to the usage of the appliances. The monitoring unit can also collaborate with a smart security system to adjust security settings according to the usage of the appliances. In this way, by collaborating with other smart devices, the overall usage situation within the home can be grasped. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the monitoring unit can input the usage status of home appliances into a generative AI and have the generative AI perform the collaboration with other smart devices.

[0083] The monitoring unit can improve energy efficiency by simultaneously recording energy consumption when monitoring the usage of home appliances. For example, the monitoring unit can record energy consumption according to the usage of home appliances and suggest efficient usage methods. The monitoring unit can also monitor energy consumption in real time and warn of excessive consumption. The monitoring unit can also make suggestions to improve energy efficiency based on the usage history of home appliances. In this way, energy efficiency can be improved by simultaneously recording energy consumption. Some or all of the above processing in the monitoring unit may be performed using, for example, a generating AI, or without a generating AI. For example, the monitoring unit can input the usage status of home appliances and energy consumption into a generating AI and have the generating AI execute suggestions to improve energy efficiency.

[0084] The analysis unit can estimate the user's emotions and adjust the timing of notifications for unused features based on the estimated emotions. For example, if the user is relaxed, the analysis unit will frequently notify the user of unused features. If the user is stressed, the analysis unit may also reduce the frequency of notifications for unused features. If the user is in a hurry, the analysis unit may also minimize notifications for unused features. This allows notifications to be delivered at the optimal time for the user by adjusting the timing of notifications for unused features based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the notification timing.

[0085] The analysis unit can analyze appliance usage patterns in detail and propose optimal usage methods. For example, the analysis unit can propose optimal usage methods based on appliance usage history. The analysis unit can also analyze appliance usage patterns and propose energy-efficient usage methods. The analysis unit can also propose optimal usage methods tailored to the user's lifestyle based on appliance usage data. In this way, by analyzing appliance usage patterns in detail, it is possible to propose optimal usage methods. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input appliance usage patterns into a generative AI and have the generative AI propose optimal usage methods.

[0086] The analysis unit can store appliance usage data in the cloud and analyze it by comparing it with data from other users. For example, the analysis unit can analyze appliance usage patterns by comparing them with data from other users. The analysis unit can also analyze appliance usage trends based on the data stored in the cloud. The analysis unit can also suggest optimal usage methods by comparing them with data from other users. This allows for the suggestion of optimal usage methods by storing appliance usage data in the cloud and analyzing it by comparing it with data from other users. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input appliance usage data into generative AI and have the generative AI perform an analysis by comparing it with data from other users.

[0087] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. By adjusting the display method of the analysis results based on the user's emotions, the optimal display for the user can be achieved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the analysis results.

[0088] The analysis unit can perform analysis of appliance usage patterns while taking into account the user's lifestyle data. For example, the analysis unit can analyze appliance usage patterns based on the user's lifestyle data. The analysis unit can also propose the optimal usage method tailored to the user's daily rhythm. The analysis unit can also propose energy-efficient usage methods while taking the user's lifestyle data into consideration. In this way, by performing analysis while taking the user's lifestyle data into consideration, the optimal usage method can be proposed. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the user's lifestyle data into a generative AI and have the generative AI perform the analysis of usage patterns.

[0089] The analysis unit can perform its analysis of appliance usage patterns while taking appliance maintenance information into consideration. For example, the analysis unit can analyze usage patterns based on appliance maintenance information. The analysis unit can also propose optimal usage methods while considering maintenance information. The analysis unit can also propose energy-efficient usage methods based on appliance maintenance information. In this way, by performing the analysis while considering appliance maintenance information, it is possible to propose optimal usage methods. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input appliance maintenance information into a generative AI and have the generative AI perform the usage pattern analysis.

[0090] The generation unit can estimate the user's emotions and adjust the presentation of the summary and video explanation based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a video that progresses at a leisurely pace. If the user is in a hurry, the generation unit can also generate a video that emphasizes the shortest route. If the user is excited, the generation unit can also generate a video with visually stimulating effects. This allows for optimal presentation for the user by adjusting the presentation of the summary and video explanation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the presentation of the summary and video explanation.

