system

Abstract

The system according to this embodiment aims to centrally manage information on home appliances and to provide troubleshooting, replacement suggestions, maintenance schedule management, and support for suggesting and purchasing spare parts. [Solution] The system according to the embodiment comprises a collection unit, a management unit, a troubleshooting unit, a proposal unit, a maintenance unit, and a parts proposal unit. The collection unit collects information on home appliances. The management unit manages the information collected by the collection unit. The troubleshooting unit performs troubleshooting based on the information managed by the management unit. The proposal unit makes replacement suggestions based on the information obtained by the troubleshooting unit. The maintenance unit manages the maintenance schedule based on the information proposed by the proposal unit. The parts proposal unit proposes and provides purchase support for spare parts based on the information managed by the maintenance 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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the information management of household appliances, troubleshooting, replacement proposals, maintenance schedule management, spare part proposals, and purchase support are not carried out integrally, and there is room for improvement.

[0005] The system according to the embodiment aims to integrally manage the information of household appliances and perform troubleshooting, replacement proposals, maintenance schedule management, spare part proposals, and purchase support.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, a management unit, a troubleshooting unit, a proposal unit, a maintenance unit, and a parts proposal unit. The data collection unit collects information on home appliances. The management unit manages the information collected by the data collection unit. The troubleshooting unit performs troubleshooting based on the information managed by the management unit. The proposal unit makes replacement suggestions based on the information obtained by the troubleshooting unit. The maintenance unit manages the maintenance schedule based on the information proposed by the proposal unit. The parts proposal unit proposes and provides purchase support for spare parts based on the information managed by the maintenance unit. [Effects of the Invention]

[0007] The system according to this embodiment can centrally manage information on home appliances and provide troubleshooting, replacement suggestions, maintenance schedule management, and support for suggesting and purchasing spare parts. [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 tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memories (SSDs (Solid State Drives)), magnetic disks (e.g., hard disks), or magnetic tapes.

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

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

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, a specific processing unit 290 (see FIG. 2) acquires data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example of form 1) The home appliance information management system according to an embodiment of the present invention is an AI application that visualizes home appliance information to facilitate management. This home appliance information management system automatically collects and registers information about a home appliance when a user purchases it and takes a picture of the cover of the instruction manual with their smartphone camera. Next, based on the registered home appliance information, the AI ​​manages the instruction manual, manages the warranty period, provides troubleshooting support, suggests replacements, manages maintenance schedules, and suggests and supports the purchase of spare parts. For example, when a user purchases a home appliance, they take a picture of the cover of the instruction manual with their smartphone camera. At this time, the AI ​​uses image recognition technology to recognize the model name and manufacturer of the home appliance, and searches for and displays the manual on the internet. This eliminates the need to store the home appliance manual and makes it easily accessible at any time. Next, the AI ​​records the purchase information and automatically calculates the warranty period. By sending a notification before the warranty period expires, necessary actions can be taken before the warranty expires. For example, if the warranty period is running out, it supports the decision of whether to repair or replace the appliance. Furthermore, if the home appliance malfunctions, the AI ​​instantly displays the appropriate contact information and countermeasures. When the user communicates trouble information by voice, the AI ​​collects information from the instruction manual and the manufacturer's website Q&A, and reads out the countermeasures. This allows users to quickly address problems. The AI ​​also analyzes appliance usage and the latest market information, suggesting replacement timings and recommending new products. This helps consumers choose the optimal appliances. Furthermore, the AI ​​automatically creates appliance maintenance schedules and notifies users of regular maintenance times. For example, it supports often-forgotten maintenance tasks such as cleaning air conditioner filters or refrigerator drain pipes. Finally, the AI ​​analyzes the storage volume and usage of registered appliance spare parts and suggests necessary spare parts. It also provides support such as finding suppliers and comparing prices for spare parts. This allows users to purchase necessary parts at the right time. In this way, utilizing an appliance information management system makes appliance management easier and allows users to respond calmly to sudden problems.This invention will be of great help, especially for elderly households, where managing home appliances is often difficult. As a result, the home appliance information management system will be able to efficiently collect, manage, troubleshoot, suggest solutions, perform maintenance, and recommend parts for home appliances.

[0029] The home appliance information management system according to this embodiment comprises a collection unit, a management unit, a troubleshooting unit, a suggestion unit, a maintenance unit, and a parts suggestion unit. The collection unit collects information about home appliances. For example, the collection unit collects information about home appliances by taking a picture of the cover of the instruction manual with a smartphone camera. For example, the collection unit recognizes the model name and manufacturer of the home appliance and searches for and displays the manual on the internet. For example, the collection unit records the purchase date and usage status of the home appliance. The management unit manages the information collected by the collection unit. For example, the management unit stores the collected information about home appliances in a database and automatically calculates the warranty period. For example, the management unit sends a notification before the warranty period expires. For example, the management unit supports the decision of whether to repair or replace the appliance when the warranty period is running out. The troubleshooting unit performs troubleshooting based on the information managed by the management unit. For example, the troubleshooting unit displays appropriate contact information and countermeasures when a home appliance malfunctions. For example, when a user provides trouble information by voice, the troubleshooting unit collects information from the instruction manual and the Q&A section of the manufacturer's website and reads out countermeasures. The troubleshooting department supports users in quickly resolving problems. The proposal department makes replacement suggestions based on the information obtained by the troubleshooting department. The proposal department analyzes the usage status of home appliances and the latest market information to suggest replacement timing and recommended new products. The proposal department helps consumers choose the optimal home appliance. The maintenance department manages maintenance schedules based on the information suggested by the proposal department. The maintenance department automatically creates maintenance schedules for home appliances and notifies users of regular maintenance times. The maintenance department supports often-forgotten maintenance tasks, such as cleaning air conditioner filters and refrigerator drain pipes. The parts proposal department suggests and supports the purchase of spare parts based on the information managed by the maintenance department. The parts proposal department analyzes the storage volume and usage status of spare parts for registered home appliances and suggests necessary spare parts. The parts proposal department provides support such as finding suppliers for spare parts and comparing prices.As a result, the home appliance information management system according to this embodiment can efficiently collect, manage, troubleshoot, propose solutions, perform maintenance, and suggest parts for home appliances.

[0030] The data collection unit collects information about home appliances. For example, it collects information about home appliances by taking a picture of the instruction manual cover with a smartphone camera. Specifically, when the smartphone camera is used to photograph the instruction manual cover, image recognition technology is used to automatically extract the model name and manufacturer of the home appliance. This information is cross-referenced with an online database to search for the corresponding manual and product information. The data collection unit also records, for example, the purchase date and usage status of home appliances. The purchase date is automatically recorded by taking a picture of the receipt or proof of purchase, and the usage status can be entered by the user through an application, or if the home appliance itself is connected to the internet, the usage data can be automatically transmitted. This allows the data collection unit to efficiently collect detailed information about home appliances and store it in a database. Furthermore, the data collection unit also collects detailed information such as the serial number and manufacturing date of the home appliance, and this information can be used to manage the warranty period and create maintenance schedules. The data collection unit improves user convenience by eliminating the need for manual input by the user and automatically collecting information using the smartphone camera and the sensors of the home appliance itself.

[0031] The management department manages the information collected by the collection department. For example, the management department stores the collected information on home appliances in a database and automatically calculates the warranty period. Specifically, it registers information such as the model name, manufacturer, and purchase date of the collected home appliances in the database and uses an algorithm to automatically calculate the start and end dates of the warranty period. For example, the management department sends a notification before the warranty period expires. The notification is sent to the user's smartphone via push notification or email, and if the warranty period is nearing its end, it also provides information to support the decision of whether to repair or replace the appliance. For example, the management department centrally manages the usage status and maintenance history of home appliances, making it accessible to users at any time. This makes it easier for users to understand the condition of their appliances and perform maintenance and repairs at the appropriate time. Furthermore, the management department can analyze the usage data of home appliances and propose maintenance schedules according to the frequency of use and wear and tear. In this way, the management department can efficiently manage information on home appliances and provide appropriate support to users.

[0032] The troubleshooting department performs troubleshooting based on information managed by the management department. For example, when a home appliance malfunctions, the troubleshooting department displays the appropriate contact information and solutions. Specifically, when a user enters trouble information about a home appliance through the application, the troubleshooting department searches for relevant information from the instruction manual and the manufacturer's website's Q&A database and displays the appropriate solution. For example, if a user communicates trouble information by voice, the troubleshooting department uses voice recognition technology to analyze the trouble and reads out the solution. This helps users to quickly deal with problems. Furthermore, the troubleshooting department can also suggest solutions for similar problems that may occur in the future based on past trouble history. In this way, the troubleshooting department can support users in quickly and appropriately dealing with home appliance problems and reduce stress while using the appliances.

[0033] The Proposal Department makes replacement suggestions based on information obtained by the Troubleshooting Department. For example, the Proposal Department analyzes the usage status of home appliances and the latest market information to suggest the timing of replacement and recommend new products. Specifically, it uses an algorithm that analyzes the frequency of use and wear and tear of home appliances to calculate the optimal replacement time. It also collects the latest market information and proposes new products that meet the user's needs. For example, the Proposal Department provides information such as product features, prices, and reviews to help consumers choose the best home appliance for them. This allows users to choose the home appliance that is right for them. Furthermore, the Proposal Department can also make individually customized suggestions based on the user's past purchase history and usage status. In this way, the Proposal Department can make optimal replacement suggestions to users and optimize the lifecycle of home appliances.

[0034] The Maintenance Department manages maintenance schedules based on information proposed by the Proposal Department. For example, the Maintenance Department automatically creates maintenance schedules for home appliances and notifies users of the timing of regular maintenance. Specifically, it uses an algorithm that calculates the optimal maintenance schedule based on the usage status of the appliance and the manufacturer's recommended maintenance cycle. The Maintenance Department supports often-forgotten maintenance tasks, such as cleaning air conditioner filters or refrigerator drain pipes. This ensures that users do not forget to maintain their appliances and can always use them in optimal condition. Furthermore, the Maintenance Department can manage maintenance history and predict the timing of the next maintenance based on past maintenance records. This allows the Maintenance Department to efficiently manage appliance maintenance and extend the lifespan of appliances.

[0035] The Parts Proposal Department provides support for the proposal and purchase of spare parts based on information managed by the Maintenance Department. For example, the Parts Proposal Department analyzes the storage volume and usage status of spare parts for registered home appliances and proposes the necessary spare parts. Specifically, it uses an algorithm to calculate the need for spare parts based on the frequency of use and wear and tear of the home appliance. The Parts Proposal Department provides support such as finding suppliers for spare parts and comparing prices. This allows users to efficiently purchase the necessary spare parts. Furthermore, the Parts Proposal Department supports inventory management of spare parts, ensuring that necessary parts are always on hand. This enables the Parts Proposal Department to provide support for prompt maintenance and repair of home appliances, minimizing downtime.

