system

Abstract

The system according to the embodiment aims to efficiently manage instruction manuals and warranty cards and to respond quickly when a problem occurs. [Solution] The system according to the embodiment comprises an import unit, an analysis unit, a search unit, a provision unit, and a management unit. The import unit imports an instruction manual. The analysis unit analyzes the problem based on the instruction manual imported by the import unit. The search unit searches for information based on the problem analyzed by the analysis unit. The provision unit provides the information searched by the search unit. The management unit manages the expiration date of the warranty.

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 that managing instruction manuals and warranties is cumbersome, making it difficult to respond quickly when a problem occurs.

[0005] The system according to the embodiment aims to efficiently manage instruction manuals and warranty cards and to respond quickly when a problem occurs. [Means for solving the problem]

[0006] The system according to the embodiment includes an import unit, an analysis unit, a search unit, a provision unit, and a management unit. The import unit imports an instruction manual. The analysis unit analyzes a problem based on the instruction manual imported by the import unit. The search unit searches for information based on the problem analyzed by the analysis unit. The provision unit provides the information searched by the search unit. The management unit manages the expiration date of the warranty. [Effects of the Invention]

[0007] The system according to the embodiment efficiently manages instruction manuals and warranty cards, and can respond quickly when a problem occurs. [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 system according to an embodiment of the present invention uses a generation AI to centrally manage instruction manuals and user manuals, providing rapid solutions when device problems occur. This system also manages warranties and automatically manages expiration dates. Specifically, the generation AI first imports instruction manuals and user manuals for home appliances. This allows users to centrally manage manuals. For example, manuals for appliances such as refrigerators, washing machines, and air conditioners can be centrally managed. Next, when a problem occurs with an appliance, the user inputs a question into the generation AI. For example, the AI ​​might input a question such as, "What is causing the light on my refrigerator to flash?" The generation AI then searches the imported manuals for relevant information and quickly provides an answer. This allows users to quickly identify the cause of the problem and learn the appropriate solution. The system also manages warranties. The generation AI imports warranties for each appliance and automatically manages expiration dates. For example, the AI ​​can check the number of days remaining on a refrigerator's warranty. It can also search for contact information based on the warranty period. This allows users to receive appropriate support within the warranty period. This invention is also beneficial for retailers and manufacturers. Using generative AI can reduce user inquiries and potentially lower costs. It can also create new customer touchpoints, contributing to improved customer satisfaction. For example, manufacturers can use generative AI to provide users with information about new products and campaigns. This can invigorate communication with users and potentially improve brand loyalty. This invention thus centralizes the management of instruction manuals and user's manuals for home appliances, providing rapid solutions when problems arise, as well as managing warranties, making it a system beneficial to both users and manufacturers. This allows the system to centralize the management of instruction manuals and user's manuals for home appliances and provide rapid solutions when problems arise. Furthermore, by automatically managing warranty expiration dates, users can receive appropriate support within the warranty period.Furthermore, manufacturers and retailers can expect to reduce costs by reducing inquiries and create new points of contact with customers.

[0029] The system according to the embodiment includes an import unit, an analysis unit, a search unit, a provision unit, and a management unit. The import unit imports instruction manuals. For example, it can import instruction manuals for household appliances all at once. For example, the import unit can import instruction manuals for appliances such as refrigerators, washing machines, and air conditioners to centrally manage them. The analysis unit analyzes a problem based on the instruction manual imported by the import unit. For example, the analysis unit can analyze the cause of a flashing light on a refrigerator. The analysis unit can perform analysis to identify the cause of the problem, such as a sensor abnormality or a software malfunction. The search unit searches for information based on the problem analyzed by the analysis unit. For example, the search unit can search for information such as repair procedures, FAQs, and support contact information corresponding to the analyzed problem. The provision unit provides the information searched by the search unit to a user. For example, the provision unit can quickly provide the searched information to the user. For example, if a user wants to know the cause of a flashing light on a refrigerator, the provision unit can provide the relevant information. The management unit manages the expiration date of a warranty. For example, the management unit can import the warranty of each home appliance and automatically manage its expiration date. For example, the management unit can check how many days remain in the warranty period of a refrigerator. The management unit can also search for contact information based on the warranty period. For example, the management unit can search for contact information for receiving appropriate support within the warranty period. As a result, the system according to the embodiment can centrally manage instruction manuals and user's manuals for home appliances and quickly provide solutions when problems occur. Furthermore, by automatically managing the expiration date of warranty certificates, users can receive appropriate support within the warranty period. Furthermore, manufacturers and retailers can expect to reduce costs by reducing inquiries and create new customer contact points.

[0030] The import unit can import instruction manuals for household appliances in bulk. Examples of household appliances include, but are not limited to, refrigerators, washing machines, and microwave ovens. The import unit can import instruction manuals for refrigerators in bulk, for example. The import unit can also import instruction manuals for washing machines in bulk. The import unit can also import instruction manuals for air conditioners in bulk. For example, the import unit can import instruction manuals for household appliances such as refrigerators, washing machines, and air conditioners in order to centrally manage them. This makes management easier by importing instruction manuals for household appliances in bulk. Some or all of the above-mentioned processing in the import unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the import unit can input instruction manuals for household appliances into the generation AI and cause the generation AI to import the instruction manuals.

[0031] The analysis unit can analyze the cause of a problem. For example, the analysis unit can analyze the cause of a flashing light on a refrigerator. For example, the analysis unit can perform analysis to identify the cause of the problem, such as a sensor abnormality or a software malfunction. The analysis unit can also analyze the cause of a malfunctioning washing machine. For example, the analysis unit can perform analysis to identify the cause of a washing machine problem, such as a motor failure or a wiring malfunction. The analysis unit can also analyze the cause of poor cooling in an air conditioner. For example, the analysis unit can perform analysis to identify the cause of an air conditioner problem, such as a refrigerant leak or a clogged filter. By analyzing the cause of the problem, a quick solution can be provided. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the cause of the problem into the generation AI and have the generation AI analyze the cause of the problem.

[0032] The search unit can search for relevant information based on the analyzed problem. The search unit can search for information such as repair procedures, FAQs, and support contact information corresponding to the analyzed problem. For example, the search unit can search for repair procedures corresponding to the cause of a flashing light on a refrigerator. The search unit can also search for FAQs corresponding to a malfunctioning washing machine. The search unit can also search for support contact information corresponding to a cooling problem of an air conditioner. For example, the search unit can search for repair procedures corresponding to the cause of a flashing light on a refrigerator and provide them to the user. The search unit can also search for FAQs corresponding to a malfunctioning washing machine and provide them to the user. The search unit can also search for support contact information corresponding to a cooling problem of an air conditioner and provide them to the user. In this way, appropriate information can be provided by searching for information based on the analyzed problem. Some or all of the above-described processing in the search unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the search unit can input information based on the analyzed problem to the generation AI and cause the generation AI to search for information.

[0033] The providing unit can provide the searched information to the user. The providing unit can, for example, quickly provide the searched information to the user. For example, the providing unit can provide the user with a repair procedure corresponding to the cause of a flashing light on a refrigerator. The providing unit can also provide the user with FAQs corresponding to a malfunction of a washing machine. Furthermore, the providing unit can provide the user with support contact information for a cooling problem of an air conditioner. For example, the providing unit can provide the user with a repair procedure corresponding to the cause of a flashing light on a refrigerator, allowing the user to respond quickly. The providing unit can also provide the user with FAQs corresponding to a malfunction of a washing machine, allowing the user to respond quickly. Furthermore, the providing unit can provide the user with support contact information for a cooling problem of an air conditioner, allowing the user to respond quickly. In this way, by providing the searched information to the user, the user can respond quickly. Some or all of the above-described processing by the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input the searched information to a generation AI and cause the generation AI to provide the information.

[0034] The management unit can automatically manage warranty expiration dates. For example, the management unit can import warranty cards for each home appliance and automatically manage the expiration dates. For example, the management unit can check how many days remain in the warranty period for a refrigerator. The management unit can also check how many days remain in the warranty period for a washing machine. Furthermore, the management unit can check how many days remain in the warranty period for an air conditioner. For example, the management unit can check how many days remain in the warranty period for a refrigerator and notify the user. The management unit can also check how many days remain in the warranty period for a washing machine and notify the user. Furthermore, the management unit can check how many days remain in the warranty period for an air conditioner and notify the user. This makes it easier for users to understand the warranty period by automatically managing the warranty expiration dates. Some or all of the above-described processing in the management unit may be performed using, or without, a generation AI. For example, the management unit can input the warranty expiration date into the generation AI and have the generation AI manage the expiration date.

[0035] The management unit can search for a contact point based on the warranty period. The management unit can search for an appropriate contact point based on, for example, the warranty period. For example, when the warranty period for a refrigerator remains, the management unit can search for a contact point for a refrigerator support center. Furthermore, when the warranty period for a washing machine remains, the management unit can search for a contact point for a washing machine support center. Furthermore, when the warranty period for an air conditioner remains, the management unit can search for a contact point for a support center for the air conditioner. For example, when the warranty period for a refrigerator remains, the management unit can search for a contact point for a refrigerator support center and provide it to the user. Furthermore, when the warranty period for a washing machine remains, the management unit can search for a contact point for a washing machine support center and provide it to the user. Furthermore, when the warranty period for an air conditioner remains, the management unit can search for a contact point for a support center for the air conditioner and provide it to the user. This makes it easier to receive appropriate support by searching for a contact point based on the warranty period. Some or all of the above-described processing in the management unit may be performed, for example, using a generation AI or without using a generation AI. For example, the management department can input contact information into the generation AI according to the warranty period and have the generation AI perform a search for the contact information.