[0091] The generation unit can add a function to highlight important points when scanning appliance instruction manuals. For example, the generation unit can automatically detect and highlight important points in the instruction manual. The generation unit can also color-code important points to improve visibility. The generation unit can also display important points as pop-ups to draw the user's attention. This ensures that users understand the important parts of the instruction manual without missing anything by highlighting the important points. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input an appliance instruction manual into a generation AI and have the generation AI perform the highlighting of important points.

[0092] The generation unit can generate summaries and video explanations in multiple languages ​​when scanning appliance instruction manuals. For example, the generation unit scans the instruction manual and generates summaries in multiple languages. The generation unit can also generate video explanations in multiple languages ​​and provide them to the user. The generation unit can also provide summaries and video explanations in the appropriate language based on the user's language settings. This allows the unit to accommodate users who speak different languages ​​by generating summaries and video explanations in multiple languages. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input an appliance instruction manual into a generation AI and have the generation AI generate summaries and video explanations in multiple languages.

[0093] The generation unit can estimate the user's emotions and adjust the length of the summary / video explanation based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise video. If the user is relaxed, the generation unit can also generate a longer video with detailed explanations. If the user is excited, the generation unit can also generate a video with visually stimulating effects. By adjusting the length of the summary / video explanation based on the user's emotions, the system can provide an explanation of the optimal length for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the summary / video explanation.

[0094] The generation unit can generate customized summaries and video explanations by scanning home appliance instruction manuals, taking into account the user's past usage history. For example, the generation unit can generate a customized summary based on the user's past usage history. The generation unit can also generate the most optimal video explanation by considering the user's usage patterns. The generation unit can also analyze the user's past usage history and generate the most efficient summary and video explanation. This allows the generation unit to provide the user with the most suitable explanation by generating customized summaries and video explanations that take into account the user's past usage history. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's past usage history into a generation AI and have the generation AI perform the generation of customized summaries and video explanations.

[0095] The generation unit can automatically add relevant FAQs and troubleshooting information when scanning appliance instruction manuals. For example, the generation unit can scan the manual and automatically add relevant FAQs. The generation unit can also automatically add troubleshooting information and provide it to the user. The generation unit can also automatically add relevant information based on the content of the manual. This allows users to quickly obtain information that helps them solve problems by automatically adding relevant FAQs and troubleshooting information. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the appliance instruction manual into a generation AI and have the generation AI perform the addition of relevant FAQs and troubleshooting information.

[0096] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is stressed, the suggestion unit can provide simple suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. By adjusting the way it presents suggestions based on the user's emotions, it can provide the user with the most suitable suggestions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way it presents suggestions.

[0097] The suggestion unit can make optimal suggestions by referring to the user's past usage history when making suggestions. For example, the suggestion unit can make optimal suggestions based on the user's past usage history. The suggestion unit can also make optimal suggestions by considering the user's usage patterns. The suggestion unit can also analyze the user's past usage history and make the most efficient suggestions. In this way, by referring to the user's past usage history and making optimal suggestions, the suggestion unit can provide the user with the most suitable suggestions. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's past usage history into a generative AI and have the generative AI generate optimal suggestions.

[0098] The suggestion unit can make suggestions at the optimal time based on the usage status of home appliances. For example, the suggestion unit can make suggestions at the optimal time based on the usage status of home appliances. The suggestion unit can also analyze the usage patterns of home appliances and make suggestions at the optimal time. The suggestion unit can also make suggestions at the most efficient time based on the usage data of home appliances. As a result, by making suggestions at the optimal time based on the usage status of home appliances, suggestions can be provided to the user at the optimal time. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the usage status of home appliances into a generative AI and have the generative AI execute a suggestion at the optimal time.

[0099] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit may prioritize detailed suggestions. If the user is stressed, the suggestion unit may also prioritize simple suggestions. If the user is in a hurry, the suggestion unit may also prioritize concise suggestions. By prioritizing suggestions based on the user's emotions, the system can provide the user with the most suitable suggestions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI determine the priority of suggestions.