[0036] The data collection unit can collect information about home appliances by taking a picture of the instruction manual cover with a smartphone camera. For example, the data collection unit automatically collects information about the home appliance by taking a picture of the instruction manual cover with a smartphone camera. The data collection unit recognizes the model name and manufacturer of the home appliance, searches for and displays the manual on the internet, and records the purchase date and usage status of the home appliance. This makes it easy to collect information about home appliances by taking a picture of the instruction manual cover. The instruction manual cover may include, but is not limited to, the product name, model number, and barcode. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input image data obtained by taking a picture of the instruction manual cover with a smartphone camera into a generating AI, and have the generating AI collect information about the home appliance from the image data.

[0037] The management unit can manage the collected appliance information and automatically calculate the warranty period. For example, the management unit can store the collected appliance information in a database and automatically calculate the warranty period. For example, the management unit can send notifications before the warranty period expires. For example, the management unit can support decisions regarding repair or replacement when the warranty period is nearing its end. This makes warranty period management easier by automatically calculating the warranty period. The warranty period includes, but is not limited to, the period from the date of purchase and the warranty coverage. Some or all of the above processes in the management unit may be performed using, for example, AI, or not using AI. For example, the management unit can input the collected appliance information into a generating AI and have the generating AI perform the warranty period calculation.

[0038] The troubleshooting unit can display appropriate contact information and solutions when an appliance malfunctions. For example, when an appliance malfunctions, the troubleshooting unit can display appropriate contact information and solutions. For example, when a user provides trouble information by voice, the troubleshooting unit can collect information from the instruction manual and the manufacturer's website Q&A, and read out solutions. The troubleshooting unit can support the user in quickly dealing with the problem. This allows users to quickly deal with problems by quickly displaying solutions when an appliance malfunctions. Appropriate contact information includes, but is not limited to, the manufacturer's customer support and repair companies. Solutions include, but are not limited to, reset methods and parts replacement procedures. Some or all of the above processes in the troubleshooting unit may be performed using, for example, AI, or not using AI. For example, the troubleshooting unit can input trouble information provided by the user by voice into a generating AI, and have the generating AI display appropriate contact information and solutions.

[0039] The suggestion department can analyze the usage status of home appliances and the latest market information to suggest the best time to replace appliances and recommend new products. For example, the suggestion department can analyze the usage status of home appliances and the latest market information to suggest the best time to replace appliances and recommend new products. For example, the suggestion department can help consumers choose the best home appliances. By analyzing the usage status of home appliances and market information, it can suggest the best time to replace appliances and recommend new products. The latest market information includes, but is not limited to, new product release information and price trends. Some or all of the above processing in the suggestion department may be performed using AI, for example, or not using AI. For example, the suggestion department can input the usage status of home appliances and market information into a generating AI and have the generating AI perform the task of suggesting the best time to replace appliances and recommending new products.

[0040] The maintenance unit can automatically create maintenance schedules for home appliances and notify users of the timing of regular maintenance. For example, the maintenance unit can automatically create maintenance schedules for home appliances and notify users of the timing of regular maintenance. The maintenance unit supports often-forgotten maintenance tasks, such as cleaning air conditioner filters or refrigerator drain pipes. This makes home appliance maintenance easier by automatically creating maintenance schedules and notifying users of the timing of regular maintenance. The maintenance schedule includes, but is not limited to, the frequency of regular inspections and notification methods. Some or all of the above-described processes in the maintenance unit may be performed using AI, for example, or without AI. For example, the maintenance unit can input the usage status of home appliances into a generating AI and have the generating AI create the maintenance schedule.

[0041] The parts suggestion department can analyze the storage volume and usage status of spare parts for registered home appliances, suggest necessary spare parts, and provide support for purchasing and price comparisons. For example, the parts suggestion department analyzes the storage volume and usage status of spare parts for registered home appliances and suggests necessary spare parts. The parts suggestion department provides support such as choosing suppliers for spare parts and comparing prices. This allows for the purchase of parts at the appropriate time by analyzing the storage volume and usage status of spare parts and suggesting necessary spare parts. The storage volume of spare parts includes, but is not limited to, an inventory management system and storage volume based on usage frequency. The usage status includes, but is not limited to, usage frequency and usage time. Some or all of the above processing in the parts suggestion department may be performed using, for example, AI, or not using AI. For example, the parts suggestion department can input the usage status of home appliances into a generating AI and have the generating AI execute spare parts suggestions.

[0042] The data collection unit can analyze the purchase history of home appliances and select the optimal data collection method. For example, the data collection unit can analyze the brands and models of home appliances that the user has purchased in the past and prioritize the collection of information on appliances of the same brand and model. For example, the data collection unit can prioritize the collection of information on home appliances of a specific category from the user's purchase history. For example, the data collection unit can automatically collect information on frequently purchased home appliances based on the user's purchase history. This allows the optimal data collection method to be selected by analyzing the purchase history of home appliances. Purchase history includes, but is not limited to, the date of purchase, the place of purchase, and the price of purchase. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the purchase history of home appliances into a generating AI and have the generating AI select the optimal data collection method.

[0043] The data collection unit can filter the collected home appliance information based on the user's current living situation and areas of interest. For example, when a user purchases a new home appliance, the data collection unit collects relevant home appliance information based on the user's current living situation. For example, the data collection unit prioritizes collecting home appliance information in specific categories based on the user's areas of interest. For example, the data collection unit filters and collects necessary home appliance information according to the user's lifestyle. This allows for efficient collection of necessary home appliance information by filtering based on the user's living situation and areas of interest. Living situation includes, but is not limited to, family structure and lifestyle patterns. Areas of interest include, but is not limited to, hobbies and topics of interest. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0044] The data collection unit can prioritize the collection of highly relevant information based on the user's geographical location when collecting information on home appliances. For example, if the user lives in a specific region, the data collection unit will prioritize the collection of information on home appliances used in that region. For example, if the user is traveling, the data collection unit will collect information on home appliances needed at their travel destination. For example, the data collection unit will collect region-specific home appliance information based on the user's geographical location. This allows for the efficient collection of region-specific information by prioritizing the collection of highly relevant information based on the user's geographical location. Geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant information.

[0045] The data collection unit can analyze the user's social media activity and collect relevant information when collecting information on home appliances. For example, the data collection unit can collect information about home appliances that the user has shared on social media. For example, the data collection unit can collect information on home appliances of interest from the user's social media activity. For example, the data collection unit can prioritize collecting information on home appliance manufacturers that the user follows. This allows for the efficient collection of relevant home appliance information by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and the number of likes. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into a generating AI and have the generating AI collect relevant information.

[0046] The management department can determine management priorities based on the remaining warranty period when managing home appliance information. For example, the management department can prioritize managing information on home appliances with a short remaining warranty period. For example, the management department can postpone managing information on home appliances with a long remaining warranty period. For example, the management department can prioritize necessary actions based on the remaining warranty period. In this way, by determining management priorities based on the remaining warranty period, important information can be managed preferentially. The remaining warranty period includes, but is not limited to, the number of days elapsed since the date of purchase and the warranty details. Some or all of the above processing in the management department may be performed using, for example, AI, or not using AI. For example, the management department can input the remaining warranty period into a generating AI and have the generating AI perform the determination of management priorities.

[0047] The management unit can apply different management algorithms to each appliance category when managing appliance information. For example, the management unit can provide different management methods for each appliance category. For example, the management unit can apply the optimal management algorithm according to the appliance category. For example, the management unit can set different management priorities for each appliance category. This allows for the provision of an optimal management method by applying different management algorithms to each appliance category. Appliance categories include, but are not limited to, kitchen appliances and entertainment appliances. Some or all of the above processing in the management unit may be performed using, for example, AI, or not using AI. For example, the management unit can input appliance categories into a generating AI and have the generating AI execute the application of management algorithms.

[0048] The management unit can select the optimal management method when managing home appliance information by referring to the user's past management history. For example, the management unit may prioritize suggesting management methods previously used by the user. For example, the management unit may select the optimal management method from the user's past management history. For example, the management unit may analyze the user's management history and suggest an efficient management method. This allows the optimal management method to be selected by referring to the user's past management history. Past management history includes, but is not limited to, past maintenance records and usage history. Some or all of the above processes in the management unit may be performed using, for example, AI, or not using AI. For example, the management unit may input the user's past management history into a generating AI and have the generating AI select the optimal management method.

[0049] The management unit can select the optimal management method when managing home appliance information, taking into account the user's device information. For example, if the user is using a smartphone, the management unit provides a management method optimized for smartphones. For example, if the user is using a tablet, the management unit provides a management method optimized for tablets. For example, if the user is using a personal computer, the management unit provides a management method optimized for personal computers. This allows the management unit to select the optimal management method by taking into account the user's device information. Device information includes, but is not limited to, the type of device, performance, and usage status. Some or all of the above processing in the management unit may be performed using, for example, AI, or not using AI. For example, the management unit can input the user's device information into a generating AI and have the generating AI select the optimal management method.

[0050] The troubleshooting unit can select the optimal solution by referring to the appliance's failure history during troubleshooting. For example, the troubleshooting unit can propose the optimal solution based on the appliance's failure history. For example, the troubleshooting unit can prioritize suggesting solutions for frequently occurring problems based on the appliance's failure history. For example, the troubleshooting unit can analyze the appliance's failure history and select an efficient solution. This allows the optimal solution to be selected by referring to the appliance's failure history. The failure history includes, but is not limited to, past failure causes and repair history. Some or all of the above processes in the troubleshooting unit may be performed using AI, for example, or without AI. For example, the troubleshooting unit can input the appliance's failure history into a generating AI and have the generating AI select the optimal solution.

[0051] The troubleshooting unit can apply different troubleshooting algorithms to each category of home appliance during troubleshooting. For example, the troubleshooting unit provides different troubleshooting methods for each category of home appliance. For example, the troubleshooting unit applies the optimal troubleshooting algorithm according to the category of home appliance. For example, the troubleshooting unit sets different troubleshooting priorities for each category of home appliance. This allows the optimal troubleshooting method to be provided by applying different troubleshooting algorithms to each category of home appliance. Categories of home appliances include, but are not limited to, kitchen appliances and entertainment appliances. Some or all of the above-described processes in the troubleshooting unit may be performed using, for example, AI, or not using AI. For example, the troubleshooting unit can input the category of home appliance into a generating AI and have the generating AI execute the application of the troubleshooting algorithm.

[0052] The troubleshooting unit can select the most appropriate solution based on the user's geographical location information during troubleshooting. For example, if the user lives in a specific region, the troubleshooting unit will prioritize suggesting solutions used in that region. For example, if the user is traveling, the troubleshooting unit will suggest solutions needed at their travel destination. For example, the troubleshooting unit will suggest region-specific solutions based on the user's geographical location information. This allows the system to provide region-specific solutions by selecting the most appropriate solution based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above-described processes in the troubleshooting unit may be performed using, for example, AI, or not using AI. For example, the troubleshooting unit can input the user's geographical location information into a generating AI and have the generating AI select the most appropriate solution.