[0036] The import unit can analyze the user's past usage history and select an appropriate import method when importing an instruction manual. For example, the import unit can analyze the user's past usage history and select the optimal import method when importing an instruction manual. For example, the import unit can prioritize importing instruction manuals for home appliances that the user has used frequently in the past. It can also automatically select and import instruction manuals for specific home appliances based on the user's past usage history. It can also analyze the format (PDF, image, etc.) of instruction manuals used by the user in the past and select the optimal import method. For example, the import unit can collect the user's past usage history from an operation log and prioritize importing instruction manuals for frequently used home appliances. It can also analyze the user's past usage history based on usage time and automatically select and import instruction manuals for specific home appliances. It can also analyze the format of instruction manuals used by the user in the past and select the optimal import method. This allows the optimal import method to be selected by analyzing the user's past usage history. Some or all of the above-described processing in the import unit may be performed, for example, using a generation AI or without a generation AI. For example, the capture unit can input the user's past usage history data into the generation AI and have the generation AI select the optimal capture method.

[0037] The import unit can filter instruction manuals based on the user's current usage status and areas of interest when importing them. For example, the import unit can prioritize importing instruction manuals for home appliances currently used by the user. The import unit can also filter and import related instruction manuals based on the user's areas of interest (e.g., kitchen appliances). Furthermore, the import unit can prioritize importing instruction manuals related to a problem the user is currently facing. For example, the import unit can collect the user's current usage status from an operation log and prioritize importing instruction manuals for the home appliances currently used. The import unit can also analyze the user's areas of interest from social media posts, filter, and import related instruction manuals. Furthermore, the import unit can collect the problem the user is currently facing from support history and prioritize importing related instruction manuals. This allows for filtering based on the user's current usage status and areas of interest, thereby importing highly relevant instruction manuals. Some or all of the above-described processing by the import unit may be performed using, or without, a generation AI. For example, the capture unit can input the user's current usage data into the generation AI and have the generation AI perform filtering.

[0038] When importing an instruction manual, the import unit can prioritize importing highly relevant instruction manuals by taking into account the user's geographical location information. For example, when importing an instruction manual, the import unit can prioritize importing highly relevant instruction manuals by taking into account the user's geographical location information. For example, the import unit can prioritize importing instruction manuals for home appliances sold in the user's current area. Region-specific instruction manuals can also be imported based on the user's geographical location information. Furthermore, when the user is traveling, the import unit can prioritize importing instruction manuals for portable home appliances. For example, the import unit can collect the user's geographical location information from GPS data and prioritize importing instruction manuals for home appliances sold in the user's current area. Furthermore, the import unit can collect the user's geographical location information from an IP address and prioritize importing region-specific instruction manuals. Furthermore, when the user is traveling, the import unit can prioritize importing instruction manuals for portable home appliances. In this way, highly relevant instruction manuals can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing by the import unit may be performed, for example, using a generation AI or without using a generation AI. For example, the import unit can input the user's geographical location information data into the generation AI and cause the generation AI to select highly relevant instruction manuals.

[0039] The import unit can analyze the user's social media activity when importing an instruction manual and import related instruction manuals. For example, the import unit can analyze the user's social media activity when importing an instruction manual and import related instruction manuals. For example, the import unit can prioritize importing instruction manuals for home appliances mentioned by the user on social media. It can also import instruction manuals for home appliances of interest from the user's social media activity. It can also import instruction manuals related to problems the user shared on social media. For example, the import unit can analyze the content of the user's social media posts and prioritize importing instruction manuals for mentioned home appliances. It can also identify home appliances of interest from the user's social media activity and import their instruction manuals. It can also import instruction manuals related to problems the user shared on social media. In this way, related instruction manuals can be imported by analyzing the user's social media activity. Some or all of the above-described processing by the import unit may be performed using, or without, a generation AI. For example, the import unit can input the user's social media activity data into the generation AI and cause the generation AI to select related instruction manuals.

[0040] The analysis unit can appropriately adjust the analysis algorithm by referring to past trouble data when analyzing a trouble. For example, the analysis unit can optimize the analysis algorithm by referring to past trouble data when analyzing a trouble. For example, the analysis unit can prioritize analysis of the most frequently occurring troubles based on past trouble data. It can also extract specific trouble patterns from past trouble data and optimize the analysis algorithm. It can also analyze past trouble data to optimize an algorithm for identifying the cause of the trouble. For example, the analysis unit can collect past trouble data from a failure history and prioritize analysis of the most frequently occurring troubles. It can also analyze past trouble data from repair records, extract specific trouble patterns, and optimize the analysis algorithm. It can also analyze past trouble data and optimize an algorithm for identifying the cause of the trouble. By referring to past trouble data, the analysis algorithm can be optimized and the analysis accuracy can be improved. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input past trouble data into the generation AI and cause the generation AI to optimize the analysis algorithm.

[0041] The analysis unit can apply different analysis methods depending on the usage status of the device when analyzing a problem. For example, the analysis unit can apply different analysis methods depending on the usage status of the device when analyzing a problem. For example, if the device is used frequently, a detailed analysis method can be applied. Also, if the device has not been used for a long period of time, a simple analysis method can be applied. Furthermore, the analysis unit can select an analysis method for a specific problem depending on the usage status of the device. For example, the analysis unit collects device usage status from usage time, and if the device is used frequently, a detailed analysis method can be applied. Also, the analysis unit collects device usage status from operation logs, and if the device has not been used for a long period of time, a simple analysis method can be applied. Furthermore, the analysis unit can select an analysis method for a specific problem depending on the usage status of the device. By applying an analysis method depending on the usage status of the device, appropriate analysis results can be provided. 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 device usage status data to the generation AI and have the generation AI select an analysis method.

[0042] The analysis unit can take into account the geographical distribution of devices when analyzing a problem. For example, the analysis unit can take into account the geographical distribution of devices when analyzing a problem. For example, the analysis unit can take into account the climatic conditions of the area where the devices are installed. Furthermore, based on the geographical distribution of devices, it can prioritize analysis of problems that tend to occur in a specific area. Furthermore, it can analyze the geographical distribution of devices to identify region-specific trouble patterns. For example, the analysis unit can collect data on the geographical distribution of devices from sales areas and perform analysis taking into account climatic conditions. Furthermore, the analysis unit can collect data on the geographical distribution of devices from installation locations and prioritize analysis of problems that tend to occur in a specific area. Furthermore, the analysis unit can analyze the geographical distribution of devices and identify region-specific trouble patterns. Thus, by taking the geographical distribution of devices into account, it is possible to improve the accuracy of analysis of region-specific troubles. 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 data on the geographical distribution of devices into the generation AI and have the generation AI perform the analysis.

[0043] The analysis unit can improve the accuracy of the analysis by referring to related literature when analyzing a problem. For example, the analysis unit can improve the accuracy of the analysis by referring to related literature when analyzing a problem. For example, the analysis unit can optimize an analysis method for identifying the cause of the problem based on the related literature. Solutions to specific problems can also be extracted from the related literature and reflected in the analysis. Furthermore, the reliability of the analysis results can be improved by referring to the related literature. For example, the analysis unit can collect related literature from technical papers and optimize an analysis method for identifying the cause of the problem. The analysis unit can also collect related literature from patent documents and extract solutions to specific problems and reflect them in the analysis. Furthermore, the analysis unit can improve the reliability of the analysis results by referring to the related literature. Thus, the accuracy of the analysis can be improved by referring to the related literature. Some or all of the above-described processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] The search unit can appropriately adjust the search algorithm by referring to past search history when performing a search. For example, the search unit can optimize the search algorithm by referring to past search history when performing a search. For example, the search unit can prioritize displaying the most frequently searched information based on the user's past search history. It can also extract specific search patterns from the user's past search history and optimize the search algorithm. It can also analyze the user's past search history and optimize the algorithm for providing the most relevant information. For example, the search unit can collect the user's past search history based on search keywords and prioritize displaying the most frequently searched information. It can also collect the user's past search history based on search date and time, extract specific search patterns, and optimize the search algorithm. It can also analyze the user's past search history and optimize the algorithm for providing the most relevant information. By referring to past search history, the search algorithm can be optimized and search accuracy can be improved. Some or all of the above-described processing in the search unit may be performed, for example, using a generation AI or without a generation AI. For example, the search unit can input past search history data into the generation AI and have the generation AI optimize the search algorithm.

[0045] The search unit can apply different search methods depending on the trouble category during a search. For example, the search unit can apply different search methods depending on the trouble category during a search. For example, if the trouble category is an electrical system, it can prioritize searching for electrical-related information. Also, if the trouble category is a mechanical system, it can prioritize searching for mechanical-related information. Furthermore, it can select an optimal search method depending on the trouble category and provide information. For example, the search unit can identify the trouble category as an electrical system and prioritize searching for electrical-related information. Also, the search unit can identify the trouble category as a mechanical system and prioritize searching for mechanical-related information. Furthermore, the search unit can select an optimal search method depending on the trouble category and provide information. By applying the optimal search method depending on the trouble category, it is possible to provide highly relevant information. Some or all of the above-described processing in the search unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the search unit can input trouble category data to the generation AI and cause the generation AI to select a search method.

[0046] The search unit may perform a search taking into account the geographical distribution of devices. For example, the search unit may perform a search taking into account the geographical distribution of devices. For example, the search unit may perform a search taking into account the climatic conditions of the region where the devices are installed. Furthermore, the search unit may prioritize searching for information related to problems that are likely to occur in a specific region based on the geographical distribution of devices. Furthermore, the search unit may analyze the geographical distribution of devices and provide information about problems that are specific to the region. For example, the search unit may collect information about the geographical distribution of devices from sales regions and perform a search taking into account climatic conditions. Furthermore, the search unit may collect information about the geographical distribution of devices from installation locations and prioritize searching for information related to problems that are likely to occur in a specific region. Furthermore, the search unit may analyze the geographical distribution of devices and provide information about problems that are specific to the region. In this way, by taking into account the geographical distribution of devices, information about problems that are specific to the region can be provided. Some or all of the above-described processing in the search unit may be performed using, or without, a generation AI. For example, the search unit may input geographical distribution data of devices into the generation AI and cause the generation AI to execute a search.

