System

Abstract

An object of a system according to an embodiment is to predict a failure of a home electric appliance and promptly perform an appropriate response.SOLUTION: A system includes a collection unit, an analysis unit, a determination unit, and a proposal unit. The collection unit collects information on a home appliance owned by a consumer. The analysis unit analyzes the information collected by the collection unit and predicts a failure of the home appliance. The determination unit determines whether repair is possible based on the failure predicted by the analysis unit. The proposal unit proposes a replacement model based on the preference of the consumer when the determination unit determines that the repair is impossible.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to predict failures in home appliances and to take appropriate measures quickly.

[0005] The system according to the embodiment aims to predict failures in home appliances and quickly take appropriate measures. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a determination unit, and a suggestion unit. The collection unit collects information about home appliances owned by consumers. The analysis unit analyzes the information collected by the collection unit and predicts failures of the home appliances. The determination unit determines whether the appliance can be repaired based on the failure predicted by the analysis unit. The suggestion unit suggests a replacement model based on the consumer's preferences if the determination unit determines that repair is not possible. [Effects of the Invention]

[0007] The system according to the embodiment can predict failures in home appliances and quickly take appropriate measures. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A home appliance lifecycle management system according to an embodiment of the present invention collects information about home appliances owned by consumers, analyzes it using a generation AI to predict failures, determines whether repairs are possible, and, if necessary, suggests replacement models. The home appliance lifecycle management system registers home appliances owned by consumers, and the generation AI analyzes the data of the registered home appliances to predict failures. Furthermore, if a failure is predicted, the system determines whether repairs are possible, and if repairs are not possible, suggests replacement models tailored to the consumer's preferences. For example, a home appliance lifecycle management system registers home appliances owned by consumers in the system. Examples of registered home appliances include refrigerators, washing machines, air conditioners, and televisions. The home appliance lifecycle management system then uses a generation AI to analyze the data of the registered home appliances and predict failures. The generation AI evaluates the failure risk of each product based on past failure data and product usage. For example, if a refrigerator is used frequently, the risk of failure is predicted to increase. The home appliance lifecycle management system then uses a generation AI to determine whether repairs are possible when a failure is predicted. The generation AI evaluates whether repairs are possible based on the product's failure location and repair history. For example, if a refrigerator's compressor breaks down, the system determines whether it can be repaired. If repair is not possible, the home appliance lifecycle management system uses generative AI to suggest a replacement model that matches the consumer's preferences. Generative AI suggests the optimal replacement model based on the consumer's past purchase history and preferences. For example, it can suggest an energy-efficient refrigerator or a washing machine with the latest features. This allows the home appliance lifecycle management system to predict home appliance failures in advance and take appropriate measures. For example, if a failure is predicted, the system can save up the purchase cost in advance. Even if repair is not possible, the system can suggest the optimal replacement model, allowing for a quick response. This makes it easier for consumers to deal with sudden failures in their daily lives and efficiently manage the lifecycle of their home appliances.

[0029] A home appliance lifecycle management system according to an embodiment includes a collection unit, an analysis unit, a determination unit, and a proposal unit. The collection unit collects information about home appliances owned by consumers. The information about home appliances owned by consumers includes, but is not limited to, the product type, manufacturer, purchase date, and frequency of use. The collection unit collects information about home appliances owned by consumers, such as refrigerators, washing machines, air conditioners, and televisions. The collection unit also stores the information entered by consumers in a database so that the analysis unit can use it. The analysis unit uses a generation AI to analyze the information collected by the collection unit and predict home appliance failures. The analysis is performed, for example, based on past failure data and product usage status, but is not limited to, for example. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze past failure data and evaluate failure risk. The analysis unit can also use a multimodal generation AI to analyze product usage status. The analysis unit can also use the generation AI to develop an algorithm for evaluating failure risk. For example, the text generation AI has learned a large amount of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses a machine learning algorithm to evaluate the risk of failure. The determination unit determines whether the product can be repaired based on the failure predicted by the analysis unit. The determination is made, for example, based on the product's fault location and repair history, but is not limited to such examples. For example, the determination unit determines whether a refrigerator compressor can be repaired if it breaks down. The determination unit can also determine whether a washing machine motor can be repaired if it breaks down. The determination unit can also determine whether an air conditioner compressor can be repaired if it breaks down. For example, the determination unit determines whether the product can be repaired based on a method for identifying the fault location and the repair history. If the determination unit determines that the product cannot be repaired, the suggestion unit suggests a replacement model based on the consumer's preferences. The suggestion is made, for example, based on the consumer's past purchase history and preferences, but is not limited to such examples. For example, the suggestion unit suggests an energy-efficient refrigerator.The suggestion unit can also suggest a washing machine with the latest features. The suggestion unit can also develop an algorithm for suggesting an optimal replacement model based on the consumer's preferences. For example, the suggestion unit suggests an optimal replacement model based on the consumer's past purchase history and preferences. This allows the home appliance lifecycle management system according to the embodiment to enable consumers to predict failures of home appliances in advance and take appropriate measures. Some or all of the above-described processing by the determination unit may be performed using, for example, AI, or without AI. For example, the determination unit may input failure information predicted by the analysis unit and determine whether the appliance can be repaired using an AI model that outputs whether the appliance can be repaired. Some or all of the above-described processing by the proposal unit may be performed using, for example, a generation AI. For example, the proposal unit may input information determined by the determination unit to be unrepairable and suggest a replacement model using a generation AI model that outputs a replacement model.

[0030] The home appliance life cycle management system includes a preference ascertaining unit for ascertaining consumer preferences. The preference ascertaining unit collects information for ascertaining consumer preferences. Consumer preferences include, but are not limited to, past purchase history and survey results. For example, the preference ascertaining unit collects the consumer's past purchase history and stores the information in a database. The preference ascertaining unit can also conduct surveys on consumers and collect the results. For example, the preference ascertaining unit ascertains the consumer's preferences based on the consumer's past purchase history. The preference ascertaining unit can also ascertain the consumer's preferences based on the results of the consumer survey. The preference ascertaining unit can also develop an algorithm for ascertaining consumer preferences. For example, the preference ascertaining unit ascertains the consumer's preferences based on the consumer's past purchase history and survey results. This enables suggestions based on the consumer's preferences. Some or all of the above-described processing in the preference ascertaining unit may be performed using, for example, AI, or may be performed without using AI. For example, the preference understanding unit can understand a consumer's preferences using an AI model that inputs the consumer's past purchase history and survey results and outputs the consumer's preferences.

[0031] The home appliance lifecycle management system includes a repair history management unit that manages repair histories. The repair history management unit manages the repair history of a home appliance. The repair history includes, for example, the repair date, repair content, and repair cost, but is not limited to these examples. For example, the repair history management unit records the repair date of the home appliance and stores it in a database. The repair history management unit can also record the repair content of the home appliance and store it in a database. For example, the repair history management unit can record the repair cost of the home appliance and store it in a database. The repair history management unit can also develop an algorithm for managing the repair history of the home appliance. For example, the repair history management unit manages the repair history based on the repair date, repair content, and repair cost of the home appliance. In this way, managing the repair history can more accurately determine whether or not the appliance can be repaired. Some or all of the above-described processing in the repair history management unit may be performed using, for example, AI, or may be performed without AI. For example, the repair history management unit can manage the repair history using an AI model that inputs the repair date, repair content, and repair cost of the home appliance and outputs the repair history.

[0032] The home appliance life cycle management system includes an expense management unit for saving purchase costs. The expense management unit manages savings for the purchase of home appliances. Purchase costs include, but are not limited to, savings plans and budget management, for example. The expense management unit, for example, creates a savings plan for the consumer and stores it in a database. The expense management unit can also manage the consumer's budget and store it in a database. For example, the expense management unit manages savings for purchase costs based on the consumer's savings plan. The expense management unit can also manage savings for purchase costs based on the consumer's budget management. The expense management unit can also develop an algorithm for saving purchase costs. For example, the expense management unit manages savings for purchase costs based on the consumer's savings plan and budget management. This enables efficient management of purchase costs. Some or all of the above-described processing in the expense management unit may be performed using, for example, AI, or may be performed without using AI. For example, the expense management unit can manage savings for purchases using an AI model that takes a consumer's savings plan and budget management as input and outputs management for saving for purchases.

[0033] The collection unit may collect information on the type of product, manufacturer, purchase date, and frequency of use. The collection unit may collect, for example, information on the type of product, manufacturer, purchase date, and frequency of use. Examples of product types include, but are not limited to, refrigerators, washing machines, air conditioners, and televisions. Examples of manufacturers include, but are not limited to, the name of a specific home appliance manufacturer. Examples of purchase dates include, but are not limited to, the specific date on which the product was purchased. Examples of frequency of use include, but are not limited to, information on how often the product is used. This allows for the collection of detailed information on home appliances, thereby improving the accuracy of failure prediction. Some or all of the above-described processing by the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit may collect information using an AI model that inputs information on the type of product, manufacturer, purchase date, and frequency of use and stores the collected information in a database.

[0034] The analysis unit can evaluate the failure risk based on past failure data and product usage conditions. The analysis unit evaluates the failure risk based on, for example, past failure data and product usage conditions. Past failure data includes, for example, but is not limited to, information on what kind of failures the product has experienced in the past. Product usage conditions include, for example, but are not limited to, information on how the product is used. This allows for highly accurate evaluation of failure risk by utilizing past data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the analysis unit can evaluate the failure risk using a generative AI model that inputs past failure data and product usage conditions and outputs a failure risk.

[0035] The determination unit can determine whether the product can be repaired based on the faulty part and repair history of the product. The determination unit determines whether the product can be repaired based on, for example, the faulty part and repair history of the product. The faulty part of the product includes, for example, but is not limited to, information on which part of the product is faulty. The repair history includes, for example, but is not limited to, information on what repairs the product has received in the past. This makes it possible to accurately determine whether the product can be repaired by taking the faulty part and repair history into consideration. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can determine whether the product can be repaired using an AI model that inputs the faulty part and repair history of the product and outputs whether the product can be repaired.

[0036] The suggestion unit can suggest a replacement model based on the consumer's past purchase history and preferences. The suggestion unit, for example, suggests a replacement model based on the consumer's past purchase history and preferences. The consumer's past purchase history includes, for example, but is not limited to, information on home appliances the consumer has previously purchased. The consumer's preferences include, for example, but are not limited to, product features and functions that the consumer prefers. This makes it possible to suggest an optimal replacement model based on the consumer's preferences. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can suggest a replacement model using a generation AI model that inputs the consumer's past purchase history and preferences and outputs a replacement model.

[0037] When collecting information about a home appliance, the collection unit can analyze the user's past usage history and select a collection method. For example, when collecting information about a home appliance, the collection unit analyzes the user's past usage history and selects an optimal collection method. The user's past usage history includes, but is not limited to, usage frequency, usage time, and usage environment. For example, the collection unit prioritizes collecting information about home appliances that the user uses frequently. The collection unit can also focus on collecting information about home appliances that the user has experienced problems with in the past. For example, the collection unit sets an optimal information collection timing based on the user's usage pattern. This enables optimal information collection by taking the user's past usage history into consideration. Some or all of the above-described processing in the collection unit may be performed using, or without using, AI. For example, the collection unit can input the user's past usage history into a generation AI and select an optimal collection method.

[0038] The collection unit may filter information about home appliances based on the user's current lifestyle and areas of interest when collecting the information. For example, the collection unit may filter information about home appliances based on the user's current lifestyle and areas of interest when collecting the information. Examples of the user's current lifestyle include, but are not limited to, a busy period or a relaxing period. Examples of the user's areas of interest include, but are not limited to, an interest in a new home appliance. For example, the collection unit collects only important information when the user is busy. Furthermore, if the user is interested in a new home appliance, the collection unit may also collect information related to the product. For example, the collection unit selects and collects appropriate information according to the user's lifestyle. This enables information collection according to the user's lifestyle and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may input the user's current lifestyle and areas of interest into a generation AI to perform filtering.

[0039] The collection unit can select a collection means according to a user's input method when collecting information about a home appliance. For example, when collecting information about a home appliance, the collection unit selects an optimal collection means according to the user's input method. User input methods include, but are not limited to, voice input, text input, and image input. For example, when the user uses voice input, the collection unit collects information using voice recognition technology. Furthermore, when the user uses text input, the collection unit can also collect information using text analysis technology. For example, when the user uses image input, the collection unit collects information using image recognition technology. This enables optimal information collection according to the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input method into a generation AI and select the optimal collection means.

[0040] When collecting information about home appliances, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, when collecting information about home appliances, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. The user's geographical location information includes, but is not limited to, GPS data, address information, etc. For example, when the user is at home, the collection unit prioritizes collecting information about home appliances used at home. Furthermore, when the user is traveling, the collection unit can prioritize collecting information about home appliances used at the travel destination. For example, when the user is at work, the collection unit prioritizes collecting information about home appliances used at work. This enables information collection based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into a generation AI and prioritize collecting highly relevant information.