[0053] The troubleshooting unit can analyze a user's social media activity and suggest relevant solutions during troubleshooting. For example, the troubleshooting unit can collect information about problems shared by the user on social media and suggest solutions. For example, the troubleshooting unit can suggest solutions of interest based on the user's social media activity. For example, the troubleshooting unit can prioritize suggesting solutions from home appliance manufacturers that the user follows. This allows for the efficient suggestion of relevant solutions by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and the number of likes. Some or all of the above processes in the troubleshooting unit may be performed using AI, for example, or without AI. For example, the troubleshooting unit can input the user's social media activity into a generating AI and have the generating AI suggest relevant solutions.

[0054] The suggestion unit can adjust the level of detail of its suggestions based on the usage of the home appliances. For example, if the appliances are used frequently, the suggestion unit will provide detailed suggestions. For example, if the appliances are used infrequently, the suggestion unit will provide concise suggestions. The suggestion unit adjusts the level of detail of its suggestions to the optimal level depending on the usage of the appliances. This allows the system to provide optimal suggestions by adjusting the level of detail based on the usage of the appliances. Usage includes, but is not limited to, frequency of use and usage time. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the usage of the appliances into a generating AI and have the generating AI adjust the level of detail of the suggestions.

[0055] The proposal unit can apply different proposal algorithms depending on the category of home appliance when making a proposal. For example, the proposal unit provides different proposal methods for each category of home appliance. For example, the proposal unit applies the optimal proposal algorithm depending on the category of home appliance. For example, the proposal unit sets different proposal priorities for each category of home appliance. This allows the optimal proposal to be provided by applying different proposal algorithms depending on the category of home appliance. Categories of home appliances include, but are not limited to, kitchen appliances and entertainment appliances. Some or all of the above processing in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal unit can input the category of home appliance into a generating AI and have the generating AI perform the application of the proposal algorithm.

[0056] The proposal unit can determine the priority of proposals based on the purchase date of the home appliance. For example, if the home appliance was purchased a long time ago, the proposal unit will prioritize suggesting replacement. For example, if the home appliance was purchased recently, the proposal unit will prioritize suggesting maintenance. The proposal unit determines the optimal priority of proposals based on the purchase date of the home appliance. This allows the proposal unit to provide the most suitable proposals by prioritizing proposals based on the purchase date of the home appliance. The purchase date includes, but is not limited to, the purchase date and purchase history. Some or all of the above processing in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal unit can input the purchase date of the home appliance into a generating AI and have the generating AI perform the determination of the proposal priority.

[0057] The suggestion unit can adjust the order of suggestions based on the relevance of the home appliances. For example, the suggestion unit will prioritize suggestions if the relevance of the home appliances is high. For example, the suggestion unit will postpone suggestions if the relevance of the home appliances is low. The suggestion unit adjusts the optimal order of suggestions based on the relevance of the home appliances. This allows the system to provide optimal suggestions by adjusting the order of suggestions based on the relevance of the home appliances. Relevance of home appliances includes, but is not limited to, products from the same manufacturer or products in the same category. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the relevance of home appliances into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0058] The maintenance unit can optimize the maintenance schedule based on the frequency of appliance use when creating the schedule. For example, if an appliance is used frequently, the maintenance unit will create a schedule that performs maintenance frequently. For example, if an appliance is used infrequently, the maintenance unit will create a schedule that reduces the frequency of maintenance. For example, the maintenance unit will create an optimal maintenance schedule based on the frequency of appliance use. This allows the system to provide an optimal maintenance schedule by optimizing the schedule based on the frequency of appliance use. Frequency of use includes, but is not limited to, usage time and number of uses. Some or all of the above processing in the maintenance unit may be performed using, for example, AI, or not using AI. For example, the maintenance unit can input the frequency of appliance use into a generating AI and have the generating AI perform schedule optimization.

[0059] The maintenance unit can apply different scheduling algorithms to each category of home appliance when creating maintenance schedules. For example, the maintenance unit can provide different maintenance schedules for each category of home appliance. For example, the maintenance unit can apply the optimal scheduling algorithm according to the category of home appliance. For example, the maintenance unit can set different maintenance priorities for each category of home appliance. This allows the maintenance unit to provide an optimal maintenance schedule by applying different scheduling algorithms to each category of home appliance. Categories of home appliances include, but are not limited to, kitchen appliances and entertainment appliances. Some or all of the above processes in the maintenance unit may be performed using, for example, AI, or not using AI. For example, the maintenance unit can input the categories of home appliances into a generating AI and have the generating AI perform the application of the scheduling algorithm.

[0060] The maintenance unit can create an optimal maintenance schedule based on the user's geographical location information. For example, if the user lives in a specific region, the maintenance unit can provide the necessary maintenance schedule for that region. For example, if the user is traveling, the maintenance unit can provide the necessary maintenance schedule for their travel destination. For example, the maintenance unit can provide region-specific maintenance schedules based on the user's geographical location information. This allows the maintenance unit to provide region-specific maintenance schedules by creating an optimal schedule based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the maintenance unit may be performed using, for example, AI, or not using AI. For example, the maintenance unit can input the user's geographical location information into a generating AI and have the generating AI create the schedule.

[0061] The maintenance department can analyze a user's social media activity and propose relevant schedules when creating maintenance schedules. For example, the maintenance department can collect maintenance-related information shared by the user on social media and propose schedules. For example, the maintenance department can propose maintenance schedules of interest based on the user's social media activity. For example, the maintenance department can prioritize proposing maintenance schedules from home appliance manufacturers that the user follows. This allows for the efficient proposal of relevant maintenance schedules by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and the number of likes. Some or all of the above processes in the maintenance department may be performed using AI, for example, or not. For example, the maintenance department can input the user's social media activity into a generating AI and have the generating AI propose schedules.

[0062] The parts suggestion unit can adjust the level of detail of its suggestions based on the usage of the home appliance when suggesting spare parts. For example, if the home appliance is used frequently, the parts suggestion unit will provide a detailed suggestion. For example, if the home appliance is used infrequently, the parts suggestion unit will provide a concise suggestion. For example, the parts suggestion unit adjusts the level of detail of the suggestion to the optimal level depending on the usage of the home appliance. This allows the unit to provide the optimal spare parts suggestion by adjusting the level of detail of the suggestion based on the usage of the home appliance. Usage conditions include, but are not limited to, usage frequency and usage time. Some or all of the above processing in the parts suggestion unit may be performed using, for example, AI. This allows the unit to provide the optimal spare parts suggestion by adjusting the level of detail of the suggestion based on the usage of the home appliance. Usage conditions include, for example, usage frequency and usage time. Some or all of the above processing in the parts suggestion unit may be performed using, for example, AI, or not using AI. For example, the parts suggestion unit can input the usage of the home appliance into a generating AI and have the generating AI perform the adjustment of the level of detail of the suggestion.

[0063] The parts suggestion unit can apply different suggestion algorithms for each category of home appliance when suggesting spare parts. For example, the parts suggestion unit can provide different suggestion methods for each category of home appliance. For example, the parts suggestion unit can apply the optimal suggestion algorithm according to the category of home appliance. For example, the parts suggestion unit can set different suggestion priorities for each category of home appliance. This allows for the provision of optimal spare parts suggestions by applying different suggestion algorithms for each category of home appliance. Categories of home appliances include, but are not limited to, kitchen appliances and entertainment appliances. Some or all of the above processing in the parts suggestion unit may be performed using, for example, AI, or not using AI. For example, the parts suggestion unit can input the category of home appliance into a generating AI and have the generating AI perform the application of the suggestion algorithm.

[0064] The parts suggestion unit can make optimal suggestions for spare parts based on the user's geographical location information. For example, if the user lives in a specific region, the parts suggestion unit will prioritize suggesting spare parts used in that region. For example, if the user is traveling, the parts suggestion unit will suggest spare parts needed at their travel destination. For example, the parts suggestion unit will suggest region-specific spare parts based on the user's geographical location information. This allows for the efficient suggestion of region-specific spare parts by making optimal suggestions based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the above processing in the parts suggestion unit may be performed using, for example, AI, or not using AI. For example, the parts suggestion unit can input the user's geographical location information into a generating AI and have the generating AI execute the optimal suggestion.

[0065] The parts suggestion department can analyze the user's social media activity and make relevant suggestions when suggesting spare parts. For example, the parts suggestion department can collect information about spare parts that the user has shared on social media and make suggestions. For example, the parts suggestion department can suggest spare parts of interest based on the user's social media activity. For example, the parts suggestion department can prioritize suggesting spare parts from home appliance manufacturers that the user follows. In this way, relevant spare parts can be efficiently suggested by analyzing the user's social media activity. Social media activity includes, but is not limited to, the content of posts and the number of likes. Some or all of the above processing in the parts suggestion department may be performed using AI, for example, or not using AI. For example, the parts suggestion department can input the user's social media activity into a generating AI and have the generating AI execute relevant suggestions.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The home appliance information management system can learn the user's lifestyle patterns and suggest the optimal schedule for using home appliances. For example, if a user uses a coffee maker every morning, the system can automatically set a schedule for the coffee maker and prepare it to brew coffee in time for the morning. Similarly, if a user uses an air conditioner at night, the system can set a schedule for the air conditioner to maintain a comfortable temperature. Furthermore, if a user uses a vacuum cleaner on weekends, the system can set a schedule for the vacuum cleaner to support efficient cleaning. In this way, the system provides an optimal schedule for using home appliances based on the user's lifestyle patterns.

[0068] The home appliance information management system can analyze a user's purchase history and suggest the optimal timing for replacing home appliances. For example, it can predict the timing of replacement based on the lifespan of appliances the user has previously purchased and notify the user at the appropriate time. Furthermore, if a user prefers to purchase appliances from a specific brand, the system can prioritize providing information on new products from that brand. Additionally, if a user tends to replace appliances frequently, the system can periodically suggest replacements and provide the latest appliance information. This enables the system to suggest the optimal replacement timing based on the user's purchase history.

[0069] The home appliance information management system can provide region-specific home appliance information based on the user's geographical location. For example, if a user lives in a cold region, the system can prioritize providing home appliance information suitable for cold climates. Similarly, if a user lives in a tropical region, the system can provide home appliance information suitable for tropical regions, supporting the selection of appropriate appliances. Furthermore, if a user is traveling, the system can provide information on home appliances needed at their destination, supporting a comfortable trip. This enables the provision of optimal home appliance information based on the user's geographical location.