[0047] The search unit can improve the accuracy of the search by referring to related literature during the search. For example, the search unit can improve the accuracy of the search by referring to related literature during the search. For example, the search unit can optimize a search method to provide the most relevant information based on the related literature. Solutions to specific problems can also be extracted from the related literature and reflected in the search results. Furthermore, the reliability of the search results can be improved by referring to the related literature. For example, the search unit can collect related literature from technical papers and optimize a search method to provide the most relevant information. The search unit can also collect related literature from patent documents, extract solutions to specific problems, and reflect them in the search results. Furthermore, the search unit can improve the reliability of the search results by referring to the related literature. Thus, the accuracy of the search can be improved by referring to the related literature. Some or all of the above-described processing in the search unit may be performed using, or without, a generation AI. For example, the search unit can input related literature data into the generation AI and cause the generation AI to improve the search accuracy.

[0048] The providing unit can select an appropriate information providing method by referring to the user's past usage history when providing information. For example, the providing unit can select an optimal information providing method by referring to the user's past usage history when providing information. For example, the providing unit can prioritize the most frequently used information providing method based on the user's past usage history. Furthermore, the providing unit can extract a specific provision pattern from the user's past usage history and select an optimal information providing method. Furthermore, the providing unit can analyze the user's past usage history and select a method for providing the most relevant information. For example, the providing unit can collect the user's past usage history from usage time and prioritize the most frequently used information providing method. Furthermore, the providing unit can collect the user's past usage history from an operation log, extract a specific provision pattern, and select an optimal information providing method. Furthermore, the providing unit can analyze the user's past usage history and select a method for providing the most relevant information. Thus, the optimal information providing method can be selected by referring to the user's past usage history. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI or without using a generation AI. For example, the providing unit can input the user's past usage history data into the generation AI and have the generation AI select the provision method.

[0049] The providing unit can customize the information based on the user's current usage status when providing the information. The providing unit can, for example, customize the information based on the user's current usage status when providing the information. For example, the providing unit can prioritize providing information related to the home appliance currently being used by the user. The providing unit can also customize and provide the most relevant information based on the user's current usage status. Furthermore, the providing unit can prioritize providing information related to a problem the user is facing. For example, the providing unit can collect the user's current usage status from an operation log and prioritize providing information related to the home appliance currently being used. The providing unit can also collect the user's current usage status from usage time and customize and provide the most relevant information. The providing unit can also collect the problem the user is facing from a support history and prioritize providing the related information. In this way, highly relevant information can be provided by customizing the information based on the user's current usage status. Some or all of the above-described processing by the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input the user's current usage status data into the generation AI and cause the generation AI to customize the information.

[0050] The providing unit can provide appropriate information by taking into account the user's geographical location information when providing information. For example, the providing unit can provide appropriate information by taking into account the user's geographical location information when providing information. For example, the providing unit can provide information by taking into account the climatic conditions of the user's current location. Region-specific information can also be provided based on the user's geographical location information. Furthermore, when the user is traveling, information related to portable home appliances can be preferentially provided. For example, the providing unit can collect the user's geographical location information from GPS data and provide information by taking into account the climatic conditions of the user's current location. Furthermore, the providing unit can collect the user's geographical location information from an IP address and provide region-specific information. Furthermore, when the user is traveling, the providing unit can preferentially provide information related to portable home appliances. This allows for the provision of highly relevant information by taking into account the user's geographical location information. Some or all of the above-described processing by the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input the user's geographical location information data into the generation AI and cause the generation AI to provide appropriate information.

[0051] The providing unit can analyze the user's social media activity and adjust the method of providing information when providing information. For example, the providing unit can analyze the user's social media activity and adjust the method of providing information when providing information. For example, the providing unit can prioritize providing information related to home appliances mentioned by the user on social media. It can also provide information related to home appliances of interest based on the user's social media activity. It can also prioritize providing information related to problems shared by the user on social media. For example, the providing unit can analyze the content of the user's social media posts and prioritize providing information related to the mentioned home appliances. It can also identify home appliances of interest based on the user's social media activity and provide related information. It can also prioritize providing information related to problems shared by the user on social media. This makes it possible to provide highly relevant information by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's social media activity data into the generation AI and cause the generation AI to adjust the method of providing information.

[0052] The management unit can appropriately adjust the management algorithm by referring to past warranty data when managing the warranty expiration date. For example, the management unit can optimize the management algorithm by referring to past warranty data when managing the warranty expiration date. For example, the management unit can prioritize the most frequently used management method based on the past warranty data. It can also extract a specific management pattern from the past warranty data and optimize the management algorithm. It can also analyze the past warranty data and optimize the algorithm for providing the most relevant management method. For example, the management unit can collect past warranty data from the warranty issue date and prioritize the most frequently used management method. It can also collect past warranty data from the warranty content, extract a specific management pattern, and optimize the management algorithm. It can also analyze the past warranty data and optimize the algorithm for providing the most relevant management method. By referring to the past warranty data, the management algorithm can be optimized and management accuracy can be improved. Some or all of the above-described processing in the management unit can be performed, for example, using a generation AI or without a generation AI. For example, the management department can input past warranty data into the generation AI and have the generation AI optimize the management algorithm.

[0053] The management unit can apply different management methods depending on the usage status of the device when managing the expiration date of the warranty. For example, the management unit can apply different management methods depending on the usage status of the device when managing the expiration date of the warranty. For example, if the device is used frequently, a detailed management method can be applied. Also, if the device has not been used for a long period of time, a simple management method can be applied. Furthermore, a specific management method can be selected depending on the usage status of the device. For example, the management unit can collect device usage status from usage time and apply a detailed management method if the device is used frequently. Also, the management unit can collect device usage status from operation logs and apply a simple management method if the device has not been used for a long period of time. Furthermore, the management unit can select a specific management method depending on the usage status of the device. In this way, an appropriate management method can be provided by applying a management method depending on the usage status of the device. Some or all of the above-mentioned processing in the management unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the management unit can input device usage data into the generation AI and have the generation AI select a management method.

[0054] The management unit can search for the optimal contact point by taking into account the user's geographical location information when managing the warranty expiration date. For example, the management unit can search for the optimal contact point by taking into account the user's geographical location information when managing the warranty expiration date. For example, the management unit can prioritize searching for a support center in the user's current area. Region-specific contact points can also be searched for based on the user's geographical location information. Furthermore, if the user is traveling, the management unit can prioritize searching for the nearest support center. For example, the management unit can collect the user's geographical location information from GPS data and prioritize searching for a support center in the user's current area. Furthermore, the management unit can collect the user's geographical location information from an IP address and prioritize searching for a region-specific contact point. Furthermore, if the user is traveling, the management unit can prioritize searching for the nearest support center. This allows the optimal contact point to be provided by taking the user's geographical location information into account. Some or all of the above-described processing in the management unit may be performed using, or without, a generation AI. For example, the management unit can input the user's geographical location information data into the generation AI and cause the generation AI to search for the optimal contact point.

[0055] The management unit can improve the accuracy of management by referring to related literature when managing the expiration date of a warranty. For example, the management unit can improve the accuracy of management by referring to related literature when managing the expiration date of a warranty. For example, the management unit can optimize a method for providing the most relevant management method based on the related literature. It can also extract specific management methods from the related literature and reflect them in the management. Furthermore, the reliability of the management results can be improved by referring to the related literature. For example, the management unit can collect related literature from technical papers and optimize a method for providing the most relevant management method. It can also collect related literature from patent documents, extract specific management methods, and reflect them in the management. It can also improve the reliability of the management results by referring to the related literature. Thus, the accuracy of management can be improved by referring to the related literature. Some or all of the above-described processing in the management unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the management unit can input related literature data into the generation AI and have the generation AI improve the accuracy of the management.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] When analyzing a problem, the analysis unit can optimize the analysis algorithm by referring to past problem data. For example, the analysis unit can prioritize analysis of the most frequently occurring problems based on the past problem data. It can also extract specific problem patterns from the past problem data and optimize the analysis algorithm. Furthermore, it can analyze the past problem data and optimize the algorithm for identifying the cause of the problem. For example, the analysis unit can collect past problem data from a failure history and prioritize analysis of the most frequently occurring problems. It can also analyze the past problem data from repair records, extract specific problem patterns, and optimize the analysis algorithm. It can also analyze the past problem data and optimize the algorithm for identifying the cause of the problem. By referring to the past problem data, the analysis algorithm can be optimized and the analysis accuracy can be improved. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input past problem data into the generation AI and have the generation AI optimize the analysis algorithm.

[0058] When searching, the search unit can optimize the search algorithm by referring to the user's past search history. For example, the search unit can prioritize displaying the most frequently searched information based on the user's past search history. It can also extract specific search patterns from the user's past search history and optimize the search algorithm. It can also analyze the user's past search history and optimize the algorithm for providing the most relevant information. For example, the search unit can collect the user's past search history based on search keywords and prioritize displaying the most frequently searched information. It can also collect the user's past search history based on search date and time, extract specific search patterns, and optimize the search algorithm. It can also analyze the user's past search history and optimize the algorithm for providing the most relevant information. By referring to the past search history, the search algorithm can be optimized and search accuracy can be improved. Some or all of the above-described processing in the search unit can be performed using, or without, a generation AI. For example, the search unit can input past search history data into the generation AI and cause the generation AI to optimize the search algorithm.