[0100] The proposal unit can make customized proposals by taking into account the user's lifestyle data. For example, the proposal unit can make customized proposals based on the user's lifestyle data. The proposal unit can also make optimal proposals that match the user's daily rhythm. The proposal unit can also make energy-efficient proposals by taking into account the user's lifestyle data. In this way, by making customized proposals that take into account the user's lifestyle data, the optimal proposal can be provided to the user. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the proposal unit can input the user's lifestyle data into a generative AI and have the generative AI execute the generation of customized proposals.

[0101] The proposal unit can make proposals while considering appliance maintenance information. For example, the proposal unit can make the optimal proposal based on appliance maintenance information. The proposal unit can also make the optimal proposal by considering maintenance information. The proposal unit can also make energy-efficient proposals based on appliance maintenance information. In this way, by making proposals while considering appliance maintenance information, the optimal proposal can be provided to the user. Some or all of the above processing in the proposal unit may be performed using, for example, a generation AI, or without a generation AI. For example, the proposal unit can input appliance maintenance information into a generation AI and have the generation AI execute the generation of the optimal proposal.

[0102] The feedback unit can estimate the user's emotions and adjust the way it expresses the feedback based on the estimated emotions. For example, if the user is relaxed, the feedback unit can provide detailed feedback. If the user is stressed, the feedback unit can provide simple feedback. If the user is in a hurry, the feedback unit can provide concise feedback. In this way, by adjusting the way the feedback is expressed based on the user's emotions, the optimal feedback can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input user emotion data into the generative AI and have the generative AI adjust the way the feedback is expressed.

[0103] The feedback unit can analyze appliance usage data and propose specific improvements to appliance manufacturers. For example, the feedback unit can propose specific improvements based on appliance usage data. The feedback unit can also analyze appliance usage patterns and propose improvements to appliance manufacturers. The feedback unit can also propose improvements to improve energy efficiency based on appliance usage data. In this way, technological innovation can be promoted by analyzing appliance usage data and proposing specific improvements to appliance manufacturers. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit can input appliance usage data into a generative AI and have the generative AI execute the proposal of specific improvements.

[0104] The feedback unit can collect appliance usage data over a long period and perform trend analysis. For example, the feedback unit can collect appliance usage data over a long period and analyze usage trends. Based on the trend analysis, the feedback unit can also predict appliance usage patterns. Based on the long-term data, the feedback unit can also make suggestions to improve energy efficiency. In this way, by collecting appliance usage data over a long period and performing trend analysis, usage trends can be understood and optimal suggestions can be made. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit can input appliance usage data into a generative AI and have the generative AI perform trend analysis.

[0105] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, if the user is relaxed, the feedback unit may prioritize detailed feedback. If the user is stressed, the feedback unit may also prioritize simple feedback. If the user is in a hurry, the feedback unit may also prioritize concise feedback. This allows the system to provide the user with the most appropriate feedback by prioritizing feedback based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the feedback unit may be performed using AI or not. For example, the feedback unit can input user emotion data into a generative AI and have the generative AI determine the priority of feedback.

[0106] The feedback unit can provide comprehensive feedback by considering data from other smart devices when analyzing home appliance usage data. For example, the feedback unit can provide comprehensive feedback based on data from other smart devices. The feedback unit can also compare home appliance usage data with data from other devices to provide optimal feedback. The feedback unit can also consider data from other smart devices to provide feedback aimed at improving energy efficiency. By providing comprehensive feedback that also considers data from other smart devices, the feedback unit can provide the user with the most optimal feedback. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit can input home appliance usage data and data from other smart devices into a generative AI and have the generative AI generate comprehensive feedback.

[0107] The feedback unit can provide feedback by considering energy consumption and maintenance information when analyzing appliance usage data. For example, the feedback unit can suggest efficient usage methods based on energy consumption. The feedback unit can also suggest optimal usage methods by considering maintenance information. The feedback unit can also provide feedback to improve energy efficiency based on energy consumption and maintenance information. In this way, by providing feedback while considering energy consumption and maintenance information, the feedback unit can provide the most optimal feedback for the user. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit can input appliance usage data, energy consumption, and maintenance information into a generative AI and have the generative AI generate the feedback.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The smart home appliance coach and leader system can acquire user health data and suggest how to use home appliances according to their health condition. For example, it can acquire user sleep data and suggest how to use appliances to improve sleep quality. It can also acquire user exercise data and suggest how to use appliances suitable for post-exercise recovery. Furthermore, it can acquire user dietary data and suggest how to use appliances to support a healthy diet. In this way, by suggesting how to use home appliances according to the user's health condition, it can improve their quality of life.