[0070] The home appliance information management system can analyze users' social media activity and provide relevant home appliance information. For example, it can collect information about home appliances that users have shared on social media and provide relevant information. It can also prioritize providing information from home appliance manufacturers that users follow and provide the latest product information. Furthermore, it can collect and efficiently provide information on home appliances of interest from users' social media activity. This makes it possible to provide optimal home appliance information based on users' social media activity.

[0071] The home appliance information management system can provide the optimal display method for home appliance information based on the user's device information. For example, if the user is using a smartphone, the system can provide a display method optimized for smartphones. If the user is using a tablet, the system can provide a display method optimized for tablets, improving visibility. Furthermore, if the user is using a PC, the system can provide a display method optimized for PCs, supporting efficient information management. This ensures that the optimal display method for home appliance information is provided based on the user's device information.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The data collection unit collects information about home appliances. For example, it collects information about home appliances by taking a picture of the instruction manual cover with a smartphone camera, recognizing the model name and manufacturer of the appliance, and searching for and displaying the manual online. It also records the purchase date and usage status of the appliance. Step 2: The management department manages the information collected by the collection department. For example, it stores the collected information on home appliances in a database and automatically calculates the warranty period. It sends notifications before the warranty period expires and supports decisions on repair or replacement when the warranty period is running out. Step 3: The troubleshooting department performs troubleshooting based on information managed by the management department. For example, if an appliance malfunctions, it displays the appropriate contact information and troubleshooting steps. When the user provides trouble information by voice, it gathers information from the instruction manual and the manufacturer's website Q&A, and reads out the solution. This supports the user in quickly resolving the problem. Step 4: The Proposal Department makes replacement suggestions based on the information obtained by the Troubleshooting Department. For example, they analyze the usage status of home appliances and the latest market information to suggest the best time for replacement and recommend new products. This helps consumers choose the most suitable home appliances. Step 5: The maintenance department manages the maintenance schedule based on the information proposed by the proposal department. For example, it automatically creates maintenance schedules for home appliances and notifies customers of when regular maintenance is due. It also supports often-forgotten maintenance tasks such as cleaning air conditioner filters and refrigerator drain pipes. Step 6: The parts proposal department provides support for proposing and purchasing spare parts based on information managed by the maintenance department. For example, it analyzes the storage volume and usage status of spare parts for registered home appliances and proposes necessary spare parts. It also provides support such as finding suppliers for spare parts and comparing prices.

[0074] (Example of form 2) The home appliance information management system according to an embodiment of the present invention is an AI application that visualizes home appliance information to facilitate management. This home appliance information management system automatically collects and registers information about a home appliance when a user purchases it and takes a picture of the cover of the instruction manual with their smartphone camera. Next, based on the registered home appliance information, the AI ​​manages the instruction manual, manages the warranty period, provides troubleshooting support, suggests replacements, manages maintenance schedules, and suggests and supports the purchase of spare parts. For example, when a user purchases a home appliance, they take a picture of the cover of the instruction manual with their smartphone camera. At this time, the AI ​​uses image recognition technology to recognize the model name and manufacturer of the home appliance, and searches for and displays the manual on the internet. This eliminates the need to store the home appliance manual and makes it easily accessible at any time. Next, the AI ​​records the purchase information and automatically calculates the warranty period. By sending a notification before the warranty period expires, necessary actions can be taken before the warranty expires. For example, if the warranty period is running out, it supports the decision of whether to repair or replace the appliance. Furthermore, if the home appliance malfunctions, the AI ​​instantly displays the appropriate contact information and countermeasures. When the user communicates trouble information by voice, the AI ​​collects information from the instruction manual and the manufacturer's website Q&A, and reads out the countermeasures. This allows users to quickly address problems. The AI ​​also analyzes appliance usage and the latest market information, suggesting replacement timings and recommending new products. This helps consumers choose the optimal appliances. Furthermore, the AI ​​automatically creates appliance maintenance schedules and notifies users of regular maintenance times. For example, it supports often-forgotten maintenance tasks such as cleaning air conditioner filters or refrigerator drain pipes. Finally, the AI ​​analyzes the storage volume and usage of registered appliance spare parts and suggests necessary spare parts. It also provides support such as finding suppliers and comparing prices for spare parts. This allows users to purchase necessary parts at the right time. In this way, utilizing an appliance information management system makes appliance management easier and allows users to respond calmly to sudden problems.This invention will be of great help, especially for elderly households, where managing home appliances is often difficult. As a result, the home appliance information management system will be able to efficiently collect, manage, troubleshoot, suggest solutions, perform maintenance, and recommend parts for home appliances.

[0075] The home appliance information management system according to this embodiment comprises a collection unit, a management unit, a troubleshooting unit, a suggestion unit, a maintenance unit, and a parts suggestion unit. The collection unit collects information about home appliances. For example, the collection unit collects information about home appliances by taking a picture of the cover of the instruction manual with a smartphone camera. For example, the collection unit recognizes the model name and manufacturer of the home appliance and searches for and displays the manual on the internet. For example, the collection unit records the purchase date and usage status of the home appliance. The management unit manages the information collected by the collection unit. For example, the management unit stores the collected information about home appliances in a database and automatically calculates the warranty period. For example, the management unit sends a notification before the warranty period expires. For example, the management unit supports the decision of whether to repair or replace the appliance when the warranty period is running out. The troubleshooting unit performs troubleshooting based on the information managed by the management unit. For example, the troubleshooting unit displays appropriate contact information and countermeasures when a home appliance malfunctions. For example, when a user provides trouble information by voice, the troubleshooting unit collects information from the instruction manual and the Q&A section of the manufacturer's website and reads out countermeasures. The troubleshooting department supports users in quickly resolving problems. The proposal department makes replacement suggestions based on the information obtained by the troubleshooting department. The proposal department analyzes the usage status of home appliances and the latest market information to suggest replacement timing and recommended new products. The proposal department helps consumers choose the optimal home appliance. The maintenance department manages maintenance schedules based on the information suggested by the proposal department. The maintenance department automatically creates maintenance schedules for home appliances and notifies users of regular maintenance times. The maintenance department supports often-forgotten maintenance tasks, such as cleaning air conditioner filters and refrigerator drain pipes. The parts proposal department suggests and supports the purchase of spare parts based on the information managed by the maintenance department. The parts proposal department analyzes the storage volume and usage status of spare parts for registered home appliances and suggests necessary spare parts. The parts proposal department provides support such as finding suppliers for spare parts and comparing prices.As a result, the home appliance information management system according to this embodiment can efficiently collect, manage, troubleshoot, propose solutions, perform maintenance, and suggest parts for home appliances.

[0076] The data collection unit collects information about home appliances. For example, it collects information about home appliances by taking a picture of the instruction manual cover with a smartphone camera. Specifically, when the smartphone camera is used to photograph the instruction manual cover, image recognition technology is used to automatically extract the model name and manufacturer of the home appliance. This information is cross-referenced with an online database to search for the corresponding manual and product information. The data collection unit also records, for example, the purchase date and usage status of home appliances. The purchase date is automatically recorded by taking a picture of the receipt or proof of purchase, and the usage status can be entered by the user through an application, or if the home appliance itself is connected to the internet, the usage data can be automatically transmitted. This allows the data collection unit to efficiently collect detailed information about home appliances and store it in a database. Furthermore, the data collection unit also collects detailed information such as the serial number and manufacturing date of the home appliance, and this information can be used to manage the warranty period and create maintenance schedules. The data collection unit improves user convenience by eliminating the need for manual input by the user and automatically collecting information using the smartphone camera and the sensors of the home appliance itself.

[0077] The management department manages the information collected by the collection department. For example, the management department stores the collected information on home appliances in a database and automatically calculates the warranty period. Specifically, it registers information such as the model name, manufacturer, and purchase date of the collected home appliances in the database and uses an algorithm to automatically calculate the start and end dates of the warranty period. For example, the management department sends a notification before the warranty period expires. The notification is sent to the user's smartphone via push notification or email, and if the warranty period is nearing its end, it also provides information to support the decision of whether to repair or replace the appliance. For example, the management department centrally manages the usage status and maintenance history of home appliances, making it accessible to users at any time. This makes it easier for users to understand the condition of their appliances and perform maintenance and repairs at the appropriate time. Furthermore, the management department can analyze the usage data of home appliances and propose maintenance schedules according to the frequency of use and wear and tear. In this way, the management department can efficiently manage information on home appliances and provide appropriate support to users.

[0078] The troubleshooting department performs troubleshooting based on information managed by the management department. For example, when a home appliance malfunctions, the troubleshooting department displays the appropriate contact information and solutions. Specifically, when a user enters trouble information about a home appliance through the application, the troubleshooting department searches for relevant information from the instruction manual and the manufacturer's website's Q&A database and displays the appropriate solution. For example, if a user communicates trouble information by voice, the troubleshooting department uses voice recognition technology to analyze the trouble and reads out the solution. This helps users to quickly deal with problems. Furthermore, the troubleshooting department can also suggest solutions for similar problems that may occur in the future based on past trouble history. In this way, the troubleshooting department can support users in quickly and appropriately dealing with home appliance problems and reduce stress while using the appliances.

[0079] The Proposal Department makes replacement suggestions based on information obtained by the Troubleshooting Department. For example, the Proposal Department analyzes the usage status of home appliances and the latest market information to suggest the timing of replacement and recommend new products. Specifically, it uses an algorithm that analyzes the frequency of use and wear and tear of home appliances to calculate the optimal replacement time. It also collects the latest market information and proposes new products that meet the user's needs. For example, the Proposal Department provides information such as product features, prices, and reviews to help consumers choose the best home appliance for them. This allows users to choose the home appliance that is right for them. Furthermore, the Proposal Department can also make individually customized suggestions based on the user's past purchase history and usage status. In this way, the Proposal Department can make optimal replacement suggestions to users and optimize the lifecycle of home appliances.

[0080] The Maintenance Department manages maintenance schedules based on information proposed by the Proposal Department. For example, the Maintenance Department automatically creates maintenance schedules for home appliances and notifies users of the timing of regular maintenance. Specifically, it uses an algorithm that calculates the optimal maintenance schedule based on the usage status of the appliance and the manufacturer's recommended maintenance cycle. The Maintenance Department supports often-forgotten maintenance tasks, such as cleaning air conditioner filters or refrigerator drain pipes. This ensures that users do not forget to maintain their appliances and can always use them in optimal condition. Furthermore, the Maintenance Department can manage maintenance history and predict the timing of the next maintenance based on past maintenance records. This allows the Maintenance Department to efficiently manage appliance maintenance and extend the lifespan of appliances.