[0041] The collection unit may analyze the user's social media activities and collect related information when collecting information about home appliances. For example, the collection unit may analyze the user's social media activities and collect related information when collecting information about home appliances. The user's social media activities may include, but are not limited to, the content of posts, the number of likes, and the number of followers. For example, the collection unit may collect information about home appliances mentioned by the user on social media. The collection unit may also analyze the content of the user's social media posts and collect information about related home appliances. For example, the collection unit may collect information about related home appliances by referring to the activities of the user's friends on social media. This enables information collection based on the user's social media activities. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's social media activities into a generation AI to collect related information.

[0042] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information about home appliances. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting information about home appliances. The user's past feedback includes, but is not limited to, survey results, review content, etc. For example, the collection unit improves the information collection method based on the user's past feedback. The collection unit can also prioritize the collection of specific information from the user's past feedback. For example, the collection unit adjusts the timing of information collection by reflecting the user's feedback. This allows the information collection method to be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback into a generation AI to customize the collection method.

[0043] The analysis unit can adjust the level of detail of the prediction based on the importance of the home appliance when predicting a failure. For example, the analysis unit adjusts the level of detail of the prediction based on the importance of the home appliance when predicting a failure. Examples of the importance of a home appliance include, but are not limited to, frequency of use, price, and importance of function. For example, the analysis unit performs detailed failure prediction for home appliances with high importance. The analysis unit can also perform simplified failure prediction for home appliances with low importance. For example, the analysis unit dynamically adjusts the level of detail of the prediction based on the importance of the home appliance. This enables failure prediction based on the importance of the home appliance. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the importance of the home appliance into the generation AI and adjust the level of detail of the prediction.

[0044] The analysis unit can apply different prediction algorithms depending on the category of the home appliance when predicting a failure. For example, the analysis unit applies different prediction algorithms depending on the category of the home appliance when predicting a failure. Categories of home appliances include, but are not limited to, refrigerators, washing machines, and air conditioners. For example, the analysis unit can apply an algorithm that emphasizes cooling system data to predict a failure of a refrigerator. The analysis unit can also apply an algorithm that emphasizes motor data to predict a failure of a washing machine. For example, the analysis unit can apply an algorithm that emphasizes compressor data to predict a failure of an air conditioner. This enables optimal failure prediction depending on the category of the home appliance. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the category of the home appliance into the generation AI and apply different prediction algorithms.

[0045] The analysis unit can improve the accuracy of a failure prediction by referring to the user's past prediction results. For example, the analysis unit can improve the accuracy of a failure prediction by referring to the user's past prediction results. The user's past prediction results include, but are not limited to, past failure prediction results and prediction accuracy. For example, the analysis unit can adjust a prediction algorithm based on the user's past failure prediction results. The analysis unit can also extract specific patterns from the user's past prediction results to improve the prediction accuracy. For example, the analysis unit can improve the prediction model by reflecting the user's past prediction results. This improves the prediction accuracy by referring to the user's past prediction results. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the user's past prediction results into the generation AI to improve the prediction accuracy.

[0046] The analysis unit can determine the priority of predictions based on the purchase date of the home appliance when predicting a failure. For example, the analysis unit determines the priority of predictions based on the purchase date of the home appliance when predicting a failure. The purchase date of the home appliance includes, but is not limited to, the purchase date and the start date of use. For example, the analysis unit prioritizes failure predictions for home appliances that have been purchased for a long time. The analysis unit can also lower the priority of failure predictions for home appliances that have been purchased recently. For example, the analysis unit dynamically adjusts the priority of predictions based on the purchase date of the home appliance. This enables appropriate failure predictions by determining the priority based on the purchase date of the home appliance. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the purchase date of the home appliance into the generation AI to determine the priority of predictions.

[0047] The analysis unit can adjust the order of predictions based on the relevance of home appliances when predicting failures. For example, the analysis unit adjusts the order of predictions based on the relevance of home appliances when predicting failures. Relevance of home appliances includes, but is not limited to, the same manufacturer or the same category. The analysis unit simultaneously predicts failures of home appliances that are highly related, such as refrigerators and freezers. The analysis unit can also individually predict failures of home appliances that are less related. For example, the analysis unit dynamically adjusts the order of predictions based on the relevance of home appliances. This enables efficient failure predictions by adjusting the prediction order based on the relevance of home appliances. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the relevance of home appliances into the generation AI and adjust the order of predictions.

[0048] The analysis unit can adjust the use of technical terminology in the prediction according to the user's level of expertise when predicting a failure. For example, the analysis unit can adjust the use of technical terminology in the prediction according to the user's level of expertise when predicting a failure. Examples of the user's level of expertise include, but are not limited to, survey results, past usage history, etc. For example, if the user has technical expertise, the analysis unit can provide a failure prediction using detailed technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can provide a failure prediction in simple terms. For example, the analysis unit dynamically adjusts the use of technical terminology in the prediction according to the user's level of expertise. This enables appropriate information to be provided by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the user's level of expertise into the generation AI and adjust the use of technical terminology in the prediction.

[0049] The determination unit can adjust the level of detail of the determination based on the fault location of the home appliance when determining whether the appliance can be repaired. For example, the determination unit adjusts the level of detail of the determination based on the fault location of the home appliance when determining whether the appliance can be repaired. Fault locations of the appliance include, but are not limited to, component failures and system failures. For example, the determination unit makes a detailed determination of whether the appliance can be repaired for important fault locations. The determination unit can also make a simplified determination of whether the appliance can be repaired for unimportant fault locations. For example, the determination unit dynamically adjusts the level of detail of the determination depending on the fault location of the appliance. This enables an appropriate determination of whether the appliance can be repaired by adjusting the level of detail of the determination based on the fault location of the appliance. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the fault location of the appliance into a generation AI and adjust the level of detail of the determination.

[0050] The determination unit can apply different determination algorithms depending on the category of the home appliance when determining whether the appliance can be repaired. For example, the determination unit applies different determination algorithms depending on the category of the home appliance when determining whether the appliance can be repaired. Categories of home appliances include, but are not limited to, refrigerators, washing machines, and air conditioners. For example, the determination unit applies an algorithm that emphasizes cooling system data to determine whether a refrigerator can be repaired. The determination unit can also apply an algorithm that emphasizes motor data to determine whether a washing machine can be repaired. For example, the determination unit applies an algorithm that emphasizes compressor data to determine whether an air conditioner can be repaired. This enables an optimal determination of whether the appliance can be repaired depending on the category of the home appliance. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the category of the home appliance into a generation AI and apply different determination algorithms.

[0051] The determination unit can improve the accuracy of the determination by referring to the user's past determination results when determining whether the product is repairable. For example, the determination unit improves the accuracy of the determination by referring to the user's past determination results when determining whether the product is repairable. The user's past determination results include, but are not limited to, past repairability determination results and determination accuracy. For example, the determination unit adjusts the determination algorithm based on the user's past repairability determination results. The determination unit can also extract specific patterns from the user's past determination results to improve the determination accuracy. For example, the determination unit improves the determination model by reflecting the user's past determination results. This improves the accuracy of the determination by referring to the user's past determination results. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the user's past determination results into the generation AI to improve the accuracy of the determination.

[0052] The determination unit can determine the priority of the determination based on the purchase date of the home appliance when determining whether the appliance can be repaired. For example, the determination unit determines the priority of the determination based on the purchase date of the home appliance when determining whether the appliance can be repaired. The purchase date of the home appliance includes, but is not limited to, the purchase date and the date of first use. For example, the determination unit prioritizes the determination of whether the appliance can be repaired for a home appliance that has been purchased for a long time. The determination unit can also lower the priority of the determination of whether the appliance can be repaired for a home appliance that was purchased recently. For example, the determination unit dynamically adjusts the priority of the determination based on the purchase date of the home appliance. This enables an appropriate determination of whether the appliance can be repaired by determining the priority based on the purchase date of the home appliance. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the purchase date of the home appliance into the generation AI to determine the priority of the determination.

[0053] The determination unit can adjust the order of determination based on the relevance of the home appliances when determining whether the appliances can be repaired. For example, the determination unit adjusts the order of determination based on the relevance of the home appliances when determining whether the appliances can be repaired. Examples of the relevance of the home appliances include, but are not limited to, the same manufacturer or the same category. The determination unit simultaneously determines whether highly related home appliances, such as a refrigerator and a freezer, can be repaired. The determination unit can also individually determine whether less related home appliances can be repaired. For example, the determination unit dynamically adjusts the order of determination based on the relevance of the home appliances. This enables efficient determination of whether the appliances can be repaired. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the relevance of the home appliances into a generation AI and adjust the order of determination.

[0054] The determination unit can adjust the use of technical terms for the determination depending on the user's level of expertise when determining whether the product is repairable. For example, the determination unit adjusts the use of technical terms for the determination depending on the user's level of expertise when determining whether the product is repairable. Examples of the user's level of expertise include, but are not limited to, survey results and past usage history. For example, if the user has specialized knowledge, the determination unit can provide a repairability determination using detailed technical terms. Furthermore, if the user does not have specialized knowledge, the determination unit can also provide a repairability determination in simple terms. For example, the determination unit dynamically adjusts the use of technical terms for the determination depending on the user's level of expertise. This enables appropriate information to be provided by adjusting the use of technical terms depending on the user's level of expertise. Some or all of the above-described processing by the determination unit may be performed using, for example, AI, or may be performed without AI. For example, the determination unit can input the user's level of expertise into the generation AI and adjust the use of technical terms for the determination.

[0055] The suggestion unit can adjust the level of detail of the proposal based on the importance of the home appliance when suggesting a replacement model. For example, the suggestion unit adjusts the level of detail of the proposal based on the importance of the home appliance when suggesting a replacement model. The importance of a home appliance includes, but is not limited to, frequency of use, price, and importance of functions. For example, the suggestion unit makes a detailed replacement model proposal for a home appliance with high importance. The suggestion unit can also make a simplified replacement model proposal for a home appliance with low importance. For example, the suggestion unit dynamically adjusts the level of detail of the proposal according to the importance of the home appliance. This makes it possible to suggest an appropriate replacement model by adjusting the level of detail of the proposal according to the importance of the home appliance. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the importance of the home appliance into the generation AI and adjust the level of detail of the proposal.

[0056] The suggestion unit can apply different suggestion algorithms depending on the category of the home appliance when suggesting a replacement model. For example, the suggestion unit applies different suggestion algorithms depending on the category of the home appliance when suggesting a replacement model. Home appliance categories include, but are not limited to, refrigerators, washing machines, and air conditioners. For example, the suggestion unit can apply an algorithm that emphasizes energy efficiency when suggesting a replacement for a refrigerator. The suggestion unit can also apply an algorithm that emphasizes cleaning power when suggesting a replacement for a washing machine. For example, the suggestion unit can apply an algorithm that emphasizes heating and cooling capacity when suggesting a replacement for an air conditioner. This makes it possible to suggest an optimal replacement model depending on the category of the home appliance. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the suggestion unit can input the category of the home appliance into the generation AI and apply different suggestion algorithms.

[0057] The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestions when suggesting a replacement model. For example, the suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestions when suggesting a replacement model. The user's past suggestions include, but are not limited to, past replacement suggestion results and suggestion accuracy. For example, the suggestion unit can adjust the suggestion algorithm based on the user's past suggestions. The suggestion unit can also extract specific patterns from the user's past suggestions to improve the suggestion accuracy. For example, the suggestion unit can improve the suggestion model by reflecting the user's past suggestions. By referring to the user's past suggestions, the suggestion accuracy can be improved. Some or all of the above-described processing in the suggestion unit can be performed using, or without, a generation AI. For example, the suggestion unit can input the user's past suggestions into the generation AI to improve the suggestion accuracy.

[0058] When proposing a replacement model, the suggestion unit can determine the priority of the suggestions based on the purchase date of the home appliance. For example, when proposing a replacement model, the suggestion unit determines the priority of the suggestions based on the purchase date of the home appliance. The purchase date of the home appliance includes, but is not limited to, the purchase date and the start date of use. For example, the suggestion unit prioritizes the suggestion of a replacement model for a home appliance that has been purchased for a long time. The suggestion unit can also lower the priority of the suggestion of a replacement model for a home appliance that has been purchased recently. For example, the suggestion unit dynamically adjusts the priority of the suggestions depending on the purchase date of the home appliance. This enables the suggestion of an appropriate replacement model by determining the priority based on the purchase date of the home appliance. Some or all of the above-described processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the purchase date of the home appliance into the generation AI and determine the priority of the suggestions.

[0059] The suggestion unit can adjust the order of suggestions based on the relevance of the home appliances when suggesting replacement models. For example, the suggestion unit adjusts the order of suggestions based on the relevance of the home appliances when suggesting replacement models. Examples of relevance of home appliances include, but are not limited to, the same manufacturer and the same category. The suggestion unit simultaneously suggests replacement models for highly related home appliances, such as a refrigerator and a freezer. The suggestion unit can also individually suggest replacement models for less related home appliances. For example, the suggestion unit dynamically adjusts the order of suggestions based on the relevance of the home appliances. This enables efficient suggestions for replacement models. Some or all of the above-described processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the relevance of the home appliances into the generation AI and adjust the order of suggestions.