[0059] When providing information, the providing unit can select the optimal information providing method by referring to the user's past usage history. For example, the providing unit can prioritize the most frequently used information providing method based on the user's past usage history. It can also extract a specific provision pattern from the user's past usage history and select the optimal information providing method. Furthermore, it can analyze the user's past usage history and select a method for providing the most relevant information. For example, the providing unit can collect the user's past usage history from usage time and prioritize the most frequently used information providing method. It can also collect the user's past usage history from an operation log, extract a specific provision pattern, and select the optimal information providing method. Furthermore, the providing unit can analyze the user's past usage history and select a method for providing the most relevant information. Thus, the optimal information providing method can be selected by referring to the user's past usage history. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI or without a generation AI. For example, the providing unit can input the user's past usage history data into the generation AI and have the generation AI select the information providing method.

[0060] When providing information, the providing unit can customize the information based on the user's current usage status. For example, the providing unit can prioritize providing information related to the home appliance currently being used by the user. The providing unit can also customize and provide the most relevant information based on the user's current usage status. Furthermore, the providing unit can prioritize providing information related to a problem the user is facing. For example, the providing unit can collect the user's current usage status from an operation log and prioritize providing information related to the home appliance currently being used. The providing unit can also collect the user's current usage status from usage time and customize and provide the most relevant information. The providing unit can also collect the problem the user is facing from a support history and prioritize providing the relevant information. This allows the provision of highly relevant information by customizing the information based on the user's current usage status. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's current usage status data into the generation AI and cause the generation AI to customize the information.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The import unit imports instruction manuals. For example, it is possible to import instruction manuals for household appliances all at once. The import unit can import instruction manuals for household appliances such as refrigerators, washing machines, and air conditioners in order to centrally manage them. Step 2: The analysis unit analyzes the problem based on the instruction manual imported by the import unit. For example, it can analyze the cause of a flashing light on a refrigerator. The analysis unit can perform analysis to identify the cause of the problem, such as a sensor abnormality or software malfunction. Step 3: The search unit searches for information based on the problem analyzed by the analysis unit. For example, it can search for information such as repair procedures, FAQs, and support contact information that correspond to the analyzed problem. Step 4: The providing unit provides the information searched by the searching unit to the user. For example, if the user wants to know why the light on the refrigerator is flashing, the providing unit can provide the user with the relevant information. Step 5: The management unit manages the expiration dates of warranty certificates. For example, it can import the warranty certificates of each home appliance and automatically manage the expiration dates. The management unit can check how many days are left in the warranty period of a refrigerator. It can also search for contact information based on the warranty period.

[0063] (Example 2) A system according to an embodiment of the present invention uses a generation AI to centrally manage instruction manuals and user manuals, providing rapid solutions when device problems occur. This system also manages warranties and automatically manages expiration dates. Specifically, the generation AI first imports instruction manuals and user manuals for home appliances. This allows users to centrally manage manuals. For example, manuals for appliances such as refrigerators, washing machines, and air conditioners can be centrally managed. Next, when a problem occurs with an appliance, the user inputs a question into the generation AI. For example, the AI ​​might input a question such as, "What is causing the light on my refrigerator to flash?" The generation AI then searches the imported manuals for relevant information and quickly provides an answer. This allows users to quickly identify the cause of the problem and learn the appropriate solution. The system also manages warranties. The generation AI imports warranties for each appliance and automatically manages expiration dates. For example, the AI ​​can check the number of days remaining on a refrigerator's warranty. It can also search for contact information based on the warranty period. This allows users to receive appropriate support within the warranty period. This invention is also beneficial for retailers and manufacturers. Using generative AI can reduce user inquiries and potentially lower costs. It can also create new customer touchpoints, contributing to improved customer satisfaction. For example, manufacturers can use generative AI to provide users with information about new products and campaigns. This can invigorate communication with users and potentially improve brand loyalty. This invention thus centralizes the management of instruction manuals and user's manuals for home appliances, providing rapid solutions when problems arise, as well as managing warranties, making it a system beneficial to both users and manufacturers. This allows the system to centralize the management of instruction manuals and user's manuals for home appliances and provide rapid solutions when problems arise. Furthermore, by automatically managing warranty expiration dates, users can receive appropriate support within the warranty period.Furthermore, manufacturers and retailers can expect to reduce costs by reducing inquiries and create new points of contact with customers.

[0064] The system according to the embodiment includes an import unit, an analysis unit, a search unit, a provision unit, and a management unit. The import unit imports instruction manuals. For example, it can import instruction manuals for household appliances all at once. For example, the import unit can import instruction manuals for appliances such as refrigerators, washing machines, and air conditioners to centrally manage them. The analysis unit analyzes a problem based on the instruction manual imported by the import unit. For example, the analysis unit can analyze the cause of a flashing light on a refrigerator. The analysis unit can perform analysis to identify the cause of the problem, such as a sensor abnormality or a software malfunction. The search unit searches for information based on the problem analyzed by the analysis unit. For example, the search unit can search for information such as repair procedures, FAQs, and support contact information corresponding to the analyzed problem. The provision unit provides the information searched by the search unit to a user. For example, the provision unit can quickly provide the searched information to the user. For example, if a user wants to know the cause of a flashing light on a refrigerator, the provision unit can provide the relevant information. The management unit manages the expiration date of a warranty. For example, the management unit can import the warranty of each home appliance and automatically manage its expiration date. For example, the management unit can check how many days remain in the warranty period of a refrigerator. The management unit can also search for contact information based on the warranty period. For example, the management unit can search for contact information for receiving appropriate support within the warranty period. As a result, the system according to the embodiment can centrally manage instruction manuals and user's manuals for home appliances and quickly provide solutions when problems occur. Furthermore, by automatically managing the expiration date of warranty certificates, users can receive appropriate support within the warranty period. Furthermore, manufacturers and retailers can expect to reduce costs by reducing inquiries and create new customer contact points.

[0065] The import unit can import instruction manuals for household appliances in bulk. Examples of household appliances include, but are not limited to, refrigerators, washing machines, and microwave ovens. The import unit can import instruction manuals for refrigerators in bulk, for example. The import unit can also import instruction manuals for washing machines in bulk. The import unit can also import instruction manuals for air conditioners in bulk. For example, the import unit can import instruction manuals for household appliances such as refrigerators, washing machines, and air conditioners in order to centrally manage them. This makes management easier by importing instruction manuals for household appliances in bulk. Some or all of the above-mentioned processing in the import unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the import unit can input instruction manuals for household appliances into the generation AI and cause the generation AI to import the instruction manuals.

[0066] The analysis unit can analyze the cause of a problem. For example, the analysis unit can analyze the cause of a flashing light on a refrigerator. For example, the analysis unit can perform analysis to identify the cause of the problem, such as a sensor abnormality or a software malfunction. The analysis unit can also analyze the cause of a malfunctioning washing machine. For example, the analysis unit can perform analysis to identify the cause of a washing machine problem, such as a motor failure or a wiring malfunction. The analysis unit can also analyze the cause of poor cooling in an air conditioner. For example, the analysis unit can perform analysis to identify the cause of an air conditioner problem, such as a refrigerant leak or a clogged filter. By analyzing the cause of the problem, a quick solution can be provided. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the cause of the problem into the generation AI and have the generation AI analyze the cause of the problem.

[0067] The search unit can search for relevant information based on the analyzed problem. The search unit can search for information such as repair procedures, FAQs, and support contact information corresponding to the analyzed problem. For example, the search unit can search for repair procedures corresponding to the cause of a flashing light on a refrigerator. The search unit can also search for FAQs corresponding to a malfunctioning washing machine. The search unit can also search for support contact information corresponding to a cooling problem of an air conditioner. For example, the search unit can search for repair procedures corresponding to the cause of a flashing light on a refrigerator and provide them to the user. The search unit can also search for FAQs corresponding to a malfunctioning washing machine and provide them to the user. The search unit can also search for support contact information corresponding to a cooling problem of an air conditioner and provide them to the user. In this way, appropriate information can be provided by searching for information based on the analyzed problem. Some or all of the above-described processing in the search unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the search unit can input information based on the analyzed problem to the generation AI and cause the generation AI to search for information.

[0068] The providing unit can provide the searched information to the user. The providing unit can, for example, quickly provide the searched information to the user. For example, the providing unit can provide the user with a repair procedure corresponding to the cause of a flashing light on a refrigerator. The providing unit can also provide the user with FAQs corresponding to a malfunction of a washing machine. Furthermore, the providing unit can provide the user with support contact information for a cooling problem of an air conditioner. For example, the providing unit can provide the user with a repair procedure corresponding to the cause of a flashing light on a refrigerator, allowing the user to respond quickly. The providing unit can also provide the user with FAQs corresponding to a malfunction of a washing machine, allowing the user to respond quickly. Furthermore, the providing unit can provide the user with support contact information for a cooling problem of an air conditioner, allowing the user to respond quickly. In this way, by providing the searched information to the user, the user can respond quickly. Some or all of the above-described processing by the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input the searched information to a generation AI and cause the generation AI to provide the information.

[0069] The management unit can automatically manage warranty expiration dates. For example, the management unit can import warranty cards for each home appliance and automatically manage the expiration dates. For example, the management unit can check how many days remain in the warranty period for a refrigerator. The management unit can also check how many days remain in the warranty period for a washing machine. Furthermore, the management unit can check how many days remain in the warranty period for an air conditioner. For example, the management unit can check how many days remain in the warranty period for a refrigerator and notify the user. The management unit can also check how many days remain in the warranty period for a washing machine and notify the user. Furthermore, the management unit can check how many days remain in the warranty period for an air conditioner and notify the user. This makes it easier for users to understand the warranty period by automatically managing the warranty expiration dates. Some or all of the above-described processing in the management unit may be performed using, or without, a generation AI. For example, the management unit can input the warranty expiration date into the generation AI and have the generation AI manage the expiration date.