[0081] The Parts Proposal Department provides support for the proposal and purchase of spare parts based on information managed by the Maintenance Department. For example, the Parts Proposal Department analyzes the storage volume and usage status of spare parts for registered home appliances and proposes the necessary spare parts. Specifically, it uses an algorithm to calculate the need for spare parts based on the frequency of use and wear and tear of the home appliance. The Parts Proposal Department provides support such as finding suppliers for spare parts and comparing prices. This allows users to efficiently purchase the necessary spare parts. Furthermore, the Parts Proposal Department supports inventory management of spare parts, ensuring that necessary parts are always on hand. This enables the Parts Proposal Department to provide support for prompt maintenance and repair of home appliances, minimizing downtime.

[0082] The data collection unit can collect information about home appliances by taking a picture of the instruction manual cover with a smartphone camera. For example, the data collection unit automatically collects information about the home appliance by taking a picture of the instruction manual cover with a smartphone camera. The data collection unit recognizes the model name and manufacturer of the home appliance, searches for and displays the manual on the internet, and records the purchase date and usage status of the home appliance. This makes it easy to collect information about home appliances by taking a picture of the instruction manual cover. The instruction manual cover may include, but is not limited to, the product name, model number, and barcode. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input image data obtained by taking a picture of the instruction manual cover with a smartphone camera into a generating AI, and have the generating AI collect information about the home appliance from the image data.

[0083] The management unit can manage the collected appliance information and automatically calculate the warranty period. For example, the management unit can store the collected appliance information in a database and automatically calculate the warranty period. For example, the management unit can send notifications before the warranty period expires. For example, the management unit can support decisions regarding repair or replacement when the warranty period is nearing its end. This makes warranty period management easier by automatically calculating the warranty period. The warranty period includes, but is not limited to, the period from the date of purchase and the warranty coverage. Some or all of the above processes in the management unit may be performed using, for example, AI, or not using AI. For example, the management unit can input the collected appliance information into a generating AI and have the generating AI perform the warranty period calculation.

[0084] The troubleshooting unit can display appropriate contact information and solutions when an appliance malfunctions. For example, when an appliance malfunctions, the troubleshooting unit can display appropriate contact information and solutions. For example, when a user provides trouble information by voice, the troubleshooting unit can collect information from the instruction manual and the manufacturer's website Q&A, and read out solutions. The troubleshooting unit can support the user in quickly dealing with the problem. This allows users to quickly deal with problems by quickly displaying solutions when an appliance malfunctions. Appropriate contact information includes, but is not limited to, the manufacturer's customer support and repair companies. Solutions include, but are not limited to, reset methods and parts replacement procedures. Some or all of the above processes in the troubleshooting unit may be performed using, for example, AI, or not using AI. For example, the troubleshooting unit can input trouble information provided by the user by voice into a generating AI, and have the generating AI display appropriate contact information and solutions.

[0085] The suggestion department can analyze the usage status of home appliances and the latest market information to suggest the best time to replace appliances and recommend new products. For example, the suggestion department can analyze the usage status of home appliances and the latest market information to suggest the best time to replace appliances and recommend new products. For example, the suggestion department can help consumers choose the best home appliances. By analyzing the usage status of home appliances and market information, it can suggest the best time to replace appliances and recommend new products. The latest market information includes, but is not limited to, new product release information and price trends. Some or all of the above processing in the suggestion department may be performed using AI, for example, or not using AI. For example, the suggestion department can input the usage status of home appliances and market information into a generating AI and have the generating AI perform the task of suggesting the best time to replace appliances and recommending new products.

[0086] The maintenance unit can automatically create maintenance schedules for home appliances and notify users of the timing of regular maintenance. For example, the maintenance unit can automatically create maintenance schedules for home appliances and notify users of the timing of regular maintenance. The maintenance unit supports often-forgotten maintenance tasks, such as cleaning air conditioner filters or refrigerator drain pipes. This makes home appliance maintenance easier by automatically creating maintenance schedules and notifying users of the timing of regular maintenance. The maintenance schedule includes, but is not limited to, the frequency of regular inspections and notification methods. Some or all of the above-described processes in the maintenance unit may be performed using AI, for example, or without AI. For example, the maintenance unit can input the usage status of home appliances into a generating AI and have the generating AI create the maintenance schedule.

[0087] The parts suggestion department can analyze the storage volume and usage status of spare parts for registered home appliances, suggest necessary spare parts, and provide support for purchasing and price comparisons. For example, the parts suggestion department analyzes the storage volume and usage status of spare parts for registered home appliances and suggests necessary spare parts. The parts suggestion department provides support such as choosing suppliers for spare parts and comparing prices. This allows for the purchase of parts at the appropriate time by analyzing the storage volume and usage status of spare parts and suggesting necessary spare parts. The storage volume of spare parts includes, but is not limited to, an inventory management system and storage volume based on usage frequency. The usage status includes, but is not limited to, usage frequency and usage time. Some or all of the above processing in the parts suggestion department may be performed using, for example, AI, or not using AI. For example, the parts suggestion department can input the usage status of home appliances into a generating AI and have the generating AI execute spare parts suggestions.

[0088] The data collection unit can estimate the user's emotions and adjust the timing of collecting home appliance information based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing and collect data when the user is relaxed. For example, if the user is excited, the data collection unit can advance the collection timing and collect home appliance information immediately. For example, if the user is tired, the data collection unit can adjust the collection timing and collect data after the user has rested. By adjusting the collection timing according to the user's emotions, home appliance information can be collected at a more appropriate time. User emotions include, but are not limited to, facial recognition and voice analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user facial data into a generative AI and have the generative AI perform emotion estimation.

[0089] The data collection unit can analyze the purchase history of home appliances and select the optimal data collection method. For example, the data collection unit can analyze the brands and models of home appliances that the user has purchased in the past and prioritize the collection of information on appliances of the same brand and model. For example, the data collection unit can prioritize the collection of information on home appliances of a specific category from the user's purchase history. For example, the data collection unit can automatically collect information on frequently purchased home appliances based on the user's purchase history. This allows the optimal data collection method to be selected by analyzing the purchase history of home appliances. Purchase history includes, but is not limited to, the date of purchase, the place of purchase, and the price of purchase. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the purchase history of home appliances into a generating AI and have the generating AI select the optimal data collection method.

[0090] The data collection unit can filter the collected home appliance information based on the user's current living situation and areas of interest. For example, when a user purchases a new home appliance, the data collection unit collects relevant home appliance information based on the user's current living situation. For example, the data collection unit prioritizes collecting home appliance information in specific categories based on the user's areas of interest. For example, the data collection unit filters and collects necessary home appliance information according to the user's lifestyle. This allows for efficient collection of necessary home appliance information by filtering based on the user's living situation and areas of interest. Living situation includes, but is not limited to, family structure and lifestyle patterns. Areas of interest include, but is not limited to, hobbies and topics of interest. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's living situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0091] The data collection unit can estimate the user's emotions and determine the priority of appliance information to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit will postpone collecting less important appliance information. For example, if the user is relaxed, the data collection unit will prioritize collecting more important appliance information. For example, if the user is in a hurry, the data collection unit will quickly collect the necessary appliance information. In this way, by prioritizing appliance information according to the user's emotions, important information can be collected preferentially. User emotions include, but are not limited to, facial recognition and voice analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user facial data into a generative AI and have the generative AI perform emotion estimation.

[0092] The data collection unit can prioritize the collection of highly relevant information based on the user's geographical location when collecting information on home appliances. For example, if the user lives in a specific region, the data collection unit will prioritize the collection of information on home appliances used in that region. For example, if the user is traveling, the data collection unit will collect information on home appliances needed at their travel destination. For example, the data collection unit will collect region-specific home appliance information based on the user's geographical location. This allows for the efficient collection of region-specific information by prioritizing the collection of highly relevant information based on the user's geographical location. Geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant information.

[0093] The data collection unit can analyze the user's social media activity and collect relevant information when collecting information on home appliances. For example, the data collection unit can collect information about home appliances that the user has shared on social media. For example, the data collection unit can collect information on home appliances of interest from the user's social media activity. For example, the data collection unit can prioritize collecting information on home appliance manufacturers that the user follows. This allows for the efficient collection of relevant home appliance information by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and the number of likes. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into a generating AI and have the generating AI collect relevant information.

[0094] The management unit can estimate the user's emotions and adjust the method of managing home appliance information based on the estimated user emotions. For example, if the user is stressed, the management unit provides a simple management method. For example, if the user is relaxed, the management unit provides detailed management options. For example, if the user is in a hurry, the management unit provides a way to quickly manage information. This allows for more appropriate management methods to be provided by adjusting the management method according to the user's emotions. User emotions include, but are not limited to, facial recognition and voice analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input user facial data into a generative AI and have the generative AI perform emotion estimation.

[0095] The management department can determine management priorities based on the remaining warranty period when managing home appliance information. For example, the management department can prioritize managing information on home appliances with a short remaining warranty period. For example, the management department can postpone managing information on home appliances with a long remaining warranty period. For example, the management department can prioritize necessary actions based on the remaining warranty period. In this way, by determining management priorities based on the remaining warranty period, important information can be managed preferentially. The remaining warranty period includes, but is not limited to, the number of days elapsed since the date of purchase and the warranty details. Some or all of the above processing in the management department may be performed using, for example, AI, or not using AI. For example, the management department can input the remaining warranty period into a generating AI and have the generating AI perform the determination of management priorities.

[0096] The management unit can apply different management algorithms to each appliance category when managing appliance information. For example, the management unit can provide different management methods for each appliance category. For example, the management unit can apply the optimal management algorithm according to the appliance category. For example, the management unit can set different management priorities for each appliance category. This allows for the provision of an optimal management method by applying different management algorithms to each appliance category. Appliance categories include, but are not limited to, kitchen appliances and entertainment appliances. Some or all of the above processing in the management unit may be performed using, for example, AI, or not using AI. For example, the management unit can input appliance categories into a generating AI and have the generating AI execute the application of management algorithms.

[0097] The control unit can estimate the user's emotions and adjust the display method of home appliance information based on the estimated user emotions. For example, if the user is tense, the control unit provides a simple and highly visible display method. For example, if the user is relaxed, the control unit provides a display method that includes detailed information. For example, if the user is in a hurry, the control unit provides a display method that gets straight to the point. In this way, by adjusting the display method according to the user's emotions, a more appropriate display method can be provided. User emotions include, but are not limited to, facial recognition and voice analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input user facial data into a generative AI and have the generative AI perform emotion estimation.

[0098] The management unit can select the optimal management method when managing home appliance information by referring to the user's past management history. For example, the management unit may prioritize suggesting management methods previously used by the user. For example, the management unit may select the optimal management method from the user's past management history. For example, the management unit may analyze the user's management history and suggest an efficient management method. This allows the optimal management method to be selected by referring to the user's past management history. Past management history includes, but is not limited to, past maintenance records and usage history. Some or all of the above processes in the management unit may be performed using, for example, AI, or not using AI. For example, the management unit may input the user's past management history into a generating AI and have the generating AI select the optimal management method.