[0060] The suggestion unit may adjust the use of technical terms in the proposal according to the user's level of expertise when proposing a replacement model. For example, the suggestion unit may adjust the use of technical terms in the proposal according to the user's level of expertise when proposing a replacement model. Examples of the user's level of expertise include, but are not limited to, survey results and past usage history. For example, if the user has technical expertise, the suggestion unit may provide a replacement model proposal using detailed technical terms. Furthermore, if the user does not have technical expertise, the suggestion unit may provide a replacement model proposal in simple language. For example, the suggestion unit may dynamically adjust the use of technical terms in the proposal according to the user's level of expertise. This enables appropriate information to be provided by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit may input the user's level of expertise into the generation AI and adjust the use of technical terms in the proposal.

[0061] The preference understanding unit can analyze the user's past purchase history to select the optimal preference understanding method when understanding the user's preferences. For example, the preference understanding unit analyzes the user's past purchase history to select the optimal preference understanding method when understanding the user's preferences. The user's past purchase history includes, but is not limited to, purchase dates, purchased products, and purchase frequency. The preference understanding unit selects the optimal preference understanding method based on the user's past purchase history. The preference understanding unit can also extract specific patterns from the user's past purchase history and improve the preference understanding method. For example, the preference understanding unit dynamically adjusts the preference understanding method by reflecting the user's past purchase history. This allows the optimal preference understanding method to be provided by analyzing the user's past purchase history. Some or all of the above-described processing in the preference understanding unit may be performed using, or without, AI. For example, the preference understanding unit can input the user's past purchase history into a generation AI to select the optimal preference understanding method.

[0062] The preference understanding unit can perform filtering based on the user's current living situation and areas of interest when understanding the preferences. For example, the preference understanding unit performs filtering based on the user's current living situation and areas of interest when understanding the preferences. The user's current living situation includes, but is not limited to, for example, a living environment and a method for identifying areas of interest. For example, the preference understanding unit selects an optimal preference understanding method based on the user's current living situation. The preference understanding unit can also adjust the preference understanding method based on the user's areas of interest. For example, the preference understanding unit provides an appropriate preference understanding method in accordance with the user's lifestyle. This makes it possible to understand the preferences according to the user's living situation and areas of interest. Some or all of the above-described processing in the preference understanding unit may be performed using, for example, AI, or may be performed without using AI. For example, the preference understanding unit can input the user's current living situation and areas of interest into a generation AI and perform filtering.

[0063] The preference understanding unit can prioritize highly relevant preferences when understanding the preferences by taking into account the user's geographical location information. For example, the preference understanding unit prioritizes highly relevant preferences when understanding the preferences by taking into account the user's geographical location information. The user's geographical location information includes, but is not limited to, GPS data, address information, etc. For example, when the user is at home, the preference understanding unit prioritizes understanding the preferences of home appliances used at home. Furthermore, when the user is traveling, the preference understanding unit can also prioritize understanding the preferences of home appliances used at the travel destination. For example, when the user is at work, the preference understanding unit prioritizes understanding the preferences of home appliances used at work. This makes it possible to understand preferences based on the user's geographical location information. Some or all of the above-described processing in the preference understanding unit may be performed using, for example, AI, or may be performed without using AI. For example, the preference understanding unit can input the user's geographical location information to a generation AI and prioritize understanding highly relevant preferences.

[0064] The preference understanding unit can analyze the user's social media activity and understand the related preferences when understanding the preferences. For example, the preference understanding unit analyzes the user's social media activity and understands the related preferences when understanding the preferences. The user's social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the preference understanding unit understands the preferences of home appliances mentioned by the user on social media. The preference understanding unit can also analyze the content of the user's posts on social media to understand the preferences of related home appliances. For example, the preference understanding unit understands the preferences of related home appliances by referring to the activities of the user's friends on social media. This makes it possible to understand the preferences based on the user's social media activity. Some or all of the above-described processing in the preference understanding unit may be performed using, or without, AI. For example, the preference understanding unit can input the user's social media activity into a generation AI to understand the related preferences.

[0065] The repair history management unit can adjust the level of detail of the management based on the fault location of the home appliance when managing the repair history. For example, the repair history management unit adjusts the level of detail of the management based on the fault location of the home appliance when managing the repair history. Fault locations of the home appliance include, but are not limited to, component failures and system failures. For example, the repair history management unit manages detailed repair histories for important fault locations. The repair history management unit can also manage simplified repair histories for unimportant fault locations. For example, the repair history management unit dynamically adjusts the level of detail of the management based on the fault location of the home appliance. This enables appropriate repair history management by adjusting the level of detail of the management based on the fault location of the home appliance. Some or all of the above-described processing in the repair history management unit may be performed using, for example, AI, or may be performed without using AI. For example, the repair history management unit can input the fault location of the home appliance into a generation AI and adjust the level of detail of the management.

[0066] The repair history management unit can apply different management algorithms depending on the category of the home appliance when managing the repair history. For example, the repair history management unit applies different management algorithms depending on the category of the home appliance when managing the repair history. Home appliance categories include, but are not limited to, refrigerators, washing machines, and air conditioners. For example, the repair history management unit applies an algorithm that emphasizes cooling system data when managing the repair history of refrigerators. Furthermore, the repair history management unit can also apply an algorithm that emphasizes motor data when managing the repair history of washing machines. For example, the repair history management unit applies an algorithm that emphasizes compressor data when managing the repair history of air conditioners. This enables optimal repair history management depending on the category of the home appliance. Some or all of the above-described processing in the repair history management unit may be performed using, for example, AI, or may be performed without using AI. For example, the repair history management unit can input the category of the home appliance into a generation AI and apply different management algorithms.

[0067] The repair history management unit can improve the accuracy of the repair history management by referring to the user's past repair history. For example, the repair history management unit can improve the accuracy of the management by referring to the user's past repair history. The user's past repair history includes, but is not limited to, the repair date, repair content, and repair cost. For example, the repair history management unit can adjust the management algorithm based on the user's past repair history. The repair history management unit can also extract specific patterns from the user's past repair history to improve the management accuracy. For example, the repair history management unit can improve the management model by reflecting the user's past repair history. By referring to the user's past repair history, the management accuracy can be improved. Some or all of the above-described processing in the repair history management unit can be performed using, for example, AI, or without AI. For example, the repair history management unit can input the user's past repair history into a generation AI to improve the management accuracy.

[0068] The repair history management unit can determine the management priority based on the purchase date of the home appliance when managing the repair history. For example, the repair history management unit determines the management priority based on the purchase date of the home appliance when managing the repair history. The purchase date of the home appliance includes, but is not limited to, the purchase date and the start date of use. For example, the repair history management unit prioritizes management of the repair history of a home appliance that has been purchased for a long time. The repair history management unit can also lower the priority of management of the repair history of a home appliance that was purchased recently. For example, the repair history management unit dynamically adjusts the management priority based on the purchase date of the home appliance. This enables appropriate management of the repair history by determining the priority based on the purchase date of the home appliance. Some or all of the above-described processing in the repair history management unit may be performed using, for example, AI, or may be performed without using AI. For example, the repair history management unit can input the purchase date of the home appliance into a generation AI to determine the management priority.

[0069] The repair history management unit can adjust the management order based on the relevance of home appliances when managing repair histories. For example, the repair history management unit adjusts the management order based on the relevance of home appliances when managing repair histories. Relevance of home appliances includes, but is not limited to, the same manufacturer or the same category. The repair history management unit simultaneously manages the repair histories of home appliances that are highly related, such as refrigerators and freezers. The repair history management unit can also manage the repair histories individually for home appliances that are less related. For example, the repair history management unit dynamically adjusts the management order based on the relevance of home appliances. This enables efficient management of repair histories by adjusting the management order based on the relevance of home appliances. Some or all of the above-described processing in the repair history management unit may be performed using, for example, AI, or may be performed without AI. For example, the repair history management unit can input the relevance of home appliances into a generation AI and adjust the management order.

[0070] The repair history management unit can adjust the use of technical terms in the management of repair histories according to the user's level of expertise when managing repair histories. For example, the repair history management unit can adjust the use of technical terms in the management of repair histories according to the user's level of expertise when managing repair histories. Examples of the user's level of expertise include, but are not limited to, survey results, past usage history, etc. For example, if the user has technical expertise, the repair history management unit can provide repair history management using detailed technical terms. Furthermore, if the user does not have technical expertise, the repair history management unit can also provide repair history management in simple terms. For example, the repair history management unit dynamically adjusts the use of technical terms in the management of repair histories according to the user's level of expertise. This enables appropriate information to be provided by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-described processing in the repair history management unit may be performed using, for example, AI, or may be performed without AI. For example, the repair history management unit can input the user's level of expertise into the generation AI and adjust the use of technical terms in the management of repair histories.

[0071] The expense management unit can adjust the level of detail of management based on the importance of the home appliance during expense management. For example, the expense management unit adjusts the level of detail of management based on the importance of the home appliance during expense management. The importance of the home appliance includes, but is not limited to, for example, frequency of use, price, and importance of function. For example, the expense management unit performs detailed expense management for home appliances with high importance. The expense management unit can also perform simplified expense management for home appliances with low importance. For example, the expense management unit dynamically adjusts the level of detail of management according to the importance of the home appliance. This enables appropriate expense management by adjusting the level of detail of management according to the importance of the home appliance. Some or all of the above-described processing in the expense management unit may be performed using, for example, AI, or may be performed without using AI. For example, the expense management unit can input the importance of the home appliance into a generation AI and adjust the level of detail of management.

[0072] The expense management unit can apply different management algorithms depending on the category of the home appliance during expense management. For example, the expense management unit applies different management algorithms depending on the category of the home appliance during expense management. Categories of home appliances include, but are not limited to, refrigerators, washing machines, and air conditioners. For example, the expense management unit applies an algorithm that emphasizes energy efficiency when managing the expense of a refrigerator. The expense management unit can also apply an algorithm that emphasizes cleaning power when managing the expense of a washing machine. For example, the expense management unit applies an algorithm that emphasizes heating and cooling capacity when managing the expense of an air conditioner. This enables optimal expense management according to the category of the home appliance. Some or all of the above-mentioned processing in the expense management unit may be performed using, for example, AI, or may be performed without using AI. For example, the expense management unit can input the category of the home appliance into a generation AI and apply different management algorithms.

[0073] The expense management unit can improve the accuracy of expense management by referring to the user's past expense management history during expense management. For example, the expense management unit can improve the accuracy of expense management by referring to the user's past expense management history during expense management. The user's past expense management history includes, but is not limited to, past expense management results and management accuracy. For example, the expense management unit can adjust the management algorithm based on the user's past expense management history. The expense management unit can also extract specific patterns from the user's past expense management history to improve management accuracy. For example, the expense management unit can improve the management model by reflecting the user's past expense management history. By referring to the user's past expense management history, the accuracy of management is improved. Some or all of the above-described processing in the expense management unit can be performed using, for example, AI, or without AI. For example, the expense management unit can input the user's past expense management history into a generation AI to improve management accuracy.

[0074] The expense management unit can determine management priorities based on the purchase date of the home appliances during expense management. For example, the expense management unit determines management priorities based on the purchase date of the home appliances during expense management. The purchase date of the home appliances includes, but is not limited to, the purchase date and the start date of use. For example, the expense management unit prioritizes expense management for home appliances that have been purchased for some time. The expense management unit can also lower the priority of expense management for home appliances that have been purchased recently. For example, the expense management unit dynamically adjusts management priorities based on the purchase date of the home appliances. This enables appropriate expense management by determining priorities based on the purchase date of the home appliances. Some or all of the above-described processing in the expense management unit may be performed using, for example, AI, or may be performed without using AI. For example, the expense management unit can input the purchase date of the home appliances into a generation AI to determine management priorities.

[0075] The expense management unit can adjust the management order based on the relevance of home appliances during expense management. For example, the expense management unit adjusts the management order based on the relevance of home appliances during expense management. Examples of the relevance of home appliances include, but are not limited to, appliances from the same manufacturer or the same category. The expense management unit simultaneously manages expenses for home appliances that are highly related, such as refrigerators and freezers. The expense management unit can also manage expenses individually for home appliances that are less related. For example, the expense management unit dynamically adjusts the management order based on the relevance of home appliances. This enables efficient expense management by adjusting the management order based on the relevance of home appliances. Some or all of the above-described processing in the expense management unit may be performed using, for example, AI, or may be performed without AI. For example, the expense management unit can input the relevance of home appliances into a generation AI and adjust the management order.