[0070] The management unit can search for a contact point based on the warranty period. The management unit can search for an appropriate contact point based on, for example, the warranty period. For example, when the warranty period for a refrigerator remains, the management unit can search for a contact point for a refrigerator support center. Furthermore, when the warranty period for a washing machine remains, the management unit can search for a contact point for a washing machine support center. Furthermore, when the warranty period for an air conditioner remains, the management unit can search for a contact point for a support center for the air conditioner. For example, when the warranty period for a refrigerator remains, the management unit can search for a contact point for a refrigerator support center and provide it to the user. Furthermore, when the warranty period for a washing machine remains, the management unit can search for a contact point for a washing machine support center and provide it to the user. Furthermore, when the warranty period for an air conditioner remains, the management unit can search for a contact point for a support center for the air conditioner and provide it to the user. This makes it easier to receive appropriate support by searching for a contact point based on the warranty period. Some or all of the above-described processing in the management unit may be performed, for example, using a generation AI or without using a generation AI. For example, the management department can input contact information into the generation AI according to the warranty period and have the generation AI perform a search for the contact information.

[0071] The capture unit can estimate the user's emotions and determine how to adjust the timing of the instruction manual download based on the estimated user emotions. The capture unit can, for example, estimate the user's emotions and adjust the timing of the instruction manual download based on the estimated user emotions. For example, if the user is feeling stressed, the download process can be automated to reduce the user's burden. If the user is relaxed, an option to manually perform the download process can be provided, allowing the user to proceed at their own pace. Furthermore, if the user is in a hurry, a high-speed mode can be provided to speed up the download process. For example, the capture unit can estimate the user's emotions using facial expression recognition technology and automate the download process if the user is feeling stressed. Alternatively, the capture unit can estimate the user's emotions using voice analysis technology and provide an option to manually perform the download process if the user is relaxed. Furthermore, the capture unit can estimate the user's emotions using a biometric sensor and provide a high-speed mode to speed up the download process if the user is in a hurry. This reduces the user's burden by adjusting the download timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may 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 capture unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the capture unit may input user emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0072] The import unit can analyze the user's past usage history and select an appropriate import method when importing an instruction manual. For example, the import unit can analyze the user's past usage history and select the optimal import method when importing an instruction manual. For example, the import unit can prioritize importing instruction manuals for home appliances that the user has used frequently in the past. It can also automatically select and import instruction manuals for specific home appliances based on the user's past usage history. It can also analyze the format (PDF, image, etc.) of instruction manuals used by the user in the past and select the optimal import method. For example, the import unit can collect the user's past usage history from an operation log and prioritize importing instruction manuals for frequently used home appliances. It can also analyze the user's past usage history based on usage time and automatically select and import instruction manuals for specific home appliances. It can also analyze the format of instruction manuals used by the user in the past and select the optimal import method. This allows the optimal import method to be selected by analyzing the user's past usage history. Some or all of the above-described processing in the import unit may be performed, for example, using a generation AI or without a generation AI. For example, the capture unit can input the user's past usage history data into the generation AI and have the generation AI select the optimal capture method.

[0073] The import unit can filter instruction manuals based on the user's current usage status and areas of interest when importing them. For example, the import unit can prioritize importing instruction manuals for home appliances currently used by the user. The import unit can also filter and import related instruction manuals based on the user's areas of interest (e.g., kitchen appliances). Furthermore, the import unit can prioritize importing instruction manuals related to a problem the user is currently facing. For example, the import unit can collect the user's current usage status from an operation log and prioritize importing instruction manuals for the home appliances currently used. The import unit can also analyze the user's areas of interest from social media posts, filter, and import related instruction manuals. Furthermore, the import unit can collect the problem the user is currently facing from support history and prioritize importing related instruction manuals. This allows for filtering based on the user's current usage status and areas of interest, thereby importing highly relevant instruction manuals. Some or all of the above-described processing by the import unit may be performed using, or without, a generation AI. For example, the capture unit can input the user's current usage data into the generation AI and have the generation AI perform filtering.

[0074] The capture unit can estimate the user's emotions and determine a method for prioritizing the instruction manuals to be loaded based on the estimated user's emotions. The capture unit can, for example, estimate the user's emotions and determine the priority of the instruction manuals to be loaded based on the estimated user's emotions. For example, if the user is stressed, important instruction manuals can be loaded first. Furthermore, if the user is relaxed, all instruction manuals can be loaded evenly. Furthermore, if the user is in a hurry, the most necessary instruction manuals can be loaded first. For example, the capture unit can estimate the user's emotions using facial expression recognition technology, and if the user is stressed, important instruction manuals can be loaded first. Furthermore, the capture unit can estimate the user's emotions using voice analysis technology, and if the user is relaxed, all instruction manuals can be loaded evenly. Furthermore, the capture unit can estimate the user's emotions using a biosensor, and if the user is in a hurry, the most necessary instruction manuals can be loaded first. Thus, by determining the priority of instruction manuals according to the user's emotions, important instruction manuals can be loaded first. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may 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 capture unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the capture unit may input user emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0075] When importing an instruction manual, the import unit can prioritize importing highly relevant instruction manuals by taking into account the user's geographical location information. For example, when importing an instruction manual, the import unit can prioritize importing highly relevant instruction manuals by taking into account the user's geographical location information. For example, the import unit can prioritize importing instruction manuals for home appliances sold in the user's current area. Region-specific instruction manuals can also be imported based on the user's geographical location information. Furthermore, when the user is traveling, the import unit can prioritize importing instruction manuals for portable home appliances. For example, the import unit can collect the user's geographical location information from GPS data and prioritize importing instruction manuals for home appliances sold in the user's current area. Furthermore, the import unit can collect the user's geographical location information from an IP address and prioritize importing region-specific instruction manuals. Furthermore, when the user is traveling, the import unit can prioritize importing instruction manuals for portable home appliances. In this way, highly relevant instruction manuals can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing by the import unit may be performed, for example, using a generation AI or without using a generation AI. For example, the import unit can input the user's geographical location information data into the generation AI and cause the generation AI to select highly relevant instruction manuals.

[0076] The import unit can analyze the user's social media activity when importing an instruction manual and import related instruction manuals. For example, the import unit can analyze the user's social media activity when importing an instruction manual and import related instruction manuals. For example, the import unit can prioritize importing instruction manuals for home appliances mentioned by the user on social media. It can also import instruction manuals for home appliances of interest from the user's social media activity. It can also import instruction manuals related to problems the user shared on social media. For example, the import unit can analyze the content of the user's social media posts and prioritize importing instruction manuals for mentioned home appliances. It can also identify home appliances of interest from the user's social media activity and import their instruction manuals. It can also import instruction manuals related to problems the user shared on social media. In this way, related instruction manuals can be imported by analyzing the user's social media activity. Some or all of the above-described processing by the import unit may be performed using, or without, a generation AI. For example, the import unit can input the user's social media activity data into the generation AI and cause the generation AI to select related instruction manuals.

[0077] The analysis unit can estimate the user's emotions and determine how to adjust the problem analysis method based on the estimated user emotions. The analysis unit can, for example, estimate the user's emotions and adjust the problem analysis method based on the estimated user emotions. For example, if the user is stressed, a simple and quick analysis method can be provided. Also, if the user is relaxed, a detailed analysis method can be provided. Furthermore, if the user is in a hurry, an analysis method focusing on the most important information can be provided. For example, the analysis unit can estimate the user's emotions using facial expression recognition technology and, if the user is stressed, provide a simple and quick analysis method. Also, the analysis unit can estimate the user's emotions using voice analysis technology and, if the user is relaxed, provide a detailed analysis method. Furthermore, the analysis unit can estimate the user's emotions using a biosensor and, if the user is in a hurry, provide an analysis method focusing on the most important information. By adjusting the problem analysis method based on the user's emotions, it is possible to provide analysis results appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may 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 may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input user emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0078] The analysis unit can appropriately adjust the analysis algorithm by referring to past trouble data when analyzing a trouble. For example, the analysis unit can optimize the analysis algorithm by referring to past trouble data when analyzing a trouble. For example, the analysis unit can prioritize analysis of the most frequently occurring troubles based on past trouble data. It can also extract specific trouble patterns from past trouble data and optimize the analysis algorithm. It can also analyze past trouble data to optimize an algorithm for identifying the cause of the trouble. For example, the analysis unit can collect past trouble data from a failure history and prioritize analysis of the most frequently occurring troubles. It can also analyze past trouble data from repair records, extract specific trouble patterns, and optimize the analysis algorithm. It can also analyze past trouble data and optimize an algorithm for identifying the cause of the trouble. By referring to past trouble data, the analysis algorithm can be optimized and the analysis accuracy can be improved. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input past trouble data into the generation AI and cause the generation AI to optimize the analysis algorithm.

[0079] The analysis unit can apply different analysis methods depending on the usage status of the device when analyzing a problem. For example, the analysis unit can apply different analysis methods depending on the usage status of the device when analyzing a problem. For example, if the device is used frequently, a detailed analysis method can be applied. Also, if the device has not been used for a long period of time, a simple analysis method can be applied. Furthermore, the analysis unit can select an analysis method for a specific problem depending on the usage status of the device. For example, the analysis unit collects device usage status from usage time, and if the device is used frequently, a detailed analysis method can be applied. Also, the analysis unit collects device usage status from operation logs, and if the device has not been used for a long period of time, a simple analysis method can be applied. Furthermore, the analysis unit can select an analysis method for a specific problem depending on the usage status of the device. By applying an analysis method depending on the usage status of the device, appropriate analysis results can be provided. 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 device usage status data to the generation AI and have the generation AI select an analysis method.