[0099] The management unit can select the optimal management method when managing home appliance information, taking into account the user's device information. For example, if the user is using a smartphone, the management unit provides a management method optimized for smartphones. For example, if the user is using a tablet, the management unit provides a management method optimized for tablets. For example, if the user is using a personal computer, the management unit provides a management method optimized for personal computers. This allows the management unit to select the optimal management method by taking into account the user's device information. Device information includes, but is not limited to, the type of device, performance, and usage status. Some or all of the above processing in the management unit may be performed using, for example, AI, or not using AI. For example, the management unit can input the user's device information into a generating AI and have the generating AI select the optimal management method.

[0100] The troubleshooting unit can estimate the user's emotions and adjust the way troubleshooting is displayed based on the estimated emotions. For example, if the user is nervous, the troubleshooting unit provides a simple and highly visible display. For example, if the user is relaxed, the troubleshooting unit provides a display that includes detailed information. For example, if the user is in a hurry, the troubleshooting unit provides a display that gets straight to the point. By adjusting the display according to the user's emotions, more appropriate troubleshooting can be provided. User emotions include, but are not limited to, facial recognition and voice analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the troubleshooting unit may be performed using AI, for example, or without AI. For example, the troubleshooting unit can input user facial data into a generative AI and have the generative AI perform emotion estimation.

[0101] The troubleshooting unit can select the optimal solution by referring to the appliance's failure history during troubleshooting. For example, the troubleshooting unit can propose the optimal solution based on the appliance's failure history. For example, the troubleshooting unit can prioritize suggesting solutions for frequently occurring problems based on the appliance's failure history. For example, the troubleshooting unit can analyze the appliance's failure history and select an efficient solution. This allows the optimal solution to be selected by referring to the appliance's failure history. The failure history includes, but is not limited to, past failure causes and repair history. Some or all of the above processes in the troubleshooting unit may be performed using AI, for example, or without AI. For example, the troubleshooting unit can input the appliance's failure history into a generating AI and have the generating AI select the optimal solution.

[0102] The troubleshooting unit can apply different troubleshooting algorithms to each category of home appliance during troubleshooting. For example, the troubleshooting unit provides different troubleshooting methods for each category of home appliance. For example, the troubleshooting unit applies the optimal troubleshooting algorithm according to the category of home appliance. For example, the troubleshooting unit sets different troubleshooting priorities for each category of home appliance. This allows the optimal troubleshooting method to be provided by applying different troubleshooting algorithms to each category of home appliance. Categories of home appliances include, but are not limited to, kitchen appliances and entertainment appliances. Some or all of the above-described processes in the troubleshooting unit may be performed using, for example, AI, or not using AI. For example, the troubleshooting unit can input the category of home appliance into a generating AI and have the generating AI execute the application of the troubleshooting algorithm.

[0103] The troubleshooting unit can estimate the user's emotions and determine troubleshooting priorities based on those estimated emotions. For example, if the user is stressed, the troubleshooting unit will postpone less important issues. If the user is relaxed, the troubleshooting unit will prioritize addressing more important issues. If the user is in a hurry, the troubleshooting unit will quickly address necessary issues. This allows for prioritizing important issues by determining troubleshooting priorities according to the user's emotions. User emotions include, but are not limited to, facial recognition and voice analysis. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processes in the troubleshooting unit may be performed using AI or not. For example, the troubleshooting unit can input user facial data into a generative AI and have the generative AI perform emotion estimation.

[0104] The troubleshooting unit can select the most appropriate solution based on the user's geographical location information during troubleshooting. For example, if the user lives in a specific region, the troubleshooting unit will prioritize suggesting solutions used in that region. For example, if the user is traveling, the troubleshooting unit will suggest solutions needed at their travel destination. For example, the troubleshooting unit will suggest region-specific solutions based on the user's geographical location information. This allows the system to provide region-specific solutions by selecting the most appropriate solution based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above-described processes in the troubleshooting unit may be performed using, for example, AI, or not using AI. For example, the troubleshooting unit can input the user's geographical location information into a generating AI and have the generating AI select the most appropriate solution.

[0105] The troubleshooting unit can analyze a user's social media activity and suggest relevant solutions during troubleshooting. For example, the troubleshooting unit can collect information about problems shared by the user on social media and suggest solutions. For example, the troubleshooting unit can suggest solutions of interest based on the user's social media activity. For example, the troubleshooting unit can prioritize suggesting solutions from home appliance manufacturers that the user follows. This allows for the efficient suggestion of relevant solutions by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and the number of likes. Some or all of the above processes in the troubleshooting unit may be performed using AI, for example, or without AI. For example, the troubleshooting unit can input the user's social media activity into a generating AI and have the generating AI suggest relevant solutions.

[0106] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit provides a simple and easily visible suggestion. If the user is relaxed, the suggestion unit provides a suggestion that includes detailed information. If the user is in a hurry, the suggestion unit provides a concise suggestion. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions can be provided. User emotions include, but are not limited to, facial recognition and voice analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user facial data into a generative AI and have the generative AI perform emotion estimation.

[0107] The suggestion unit can adjust the level of detail of its suggestions based on the usage of the home appliances. For example, if the appliances are used frequently, the suggestion unit will provide detailed suggestions. For example, if the appliances are used infrequently, the suggestion unit will provide concise suggestions. The suggestion unit adjusts the level of detail of its suggestions to the optimal level depending on the usage of the appliances. This allows the system to provide optimal suggestions by adjusting the level of detail based on the usage of the appliances. Usage includes, but is not limited to, frequency of use and usage time. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the usage of the appliances into a generating AI and have the generating AI adjust the level of detail of the suggestions.

[0108] The proposal unit can apply different proposal algorithms depending on the category of home appliance when making a proposal. For example, the proposal unit provides different proposal methods for each category of home appliance. For example, the proposal unit applies the optimal proposal algorithm depending on the category of home appliance. For example, the proposal unit sets different proposal priorities for each category of home appliance. This allows the optimal proposal to be provided by applying different proposal algorithms depending on the category of home appliance. Categories of home appliances include, but are not limited to, kitchen appliances and entertainment appliances. Some or all of the above processing in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal unit can input the category of home appliance into a generating AI and have the generating AI perform the application of the proposal algorithm.

[0109] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit will provide a short, to-the-point suggestion. If the user is relaxed, the suggestion unit will provide a longer suggestion with detailed explanations. If the user is excited, the suggestion unit will provide a suggestion with visually stimulating effects. By adjusting the length of the suggestions according to the user's emotions, more appropriate suggestions can be provided. User emotions include, but are not limited to, facial recognition and voice analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user facial data into a generative AI and have the generative AI perform emotion estimation.

[0110] The proposal unit can determine the priority of proposals based on the purchase date of the home appliance. For example, if the home appliance was purchased a long time ago, the proposal unit will prioritize suggesting replacement. For example, if the home appliance was purchased recently, the proposal unit will prioritize suggesting maintenance. The proposal unit determines the optimal priority of proposals based on the purchase date of the home appliance. This allows the proposal unit to provide the most suitable proposals by prioritizing proposals based on the purchase date of the home appliance. The purchase date includes, but is not limited to, the purchase date and purchase history. Some or all of the above processing in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal unit can input the purchase date of the home appliance into a generating AI and have the generating AI perform the determination of the proposal priority.

[0111] The suggestion unit can adjust the order of suggestions based on the relevance of the home appliances. For example, the suggestion unit will prioritize suggestions if the relevance of the home appliances is high. For example, the suggestion unit will postpone suggestions if the relevance of the home appliances is low. The suggestion unit adjusts the optimal order of suggestions based on the relevance of the home appliances. This allows the system to provide optimal suggestions by adjusting the order of suggestions based on the relevance of the home appliances. Relevance of home appliances includes, but is not limited to, products from the same manufacturer or products in the same category. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the relevance of home appliances into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0112] The maintenance unit can estimate the user's emotions and adjust the display method of the maintenance schedule based on the estimated user emotions. For example, if the user is tense, the maintenance unit provides a simple and highly visible display method. For example, if the user is relaxed, the maintenance unit provides a display method that includes detailed information. For example, if the user is in a hurry, the maintenance unit provides a display method that gets straight to the point. By adjusting the display method according to the user's emotions, a more appropriate maintenance schedule can be provided. User emotions include, but are not limited to, facial recognition and voice analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the maintenance unit may be performed using AI, for example, or without AI. For example, the maintenance unit can input user facial data into a generative AI and have the generative AI perform emotion estimation.

[0113] The maintenance unit can optimize the maintenance schedule based on the frequency of appliance use when creating the schedule. For example, if an appliance is used frequently, the maintenance unit will create a schedule that performs maintenance frequently. For example, if an appliance is used infrequently, the maintenance unit will create a schedule that reduces the frequency of maintenance. For example, the maintenance unit will create an optimal maintenance schedule based on the frequency of appliance use. This allows the system to provide an optimal maintenance schedule by optimizing the schedule based on the frequency of appliance use. Frequency of use includes, but is not limited to, usage time and number of uses. Some or all of the above processing in the maintenance unit may be performed using, for example, AI, or not using AI. For example, the maintenance unit can input the frequency of appliance use into a generating AI and have the generating AI perform schedule optimization.

[0114] The maintenance unit can apply different scheduling algorithms to each category of home appliance when creating maintenance schedules. For example, the maintenance unit can provide different maintenance schedules for each category of home appliance. For example, the maintenance unit can apply the optimal scheduling algorithm according to the category of home appliance. For example, the maintenance unit can set different maintenance priorities for each category of home appliance. This allows the maintenance unit to provide an optimal maintenance schedule by applying different scheduling algorithms to each category of home appliance. Categories of home appliances include, but are not limited to, kitchen appliances and entertainment appliances. Some or all of the above processes in the maintenance unit may be performed using, for example, AI, or not using AI. For example, the maintenance unit can input the categories of home appliances into a generating AI and have the generating AI perform the application of the scheduling algorithm.

[0115] The maintenance unit can estimate the user's emotions and determine the priority of the maintenance schedule based on the estimated emotions. For example, if the user is stressed, the maintenance unit will postpone less important maintenance. For example, if the user is relaxed, the maintenance unit will prioritize more important maintenance. For example, if the user is in a hurry, the maintenance unit will quickly perform necessary maintenance. In this way, by determining the priority of the maintenance schedule according to the user's emotions, important maintenance can be prioritized. User emotions include, but are not limited to, facial recognition and voice analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the maintenance unit may be performed using AI, for example, or not using AI. For example, the maintenance unit can input user facial data into a generative AI and have the generative AI perform emotion estimation.