[0076] The expense management unit can adjust the use of management terminology according to the user's level of expertise during expense management. For example, the expense management unit can adjust the use of management terminology according to the user's level of expertise during expense management. Examples of the user's level of expertise include, but are not limited to, survey results, past usage history, etc. For example, if the user has expertise, the expense management unit can provide expense management using detailed terminology. Furthermore, if the user does not have expertise, the expense management unit can also provide expense management in simple terms. For example, the expense management unit dynamically adjusts the use of management terminology according to the user's level of expertise. This enables appropriate information to be provided by adjusting the use of terminology according to the user's level of expertise. Some or all of the above-described processing in the expense management unit can be performed using, for example, AI, or without AI. For example, the expense management unit can input the user's level of expertise into the generation AI and adjust the use of management terminology.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The home appliance lifecycle management system can analyze the usage patterns of home appliances based on the consumer's lifestyle and suggest optimal usage methods. For example, if the consumer travels frequently, the analysis unit can recommend the use of energy-efficient home appliances. Also, if the consumer works from home, it can suggest a home appliance usage schedule that suits the consumer's time at home. Furthermore, if the consumer is health-conscious, it can also suggest health-conscious ways to use home appliances. This makes it possible to provide optimal home appliance usage methods that suit the consumer's lifestyle.

[0079] The home appliance lifecycle management system can collect consumer health data and suggest ways to use home appliances based on the consumer's health condition. For example, the collection unit collects the consumer's sleep data, and the analysis unit uses that data to suggest ways to use home appliances that provide an optimal sleeping environment. It can also collect the consumer's exercise data and suggest ways to use home appliances that are suitable for post-exercise recovery. It can also collect the consumer's dietary data and suggest ways to use home appliances that support a healthy diet. This makes it possible to provide optimal ways to use home appliances that are tailored to the consumer's health condition.

[0080] The home appliance lifecycle management system can collect energy consumption data from consumers and suggest ways to use home appliances to optimize energy efficiency. For example, the collection unit collects energy consumption data from consumers, and the analysis unit suggests ways to use energy-efficient home appliances based on that data. The system can also analyze consumers' energy consumption patterns and suggest usage schedules to reduce energy consumption during peak hours. It can also suggest ways to use home appliances that are in line with the consumer's energy consumption goals. This makes it possible to provide ways to use home appliances to optimize the consumer's energy consumption.

[0081] The home appliance lifecycle management system can collect environmental data from consumers and suggest environmentally friendly ways to use home appliances. For example, the collection unit collects data on the consumer's living environment, and the analysis unit uses that data to suggest environmentally friendly ways to use home appliances. It can also collect climate data from the consumer's region and suggest ways to use home appliances that are suitable for the climate. It can also suggest ways to use home appliances based on the consumer's eco-consciousness. This makes it possible to provide consumers with optimal, environmentally friendly ways to use home appliances.

[0082] The home appliance lifecycle management system can collect consumer family composition data and suggest ways to use home appliances that are suitable for all family members. For example, the collection unit collects the consumer's family composition data, and the analysis unit uses that data to suggest ways to use home appliances that are comfortable for all family members. It can also suggest ways to use home appliances that are appropriate for different age groups in the family. It can also suggest ways to use home appliances that are suited to the family's lifestyle. This makes it possible to provide optimal ways to use home appliances that are suitable for all family members.

[0083] The processing flow of the first embodiment will be briefly explained below.

[0084] Step 1: The collection unit collects information about home appliances owned by consumers. The collected information includes the product type, manufacturer, purchase date, and frequency of use. The collection unit collects information about home appliances such as refrigerators, washing machines, air conditioners, and televisions, and stores the information entered by consumers in a database. Step 2: The analysis unit analyzes the information collected by the collection unit and predicts failures of home appliances. The analysis is based on past failure data and product usage, and uses generative AI to evaluate the risk of failure. For example, text generation AI and multimodal generation AI are used to develop algorithms for evaluating failure risk. Step 3: The judgment unit determines whether or not the product can be repaired based on the failure predicted by the analysis unit. The judgment is made based on the product's fault location and repair history. For example, if a refrigerator compressor, washing machine motor, or air conditioner compressor breaks down, it determines whether the product can be repaired. Step 4: If the judgment unit determines that repair is not possible, the suggestion unit suggests a replacement model based on the consumer's preferences. The suggestion is based on the consumer's past purchase history and preferences. For example, it suggests an energy-efficient refrigerator or a washing machine with the latest features.

[0085] (Example 2) A home appliance lifecycle management system according to an embodiment of the present invention collects information about home appliances owned by consumers, analyzes it using a generation AI to predict failures, determines whether repairs are possible, and, if necessary, suggests replacement models. The home appliance lifecycle management system registers home appliances owned by consumers, and the generation AI analyzes the data of the registered home appliances to predict failures. Furthermore, if a failure is predicted, the system determines whether repairs are possible, and if repairs are not possible, suggests replacement models tailored to the consumer's preferences. For example, a home appliance lifecycle management system registers home appliances owned by consumers in the system. Examples of registered home appliances include refrigerators, washing machines, air conditioners, and televisions. The home appliance lifecycle management system then uses a generation AI to analyze the data of the registered home appliances and predict failures. The generation AI evaluates the failure risk of each product based on past failure data and product usage. For example, if a refrigerator is used frequently, the risk of failure is predicted to increase. The home appliance lifecycle management system then uses a generation AI to determine whether repairs are possible when a failure is predicted. The generation AI evaluates whether repairs are possible based on the product's failure location and repair history. For example, if a refrigerator's compressor breaks down, the system determines whether it can be repaired. If repair is not possible, the home appliance lifecycle management system uses generative AI to suggest a replacement model that matches the consumer's preferences. Generative AI suggests the optimal replacement model based on the consumer's past purchase history and preferences. For example, it can suggest an energy-efficient refrigerator or a washing machine with the latest features. This allows the home appliance lifecycle management system to predict home appliance failures in advance and take appropriate measures. For example, if a failure is predicted, the system can save up the purchase cost in advance. Even if repair is not possible, the system can suggest the optimal replacement model, allowing for a quick response. This makes it easier for consumers to deal with sudden failures in their daily lives and efficiently manage the lifecycle of their home appliances.

[0086] A home appliance lifecycle management system according to an embodiment includes a collection unit, an analysis unit, a determination unit, and a proposal unit. The collection unit collects information about home appliances owned by consumers. The information about home appliances owned by consumers includes, but is not limited to, the product type, manufacturer, purchase date, and frequency of use. The collection unit collects information about home appliances owned by consumers, such as refrigerators, washing machines, air conditioners, and televisions. The collection unit also stores the information entered by consumers in a database so that the analysis unit can use it. The analysis unit uses a generation AI to analyze the information collected by the collection unit and predict home appliance failures. The analysis is performed, for example, based on past failure data and product usage status, but is not limited to, for example. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze past failure data and evaluate failure risk. The analysis unit can also use a multimodal generation AI to analyze product usage status. The analysis unit can also use the generation AI to develop an algorithm for evaluating failure risk. For example, the text generation AI has learned a large amount of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses a machine learning algorithm to evaluate the risk of failure. The determination unit determines whether the product can be repaired based on the failure predicted by the analysis unit. The determination is made, for example, based on the product's fault location and repair history, but is not limited to such examples. For example, the determination unit determines whether a refrigerator compressor can be repaired if it breaks down. The determination unit can also determine whether a washing machine motor can be repaired if it breaks down. The determination unit can also determine whether an air conditioner compressor can be repaired if it breaks down. For example, the determination unit determines whether the product can be repaired based on a method for identifying the fault location and the repair history. If the determination unit determines that the product cannot be repaired, the suggestion unit suggests a replacement model based on the consumer's preferences. The suggestion is made, for example, based on the consumer's past purchase history and preferences, but is not limited to such examples. For example, the suggestion unit suggests an energy-efficient refrigerator.The suggestion unit can also suggest a washing machine with the latest features. The suggestion unit can also develop an algorithm for suggesting an optimal replacement model based on the consumer's preferences. For example, the suggestion unit suggests an optimal replacement model based on the consumer's past purchase history and preferences. This allows the home appliance lifecycle management system according to the embodiment to enable consumers to predict failures of home appliances in advance and take appropriate measures. Some or all of the above-described processing by the determination unit may be performed using, for example, AI, or without AI. For example, the determination unit may input failure information predicted by the analysis unit and determine whether the appliance can be repaired using an AI model that outputs whether the appliance can be repaired. Some or all of the above-described processing by the proposal unit may be performed using, for example, a generation AI. For example, the proposal unit may input information determined by the determination unit to be unrepairable and suggest a replacement model using a generation AI model that outputs a replacement model.

[0087] The home appliance life cycle management system includes a preference ascertaining unit for ascertaining consumer preferences. The preference ascertaining unit collects information for ascertaining consumer preferences. Consumer preferences include, but are not limited to, past purchase history and survey results. For example, the preference ascertaining unit collects the consumer's past purchase history and stores the information in a database. The preference ascertaining unit can also conduct surveys on consumers and collect the results. For example, the preference ascertaining unit ascertains the consumer's preferences based on the consumer's past purchase history. The preference ascertaining unit can also ascertain the consumer's preferences based on the results of the consumer survey. The preference ascertaining unit can also develop an algorithm for ascertaining consumer preferences. For example, the preference ascertaining unit ascertains the consumer's preferences based on the consumer's past purchase history and survey results. This enables suggestions based on the consumer's preferences. Some or all of the above-described processing in the preference ascertaining unit may be performed using, for example, AI, or may be performed without using AI. For example, the preference understanding unit can understand a consumer's preferences using an AI model that inputs the consumer's past purchase history and survey results and outputs the consumer's preferences.

[0088] The home appliance lifecycle management system includes a repair history management unit that manages repair histories. The repair history management unit manages the repair history of a home appliance. The repair history includes, for example, the repair date, repair content, and repair cost, but is not limited to these examples. For example, the repair history management unit records the repair date of the home appliance and stores it in a database. The repair history management unit can also record the repair content of the home appliance and store it in a database. For example, the repair history management unit can record the repair cost of the home appliance and store it in a database. The repair history management unit can also develop an algorithm for managing the repair history of the home appliance. For example, the repair history management unit manages the repair history based on the repair date, repair content, and repair cost of the home appliance. In this way, managing the repair history can more accurately determine whether or not the appliance can be repaired. Some or all of the above-described processing in the repair history management unit may be performed using, for example, AI, or may be performed without AI. For example, the repair history management unit can manage the repair history using an AI model that inputs the repair date, repair content, and repair cost of the home appliance and outputs the repair history.

[0089] The home appliance life cycle management system includes an expense management unit for saving purchase costs. The expense management unit manages savings for the purchase of home appliances. Purchase costs include, but are not limited to, savings plans and budget management, for example. The expense management unit, for example, creates a savings plan for the consumer and stores it in a database. The expense management unit can also manage the consumer's budget and store it in a database. For example, the expense management unit manages savings for purchase costs based on the consumer's savings plan. The expense management unit can also manage savings for purchase costs based on the consumer's budget management. The expense management unit can also develop an algorithm for saving purchase costs. For example, the expense management unit manages savings for purchase costs based on the consumer's savings plan and budget management. This enables efficient management of purchase costs. Some or all of the above-described processing in the expense management unit may be performed using, for example, AI, or may be performed without using AI. For example, the expense management unit can manage savings for purchases using an AI model that takes a consumer's savings plan and budget management as input and outputs management for saving for purchases.

[0090] The collection unit may collect information on the type of product, manufacturer, purchase date, and frequency of use. The collection unit may collect, for example, information on the type of product, manufacturer, purchase date, and frequency of use. Examples of product types include, but are not limited to, refrigerators, washing machines, air conditioners, and televisions. Examples of manufacturers include, but are not limited to, the name of a specific home appliance manufacturer. Examples of purchase dates include, but are not limited to, the specific date on which the product was purchased. Examples of frequency of use include, but are not limited to, information on how often the product is used. This allows for the collection of detailed information on home appliances, thereby improving the accuracy of failure prediction. Some or all of the above-described processing by the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit may collect information using an AI model that inputs information on the type of product, manufacturer, purchase date, and frequency of use and stores the collected information in a database.

[0091] The analysis unit can evaluate the failure risk based on past failure data and product usage conditions. The analysis unit evaluates the failure risk based on, for example, past failure data and product usage conditions. Past failure data includes, for example, but is not limited to, information on what kind of failures the product has experienced in the past. Product usage conditions include, for example, but are not limited to, information on how the product is used. This allows for highly accurate evaluation of failure risk by utilizing past data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the analysis unit can evaluate the failure risk using a generative AI model that inputs past failure data and product usage conditions and outputs a failure risk.

[0092] The determination unit can determine whether the product can be repaired based on the faulty part and repair history of the product. The determination unit determines whether the product can be repaired based on, for example, the faulty part and repair history of the product. The faulty part of the product includes, for example, but is not limited to, information on which part of the product is faulty. The repair history includes, for example, but is not limited to, information on what repairs the product has received in the past. This makes it possible to accurately determine whether the product can be repaired by taking the faulty part and repair history into consideration. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can determine whether the product can be repaired using an AI model that inputs the faulty part and repair history of the product and outputs whether the product can be repaired.