[0080] The analysis unit can estimate the user's emotions and determine how to adjust the display method of the analysis results based on the estimated user emotions. The analysis unit can, for example, estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. Also, if the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. For example, the analysis unit can estimate the user's emotions using facial expression recognition technology and, if the user is nervous, provide a simple, highly visible display method. Also, the analysis unit can estimate the user's emotions using voice analysis technology and, if the user is relaxed, provide a display method including detailed information. Furthermore, the analysis unit can estimate the user's emotions using a biosensor and, if the user is in a hurry, provide a display method that focuses on the main points. This allows the display method of the analysis results to be adjusted according to the user's emotions, thereby providing a display method suitable for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may 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 may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input user emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0081] The analysis unit can take into account the geographical distribution of devices when analyzing a problem. For example, the analysis unit can take into account the geographical distribution of devices when analyzing a problem. For example, the analysis unit can take into account the climatic conditions of the area where the devices are installed. Furthermore, based on the geographical distribution of devices, it can prioritize analysis of problems that tend to occur in a specific area. Furthermore, it can analyze the geographical distribution of devices to identify region-specific trouble patterns. For example, the analysis unit can collect data on the geographical distribution of devices from sales areas and perform analysis taking into account climatic conditions. Furthermore, the analysis unit can collect data on the geographical distribution of devices from installation locations and prioritize analysis of problems that tend to occur in a specific area. Furthermore, the analysis unit can analyze the geographical distribution of devices and identify region-specific trouble patterns. Thus, by taking the geographical distribution of devices into account, it is possible to improve the accuracy of analysis of region-specific troubles. 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 data on the geographical distribution of devices into the generation AI and have the generation AI perform the analysis.

[0082] The analysis unit can improve the accuracy of the analysis by referring to related literature when analyzing a problem. For example, the analysis unit can improve the accuracy of the analysis by referring to related literature when analyzing a problem. For example, the analysis unit can optimize an analysis method for identifying the cause of the problem based on the related literature. Solutions to specific problems can also be extracted from the related literature and reflected in the analysis. Furthermore, the reliability of the analysis results can be improved by referring to the related literature. For example, the analysis unit can collect related literature from technical papers and optimize an analysis method for identifying the cause of the problem. The analysis unit can also collect related literature from patent documents and extract solutions to specific problems and reflect them in the analysis. Furthermore, the analysis unit can improve the reliability of the analysis results by referring to the related literature. Thus, the accuracy of the analysis can be improved by referring to the related literature. Some or all of the above-described processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0083] The search unit can estimate a user's emotions and determine how to adjust the search method based on the estimated user emotions. For example, the search unit can estimate a user's emotions and adjust the search method based on the estimated user emotions. For example, if the user is stressed, a concise and quick search method can be provided. Furthermore, if the user is relaxed, detailed search options can be provided. Furthermore, if the user is in a hurry, a search method focusing on the most important information can be provided. For example, the search unit can estimate a user's emotions using facial expression recognition technology and provide a concise and quick search method if the user is stressed. Furthermore, the search unit can estimate a user's emotions using voice analysis technology and provide detailed search options if the user is relaxed. Furthermore, the search unit can estimate a user's emotions using a biometric sensor and provide a search method focusing on the most important information if the user is in a hurry. By adjusting the search method according to the user's emotions, search results suitable for the user can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may 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 search unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the search unit may input user emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0084] The search unit can appropriately adjust the search algorithm by referring to past search history when performing a search. For example, the search unit can optimize the search algorithm by referring to past search history when performing a search. For example, the search unit can prioritize displaying the most frequently searched information based on the user's past search history. It can also extract specific search patterns from the user's past search history and optimize the search algorithm. It can also analyze the user's past search history and optimize the algorithm for providing the most relevant information. For example, the search unit can collect the user's past search history based on search keywords and prioritize displaying the most frequently searched information. It can also collect the user's past search history based on search date and time, extract specific search patterns, and optimize the search algorithm. It can also analyze the user's past search history and optimize the algorithm for providing the most relevant information. By referring to past search history, the search algorithm can be optimized and search accuracy can be improved. Some or all of the above-described processing in the search unit may be performed, for example, using a generation AI or without a generation AI. For example, the search unit can input past search history data into the generation AI and have the generation AI optimize the search algorithm.

[0085] The search unit can apply different search methods depending on the trouble category during a search. For example, the search unit can apply different search methods depending on the trouble category during a search. For example, if the trouble category is an electrical system, it can prioritize searching for electrical-related information. Also, if the trouble category is a mechanical system, it can prioritize searching for mechanical-related information. Furthermore, it can select an optimal search method depending on the trouble category and provide information. For example, the search unit can identify the trouble category as an electrical system and prioritize searching for electrical-related information. Also, the search unit can identify the trouble category as a mechanical system and prioritize searching for mechanical-related information. Furthermore, the search unit can select an optimal search method depending on the trouble category and provide information. By applying the optimal search method depending on the trouble category, it is possible to provide highly relevant information. Some or all of the above-described processing in the search unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the search unit can input trouble category data to the generation AI and cause the generation AI to select a search method.

[0086] The search unit can estimate a user's emotions and determine how to adjust the display order of search results based on the estimated user emotions. The search unit can, for example, estimate a user's emotions and adjust the display order of search results based on the estimated user emotions. For example, if a user is feeling stressed, the search unit can prioritize displaying the most relevant information. Also, if a user is relaxed, the search unit can display search results containing detailed information. Furthermore, if a user is in a hurry, the search unit can prioritize displaying search results that focus on the main points. For example, the search unit can estimate a user's emotions using facial expression recognition technology, and prioritize displaying the most relevant information when the user is feeling stressed. Also, the search unit can estimate a user's emotions using voice analysis technology, and prioritize displaying search results containing detailed information when the user is relaxed. Furthermore, the search unit can estimate a user's emotions using a biometric sensor, and prioritize displaying search results that focus on the main points when the user is in a hurry. This allows the search unit to adjust the display order of search results 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 generation AI may 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 search unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the search unit may input user emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0087] The search unit may perform a search taking into account the geographical distribution of devices. For example, the search unit may perform a search taking into account the geographical distribution of devices. For example, the search unit may perform a search taking into account the climatic conditions of the region where the devices are installed. Furthermore, the search unit may prioritize searching for information related to problems that are likely to occur in a specific region based on the geographical distribution of devices. Furthermore, the search unit may analyze the geographical distribution of devices and provide information about problems that are specific to the region. For example, the search unit may collect information about the geographical distribution of devices from sales regions and perform a search taking into account climatic conditions. Furthermore, the search unit may collect information about the geographical distribution of devices from installation locations and prioritize searching for information related to problems that are likely to occur in a specific region. Furthermore, the search unit may analyze the geographical distribution of devices and provide information about problems that are specific to the region. In this way, by taking into account the geographical distribution of devices, information about problems that are specific to the region can be provided. Some or all of the above-described processing in the search unit may be performed using, or without, a generation AI. For example, the search unit may input geographical distribution data of devices into the generation AI and cause the generation AI to execute a search.

[0088] The search unit can improve the accuracy of the search by referring to related literature during the search. For example, the search unit can improve the accuracy of the search by referring to related literature during the search. For example, the search unit can optimize a search method to provide the most relevant information based on the related literature. Solutions to specific problems can also be extracted from the related literature and reflected in the search results. Furthermore, the reliability of the search results can be improved by referring to the related literature. For example, the search unit can collect related literature from technical papers and optimize a search method to provide the most relevant information. The search unit can also collect related literature from patent documents, extract solutions to specific problems, and reflect them in the search results. Furthermore, the search unit can improve the reliability of the search results by referring to the related literature. Thus, the accuracy of the search can be improved by referring to the related literature. Some or all of the above-described processing in the search unit may be performed using, or without, a generation AI. For example, the search unit can input related literature data into the generation AI and cause the generation AI to improve the search accuracy.

[0089] The providing unit can estimate the user's emotion and determine how to adjust the information provision method based on the estimated user's emotion. The providing unit can, for example, estimate the user's emotion and adjust the information provision method based on the estimated user's emotion. For example, if the user is stressed, a concise and quick information provision method can be provided. Furthermore, if the user is relaxed, a detailed information provision method can be provided. Furthermore, if the user is in a hurry, a provision method focusing on the most important information can be provided. For example, the providing unit can estimate the user's emotion using facial expression recognition technology and, if the user is stressed, provide a concise and quick information provision method. Furthermore, the providing unit can estimate the user's emotion using voice analysis technology and, if the user is relaxed, provide a detailed information provision method. Furthermore, the providing unit can estimate the user's emotion using a biosensor and, if the user is in a hurry, provide a provision method focusing on the most important information. This allows the information provision method to be adjusted according to the user's emotion, 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 generation AI may 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 providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0090] The providing unit can select an appropriate information providing method by referring to the user's past usage history when providing information. For example, the providing unit can select an optimal information providing method by referring to the user's past usage history when providing information. For example, the providing unit can prioritize the most frequently used information providing method based on the user's past usage history. Furthermore, the providing unit can extract a specific provision pattern from the user's past usage history and select an optimal information providing method. Furthermore, the providing unit can analyze the user's past usage history and select a method for providing the most relevant information. For example, the providing unit can collect the user's past usage history from usage time and prioritize the most frequently used information providing method. Furthermore, the providing unit can collect the user's past usage history from an operation log, extract a specific provision pattern, and select an optimal information providing method. Furthermore, the providing unit can analyze the user's past usage history and select a method for providing the most relevant information. Thus, the optimal information providing method can be selected by referring to the user's past usage history. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI or without using a generation AI. For example, the providing unit can input the user's past usage history data into the generation AI and have the generation AI select the provision method.

[0091] The providing unit can customize the information based on the user's current usage status when providing the information. The providing unit can, for example, customize the information based on the user's current usage status when providing the information. For example, the providing unit can prioritize providing information related to the home appliance currently being used by the user. The providing unit can also customize and provide the most relevant information based on the user's current usage status. Furthermore, the providing unit can prioritize providing information related to a problem the user is facing. For example, the providing unit can collect the user's current usage status from an operation log and prioritize providing information related to the home appliance currently being used. The providing unit can also collect the user's current usage status from usage time and customize and provide the most relevant information. The providing unit can also collect the problem the user is facing from a support history and prioritize providing the related information. In this way, highly relevant information can be provided by customizing the information based on the user's current usage status. Some or all of the above-described processing by the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input the user's current usage status data into the generation AI and cause the generation AI to customize the information.