[0116] The maintenance unit can create an optimal maintenance schedule based on the user's geographical location information. For example, if the user lives in a specific region, the maintenance unit can provide the necessary maintenance schedule for that region. For example, if the user is traveling, the maintenance unit can provide the necessary maintenance schedule for their travel destination. For example, the maintenance unit can provide region-specific maintenance schedules based on the user's geographical location information. This allows the maintenance unit to provide region-specific maintenance schedules by creating an optimal schedule based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the maintenance unit may be performed using, for example, AI, or not using AI. For example, the maintenance unit can input the user's geographical location information into a generating AI and have the generating AI create the schedule.

[0117] The maintenance department can analyze a user's social media activity and propose relevant schedules when creating maintenance schedules. For example, the maintenance department can collect maintenance-related information shared by the user on social media and propose schedules. For example, the maintenance department can propose maintenance schedules of interest based on the user's social media activity. For example, the maintenance department can prioritize proposing maintenance schedules from home appliance manufacturers that the user follows. This allows for the efficient proposal of relevant maintenance schedules by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and the number of likes. Some or all of the above processes in the maintenance department may be performed using AI, for example, or not. For example, the maintenance department can input the user's social media activity into a generating AI and have the generating AI propose schedules.

[0118] The parts suggestion unit can estimate the user's emotions and adjust the method of suggesting spare parts based on the estimated user emotions. For example, if the user is nervous, the parts suggestion unit provides a simple and highly visible suggestion method. For example, if the user is relaxed, the parts suggestion unit provides a suggestion method that includes detailed information. For example, if the user is in a hurry, the parts suggestion unit provides a suggestion method that gets straight to the point. By adjusting the suggestion method according to the user's emotions, it is possible to provide more appropriate suggestions for spare parts. User emotions include, but are not limited to, facial recognition and voice analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the parts suggestion unit may be performed using AI, for example, or without AI. For example, the parts suggestion unit can input user facial data into a generative AI and have the generative AI perform emotion estimation.

[0119] The parts suggestion unit can adjust the level of detail of its suggestions based on the usage of the home appliance when suggesting spare parts. For example, if the home appliance is used frequently, the parts suggestion unit will provide a detailed suggestion. For example, if the home appliance is used infrequently, the parts suggestion unit will provide a concise suggestion. For example, the parts suggestion unit adjusts the level of detail of the suggestion to the optimal level depending on the usage of the home appliance. This allows the unit to provide the optimal spare parts suggestion by adjusting the level of detail of the suggestion based on the usage of the home appliance. Usage conditions include, but are not limited to, usage frequency and usage time. Some or all of the above processing in the parts suggestion unit may be performed using, for example, AI. This allows the unit to provide the optimal spare parts suggestion by adjusting the level of detail of the suggestion based on the usage of the home appliance. Usage conditions include, for example, usage frequency and usage time. Some or all of the above processing in the parts suggestion unit may be performed using, for example, AI, or not using AI. For example, the parts suggestion unit can input the usage of the home appliance into a generating AI and have the generating AI perform the adjustment of the level of detail of the suggestion.

[0120] The parts suggestion unit can apply different suggestion algorithms for each category of home appliance when suggesting spare parts. For example, the parts suggestion unit can provide different suggestion methods for each category of home appliance. For example, the parts suggestion unit can apply the optimal suggestion algorithm according to the category of home appliance. For example, the parts suggestion unit can set different suggestion priorities for each category of home appliance. This allows for the provision of optimal spare parts suggestions by applying different suggestion algorithms for each category of home appliance. Categories of home appliances include, but are not limited to, kitchen appliances and entertainment appliances. Some or all of the above processing in the parts suggestion unit may be performed using, for example, AI, or not using AI. For example, the parts suggestion unit can input the category of home appliance into a generating AI and have the generating AI perform the application of the suggestion algorithm.

[0121] The parts suggestion unit can estimate the user's emotions and determine the priority of spare parts suggestions based on the estimated emotions. For example, if the user is stressed, the parts suggestion unit will postpone suggesting less important spare parts. For example, if the user is relaxed, the parts suggestion unit will prioritize suggesting more important spare parts. For example, if the user is in a hurry, the parts suggestion unit will quickly suggest necessary spare parts. In this way, by determining the priority of suggestions according to the user's emotions, important spare parts can be suggested preferentially. User emotions include, but are not limited to, facial recognition and voice analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the parts suggestion unit may be performed using AI, for example, or without AI. For example, the parts suggestion unit can input user facial data into a generative AI and have the generative AI perform emotion estimation.

[0122] The parts suggestion unit can make optimal suggestions for spare parts based on the user's geographical location information. For example, if the user lives in a specific region, the parts suggestion unit will prioritize suggesting spare parts used in that region. For example, if the user is traveling, the parts suggestion unit will suggest spare parts needed at their travel destination. For example, the parts suggestion unit will suggest region-specific spare parts based on the user's geographical location information. This allows for the efficient suggestion of region-specific spare parts by making optimal suggestions based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the above processing in the parts suggestion unit may be performed using, for example, AI, or not using AI. For example, the parts suggestion unit can input the user's geographical location information into a generating AI and have the generating AI execute the optimal suggestion.

[0123] The parts suggestion department can analyze the user's social media activity and make relevant suggestions when suggesting spare parts. For example, the parts suggestion department can collect information about spare parts that the user has shared on social media and make suggestions. For example, the parts suggestion department can suggest spare parts of interest based on the user's social media activity. For example, the parts suggestion department can prioritize suggesting spare parts from home appliance manufacturers that the user follows. In this way, relevant spare parts can be efficiently suggested by analyzing the user's social media activity. Social media activity includes, but is not limited to, the content of posts and the number of likes. Some or all of the above processing in the parts suggestion department may be performed using AI, for example, or not using AI. For example, the parts suggestion department can input the user's social media activity into a generating AI and have the generating AI execute relevant suggestions.

[0124] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0125] The home appliance information management system can estimate the user's emotions and analyze the usage of appliances based on those emotions. For example, if the user is stressed, the system can suggest that the appliance is being used frequently and notify the user of the need for maintenance. Conversely, if the user is relaxed, the system can determine that the appliance usage is normal and notify the user of any special notifications. Furthermore, if the user is agitated, the system can analyze the appliance usage in detail and suggest the optimal way to use it. This enables appropriate analysis of appliance usage in accordance with the user's emotions. Technologies such as facial recognition and voice analysis are used, but are not limited to, these, for estimating emotions.

[0126] The home appliance information management system can learn the user's lifestyle patterns and suggest the optimal schedule for using home appliances. For example, if a user uses a coffee maker every morning, the system can automatically set a schedule for the coffee maker and prepare it to brew coffee in time for the morning. Similarly, if a user uses an air conditioner at night, the system can set a schedule for the air conditioner to maintain a comfortable temperature. Furthermore, if a user uses a vacuum cleaner on weekends, the system can set a schedule for the vacuum cleaner to support efficient cleaning. In this way, the system provides an optimal schedule for using home appliances based on the user's lifestyle patterns.

[0127] The home appliance information management system can estimate the user's emotions and troubleshoot appliance problems based on those emotions. For example, if the user is stressed, the system can provide simple and quick troubleshooting methods. If the user is relaxed, the system can provide detailed troubleshooting steps to support the user in resolving the problem. Furthermore, if the user is in a hurry, the system can prioritize displaying the most important troubleshooting information to support a quick response. This enables appropriate troubleshooting tailored to the user's emotions. Emotion estimation may use, but is not limited to, technologies such as facial recognition and voice analysis.

[0128] The home appliance information management system can analyze a user's purchase history and suggest the optimal timing for replacing home appliances. For example, it can predict the timing of replacement based on the lifespan of appliances the user has previously purchased and notify the user at the appropriate time. Furthermore, if a user prefers to purchase appliances from a specific brand, the system can prioritize providing information on new products from that brand. Additionally, if a user tends to replace appliances frequently, the system can periodically suggest replacements and provide the latest appliance information. This enables the system to suggest the optimal replacement timing based on the user's purchase history.

[0129] The home appliance information management system can estimate the user's emotions and adjust the appliance maintenance schedule based on those emotions. For example, if the user is stressed, the system can delay the maintenance schedule so that maintenance can be performed when the user is relaxed. Conversely, if the user is relaxed, the system can speed up the maintenance schedule for more efficient maintenance. Furthermore, if the user is in a hurry, the system can prioritize notifications for important maintenance items to support a quick response. This makes it possible to adjust the maintenance schedule appropriately according to the user's emotions. Technologies such as facial recognition and voice analysis are used, but are not limited to, to estimate emotions.

[0130] The home appliance information management system can provide region-specific home appliance information based on the user's geographical location. For example, if a user lives in a cold region, the system can prioritize providing home appliance information suitable for cold climates. Similarly, if a user lives in a tropical region, the system can provide home appliance information suitable for tropical regions, supporting the selection of appropriate appliances. Furthermore, if a user is traveling, the system can provide information on home appliances needed at their destination, supporting a comfortable trip. This enables the provision of optimal home appliance information based on the user's geographical location.

[0131] The home appliance information management system can estimate a user's emotions and, based on those emotions, suggest replacement appliances. For example, if a user is stressed, the system can provide simple and easy-to-understand replacement suggestions. If a user is relaxed, the system can provide detailed suggestions to help the user make the best choice. Furthermore, if a user is in a hurry, the system can provide concise suggestions to support a quick decision. This enables appropriate replacement suggestions tailored to the user's emotions. Emotion estimation can be performed using, but is not limited to, technologies such as facial recognition and voice analysis.

[0132] The home appliance information management system can analyze users' social media activity and provide relevant home appliance information. For example, it can collect information about home appliances that users have shared on social media and provide relevant information. It can also prioritize providing information from home appliance manufacturers that users follow and provide the latest product information. Furthermore, it can collect and efficiently provide information on home appliances of interest from users' social media activity. This makes it possible to provide optimal home appliance information based on users' social media activity.

[0133] The home appliance information management system can estimate the user's emotions and provide maintenance suggestions based on those emotions. For example, if the user is stressed, the system can provide simple and easy-to-understand maintenance suggestions. If the user is relaxed, the system can provide detailed maintenance suggestions to support the user in performing optimal maintenance. Furthermore, if the user is in a hurry, the system can provide concise maintenance suggestions to support a quick response. This enables appropriate maintenance suggestions tailored to the user's emotions. Emotion estimation can be performed using, but is not limited to, technologies such as facial recognition and voice analysis.

[0134] The home appliance information management system can provide the optimal display method for home appliance information based on the user's device information. For example, if the user is using a smartphone, the system can provide a display method optimized for smartphones. If the user is using a tablet, the system can provide a display method optimized for tablets, improving visibility. Furthermore, if the user is using a PC, the system can provide a display method optimized for PCs, supporting efficient information management. This ensures that the optimal display method for home appliance information is provided based on the user's device information.

[0135] The following briefly describes the processing flow for example form 2.