[0093] The suggestion unit can suggest a replacement model based on the consumer's past purchase history and preferences. The suggestion unit, for example, suggests a replacement model based on the consumer's past purchase history and preferences. The consumer's past purchase history includes, for example, but is not limited to, information on home appliances the consumer has previously purchased. The consumer's preferences include, for example, but are not limited to, product features and functions that the consumer prefers. This makes it possible to suggest an optimal replacement model based on the consumer's preferences. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can suggest a replacement model using a generation AI model that inputs the consumer's past purchase history and preferences and outputs a replacement model.

[0094] The collection unit can analyze the user's emotions and adjust the timing of information collection by the home appliance based on the analyzed user's emotions. The collection unit, for example, analyzes the user's emotions and adjusts the timing of information collection by the home appliance based on the analyzed user's emotions. User emotions include, but are not limited to, states such as stress, relaxation, and hurry. For example, when the user is feeling stressed, the collection unit reduces the frequency of information collection to reduce the user's burden. The collection unit can also collect detailed information and provide it to the user when the user is relaxed. For example, when the user is in a hurry, the collection unit quickly collects only the minimum amount of information necessary. This adjusts the timing of information collection according to the user's emotions, thereby reducing the user's burden. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotional data into the generation AI and adjust the timing of information collection based on the emotion.

[0095] When collecting information about a home appliance, the collection unit can analyze the user's past usage history and select a collection method. For example, when collecting information about a home appliance, the collection unit analyzes the user's past usage history and selects an optimal collection method. The user's past usage history includes, but is not limited to, usage frequency, usage time, and usage environment. For example, the collection unit prioritizes collecting information about home appliances that the user uses frequently. The collection unit can also focus on collecting information about home appliances that the user has experienced problems with in the past. For example, the collection unit sets an optimal information collection timing based on the user's usage pattern. This enables optimal information collection by taking the user's past usage history into consideration. Some or all of the above-described processing in the collection unit may be performed using, or without using, AI. For example, the collection unit can input the user's past usage history into a generation AI and select an optimal collection method.

[0096] The collection unit may filter information about home appliances based on the user's current lifestyle and areas of interest when collecting the information. For example, the collection unit may filter information about home appliances based on the user's current lifestyle and areas of interest when collecting the information. Examples of the user's current lifestyle include, but are not limited to, a busy period or a relaxing period. Examples of the user's areas of interest include, but are not limited to, an interest in a new home appliance. For example, the collection unit collects only important information when the user is busy. Furthermore, if the user is interested in a new home appliance, the collection unit may also collect information related to the product. For example, the collection unit selects and collects appropriate information according to the user's lifestyle. This enables information collection according to the user's lifestyle and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may input the user's current lifestyle and areas of interest into a generation AI to perform filtering.

[0097] The collection unit can select a collection means according to a user's input method when collecting information about a home appliance. For example, when collecting information about a home appliance, the collection unit selects an optimal collection means according to the user's input method. User input methods include, but are not limited to, voice input, text input, and image input. For example, when the user uses voice input, the collection unit collects information using voice recognition technology. Furthermore, when the user uses text input, the collection unit can also collect information using text analysis technology. For example, when the user uses image input, the collection unit collects information using image recognition technology. This enables optimal information collection according to the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input method into a generation AI and select the optimal collection means.

[0098] The collection unit can analyze the user's emotions and determine the priority of the home appliance information to be collected based on the analyzed user's emotions. The collection unit, for example, analyzes the user's emotions and determines the priority of the home appliance information to be collected based on the analyzed user's emotions. User emotions include, but are not limited to, states such as stress, relaxation, and hurry. For example, when the user is stressed, the collection unit prioritizes collecting information of high importance. Furthermore, when the user is relaxed, the collection unit can collect detailed information and provide it to the user. For example, when the user is in a hurry, the collection unit quickly collects only the minimum amount of information necessary. This allows important information to be collected preferentially by determining the priority of information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or without using an AI. For example, the collection unit can input user emotional data into the generation AI and determine the priority of information based on emotions.

[0099] When collecting information about home appliances, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, when collecting information about home appliances, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. The user's geographical location information includes, but is not limited to, GPS data, address information, etc. For example, when the user is at home, the collection unit prioritizes collecting information about home appliances used at home. Furthermore, when the user is traveling, the collection unit can prioritize collecting information about home appliances used at the travel destination. For example, when the user is at work, the collection unit prioritizes collecting information about home appliances used at work. This enables information collection based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into a generation AI and prioritize collecting highly relevant information.

[0100] The collection unit may analyze the user's social media activities and collect related information when collecting information about home appliances. For example, the collection unit may analyze the user's social media activities and collect related information when collecting information about home appliances. The user's social media activities may include, but are not limited to, the content of posts, the number of likes, and the number of followers. For example, the collection unit may collect information about home appliances mentioned by the user on social media. The collection unit may also analyze the content of the user's social media posts and collect information about related home appliances. For example, the collection unit may collect information about related home appliances by referring to the activities of the user's friends on social media. This enables information collection based on the user's social media activities. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's social media activities into a generation AI to collect related information.

[0101] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information about home appliances. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting information about home appliances. The user's past feedback includes, but is not limited to, survey results, review content, etc. For example, the collection unit improves the information collection method based on the user's past feedback. The collection unit can also prioritize the collection of specific information from the user's past feedback. For example, the collection unit adjusts the timing of information collection by reflecting the user's feedback. This allows the information collection method to be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback into a generation AI to customize the collection method.

[0102] The analysis unit can analyze the user's emotions and adjust the way in which the failure prediction is expressed based on the analyzed user's emotions. The analysis unit, for example, analyzes the user's emotions and adjusts the way in which the failure prediction is expressed based on the analyzed user's emotions. User emotions include, but are not limited to, states such as tension, relaxation, and hurry. For example, if the user is tension, the analysis unit provides a simple, highly visible expression. Furthermore, if the user is relaxed, the analysis unit can also provide an expression that includes detailed information. For example, if the user is hurrying, the analysis unit provides an expression that focuses on the main points. This enables the way in which the failure prediction is expressed based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input user emotional data into the generation AI and adjust the way failure predictions are expressed based on the emotion.

[0103] The analysis unit can adjust the level of detail of the prediction based on the importance of the home appliance when predicting a failure. For example, the analysis unit adjusts the level of detail of the prediction based on the importance of the home appliance when predicting a failure. Examples of the importance of a home appliance include, but are not limited to, frequency of use, price, and importance of function. For example, the analysis unit performs detailed failure prediction for home appliances with high importance. The analysis unit can also perform simplified failure prediction for home appliances with low importance. For example, the analysis unit dynamically adjusts the level of detail of the prediction based on the importance of the home appliance. This enables failure prediction based on the importance of the home appliance. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the importance of the home appliance into the generation AI and adjust the level of detail of the prediction.

[0104] The analysis unit can apply different prediction algorithms depending on the category of the home appliance when predicting a failure. For example, the analysis unit applies different prediction algorithms depending on the category of the home appliance when predicting a failure. Categories of home appliances include, but are not limited to, refrigerators, washing machines, and air conditioners. For example, the analysis unit can apply an algorithm that emphasizes cooling system data to predict a failure of a refrigerator. The analysis unit can also apply an algorithm that emphasizes motor data to predict a failure of a washing machine. For example, the analysis unit can apply an algorithm that emphasizes compressor data to predict a failure of an air conditioner. This enables optimal failure prediction depending on the category of the home appliance. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the category of the home appliance into the generation AI and apply different prediction algorithms.

[0105] The analysis unit can improve the accuracy of a failure prediction by referring to the user's past prediction results. For example, the analysis unit can improve the accuracy of a failure prediction by referring to the user's past prediction results. The user's past prediction results include, but are not limited to, past failure prediction results and prediction accuracy. For example, the analysis unit can adjust a prediction algorithm based on the user's past failure prediction results. The analysis unit can also extract specific patterns from the user's past prediction results to improve the prediction accuracy. For example, the analysis unit can improve the prediction model by reflecting the user's past prediction results. This improves the prediction accuracy by referring to the user's past prediction results. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the user's past prediction results into the generation AI to improve the prediction accuracy.

[0106] The analysis unit can analyze the user's emotions and adjust the length of the failure prediction based on the analyzed user emotions. The analysis unit, for example, analyzes the user's emotions and adjusts the length of the failure prediction based on the analyzed user emotions. User emotions include, but are not limited to, states such as being in a hurry, being relaxed, or being excited. For example, if the user is in a hurry, the analysis unit provides a short and concise failure prediction. Furthermore, if the user is relaxed, the analysis unit can provide a longer failure prediction with detailed explanations. For example, if the user is excited, the analysis unit provides a failure prediction with a visually stimulating effect. This allows the length of the failure prediction to be adjusted according to the user's emotions, thereby providing information appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input user emotion data into the generation AI and adjust the length of the failure prediction based on the emotion.

[0107] The analysis unit can determine the priority of predictions based on the purchase date of the home appliance when predicting a failure. For example, the analysis unit determines the priority of predictions based on the purchase date of the home appliance when predicting a failure. The purchase date of the home appliance includes, but is not limited to, the purchase date and the start date of use. For example, the analysis unit prioritizes failure predictions for home appliances that have been purchased for a long time. The analysis unit can also lower the priority of failure predictions for home appliances that have been purchased recently. For example, the analysis unit dynamically adjusts the priority of predictions based on the purchase date of the home appliance. This enables appropriate failure predictions by determining the priority based on the purchase date of the home appliance. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the purchase date of the home appliance into the generation AI to determine the priority of predictions.

[0108] The analysis unit can adjust the order of predictions based on the relevance of home appliances when predicting failures. For example, the analysis unit adjusts the order of predictions based on the relevance of home appliances when predicting failures. Relevance of home appliances includes, but is not limited to, the same manufacturer or the same category. The analysis unit simultaneously predicts failures of home appliances that are highly related, such as refrigerators and freezers. The analysis unit can also individually predict failures of home appliances that are less related. For example, the analysis unit dynamically adjusts the order of predictions based on the relevance of home appliances. This enables efficient failure predictions by adjusting the prediction order based on the relevance of home appliances. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the relevance of home appliances into the generation AI and adjust the order of predictions.

[0109] The analysis unit can adjust the use of technical terminology in the prediction according to the user's level of expertise when predicting a failure. For example, the analysis unit can adjust the use of technical terminology in the prediction according to the user's level of expertise when predicting a failure. Examples of the user's level of expertise include, but are not limited to, survey results, past usage history, etc. For example, if the user has technical expertise, the analysis unit can provide a failure prediction using detailed technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can provide a failure prediction in simple terms. For example, the analysis unit dynamically adjusts the use of technical terminology in the prediction according to the user's level of expertise. This enables appropriate information to be provided by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the user's level of expertise into the generation AI and adjust the use of technical terminology in the prediction.

[0110] The determination unit can analyze the user's emotions and adjust the repairability determination method based on the analyzed user emotions. The determination unit, for example, analyzes the user's emotions and adjusts the repairability determination method based on the analyzed user emotions. User emotions include, but are not limited to, states such as tension, relaxation, and hurry. For example, if the user is tensioned, the determination unit provides a simple and highly visible determination method. Furthermore, if the user is relaxed, the determination unit can also provide a determination method that includes detailed information. For example, if the user is hurrying, the determination unit provides a determination method that focuses on the key points. This provides a repairability determination method based on the user's emotions, thereby enabling the provision of information appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the determination unit may be performed using, for example, an AI, or without an AI. For example, the judgment unit can input the user's emotional data into the generation AI and adjust the method for determining whether or not the product can be repaired based on the emotion.

[0111] The determination unit can adjust the level of detail of the determination based on the fault location of the home appliance when determining whether the appliance can be repaired. For example, the determination unit adjusts the level of detail of the determination based on the fault location of the home appliance when determining whether the appliance can be repaired. Fault locations of the appliance include, but are not limited to, component failures and system failures. For example, the determination unit makes a detailed determination of whether the appliance can be repaired for important fault locations. The determination unit can also make a simplified determination of whether the appliance can be repaired for unimportant fault locations. For example, the determination unit dynamically adjusts the level of detail of the determination depending on the fault location of the appliance. This enables an appropriate determination of whether the appliance can be repaired by adjusting the level of detail of the determination based on the fault location of the appliance. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the fault location of the appliance into a generation AI and adjust the level of detail of the determination.