[0092] The providing unit can estimate the user's emotions and determine a method for prioritizing information based on the estimated user emotions. The providing unit can, for example, estimate the user's emotions and determine the priority of information based on the estimated user emotions. For example, if the user is stressed, the most important information can be provided preferentially. Furthermore, if the user is relaxed, a method for providing detailed information can be provided. Furthermore, if the user is in a hurry, information that emphasizes the main points can be provided preferentially. For example, the providing unit can estimate the user's emotions using facial expression recognition technology and, if the user is stressed, prioritize the most important information. Furthermore, the providing unit can estimate the user's emotions using voice analysis technology and, if the user is relaxed, prioritize the method for providing detailed information. Furthermore, the providing unit can estimate the user's emotions using a biosensor and, if the user is in a hurry, prioritize the information that emphasizes the main points. Thus, by determining the priority of information according to the user's emotions, important information can be provided preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may 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 providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0093] The providing unit can provide appropriate information by taking into account the user's geographical location information when providing information. For example, the providing unit can provide appropriate information by taking into account the user's geographical location information when providing information. For example, the providing unit can provide information by taking into account the climatic conditions of the user's current location. Region-specific information can also be provided based on the user's geographical location information. Furthermore, when the user is traveling, information related to portable home appliances can be preferentially provided. For example, the providing unit can collect the user's geographical location information from GPS data and provide information by taking into account the climatic conditions of the user's current location. Furthermore, the providing unit can collect the user's geographical location information from an IP address and provide region-specific information. Furthermore, when the user is traveling, the providing unit can preferentially provide information related to portable home appliances. This allows for the provision of highly relevant information by taking into account the user's geographical location information. Some or all of the above-described processing by the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input the user's geographical location information data into the generation AI and cause the generation AI to provide appropriate information.

[0094] The providing unit can analyze the user's social media activity and adjust the method of providing information when providing information. For example, the providing unit can analyze the user's social media activity and adjust the method of providing information when providing information. For example, the providing unit can prioritize providing information related to home appliances mentioned by the user on social media. It can also provide information related to home appliances of interest based on the user's social media activity. It can also prioritize providing information related to problems shared by the user on social media. For example, the providing unit can analyze the content of the user's social media posts and prioritize providing information related to the mentioned home appliances. It can also identify home appliances of interest based on the user's social media activity and provide related information. It can also prioritize providing information related to problems shared by the user on social media. This makes it possible to provide highly relevant information by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's social media activity data into the generation AI and cause the generation AI to adjust the method of providing information.

[0095] The management unit can estimate the user's emotions and determine how to adjust the warranty expiration date management method based on the estimated user emotions. The management unit can, for example, estimate the user's emotions and adjust the warranty expiration date management method based on the estimated user emotions. For example, if the user is stressed, a simple and quick expiration date management method can be provided. Also, if the user is relaxed, a detailed expiration date management method can be provided. Furthermore, if the user is in a hurry, a validity date management method that focuses on the most important information can be provided. For example, the management unit can estimate the user's emotions using facial expression recognition technology and, if the user is stressed, provide a simple and quick expiration date management method. Also, the management unit can estimate the user's emotions using voice analysis technology and, if the user is relaxed, provide a detailed expiration date management method. Furthermore, the management unit can estimate the user's emotions using a biometric sensor and, if the user is in a hurry, provide a validity date management method that focuses on the most important information. This allows the warranty expiration date management method to be adjusted according to the user's emotions, thereby providing a management method that is appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may 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 management unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the management unit may input user emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0096] The management unit can appropriately adjust the management algorithm by referring to past warranty data when managing the warranty expiration date. For example, the management unit can optimize the management algorithm by referring to past warranty data when managing the warranty expiration date. For example, the management unit can prioritize the most frequently used management method based on the past warranty data. It can also extract a specific management pattern from the past warranty data and optimize the management algorithm. It can also analyze the past warranty data and optimize the algorithm for providing the most relevant management method. For example, the management unit can collect past warranty data from the warranty issue date and prioritize the most frequently used management method. It can also collect past warranty data from the warranty content, extract a specific management pattern, and optimize the management algorithm. It can also analyze the past warranty data and optimize the algorithm for providing the most relevant management method. By referring to the past warranty data, the management algorithm can be optimized and management accuracy can be improved. Some or all of the above-described processing in the management unit can be performed, for example, using a generation AI or without a generation AI. For example, the management department can input past warranty data into the generation AI and have the generation AI optimize the management algorithm.

[0097] The management unit can apply different management methods depending on the usage status of the device when managing the expiration date of the warranty. For example, the management unit can apply different management methods depending on the usage status of the device when managing the expiration date of the warranty. For example, if the device is used frequently, a detailed management method can be applied. Also, if the device has not been used for a long period of time, a simple management method can be applied. Furthermore, a specific management method can be selected depending on the usage status of the device. For example, the management unit can collect device usage status from usage time and apply a detailed management method if the device is used frequently. Also, the management unit can collect device usage status from operation logs and apply a simple management method if the device has not been used for a long period of time. Furthermore, the management unit can select a specific management method depending on the usage status of the device. In this way, an appropriate management method can be provided by applying a management method depending on the usage status of the device. Some or all of the above-mentioned processing in the management unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the management unit can input device usage data into the generation AI and have the generation AI select a management method.

[0098] The management unit can estimate a user's emotions and determine how to adjust the inquiry search method based on the estimated user emotions. For example, the management unit can estimate a user's emotions and adjust the inquiry search method based on the estimated user emotions. For example, if the user is stressed, a concise and quick inquiry search method can be provided. Furthermore, if the user is relaxed, a detailed inquiry search method can be provided. Furthermore, if the user is in a hurry, a contact search method that focuses on the most important information can be provided. For example, the management unit can estimate a user's emotions using facial expression recognition technology and, if the user is stressed, provide a concise and quick inquiry search method. Furthermore, the management unit can estimate a user's emotions using voice analysis technology and, if the user is relaxed, provide a detailed inquiry search method. Furthermore, the management unit can estimate a user's emotions using a biometric sensor and, if the user is in a hurry, provide a contact search method that focuses on the most important information. This allows the system to adjust the inquiry search method according to the user's emotions, thereby providing contacts that are appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may 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 management unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the management unit may input user emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0099] The management unit can search for the optimal contact point by taking into account the user's geographical location information when managing the warranty expiration date. For example, the management unit can search for the optimal contact point by taking into account the user's geographical location information when managing the warranty expiration date. For example, the management unit can prioritize searching for a support center in the user's current area. Region-specific contact points can also be searched for based on the user's geographical location information. Furthermore, if the user is traveling, the management unit can prioritize searching for the nearest support center. For example, the management unit can collect the user's geographical location information from GPS data and prioritize searching for a support center in the user's current area. Furthermore, the management unit can collect the user's geographical location information from an IP address and prioritize searching for a region-specific contact point. Furthermore, if the user is traveling, the management unit can prioritize searching for the nearest support center. This allows the optimal contact point to be provided by taking the user's geographical location information into account. Some or all of the above-described processing in the management unit may be performed using, or without, a generation AI. For example, the management unit can input the user's geographical location information data into the generation AI and cause the generation AI to search for the optimal contact point.

[0100] The management unit can improve the accuracy of management by referring to related literature when managing the expiration date of a warranty. For example, the management unit can improve the accuracy of management by referring to related literature when managing the expiration date of a warranty. For example, the management unit can optimize a method for providing the most relevant management method based on the related literature. It can also extract specific management methods from the related literature and reflect them in the management. Furthermore, the reliability of the management results can be improved by referring to the related literature. For example, the management unit can collect related literature from technical papers and optimize a method for providing the most relevant management method. It can also collect related literature from patent documents, extract specific management methods, and reflect them in the management. It can also improve the reliability of the management results by referring to the related literature. Thus, the accuracy of management can be improved by referring to the related literature. Some or all of the above-described processing in the management unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the management unit can input related literature data into the generation AI and have the generation AI improve the accuracy of the management. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned import unit, analysis unit, search unit, provision unit, and management unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the import unit is realized by the control unit 46A of the smart device 14 and can import instruction manuals for household appliances all at once. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes a problem based on the imported instruction manuals. The search unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and searches for information corresponding to the analyzed problem. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the searched information to the user. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and manages the expiration date of the warranty. === Hard Collateral 1-2 === Each of the multiple elements including the above-described import unit, analysis unit, search unit, provision unit, and management unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the import unit is realized by the control unit 46A of the smart glasses 214 and can import instruction manuals for household appliances in bulk. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes a problem based on the imported instruction manuals. The search unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and searches for information corresponding to the analyzed problem. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the searched information to the user. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and manages the expiration date of the warranty. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned import unit, analysis unit, search unit, provision unit, and management unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the import unit is realized by the control unit 46A of the headset type terminal 314 and can import instruction manuals for household appliances all at once. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes a problem based on the imported instruction manuals. The search unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and searches for information corresponding to the analyzed problem. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides the searched information to the user. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and manages the expiration date of the warranty. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned import unit, analysis unit, search unit, provision unit, and management unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the import unit is realized by the control unit 46A of the robot 414 and can import instruction manuals for household appliances all at once. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes a problem based on the imported instruction manuals. The search unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and searches for information corresponding to the analyzed problem. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the searched information to the user. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and manages the expiration date of the warranty.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The analysis unit can estimate the user's emotions and adjust the method of analyzing the problem based on the estimated user's emotions. For example, if the user is stressed, a simple and quick analysis method can be provided. Furthermore, if the user is relaxed, a detailed analysis method can be provided. Furthermore, if the user is in a hurry, an analysis method focusing on the most important information can be provided. For example, the analysis unit can estimate the user's emotions using facial expression recognition technology and, if the user is stressed, provide a simple and quick analysis method. Furthermore, the analysis unit can estimate the user's emotions using voice analysis technology and, if the user is relaxed, provide a detailed analysis method. Furthermore, the analysis unit can estimate the user's emotions using a biosensor and, if the user is in a hurry, provide an analysis method focusing on the most important information. This allows the system to adjust the method of analyzing the problem based on the user's emotions and provide analysis results that are 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 analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input user emotion data into the generation AI and have the generation AI estimate the emotion.