[0136] Step 1: The data collection unit collects information about home appliances. For example, it collects information about home appliances by taking a picture of the instruction manual cover with a smartphone camera, recognizing the model name and manufacturer of the appliance, and searching for and displaying the manual online. It also records the purchase date and usage status of the appliance. Step 2: The management department manages the information collected by the collection department. For example, it stores the collected information on home appliances in a database and automatically calculates the warranty period. It sends notifications before the warranty period expires and supports decisions on repair or replacement when the warranty period is running out. Step 3: The troubleshooting department performs troubleshooting based on information managed by the management department. For example, if an appliance malfunctions, it displays the appropriate contact information and troubleshooting steps. When the user provides trouble information by voice, it gathers information from the instruction manual and the manufacturer's website Q&A, and reads out the solution. This supports the user in quickly resolving the problem. Step 4: The Proposal Department makes replacement suggestions based on the information obtained by the Troubleshooting Department. For example, they analyze the usage status of home appliances and the latest market information to suggest the best time for replacement and recommend new products. This helps consumers choose the most suitable home appliances. Step 5: The maintenance department manages the maintenance schedule based on the information proposed by the proposal department. For example, it automatically creates maintenance schedules for home appliances and notifies customers of when regular maintenance is due. It also supports often-forgotten maintenance tasks such as cleaning air conditioner filters and refrigerator drain pipes. Step 6: The parts proposal department provides support for proposing and purchasing spare parts based on information managed by the maintenance department. For example, it analyzes the storage volume and usage status of spare parts for registered home appliances and proposes necessary spare parts. It also provides support such as finding suppliers for spare parts and comparing prices.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the data collection unit, management unit, troubleshooting unit, proposal unit, maintenance unit, and parts proposal unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects information on home appliances using the camera 42 of the smart device 14 and recognizes the model name and manufacturer by the control unit 46A. The management unit stores the collected information in the database 24 by the specific processing unit 290 of the data processing unit 12 and calculates the warranty period. The troubleshooting unit displays how to deal with malfunctions by the specific processing unit 290 of the data processing unit 12 and analyzes trouble information communicated by the user by voice. The proposal unit analyzes the usage status and market information of home appliances by the specific processing unit 290 of the data processing unit 12 and proposes replacement timing and new products. The maintenance unit creates a maintenance schedule and sends notifications by the specific processing unit 290 of the data processing unit 12. The parts proposal unit analyzes the amount and usage status of spare parts by the specific processing unit 290 of the data processing unit 12 and supports purchasing and price comparison. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0141] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0142] 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.

[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 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.

[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 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.

[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 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.

[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 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.

[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 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.

[0156] Each of the multiple elements described above, including the collection unit, management unit, troubleshooting unit, proposal unit, maintenance unit, and parts proposal unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects information on home appliances using the camera 42 of the smart glasses 214 and recognizes the model name and manufacturer by the control unit 46A. The management unit stores the collected information in the database 24 by the specific processing unit 290 of the data processing unit 12 and calculates the warranty period. The troubleshooting unit displays how to deal with malfunctions by the specific processing unit 290 of the data processing unit 12 and analyzes trouble information communicated by the user by voice. The proposal unit analyzes the usage status and market information of home appliances by the specific processing unit 290 of the data processing unit 12 and proposes replacement timing and new products. The maintenance unit creates a maintenance schedule and sends notifications by the specific processing unit 290 of the data processing unit 12. The parts proposal unit analyzes the amount and usage status of spare parts by the specific processing unit 290 of the data processing unit 12 and supports purchasing and price comparison. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0157] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0158] 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.

[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 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.

[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 (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).

[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] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Each of the multiple elements described above, including the data collection unit, management unit, troubleshooting unit, proposal unit, maintenance unit, and parts proposal unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects information on home appliances using the camera 42 of the headset terminal 314 and recognizes the model name and manufacturer by the control unit 46A. The management unit stores the collected information in the database 24 by the specific processing unit 290 of the data processing unit 12 and calculates the warranty period. The troubleshooting unit displays how to deal with malfunctions by the specific processing unit 290 of the data processing unit 12 and analyzes the trouble information communicated by the user by voice. The proposal unit analyzes the usage status and market information of home appliances by the specific processing unit 290 of the data processing unit 12 and proposes replacement timing and new products. The maintenance unit creates a maintenance schedule and sends notifications by the specific processing unit 290 of the data processing unit 12. The parts proposal unit analyzes the amount and usage status of spare parts by the specific processing unit 290 of the data processing unit 12 and supports purchasing and price comparison. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0173] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.).

[0186] 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.

[0187] 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.

[0188] 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.

[0189] Each of the multiple elements described above, including the collection unit, management unit, troubleshooting unit, proposal unit, maintenance unit, and parts proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects information on home appliances using the camera 42 of the robot 414 and recognizes the model name and manufacturer by the control unit 46A. The management unit stores the collected information in the database 24 by the specific processing unit 290 of the data processing unit 12 and calculates the warranty period. The troubleshooting unit displays how to deal with malfunctions by the specific processing unit 290 of the data processing unit 12 and analyzes trouble information communicated by the user by voice. The proposal unit analyzes the usage status and market information of home appliances by the specific processing unit 290 of the data processing unit 12 and proposes replacement timing and new products. The maintenance unit creates a maintenance schedule and sends notifications by the specific processing unit 290 of the data processing unit 12. The parts proposal unit analyzes the amount and usage status of spare parts by the specific processing unit 290 of the data processing unit 12 and supports purchasing and price comparison. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] (Note 1) A collection unit that collects information on home appliances, A management unit manages the information collected by the aforementioned collection unit, A troubleshooting unit that performs troubleshooting based on information managed by the aforementioned management unit, A proposal unit that makes replacement suggestions based on the information obtained by the troubleshooting unit, A maintenance unit manages the maintenance schedule based on the information proposed by the aforementioned proposal unit, The system includes a parts suggestion unit that provides suggestions and purchase support for spare parts based on information managed by the aforementioned maintenance unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Information about home appliances is collected by taking a picture of the instruction manual cover with a smartphone camera. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned management department, It manages collected information on home appliances and automatically calculates the warranty period. The system described in Appendix 1, characterized by the features described herein. (Note 4) The troubleshooting unit described above, Displaying appropriate contact information and troubleshooting steps in case of appliance malfunction. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We analyze home appliance usage patterns and the latest market information to suggest the best time to replace appliances and recommend new products. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned maintenance unit is Automatically creates maintenance schedules for home appliances and notifies users of scheduled maintenance times. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned parts proposal unit, We analyze the storage volume and usage status of spare parts for registered home appliances, suggest necessary spare parts, and provide support for purchasing and price comparison. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting home appliance information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We analyze the purchase history of home appliances and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting information on home appliances, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and determines the priority of home appliance information to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting information on home appliances, the system prioritizes collecting highly relevant information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting information on home appliances, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned management department, The system estimates the user's emotions and adjusts the method of managing home appliance information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned management department, When managing home appliance information, prioritize management based on the remaining warranty period. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned management department, When managing home appliance information, different management algorithms are applied to each category of home appliance. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned management department, The system estimates the user's emotions and adjusts how home appliance information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned management department, When managing home appliance information, the system selects the optimal management method by referring to the user's past management history. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned management department, When managing home appliance information, the optimal management method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The troubleshooting unit described above, It estimates the user's emotions and adjusts how troubleshooting is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The troubleshooting unit described above, During troubleshooting, refer to the appliance's failure history to select the most appropriate solution. The system described in Appendix 1, characterized by the features described herein. (Note 22) The troubleshooting unit described above, When troubleshooting, different troubleshooting algorithms are applied depending on the category of home appliance. The system described in Appendix 1, characterized by the features described herein. (Note 23) The troubleshooting unit described above, It estimates the user's emotions and prioritizes troubleshooting based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The troubleshooting unit described above, During troubleshooting, the system selects the most appropriate course of action based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The troubleshooting unit described above, During troubleshooting, we analyze the user's social media activity and suggest relevant solutions. The system described in Appendix 1, characterized by the features described herein. (Note 26) 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 27) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on how your home appliances are being used. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the category of home appliance. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When making proposals, prioritize them based on when the home appliances were purchased. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, When making a proposal, adjust the order of suggestions based on the relevance of the home appliances. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned maintenance unit is The system estimates user sentiment and adjusts how maintenance schedules are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned maintenance unit is When creating a maintenance schedule, optimize the schedule based on how often you use your appliances. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned maintenance unit is When creating a maintenance schedule, different scheduling algorithms are applied to each category of home appliance. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned maintenance unit is The system estimates user sentiment and prioritizes maintenance schedules based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned maintenance unit is When creating a maintenance schedule, the system will create the optimal schedule based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned maintenance unit is When creating maintenance schedules, we analyze users' social media activity and suggest relevant schedules. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned parts proposal unit, The system estimates the user's emotions and adjusts the method of suggesting spare parts based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned parts proposal unit, When suggesting spare parts, adjust the level of detail in the suggestion based on how the appliance is being used. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned parts proposal unit, When suggesting spare parts, different suggestion algorithms are applied for each category of home appliance. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned parts proposal unit, The system estimates the user's emotions and prioritizes the suggestion of spare parts based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned parts proposal unit, When suggesting spare parts, we provide optimal suggestions based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned parts proposal unit, When proposing spare parts, we analyze the user's social media activity and make relevant suggestions. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0209] 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 collection unit that collects information on home appliances, A management unit manages the information collected by the aforementioned collection unit, A troubleshooting unit that performs troubleshooting based on information managed by the aforementioned management unit, A proposal unit that makes replacement suggestions based on the information obtained by the troubleshooting unit, A maintenance unit manages the maintenance schedule based on the information proposed by the aforementioned proposal unit, The system includes a parts suggestion unit that provides suggestions and purchase support for spare parts based on information managed by the aforementioned maintenance unit. A system characterized by the following features.

2. The aforementioned collection unit is Information about home appliances is collected by taking a picture of the instruction manual cover with a smartphone camera. The system according to feature 1.

3. The aforementioned management department, It manages collected information on home appliances and automatically calculates the warranty period. The system according to feature 1.

4. The troubleshooting unit described above, Displaying appropriate contact information and troubleshooting steps in case of appliance malfunction. The system according to feature 1.

5. The aforementioned proposal section is, We analyze home appliance usage patterns and the latest market information to suggest the best time to replace appliances and recommend new products. The system according to feature 1.

6. The aforementioned maintenance unit is Automatically creates maintenance schedules for home appliances and notifies users of scheduled maintenance times. The system according to feature 1.

7. The aforementioned parts proposal unit, We analyze the storage volume and usage status of spare parts for registered home appliances, suggest necessary spare parts, and provide support for purchasing and price comparison. The system according to feature 1.

8. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting home appliance information based on those estimated emotions. The system according to feature 1.

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