[0112] The determination unit can apply different determination algorithms depending on the category of the home appliance when determining whether the appliance can be repaired. For example, the determination unit applies different determination algorithms depending on the category of the home appliance when determining whether the appliance can be repaired. Categories of home appliances include, but are not limited to, refrigerators, washing machines, and air conditioners. For example, the determination unit applies an algorithm that emphasizes cooling system data to determine whether a refrigerator can be repaired. The determination unit can also apply an algorithm that emphasizes motor data to determine whether a washing machine can be repaired. For example, the determination unit applies an algorithm that emphasizes compressor data to determine whether an air conditioner can be repaired. This enables an optimal determination of whether the appliance can be repaired depending on the category of the home appliance. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the category of the home appliance into a generation AI and apply different determination algorithms.

[0113] The determination unit can improve the accuracy of the determination by referring to the user's past determination results when determining whether the product is repairable. For example, the determination unit improves the accuracy of the determination by referring to the user's past determination results when determining whether the product is repairable. The user's past determination results include, but are not limited to, past repairability determination results and determination accuracy. For example, the determination unit adjusts the determination algorithm based on the user's past repairability determination results. The determination unit can also extract specific patterns from the user's past determination results to improve the determination accuracy. For example, the determination unit improves the determination model by reflecting the user's past determination results. This improves the accuracy of the determination by referring to the user's past determination results. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the user's past determination results into the generation AI to improve the accuracy of the determination.

[0114] The determination unit can analyze the user's emotions and adjust the length of the repairability determination based on the analyzed user emotions. The determination unit, for example, analyzes the user's emotions and adjusts the length of the repairability determination based on the analyzed user emotions. User emotions include, but are not limited to, states such as being in a hurry, being relaxed, and being excited. For example, if the user is in a hurry, the determination unit provides a short and concise repairability determination. Furthermore, if the user is relaxed, the determination unit can provide a longer repairability determination including detailed explanations. For example, if the user is excited, the determination unit provides a repairability determination with a visually stimulating effect. This allows the provision of information appropriate for the user by adjusting the length of the repairability determination according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit may input user emotion data into the generation AI and adjust the length of the repairability determination based on the emotion.

[0115] The determination unit can determine the priority of the determination based on the purchase date of the home appliance when determining whether the appliance can be repaired. For example, the determination unit determines the priority of the determination based on the purchase date of the home appliance when determining whether the appliance can be repaired. The purchase date of the home appliance includes, but is not limited to, the purchase date and the date of first use. For example, the determination unit prioritizes the determination of whether the appliance can be repaired for a home appliance that has been purchased for a long time. The determination unit can also lower the priority of the determination of whether the appliance can be repaired for a home appliance that was purchased recently. For example, the determination unit dynamically adjusts the priority of the determination based on the purchase date of the home appliance. This enables an appropriate determination of whether the appliance can be repaired by determining the priority based on the purchase date of the home appliance. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the purchase date of the home appliance into the generation AI to determine the priority of the determination.

[0116] The determination unit can adjust the order of determination based on the relevance of the home appliances when determining whether the appliances can be repaired. For example, the determination unit adjusts the order of determination based on the relevance of the home appliances when determining whether the appliances can be repaired. Examples of the relevance of the home appliances include, but are not limited to, the same manufacturer or the same category. The determination unit simultaneously determines whether highly related home appliances, such as a refrigerator and a freezer, can be repaired. The determination unit can also individually determine whether less related home appliances can be repaired. For example, the determination unit dynamically adjusts the order of determination based on the relevance of the home appliances. This enables efficient determination of whether the appliances can be repaired. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the relevance of the home appliances into a generation AI and adjust the order of determination.

[0117] The determination unit can adjust the use of technical terms for the determination depending on the user's level of expertise when determining whether the product is repairable. For example, the determination unit adjusts the use of technical terms for the determination depending on the user's level of expertise when determining whether the product is repairable. Examples of the user's level of expertise include, but are not limited to, survey results and past usage history. For example, if the user has specialized knowledge, the determination unit can provide a repairability determination using detailed technical terms. Furthermore, if the user does not have specialized knowledge, the determination unit can also provide a repairability determination in simple terms. For example, the determination unit dynamically adjusts the use of technical terms for the determination depending on the user's level of expertise. This enables appropriate information to be provided by adjusting the use of technical terms depending on the user's level of expertise. Some or all of the above-described processing by the determination unit may be performed using, for example, AI, or may be performed without AI. For example, the determination unit can input the user's level of expertise into the generation AI and adjust the use of technical terms for the determination.

[0118] The suggestion unit can analyze the user's emotions and adjust the replacement model suggestion method based on the analyzed user emotions. The suggestion unit, for example, analyzes the user's emotions and adjusts the replacement model suggestion method based on the analyzed user emotions. User emotions include, but are not limited to, states such as tension, relaxation, and hurry. For example, when the user is tensioned, the suggestion unit provides a simple and highly visible suggestion method. Furthermore, when the user is relaxed, the suggestion unit can provide a suggestion method that includes detailed information. For example, when the user is hurrying, the suggestion unit provides a suggestion method that focuses on the main points. This enables the provision of information appropriate for the user by providing a replacement model suggestion method that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, the generation AI, or without the generation AI. For example, the suggestion unit can input user emotion data into the generation AI and adjust the method of suggesting replacement models based on the emotion.

[0119] The suggestion unit can adjust the level of detail of the proposal based on the importance of the home appliance when suggesting a replacement model. For example, the suggestion unit adjusts the level of detail of the proposal based on the importance of the home appliance when suggesting a replacement model. The importance of a home appliance includes, but is not limited to, frequency of use, price, and importance of functions. For example, the suggestion unit makes a detailed replacement model proposal for a home appliance with high importance. The suggestion unit can also make a simplified replacement model proposal for a home appliance with low importance. For example, the suggestion unit dynamically adjusts the level of detail of the proposal according to the importance of the home appliance. This makes it possible to suggest an appropriate replacement model by adjusting the level of detail of the proposal according to the importance of the home appliance. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the importance of the home appliance into the generation AI and adjust the level of detail of the proposal.

[0120] The suggestion unit can apply different suggestion algorithms depending on the category of the home appliance when suggesting a replacement model. For example, the suggestion unit applies different suggestion algorithms depending on the category of the home appliance when suggesting a replacement model. Home appliance categories include, but are not limited to, refrigerators, washing machines, and air conditioners. For example, the suggestion unit can apply an algorithm that emphasizes energy efficiency when suggesting a replacement for a refrigerator. The suggestion unit can also apply an algorithm that emphasizes cleaning power when suggesting a replacement for a washing machine. For example, the suggestion unit can apply an algorithm that emphasizes heating and cooling capacity when suggesting a replacement for an air conditioner. This makes it possible to suggest an optimal replacement model depending on the category of the home appliance. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the suggestion unit can input the category of the home appliance into the generation AI and apply different suggestion algorithms.

[0121] The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestions when suggesting a replacement model. For example, the suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestions when suggesting a replacement model. The user's past suggestions include, but are not limited to, past replacement suggestion results and suggestion accuracy. For example, the suggestion unit can adjust the suggestion algorithm based on the user's past suggestions. The suggestion unit can also extract specific patterns from the user's past suggestions to improve the suggestion accuracy. For example, the suggestion unit can improve the suggestion model by reflecting the user's past suggestions. By referring to the user's past suggestions, the suggestion accuracy can be improved. Some or all of the above-described processing in the suggestion unit can be performed using, or without, a generation AI. For example, the suggestion unit can input the user's past suggestions into the generation AI to improve the suggestion accuracy.

[0122] The suggestion unit can analyze the user's emotions and adjust the length of the replacement model suggestion based on the analyzed user emotions. The suggestion unit, for example, analyzes the user's emotions and adjusts the length of the replacement model suggestion based on the analyzed user emotions. User emotions include, but are not limited to, states such as being in a hurry, being relaxed, or being excited. For example, if the user is in a hurry, the suggestion unit can provide a short and concise replacement model suggestion. Furthermore, if the user is relaxed, the suggestion unit can provide a longer replacement model suggestion with detailed explanations. For example, if the user is excited, the suggestion unit can provide a replacement model suggestion with visually stimulating effects. This allows the provision of information appropriate for the user by adjusting the length of the suggestion based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit may input user emotion data into the generation AI and adjust the length of the suggestion based on the emotion.

[0123] When proposing a replacement model, the suggestion unit can determine the priority of the suggestions based on the purchase date of the home appliance. For example, when proposing a replacement model, the suggestion unit determines the priority of the suggestions based on the purchase date of the home appliance. The purchase date of the home appliance includes, but is not limited to, the purchase date and the start date of use. For example, the suggestion unit prioritizes the suggestion of a replacement model for a home appliance that has been purchased for a long time. The suggestion unit can also lower the priority of the suggestion of a replacement model for a home appliance that has been purchased recently. For example, the suggestion unit dynamically adjusts the priority of the suggestions depending on the purchase date of the home appliance. This enables the suggestion of an appropriate replacement model by determining the priority based on the purchase date of the home appliance. Some or all of the above-described processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the purchase date of the home appliance into the generation AI and determine the priority of the suggestions.

[0124] The suggestion unit can adjust the order of suggestions based on the relevance of the home appliances when suggesting replacement models. For example, the suggestion unit adjusts the order of suggestions based on the relevance of the home appliances when suggesting replacement models. Examples of relevance of home appliances include, but are not limited to, the same manufacturer and the same category. The suggestion unit simultaneously suggests replacement models for highly related home appliances, such as a refrigerator and a freezer. The suggestion unit can also individually suggest replacement models for less related home appliances. For example, the suggestion unit dynamically adjusts the order of suggestions based on the relevance of the home appliances. This enables efficient suggestions for replacement models. Some or all of the above-described processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the relevance of the home appliances into the generation AI and adjust the order of suggestions.

[0125] The suggestion unit may adjust the use of technical terms in the proposal according to the user's level of expertise when proposing a replacement model. For example, the suggestion unit may adjust the use of technical terms in the proposal according to the user's level of expertise when proposing a replacement model. Examples of the user's level of expertise include, but are not limited to, survey results and past usage history. For example, if the user has technical expertise, the suggestion unit may provide a replacement model proposal using detailed technical terms. Furthermore, if the user does not have technical expertise, the suggestion unit may provide a replacement model proposal in simple language. For example, the suggestion unit may dynamically adjust the use of technical terms in the proposal according to the user's level of expertise. This enables appropriate information to be provided by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit may input the user's level of expertise into the generation AI and adjust the use of technical terms in the proposal.

[0126] The preference grasping unit can analyze the user's emotions and adjust the preference grasping method based on the analyzed user's emotions. The preference grasping unit, for example, analyzes the user's emotions and adjusts the preference grasping method based on the analyzed user's emotions. The user's emotions include, but are not limited to, states such as tension, relaxation, and hurry. For example, when the user is tensioned, the preference grasping unit provides a simple and highly visible preference grasping method. Furthermore, when the user is relaxed, the preference grasping unit can also provide a preference grasping method including detailed information. For example, when the user is hurrying, the preference grasping unit provides a preference grasping method that focuses on the main points. This provides a preference grasping method according to the user's emotions, making it possible to provide information appropriate for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the preference grasping unit may be performed using, for example, AI, or may be performed without using AI. For example, the preference grasping unit may input user emotion data into the generation AI and adjust the preference grasping method based on the emotion.

[0127] The preference understanding unit can analyze the user's past purchase history to select the optimal preference understanding method when understanding the user's preferences. For example, the preference understanding unit analyzes the user's past purchase history to select the optimal preference understanding method when understanding the user's preferences. The user's past purchase history includes, but is not limited to, purchase dates, purchased products, and purchase frequency. The preference understanding unit selects the optimal preference understanding method based on the user's past purchase history. The preference understanding unit can also extract specific patterns from the user's past purchase history and improve the preference understanding method. For example, the preference understanding unit dynamically adjusts the preference understanding method by reflecting the user's past purchase history. This allows the optimal preference understanding method to be provided by analyzing the user's past purchase history. Some or all of the above-described processing in the preference understanding unit may be performed using, or without, AI. For example, the preference understanding unit can input the user's past purchase history into a generation AI to select the optimal preference understanding method.

[0128] The preference understanding unit can perform filtering based on the user's current living situation and areas of interest when understanding the preferences. For example, the preference understanding unit performs filtering based on the user's current living situation and areas of interest when understanding the preferences. The user's current living situation includes, but is not limited to, for example, a living environment and a method for identifying areas of interest. For example, the preference understanding unit selects an optimal preference understanding method based on the user's current living situation. The preference understanding unit can also adjust the preference understanding method based on the user's areas of interest. For example, the preference understanding unit provides an appropriate preference understanding method in accordance with the user's lifestyle. This makes it possible to understand the preferences according to the user's living situation and areas of interest. Some or all of the above-described processing in the preference understanding unit may be performed using, for example, AI, or may be performed without using AI. For example, the preference understanding unit can input the user's current living situation and areas of interest into a generation AI and perform filtering.