[0110] The smart home appliance coach and leader system can estimate the user's emotions and suggest how to use home appliances based on those emotions. For example, if the user is stressed, it can suggest how to use appliances that have a relaxing effect. If the user is tired, it can suggest how to use appliances that help with fatigue recovery. Furthermore, if the user is energetic, it can suggest how to use appliances that support an active lifestyle. In this way, by suggesting how to use home appliances in a way that matches the user's emotions, it can improve the user's quality of life.

[0111] The smart home appliance coach and leader system can monitor the usage of home appliances and display energy consumption in real time. For example, it can monitor the usage of a refrigerator and display energy consumption in real time. It can also monitor the usage of a washing machine and display energy consumption in real time. Furthermore, it can monitor the usage of an air conditioner and display energy consumption in real time. This allows users to understand the energy consumption of their appliances in real time and choose efficient usage methods.

[0112] The smart home appliance coach and leader system can estimate the user's emotions and adjust the frequency of appliance use based on those emotions. For example, if the user is stressed, it can reduce the frequency of appliance use to provide a relaxing environment. If the user is relaxed, it can increase the frequency of appliance use to provide a comfortable environment. Furthermore, if the user is in a hurry, it can minimize the frequency of appliance use and suggest more efficient usage methods. In this way, by adjusting the frequency of appliance use to match the user's emotions, it can improve the user's quality of life.

[0113] The smart home appliance coach and leader system monitors appliance usage and integrates with other smart devices to provide a comprehensive understanding of household usage. For example, it can integrate with smart lighting to adjust brightness based on appliance usage. It can also integrate with smart thermostats to adjust room temperature based on appliance usage. Furthermore, it can integrate with smart security systems to adjust security settings based on appliance usage. This integration with other smart devices allows for a comprehensive understanding of household usage.

[0114] The smart home appliance coach and leader system can estimate the user's emotions and customize how they use their appliances based on those emotions. For example, if the user is relaxed, it can suggest ways to use appliances that promote relaxation. If the user is stressed, it can suggest ways to use appliances that help relieve stress. Furthermore, if the user is energetic, it can suggest ways to use appliances that support an active lifestyle. By customizing how users use their appliances to match their emotions, the system can improve the user's quality of life.

[0115] The smart home appliance coach and leader system can monitor the usage of home appliances, detect abnormal usage, and notify the user. For example, it can monitor the usage of a refrigerator and notify the user if it detects abnormal temperature changes. It can also monitor the usage of a washing machine and notify the user if it detects abnormal vibrations. Furthermore, it can monitor the usage of an air conditioner and notify the user if it detects abnormal operation. This allows for the detection of abnormal usage of home appliances and prompt response.

[0116] The smart home appliance coach and leader system can estimate the user's emotions and suggest how to use home appliances based on those emotions. For example, if the user is relaxed, it can suggest how to use appliances that promote relaxation. If the user is stressed, it can suggest how to use appliances that help relieve stress. Furthermore, if the user is energetic, it can suggest how to use appliances that support an active lifestyle. In this way, by suggesting how to use home appliances that match the user's emotions, it can improve the user's quality of life.

[0117] The smart home appliance coach and leader system can monitor the usage of home appliances and display energy consumption in real time. For example, it can monitor the usage of a refrigerator and display energy consumption in real time. It can also monitor the usage of a washing machine and display energy consumption in real time. Furthermore, it can monitor the usage of an air conditioner and display energy consumption in real time. This allows users to understand the energy consumption of their appliances in real time and choose efficient usage methods.