[0103] The search unit can estimate a user's emotions and adjust the search method based on the estimated user's emotions. For example, if the user is stressed, a simple and quick search method can be provided. Furthermore, if the user is relaxed, detailed search options can be provided. Furthermore, if the user is in a hurry, a search method focusing on the most important information can be provided. For example, the search unit can estimate a user's emotions using facial expression recognition technology and, if the user is stressed, provide a simple and quick search method. Furthermore, the search unit can estimate a user's emotions using voice analysis technology and, if the user is relaxed, provide detailed search options. Furthermore, the search unit can estimate a user's emotions using a biometric sensor and, if the user is in a hurry, provide a search method focusing on the most important information. By adjusting the search method according to the user's emotions, search results suitable for the user can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using 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 search unit may be performed using, for example, a generation AI. For example, the search unit may input user emotion data to the generation AI and have the generation AI estimate the emotion.

[0104] The providing unit can estimate the user's emotions and adjust the information provision method based on the estimated user's emotions. For example, if the user is stressed, a concise and quick information provision method can be provided. Furthermore, if the user is relaxed, a detailed information provision method can be provided. Furthermore, if the user is in a hurry, a method focusing on the most important information can be provided. For example, the providing unit can estimate the user's emotions using facial expression recognition technology and, if the user is stressed, provide a concise and quick information provision method. Furthermore, the providing unit can estimate the user's emotions using voice analysis technology and, if the user is relaxed, provide a detailed information provision method. Furthermore, the providing unit can estimate the user's emotions using a biosensor and, if the user is in a hurry, provide a method focusing on the most important information. This allows the information provision method 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, 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 providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0105] The management unit can estimate the user's emotions and adjust the warranty expiration date management method based on the estimated user emotions. For example, if the user is stressed, a simple and quick expiration date management method can be provided. Furthermore, if the user is relaxed, a detailed expiration date management method can be provided. Furthermore, if the user is in a hurry, a validity expiration date management method that focuses on the most important information can be provided. For example, the management unit can estimate the user's emotions using facial expression recognition technology and, if the user is stressed, provide a simple and quick expiration date management method. Furthermore, the management unit can estimate the user's emotions using voice analysis technology and, if the user is relaxed, provide a detailed expiration date management method. Furthermore, the management unit can estimate the user's emotions using a biometric sensor and, if the user is in a hurry, provide a validity expiration date management method that focuses on the most important information. This allows the warranty expiration date management method to be adjusted according to the user's emotions, thereby providing a management method that is 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 management unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the management unit may input user emotion data into the generation AI and have the generation AI estimate the emotion.

[0106] The management unit can estimate the user's emotions and adjust the inquiry search method based on the estimated user emotions. For example, if the user is stressed, a concise and quick inquiry search method can be provided. Furthermore, if the user is relaxed, a detailed inquiry search method can be provided. Furthermore, if the user is in a hurry, a contact search method that focuses on the most important information can be provided. For example, the management unit can estimate the user's emotions using facial expression recognition technology and, if the user is stressed, provide a concise and quick inquiry search method. Furthermore, the management unit can estimate the user's emotions using voice analysis technology and, if the user is relaxed, provide a detailed inquiry search method. Furthermore, the management unit can estimate the user's emotions using a biometric sensor and, if the user is in a hurry, provide a contact search method that focuses on the most important information. This allows the system to adjust the inquiry search method according to the user's emotions and provide contacts that are 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 management unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the management unit may input user emotion data into the generation AI and have the generation AI estimate the emotion.

[0107] When analyzing a problem, the analysis unit can optimize the analysis algorithm by referring to past problem data. For example, the analysis unit can prioritize analysis of the most frequently occurring problems based on the past problem data. It can also extract specific problem patterns from the past problem data and optimize the analysis algorithm. Furthermore, it can analyze the past problem data and optimize the algorithm for identifying the cause of the problem. For example, the analysis unit can collect past problem data from a failure history and prioritize analysis of the most frequently occurring problems. It can also analyze the past problem data from repair records, extract specific problem patterns, and optimize the analysis algorithm. It can also analyze the past problem data and optimize the algorithm for identifying the cause of the problem. By referring to the past problem data, the analysis algorithm can be optimized and the analysis accuracy can be improved. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input past problem data into the generation AI and have the generation AI optimize the analysis algorithm.

[0108] When searching, the search unit can optimize the search algorithm by referring to the user's past search history. For example, the search unit can prioritize displaying the most frequently searched information based on the user's past search history. It can also extract specific search patterns from the user's past search history and optimize the search algorithm. It can also analyze the user's past search history and optimize the algorithm for providing the most relevant information. For example, the search unit can collect the user's past search history based on search keywords and prioritize displaying the most frequently searched information. It can also collect the user's past search history based on search date and time, extract specific search patterns, and optimize the search algorithm. It can also analyze the user's past search history and optimize the algorithm for providing the most relevant information. By referring to the past search history, the search algorithm can be optimized and search accuracy can be improved. Some or all of the above-described processing in the search unit can be performed using, or without, a generation AI. For example, the search unit can input past search history data into the generation AI and cause the generation AI to optimize the search algorithm.

[0109] When providing information, the providing unit can select the optimal information providing method by referring to the user's past usage history. For example, the providing unit can prioritize the most frequently used information providing method based on the user's past usage history. It can also extract a specific provision pattern from the user's past usage history and select the optimal information providing method. Furthermore, it can analyze the user's past usage history and select a method for providing the most relevant information. For example, the providing unit can collect the user's past usage history from usage time and prioritize the most frequently used information providing method. It can also collect the user's past usage history from an operation log, extract a specific provision pattern, and select the optimal information providing method. Furthermore, the providing unit can analyze the user's past usage history and select a method for providing the most relevant information. Thus, the optimal information providing method can be selected by referring to the user's past usage history. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI or without a generation AI. For example, the providing unit can input the user's past usage history data into the generation AI and have the generation AI select the information providing method.

[0110] When providing information, the providing unit can customize the information based on the user's current usage status. For example, the providing unit can prioritize providing information related to the home appliance currently being used by the user. The providing unit can also customize and provide the most relevant information based on the user's current usage status. Furthermore, the providing unit can prioritize providing information related to a problem the user is facing. For example, the providing unit can collect the user's current usage status from an operation log and prioritize providing information related to the home appliance currently being used. The providing unit can also collect the user's current usage status from usage time and customize and provide the most relevant information. The providing unit can also collect the problem the user is facing from a support history and prioritize providing the relevant information. This allows the provision of highly relevant information by customizing the information based on the user's current usage status. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's current usage status data into the generation AI and cause the generation AI to customize the information.

[0111] The providing unit can estimate the user's emotions and prioritize information based on the estimated user emotions. For example, if the user is feeling stressed, the most important information can be provided preferentially. Furthermore, if the user is relaxed, detailed information can be provided preferentially. Furthermore, if the user is in a hurry, information that emphasizes the main points can be provided preferentially. For example, the providing unit can estimate the user's emotions using facial expression recognition technology, and if the user is feeling stressed, the most important information can be provided preferentially. Furthermore, the providing unit can estimate the user's emotions using voice analysis technology, and if the user is relaxed, the information can be provided preferentially. Furthermore, the providing unit can estimate the user's emotions using a biosensor, and if the user is in a hurry, detailed information can be provided preferentially. Thus, by prioritizing information according to the user's emotions, important information can be provided preferentially. 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 providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The import unit imports instruction manuals. For example, it is possible to import instruction manuals for household appliances all at once. The import unit can import instruction manuals for household appliances such as refrigerators, washing machines, and air conditioners in order to centrally manage them. Step 2: The analysis unit analyzes the problem based on the instruction manual imported by the import unit. For example, it can analyze the cause of a flashing light on a refrigerator. The analysis unit can perform analysis to identify the cause of the problem, such as a sensor abnormality or software malfunction. Step 3: The search unit searches for information based on the problem analyzed by the analysis unit. For example, it can search for information such as repair procedures, FAQs, and support contact information that correspond to the analyzed problem. Step 4: The providing unit provides the information searched by the searching unit to the user. For example, if the user wants to know why the light on the refrigerator is flashing, the providing unit can provide the user with the relevant information. Step 5: The management unit manages the expiration dates of warranty certificates. For example, it can import the warranty certificates of each home appliance and automatically manage the expiration dates. The management unit can check how many days are left in the warranty period of a refrigerator. It can also search for contact information based on the warranty period.

[0114] 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.

[0115] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0116] 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an import unit for importing an instruction manual; an analysis unit that analyzes a problem based on the instruction manual imported by the import unit; a search unit that searches for information based on the trouble analyzed by the analysis unit; a providing unit that provides the information searched by the searching unit; A management unit that manages the expiration date of the warranty card. A system characterized by:

2. The capture unit is Import all the instruction manuals for household appliances at once The system of claim 1 .

3. The analysis unit Analyze the cause of the problem The system of claim 1 .

4. The search unit Search for relevant information based on the analyzed problem The system of claim 1 .

5. The providing unit Providing the searched information to the user The system of claim 1 .

6. The management unit Automatically manage warranty expiration dates The system of claim 1 .

7. The management unit Search for contact information based on warranty period The system of claim 1 .

8. The capture unit is Estimate the user's emotions and determine how to adjust the timing of downloading instruction manuals based on the estimated user's emotions. The system of claim 1 .

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