[0129] The preference understanding unit can analyze the user's emotions and determine the priority of preferences based on the analyzed user emotions. The preference understanding unit, for example, analyzes the user's emotions and determines the priority of preferences based on the analyzed user emotions. User emotions include, but are not limited to, states such as stress, relaxation, and hurry. For example, when the user is stressed, the preference understanding unit prioritizes important preferences. Furthermore, when the user is relaxed, the preference understanding unit can also grasp detailed preferences. For example, when the user is in a hurry, the preference understanding unit quickly grasps only the minimum necessary preferences. This allows the priority of preferences to be determined according to the user's emotions, thereby prioritizing important preferences. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the preference understanding unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the preference understanding unit can input the user's emotional data into the generation AI and determine preference priorities based on the emotions.

[0130] The preference understanding unit can prioritize highly relevant preferences when understanding the preferences by taking into account the user's geographical location information. For example, the preference understanding unit prioritizes highly relevant preferences when understanding the preferences by taking into account the user's geographical location information. The user's geographical location information includes, but is not limited to, GPS data, address information, etc. For example, when the user is at home, the preference understanding unit prioritizes understanding the preferences of home appliances used at home. Furthermore, when the user is traveling, the preference understanding unit can also prioritize understanding the preferences of home appliances used at the travel destination. For example, when the user is at work, the preference understanding unit prioritizes understanding the preferences of home appliances used at work. This makes it possible to understand preferences based on the user's geographical location information. Some or all of the above-described processing in the preference understanding unit may be performed using, for example, AI, or may be performed without using AI. For example, the preference understanding unit can input the user's geographical location information to a generation AI and prioritize understanding highly relevant preferences.

[0131] The preference understanding unit can analyze the user's social media activity and understand the related preferences when understanding the preferences. For example, the preference understanding unit analyzes the user's social media activity and understands the related preferences when understanding the preferences. The user's social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the preference understanding unit understands the preferences of home appliances mentioned by the user on social media. The preference understanding unit can also analyze the content of the user's posts on social media to understand the preferences of related home appliances. For example, the preference understanding unit understands the preferences of related home appliances by referring to the activities of the user's friends on social media. This makes it possible to understand the preferences based on the user's social media activity. Some or all of the above-described processing in the preference understanding unit may be performed using, or without, AI. For example, the preference understanding unit can input the user's social media activity into a generation AI to understand the related preferences.

[0132] The repair history management unit can analyze the user's emotions and adjust the repair history management method based on the analyzed user emotions. The repair history management unit, for example, analyzes the user's emotions and adjusts the repair history management method based on the analyzed user emotions. User emotions include, but are not limited to, states such as tension, relaxation, and hurry. For example, when the user is tensioned, the repair history management unit provides a simple and highly visible repair history management method. Furthermore, when the user is relaxed, the repair history management unit can provide a repair history management method that includes detailed information. For example, when the user is hurrying, the repair history management unit provides a repair history management method that focuses on the main points. This provides a repair history management method that corresponds to the user's emotions, thereby enabling the provision of information appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the repair history management unit may be performed using, for example, AI, or without AI. For example, the repair history management unit can input user emotion data into the generation AI and adjust the repair history management method based on the emotion.

[0133] The repair history management unit can adjust the level of detail of the management based on the fault location of the home appliance when managing the repair history. For example, the repair history management unit adjusts the level of detail of the management based on the fault location of the home appliance when managing the repair history. Fault locations of the home appliance include, but are not limited to, component failures and system failures. For example, the repair history management unit manages detailed repair histories for important fault locations. The repair history management unit can also manage simplified repair histories for unimportant fault locations. For example, the repair history management unit dynamically adjusts the level of detail of the management based on the fault location of the home appliance. This enables appropriate repair history management by adjusting the level of detail of the management based on the fault location of the home appliance. Some or all of the above-described processing in the repair history management unit may be performed using, for example, AI, or may be performed without using AI. For example, the repair history management unit can input the fault location of the home appliance into a generation AI and adjust the level of detail of the management.

[0134] The repair history management unit can apply different management algorithms depending on the category of the home appliance when managing the repair history. For example, the repair history management unit applies different management algorithms depending on the category of the home appliance when managing the repair history. Home appliance categories include, but are not limited to, refrigerators, washing machines, and air conditioners. For example, the repair history management unit applies an algorithm that emphasizes cooling system data when managing the repair history of refrigerators. Furthermore, the repair history management unit can also apply an algorithm that emphasizes motor data when managing the repair history of washing machines. For example, the repair history management unit applies an algorithm that emphasizes compressor data when managing the repair history of air conditioners. This enables optimal repair history management depending on the category of the home appliance. Some or all of the above-described processing in the repair history management unit may be performed using, for example, AI, or may be performed without using AI. For example, the repair history management unit can input the category of the home appliance into a generation AI and apply different management algorithms.

[0135] The repair history management unit can improve the accuracy of the repair history management by referring to the user's past repair history. For example, the repair history management unit can improve the accuracy of the management by referring to the user's past repair history. The user's past repair history includes, but is not limited to, the repair date, repair content, and repair cost. For example, the repair history management unit can adjust the management algorithm based on the user's past repair history. The repair history management unit can also extract specific patterns from the user's past repair history to improve the management accuracy. For example, the repair history management unit can improve the management model by reflecting the user's past repair history. By referring to the user's past repair history, the management accuracy can be improved. Some or all of the above-described processing in the repair history management unit can be performed using, for example, AI, or without AI. For example, the repair history management unit can input the user's past repair history into a generation AI to improve the management accuracy.

[0136] The repair history management unit can analyze the user's emotions and adjust the length of the repair history management based on the analyzed user emotions. The repair history management unit, for example, analyzes the user's emotions and adjusts the length of the repair history management based on the analyzed user emotions. User emotions include, but are not limited to, states such as being in a hurry, being relaxed, or being excited. For example, if the user is in a hurry, the repair history management unit can provide a short and concise repair history management. Furthermore, if the user is relaxed, the repair history management unit can provide a longer repair history management with detailed explanations. For example, if the user is excited, the repair history management unit can provide a repair history management with a visually stimulating effect. This allows the length of the repair history management to be adjusted according to the user's emotions, thereby providing information appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the repair history management unit may be performed using, for example, AI, or may be performed without using AI. For example, the repair history management unit may input user emotion data into the generation AI and adjust the length of repair history management based on the emotion.

[0137] The repair history management unit can determine the management priority based on the purchase date of the home appliance when managing the repair history. For example, the repair history management unit determines the management priority based on the purchase date of the home appliance when managing the repair history. The purchase date of the home appliance includes, but is not limited to, the purchase date and the start date of use. For example, the repair history management unit prioritizes management of the repair history of a home appliance that has been purchased for a long time. The repair history management unit can also lower the priority of management of the repair history of a home appliance that was purchased recently. For example, the repair history management unit dynamically adjusts the management priority based on the purchase date of the home appliance. This enables appropriate management of the repair history by determining the priority based on the purchase date of the home appliance. Some or all of the above-described processing in the repair history management unit may be performed using, for example, AI, or may be performed without using AI. For example, the repair history management unit can input the purchase date of the home appliance into a generation AI to determine the management priority.

[0138] The repair history management unit can adjust the management order based on the relevance of home appliances when managing repair histories. For example, the repair history management unit adjusts the management order based on the relevance of home appliances when managing repair histories. Relevance of home appliances includes, but is not limited to, the same manufacturer or the same category. The repair history management unit simultaneously manages the repair histories of home appliances that are highly related, such as refrigerators and freezers. The repair history management unit can also manage the repair histories individually for home appliances that are less related. For example, the repair history management unit dynamically adjusts the management order based on the relevance of home appliances. This enables efficient management of repair histories by adjusting the management order based on the relevance of home appliances. Some or all of the above-described processing in the repair history management unit may be performed using, for example, AI, or may be performed without AI. For example, the repair history management unit can input the relevance of home appliances into a generation AI and adjust the management order.

[0139] The repair history management unit can adjust the use of technical terms in the management of repair histories according to the user's level of expertise when managing repair histories. For example, the repair history management unit can adjust the use of technical terms in the management of repair histories according to the user's level of expertise when managing repair histories. Examples of the user's level of expertise include, but are not limited to, survey results, past usage history, etc. For example, if the user has technical expertise, the repair history management unit can provide repair history management using detailed technical terms. Furthermore, if the user does not have technical expertise, the repair history management unit can also provide repair history management in simple terms. For example, the repair history management unit dynamically adjusts the use of technical terms in the management of repair histories according to the user's level of expertise. This enables appropriate information to be provided by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-described processing in the repair history management unit may be performed using, for example, AI, or may be performed without AI. For example, the repair history management unit can input the user's level of expertise into the generation AI and adjust the use of technical terms in the management of repair histories.

[0140] The expense management unit can analyze the user's emotions and adjust the expense management method based on the analyzed user emotions. For example, the expense management unit can analyze the user's emotions and adjust the expense management method based on the analyzed user emotions. User emotions include, but are not limited to, states such as tension, relaxation, and hurry. For example, when the user is tensioned, the expense management unit can provide a simple, highly visible expense management method. Furthermore, when the user is relaxed, the expense management unit can provide an expense management method that includes detailed information. For example, when the user is hurrying, the expense management unit can provide an expense management method that focuses on the key points. This provides an expense management method that corresponds to the user's emotions, thereby enabling the provision of information appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the expense management unit can be performed using, for example, AI, or without AI. For example, the expense management department can input the user's emotional data into the generation AI and adjust the expense management method based on the emotion.

[0141] The expense management unit can adjust the level of detail of management based on the importance of the home appliance during expense management. For example, the expense management unit adjusts the level of detail of management based on the importance of the home appliance during expense management. The importance of the home appliance includes, but is not limited to, for example, frequency of use, price, and importance of function. For example, the expense management unit performs detailed expense management for home appliances with high importance. The expense management unit can also perform simplified expense management for home appliances with low importance. For example, the expense management unit dynamically adjusts the level of detail of management according to the importance of the home appliance. This enables appropriate expense management by adjusting the level of detail of management according to the importance of the home appliance. Some or all of the above-described processing in the expense management unit may be performed using, for example, AI, or may be performed without using AI. For example, the expense management unit can input the importance of the home appliance into a generation AI and adjust the level of detail of management.

[0142] The expense management unit can apply different management algorithms depending on the category of the home appliance during expense management. For example, the expense management unit applies different management algorithms depending on the category of the home appliance during expense management. Categories of home appliances include, but are not limited to, refrigerators, washing machines, and air conditioners. For example, the expense management unit applies an algorithm that emphasizes energy efficiency when managing the expense of a refrigerator. The expense management unit can also apply an algorithm that emphasizes cleaning power when managing the expense of a washing machine. For example, the expense management unit applies an algorithm that emphasizes heating and cooling capacity when managing the expense of an air conditioner. This enables optimal expense management according to the category of the home appliance. Some or all of the above-mentioned processing in the expense management unit may be performed using, for example, AI, or may be performed without using AI. For example, the expense management unit can input the category of the home appliance into a generation AI and apply different management algorithms.

[0143] The expense management unit can improve the accuracy of expense management by referring to the user's past expense management history during expense management. For example, the expense management unit can improve the accuracy of expense management by referring to the user's past expense management history during expense management. The user's past expense management history includes, but is not limited to, past expense management results and management accuracy. For example, the expense management unit can adjust the management algorithm based on the user's past expense management history. The expense management unit can also extract specific patterns from the user's past expense management history to improve management accuracy. For example, the expense management unit can improve the management model by reflecting the user's past expense management history. By referring to the user's past expense management history, the accuracy of management is improved. Some or all of the above-described processing in the expense management unit can be performed using, for example, AI, or without AI. For example, the expense management unit can input the user's past expense management history into a generation AI to improve management accuracy.

[0144] The expense management unit can analyze the user's emotions and determine expense management priorities based on the analyzed user emotions. The expense management unit, for example, analyzes the user's emotions and determines expense management priorities based on the analyzed user emotions. User emotions include, but are not limited to, states such as stress, relaxation, and hurry. For example, when the user is stressed, the expense management unit prioritizes important expense management. Furthermore, when the user is relaxed, the expense management unit can perform detailed expense management. For example, when the user is in a hurry, the expense management unit quickly performs only the minimum necessary expense management. This allows for prioritizing expense management based on the user's emotions, thereby prioritizing important expense management. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the expense management unit may be performed using, for example, an AI, or without an AI. For example, the expense management department can input the user's emotional data into the generation AI and determine expense management priorities based on emotions.

[0145] The expense management unit can determine management priorities based on the purchase date of the home appliances during expense management. For example, the expense management unit determines management priorities based on the purchase date of the home appliances during expense management. The purchase date of the home appliances includes, but is not limited to, the purchase date and the start date of use. For example, the expense management unit prioritizes expense management for home appliances that have been purchased for some time. The expense management unit can also lower the priority of expense management for home appliances that have been purchased recently. For example, the expense management unit dynamically adjusts management priorities based on the purchase date of the home appliances. This enables appropriate expense management by determining priorities based on the purchase date of the home appliances. Some or all of the above-described processing in the expense management unit may be performed using, for example, AI, or may be performed without using AI. For example, the expense management unit can input the purchase date of the home appliances into a generation AI to determine management priorities.