[0118] The smart home appliance coach and leader system can estimate the user's emotions and adjust the frequency of appliance use based on those emotions. For example, if the user is stressed, it can reduce the frequency of appliance use to provide a relaxing environment. If the user is relaxed, it can increase the frequency of appliance use to provide a comfortable environment. Furthermore, if the user is in a hurry, it can minimize the frequency of appliance use and suggest more efficient usage methods. In this way, by adjusting the frequency of appliance use to match the user's emotions, it can improve the user's quality of life.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The monitoring unit monitors the usage status of home appliances. For example, it monitors the frequency of use, usage time, and usage method of home appliances, and analyzes the usage patterns of home appliances in detail. This makes it possible to identify which functions are not being used. The monitoring unit can also store the usage history of home appliances over a long period of time and analyze seasonal usage patterns. Step 2: The analysis unit analyzes the data collected by the monitoring unit and identifies unused functions. For example, it analyzes the usage patterns of home appliances in detail to identify unused functions. The analysis unit can also store the home appliance usage data in the cloud and analyze it by comparing it with data from other users. Step 3: The generation unit scans the appliance manual and generates a summary and video explanation. For example, it scans the appliance manual, summarizes the key points, and provides a video explanation. The generation unit can also generate summaries and video explanations in multiple languages. Step 4: The suggestion unit proposes the summary and video explanation generated by the generation unit to the user. For example, it may explain how to use unused functions through videos and audio via a smartphone app or home appliance display. The suggestion unit can also estimate the user's emotions and adjust the way the suggestion is presented based on the estimated user emotions. Step 5: The Feedback Department provides feedback to the home appliance manufacturers based on the information proposed by the Proposal Department. For example, it analyzes home appliance usage data and provides feedback to home appliance manufacturers for product development. The Feedback Department can also collect home appliance usage data over a long period and conduct trend analysis.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0123] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements described above, including the monitoring unit, analysis unit, generation unit, proposal unit, and feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the smart device 14 and monitors the frequency and duration of use of the home appliance. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the data collected from the monitoring unit to identify unused functions. The generation unit is implemented by the control unit 46A of the smart device 14 and scans the instruction manual for the home appliance to generate a summary and video explanation. The proposal unit is implemented by the control unit 46A of the smart device 14 and proposes the generated summary and video explanation to the user. The feedback unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the usage data of the home appliance to provide feedback to the home appliance manufacturer. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the monitoring unit, analysis unit, generation unit, proposal unit, and feedback unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the smart glasses 214 and monitors the frequency and duration of use of home appliances. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the data collected from the monitoring unit to identify unused functions. The generation unit is implemented by the control unit 46A of the smart glasses 214 and scans the instruction manual for home appliances to generate a summary and video explanation. The proposal unit is implemented by the control unit 46A of the smart glasses 214 and proposes the generated summary and video explanation to the user. The feedback unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the usage data of home appliances to provide feedback to home appliance manufacturers. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the monitoring unit, analysis unit, generation unit, proposal unit, and feedback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the headset terminal 314 and monitors the frequency and duration of use of home appliances. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the data collected from the monitoring unit to identify unused functions. The generation unit is implemented by the control unit 46A of the headset terminal 314 and scans the instruction manual for home appliances to generate a summary and video explanation. The proposal unit is implemented by the control unit 46A of the headset terminal 314 and proposes the generated summary and video explanation to the user. The feedback unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the usage data of home appliances to provide feedback to home appliance manufacturers. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0165] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the monitoring unit, analysis unit, generation unit, proposal unit, and feedback unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the robot 414 and monitors the frequency and duration of use of the home appliance. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the data collected from the monitoring unit to identify unused functions. The generation unit is implemented by the control unit 46A of the robot 414 and scans the instruction manual for the home appliance to generate a summary and video explanation. The proposal unit is implemented by the control unit 46A of the robot 414 and proposes the generated summary and video explanation to the user. The feedback unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the usage data of the home appliance to provide feedback to the home appliance manufacturer. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0174] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0176] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0177] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0178] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0182] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0183] 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.