[0146] The expense management unit can adjust the management order based on the relevance of home appliances during expense management. For example, the expense management unit adjusts the management order based on the relevance of home appliances during expense management. Examples of the relevance of home appliances include, but are not limited to, appliances from the same manufacturer or the same category. The expense management unit simultaneously manages expenses for home appliances that are highly related, such as refrigerators and freezers. The expense management unit can also manage expenses individually for home appliances that are less related. For example, the expense management unit dynamically adjusts the management order based on the relevance of home appliances. This enables efficient expense management by adjusting the management order based on the relevance of home appliances. Some or all of the above-described processing in the expense management unit may be performed using, for example, AI, or may be performed without AI. For example, the expense management unit can input the relevance of home appliances into a generation AI and adjust the management order.

[0147] The expense management unit can adjust the use of management terminology according to the user's level of expertise during expense management. For example, the expense management unit can adjust the use of management terminology according to the user's level of expertise during expense management. Examples of the user's level of expertise include, but are not limited to, survey results, past usage history, etc. For example, if the user has expertise, the expense management unit can provide expense management using detailed terminology. Furthermore, if the user does not have expertise, the expense management unit can also provide expense management in simple terms. For example, the expense management unit dynamically adjusts the use of management terminology according to the user's level of expertise. This enables appropriate information to be provided by adjusting the use of terminology according to the user's level of expertise. Some or all of the above-described processing in the expense management unit can be performed using, for example, AI, or without AI. For example, the expense management unit can input the user's level of expertise into the generation AI and adjust the use of management terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, determination unit, suggestion unit, preference understanding unit, repair history management unit, and cost management unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects information about the home appliance using the camera 42 and microphone 38B of the smart device 14, and the collected information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and performs failure prediction based on the collected information. The determination unit is realized by the specific processing unit 290 of the data processing device 12 and determines whether repair is possible based on the failure prediction result. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests a replacement model based on the consumer's preferences if repair is not possible. The preference understanding unit is realized by the control unit 46A of the smart device 14 and collects consumer preference information. The repair history management unit is realized by the specific processing unit 290 of the data processing device 12 and manages the repair history of the home appliance. The expense management unit is realized by the specific processing unit 290 of the data processing device 12 and manages purchase expenses. The collection unit analyzes the user's emotions using the camera 42 and microphone 38B of the smart device 14, and adjusts the timing of information collection by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, determination unit, suggestion unit, preference understanding unit, repair history management unit, and cost management unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects information about the home appliance using the camera 42 and microphone 238 of the smart glasses 214, and the collected information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and performs failure prediction based on the collected information. The determination unit is realized by the specific processing unit 290 of the data processing device 12 and determines whether repair is possible based on the failure prediction result. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests a replacement model based on the consumer's preferences if repair is not possible. The preference understanding unit is realized by the control unit 46A of the smart glasses 214 and collects consumer preference information. The repair history management unit is realized by the specific processing unit 290 of the data processing device 12 and manages the repair history of the home appliance. The expense management unit is realized by the specific processing unit 290 of the data processing device 12 and manages purchase expenses. The collection unit analyzes the user's emotions using the camera 42 and microphone 238 of the smart glasses 214, and adjusts the timing of information collection by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, determination unit, suggestion unit, preference understanding unit, repair history management unit, and cost management unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects information about the home appliance using the camera 42 and microphone 238 of the headset terminal 314, and the collected information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and performs failure prediction based on the collected information. The determination unit is realized by the specific processing unit 290 of the data processing device 12 and determines whether repair is possible based on the failure prediction result. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests a replacement model based on the consumer's preferences if repair is not possible. The preference understanding unit is realized by the control unit 46A of the headset terminal 314 and collects consumer preference information. The repair history management unit is realized by the specific processing unit 290 of the data processing device 12 and manages the repair history of the home appliance. The expense management unit is realized by the specific processing unit 290 of the data processing device 12 and manages purchase expenses. The collection unit analyzes the user's emotions using the camera 42 and microphone 238 of the headset type terminal 314, and adjusts the timing of information collection by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, determination unit, suggestion unit, preference understanding unit, repair history management unit, and cost management unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects information about the home appliance using the camera 42 and microphone 238 of the robot 414, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and predicts failure based on the collected information. The determination unit is realized by the specific processing unit 290 of the data processing device 12 and determines whether repair is possible based on the failure prediction result. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests a replacement model based on the consumer's preferences if repair is not possible. The preference understanding unit is realized by the control unit 46A of the robot 414 and collects consumer preference information. The repair history management unit is realized by the specific processing unit 290 of the data processing device 12 and manages the repair history of the home appliance. The expense management unit is realized by the specific processing unit 290 of the data processing device 12 and manages purchase expenses. The collection unit analyzes the user's emotions using the camera 42 and microphone 238 of the robot 414, and adjusts the timing of information collection by the specific processing unit 290 of the data processing device 12.

[0148] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0149] The home appliance lifecycle management system can analyze the usage patterns of home appliances based on the consumer's lifestyle and suggest optimal usage methods. For example, if the consumer travels frequently, the analysis unit can recommend the use of energy-efficient home appliances. Also, if the consumer works from home, it can suggest a home appliance usage schedule that suits the consumer's time at home. Furthermore, if the consumer is health-conscious, it can also suggest health-conscious ways to use home appliances. This makes it possible to provide optimal home appliance usage methods that suit the consumer's lifestyle.

[0150] The home appliance lifecycle management system can analyze a consumer's emotions and provide advice on how to use home appliances based on the analyzed emotions. For example, if the consumer is feeling stressed, the analysis unit can recommend using a home appliance that has a relaxing effect. Also, if the consumer is relaxed, it can suggest using a home appliance with an entertainment function. Furthermore, if the consumer is in a hurry, it can suggest how to use a home appliance that saves time. In this way, it is possible to provide optimal advice on how to use home appliances based on the consumer's emotions.

[0151] The home appliance lifecycle management system can collect consumer health data and suggest ways to use home appliances based on the consumer's health condition. For example, the collection unit collects the consumer's sleep data, and the analysis unit uses that data to suggest ways to use home appliances that provide an optimal sleeping environment. It can also collect the consumer's exercise data and suggest ways to use home appliances that are suitable for post-exercise recovery. It can also collect the consumer's dietary data and suggest ways to use home appliances that support a healthy diet. This makes it possible to provide optimal ways to use home appliances that are tailored to the consumer's health condition.

[0152] The home appliance lifecycle management system can analyze consumer emotions and adjust the maintenance schedule of home appliances based on the analyzed emotions. For example, if the consumer is feeling stressed, the analysis unit can reduce the frequency of maintenance to ease the consumer's burden. It can also provide a detailed maintenance schedule if the consumer is relaxed. Furthermore, if the consumer is in a hurry, it can suggest a schedule that quickly performs only the minimum necessary maintenance. This makes it possible to provide an optimal maintenance schedule according to the consumer's emotions.

[0153] The home appliance lifecycle management system can collect energy consumption data from consumers and suggest ways to use home appliances to optimize energy efficiency. For example, the collection unit collects energy consumption data from consumers, and the analysis unit suggests ways to use energy-efficient home appliances based on that data. The system can also analyze consumers' energy consumption patterns and suggest usage schedules to reduce energy consumption during peak hours. It can also suggest ways to use home appliances that are in line with the consumer's energy consumption goals. This makes it possible to provide ways to use home appliances to optimize the consumer's energy consumption.

[0154] The home appliance lifecycle management system can analyze consumer emotions and suggest the best time to purchase a home appliance based on the analyzed emotions. For example, if a consumer is feeling stressed, the analysis unit can recommend that the consumer take their time to make the purchase and stay relaxed. If the consumer is relaxed, the system can also provide detailed product information to encourage the purchase. Furthermore, if the consumer is in a hurry, the system can suggest products that can be purchased quickly. This makes it possible to suggest the optimal purchase timing based on the consumer's emotions.

[0155] The home appliance lifecycle management system can collect environmental data from consumers and suggest environmentally friendly ways to use home appliances. For example, the collection unit collects data on the consumer's living environment, and the analysis unit uses that data to suggest environmentally friendly ways to use home appliances. It can also collect climate data from the consumer's region and suggest ways to use home appliances that are suitable for the climate. It can also suggest ways to use home appliances based on the consumer's eco-consciousness. This makes it possible to provide consumers with optimal, environmentally friendly ways to use home appliances.

[0156] The home appliance lifecycle management system can analyze consumer emotions and suggest customization options for home appliances based on the analyzed emotions. For example, if the consumer is feeling stressed, the analysis unit can suggest customization options that have a relaxing effect. Also, if the consumer is feeling relaxed, the analysis unit can suggest customization options that enhance entertainment functions. Furthermore, if the consumer is in a hurry, it can suggest customization options that have a time-saving effect. In this way, it is possible to provide optimal customization options according to the consumer's emotions.

[0157] The home appliance lifecycle management system can collect consumer family composition data and suggest ways to use home appliances that are suitable for all family members. For example, the collection unit collects the consumer's family composition data, and the analysis unit uses that data to suggest ways to use home appliances that are comfortable for all family members. It can also suggest ways to use home appliances that are appropriate for different age groups in the family. It can also suggest ways to use home appliances that are suited to the family's lifestyle. This makes it possible to provide optimal ways to use home appliances that are suitable for all family members.

[0158] The home appliance lifecycle management system can analyze the consumer's emotions and suggest the timing for disposing of home appliances based on the analyzed emotions. For example, if the consumer is feeling stressed, the analysis unit can recommend that the consumer dispose of the appliance in a relaxed state rather than rushing. Also, if the consumer is relaxed, it can provide detailed disposal instructions to encourage disposal. Furthermore, if the consumer is in a hurry, it can also suggest a method for disposing of the appliance quickly. This makes it possible to suggest the optimal disposal timing according to the consumer's emotions.

[0159] The processing flow of the second embodiment will be briefly explained below.

[0160] Step 1: The collection unit collects information about home appliances owned by consumers. The collected information includes the product type, manufacturer, purchase date, and frequency of use. The collection unit collects information about home appliances such as refrigerators, washing machines, air conditioners, and televisions, and stores the information entered by consumers in a database. Step 2: The analysis unit analyzes the information collected by the collection unit and predicts failures of home appliances. The analysis is based on past failure data and product usage, and uses generative AI to evaluate the risk of failure. For example, text generation AI and multimodal generation AI are used to develop algorithms for evaluating failure risk. Step 3: The judgment unit determines whether or not the product can be repaired based on the failure predicted by the analysis unit. The judgment is made based on the product's fault location and repair history. For example, if a refrigerator compressor, washing machine motor, or air conditioner compressor breaks down, it determines whether the product can be repaired. Step 4: If the judgment unit determines that repair is not possible, the suggestion unit suggests a replacement model based on the consumer's preferences. The suggestion is based on the consumer's past purchase history and preferences. For example, it suggests an energy-efficient refrigerator or a washing machine with the latest features.

[0161] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0163] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0165] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0166] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0167] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0168] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0169] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0171] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0172] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0173] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0174] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0175] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0176] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0177] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0178] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0179] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0180] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0181] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0182] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0183] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0184] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0185] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0186] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0187] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0188] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0189] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0190] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0191] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0192] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0193] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0194] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0195] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0196] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0197] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0198] 7, a 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.

[0199] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0200] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0201] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0202] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0203] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0204] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0205] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0206] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0207] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0208] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0209] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0210] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0211] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0212] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0213] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0214] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0215] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0216] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0217] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0218] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0219] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0220] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0221] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0222] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0223] 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.

[0224] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0225] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0226] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0227] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0228] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0229] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0230] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0231] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0232] [Explanation of symbols]

[0233] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects information on home appliances owned by consumers; an analysis unit that analyzes the information collected by the collection unit and predicts a failure of the home appliance; a determination unit that determines whether or not repair is possible based on the failure predicted by the analysis unit; a suggestion unit that suggests a replacement model based on the consumer's preferences when the judgment unit determines that repair is impossible; Equipped with A system characterized by:

2. Equipped with a preference grasping unit for grasping consumer preferences 2. The system of claim 1.

3. Equipped with a repair history management department that manages repair history 2. The system of claim 1.

4. Have an expense management department to save money on purchases 2. The system of claim 1.

5. The collecting unit Collect information about product type, manufacturer, purchase date, and frequency of use 2. The system of claim 1.

6. The analysis unit Evaluate the risk of failure based on past failure data and product usage conditions 2. The system of claim 1.

7. The determination unit Determine whether or not the product can be repaired based on the product's fault location and repair history 2. The system of claim 1.

8. The proposal unit Suggest replacement models based on consumers' past purchase history and preferences 2. The system of claim 1.

9. The collecting unit Analyzes user emotions and adjusts the timing of information collection for home appliances based on the analyzed user emotions.

2. The system of claim 1.

Images