[0184] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0185] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0186] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0187] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0189] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0190] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0191] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0192] (Note 1) A monitoring unit that monitors the usage status of home appliances, An analysis unit analyzes the data collected by the monitoring unit and identifies unused functions, A generation unit that scans home appliance instruction manuals and generates summaries and video explanations, A proposal unit that proposes the summary and video explanation generated by the generation unit to the user, The system includes a feedback unit that provides back the information proposed by the proposal unit to the home appliance manufacturer. A system characterized by the following features. (Note 2) The monitoring unit, We analyze the usage patterns of home appliances in detail to identify which functions are not being used. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Scan appliance instruction manuals, summarize key points, and explain them in videos. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, The system uses smartphone apps and home appliance displays to explain how to use unused features through videos and audio. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned feedback unit is We analyze consumer electronics usage data and provide feedback to consumer electronics manufacturers for product development. The system described in Appendix 1, characterized by the features described herein. (Note 6) The monitoring unit, It estimates the user's emotions and adjusts the frequency of monitoring appliance usage based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The monitoring unit, It monitors appliance usage patterns in real time and detects abnormal usage. The system described in Appendix 1, characterized by the features described herein. (Note 8) The monitoring unit, We store the usage history of home appliances over a long period and analyze seasonal usage patterns. The system described in Appendix 1, characterized by the features described herein. (Note 9) The monitoring unit, It estimates the user's emotions and adjusts how monitoring results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The monitoring unit, When monitoring the usage of home appliances, it integrates with other smart devices to provide an overall understanding of household usage. The system described in Appendix 1, characterized by the features described herein. (Note 11) The monitoring unit, When monitoring the usage of home appliances, energy consumption is recorded simultaneously to improve energy efficiency. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is It estimates the user's emotions and adjusts the timing of notifications for unused features based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is We analyze your home appliance usage patterns in detail and suggest the optimal way to use them. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is Home appliance usage data is stored in the cloud and analyzed by comparing it with data from other users. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is When analyzing home appliance usage patterns, the analysis should take into account user lifestyle data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is When analyzing the usage patterns of home appliances, the analysis should take into account the maintenance information of those appliances. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is It estimates the user's emotions and adjusts the way summaries and video explanations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is Add a feature to highlight important points when scanning appliance instruction manuals. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When scanning instruction manuals for home appliances, generate summaries and video explanations in multiple languages. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and adjusts the length of the summary / video explanation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When scanning appliance manuals, the system generates customized summaries and video explanations that take into account the user's past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When scanning appliance manuals, automatically add relevant FAQs and troubleshooting information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making a proposal, we refer to the user's past usage history to provide the most suitable suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, we will make it at the optimal time based on the usage of home appliances. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When making a proposal, we will provide customized suggestions that take into account the user's lifestyle data. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making a proposal, we will take into account the maintenance information of home appliances. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned feedback unit is It estimates the user's emotions and adjusts how feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned feedback unit is We analyze usage data for home appliances and propose specific improvements to appliance manufacturers. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned feedback unit is We collect usage data for home appliances over a long period of time and perform trend analysis. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned feedback unit is When analyzing home appliance usage data, we also consider data from other smart devices to provide comprehensive feedback. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned feedback unit is When analyzing home appliance usage data, we also consider energy consumption and maintenance information to provide feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A monitoring unit that monitors the usage status of home appliances, An analysis unit analyzes the data collected by the monitoring unit and identifies unused functions, A generation unit that scans home appliance instruction manuals and generates summaries and video explanations, A proposal unit that proposes the summary and video explanation generated by the generation unit to the user, The system includes a feedback unit that provides back the information proposed by the proposal unit to the home appliance manufacturer. A system characterized by the following features.

2. The monitoring unit, We analyze the usage patterns of home appliances in detail to identify which functions are not being used. The system according to feature 1.

3. The generating unit is Scan appliance instruction manuals, summarize key points, and explain them in videos. The system according to feature 1.

4. The aforementioned proposal section is, The system uses smartphone apps and home appliance displays to explain how to use unused features through videos and audio. The system according to feature 1.

5. The aforementioned feedback unit is We analyze consumer electronics usage data and provide feedback to consumer electronics manufacturers for product development. The system according to feature 1.

6. The monitoring unit, It estimates the user's emotions and adjusts the frequency of monitoring appliance usage based on the estimated emotions. The system according to feature 1.

7. The monitoring unit, It monitors appliance usage patterns in real time and detects abnormal usage. The system according to feature 1.

8. The monitoring unit, We store the usage history of home appliances over a long period and analyze seasonal usage patterns. The system according to feature 1.

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