System

Abstract

An object of a system according to an embodiment is to provide appropriate support for a failure occurring in a home without expert knowledge.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, an inquiry unit, a guide unit, a specification unit, a provision unit, and an advice unit. The reception unit receives information from a user. The analysis unit analyzes the information received by the reception unit. The inquiry unit makes an inquiry about a confirmation point based on the information analyzed by the analysis unit. The guide part guides the repair point based on the confirmation point specified by the medical inquiry part. The specifying unit specifies the replacement part specified by the guide unit. The providing unit provides a link for purchasing the replacement part specified by the specifying unit. The advice part gives advice when the repair is impossible or a specialist is required by the guide part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to provide appropriate support for breakdowns that occurred in the home, and repairs were difficult without specialized knowledge.

[0005] The system according to the embodiment aims to provide appropriate support for breakdowns that occur in the home, even if the user does not have specialized knowledge. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an inquiry unit, a guide unit, an identification unit, a provision unit, and an advice unit. The reception unit receives information from a user. The analysis unit analyzes the information received by the reception unit. The inquiry unit inquires about check points based on the information analyzed by the analysis unit. The guide unit guides the user to repair points based on the check points identified by the inquiry unit. The identification unit identifies replacement parts identified by the guide unit. The provision unit provides a link to purchase the replacement parts identified by the identification unit. The advice unit provides advice when the product is unrepairable or when an expert is needed, as determined by the guide unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide appropriate support for breakdowns that occur in the home without requiring specialized knowledge. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AGI system according to an embodiment of the present invention provides support for troubleshooting household problems. This system allows users to take photos of broken home appliances or home equipment with their smartphones and input the symptoms of the malfunction. The AGI then analyzes the photos and the input symptoms, asks about checkpoints, and provides guidance on repairs. For example, if an air conditioner is not cooling, the AGI instructs users to check for dirty filters or insufficient refrigerant. It also identifies necessary replacement parts and guides users to purchase them through e-commerce site links (e.g., product links (URLs) on Yahoo! Shopping). For example, if the filter is dirty, the system provides links to appropriate filters. It also provides advice on repairs that are beyond repair or require specialists. For example, if refrigerant needs to be refilled, the system provides contact information for specialists. This allows users with a little knowledge to resolve malfunctions themselves at low cost. This allows the AGI system to quickly and efficiently resolve problems with broken home appliances and home equipment. For example, users can perform simple repairs themselves, such as cleaning or replacing air conditioner filters. Even if a specialist is needed, they can receive appropriate advice, providing peace of mind.

[0029] The AGI system according to the embodiment includes a reception unit, an analysis unit, an interview unit, a guide unit, an identification unit, a provision unit, and an advice unit. The reception unit receives information from a user. The information from the user includes, for example, photos of the broken home appliance or home equipment, text information about the malfunction symptoms, and the like, but is not limited to these examples. The reception unit receives, for example, photos taken with a smartphone and provides an interface for inputting the malfunction symptoms. The analysis unit analyzes the information received by the reception unit. The analysis unit, for example, analyzes the photos using an image analysis algorithm to identify the malfunction location. The analysis unit can also analyze the malfunction symptoms using text analysis technology to identify the cause of the malfunction. The interview unit asks about points to check based on the information analyzed by the analysis unit. The interview unit instructs the user, for example, on how to identify the malfunction location and items to check. The guide unit guides the user to repair points based on the check points identified by the interview unit. The guide unit, for example, guides the user on repair procedures and necessary tools. The identification unit identifies the replacement part identified by the guide unit. The identification unit, for example, identifies the part model number or compatible parts and provides an appropriate link. The provision unit provides a link for purchasing the replacement part identified by the identification unit. The provision unit, for example, provides a link to a product on an e-commerce site or a link to a technical support page. The advice unit uses the guide unit to give advice when the product is unrepairable or when an expert is needed. The advice unit advises the user, for example, how to handle the unrepairable product or how to contact an expert. As a result, the AGI system according to the embodiment can accept and analyze user information, ask about points to check, guide users to repair points, identify replacement parts, provide links, and give advice when the product is unrepairable or when an expert is needed.

[0030] The reception unit allows the user to take a photo of the faulty home appliance or household equipment with their smartphone and input the fault symptoms. The reception unit, for example, accepts photos taken by the user with their smartphone. The reception unit can also provide an interface for inputting the fault symptoms. For example, the user can take a photo of an air conditioner and input "not cooling." This allows the user to take a photo of the faulty home appliance or household equipment with their smartphone and input the fault symptoms.

[0031] The analysis unit can analyze the input information and identify the cause of the failure. For example, the analysis unit can analyze a photo using an image analysis algorithm and identify the location of the failure. The analysis unit can also analyze the symptoms of the failure using text analysis technology and identify the cause of the failure. For example, the analysis unit can analyze a photo of an air conditioner and identify a dirty filter or a lack of refrigerant. This makes it possible to analyze the input information and identify the cause of the failure.

[0032] The questioning unit can identify points to check based on the cause of the malfunction and instruct the user. The questioning unit, for example, instructs the user on how to identify the malfunction and items to check. For example, if the air conditioner is not cooling, the questioning unit can instruct the user to check for dirty filters, a lack of refrigerant, etc. This allows the questioning unit to identify points to check based on the cause of the malfunction and instruct the user.

[0033] The guide unit can guide the user to the repair points based on the check points. The guide unit guides the user, for example, to the repair procedure and the necessary tools. For example, if the filter is dirty, the guide unit can guide the user on how to clean the filter. This makes it possible to guide the user to the repair points based on the check points.

[0034] The identification unit can identify the required replacement part and provide a link. For example, the identification unit can identify the part model number or compatible parts and provide an appropriate link. For example, if the filter is dirty, a link to the appropriate filter can be provided. This allows the required replacement part to be identified and the appropriate link to be provided.

[0035] The advice unit can provide advice when the product is beyond repair or when an expert is needed. For example, the advice unit advises the user on how to handle the product that is beyond repair or how to contact an expert. For example, if refrigerant needs to be refilled, the advice unit can provide the contact information of a specialist. This allows advice to be given when the product is beyond repair or when an expert is needed.

[0036] The reception unit can analyze the user's past failure history and suggest the optimal input format. For example, the reception unit automatically displays failure details that the user has frequently entered in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict failures that will occur in specific time periods based on the user's past failure history and suggest an input format. This makes it possible to analyze the user's past failure history and suggest the optimal input format.

[0037] The reception unit can customize the input method based on the user's current situation when receiving malfunction information. For example, when entering malfunction information at night, the reception unit can provide a simple input method to enable a quick response. Furthermore, when the user is at home, the reception unit can provide detailed input options to collect accurate information. Furthermore, when the user is away from home, the reception unit can provide voice input or simple options to enable quick input. This makes it possible to customize the input method based on the user's current situation.

[0038] When receiving the malfunction information, the reception unit can select the optimum input means according to the user's input method. For example, when the user inputs the malfunction information by voice, the reception unit automatically converts it into text using voice recognition technology. Also, when the user inputs the malfunction information as text, the reception unit can analyze the input content and convert it into an appropriate format. Furthermore, when the user inputs the malfunction information as an image, the reception unit can identify the malfunction location using image analysis technology and complete the input content. This makes it possible to select the optimum input means according to the user's input method.

[0039] When receiving malfunction information, the reception unit can preferentially receive highly relevant information in consideration of the user's geographical location information. For example, when the user is in a specific area, the reception unit preferentially receives information about malfunctions that frequently occur in that area. Furthermore, when the user is in a specific building, the reception unit can also preferentially receive information about malfunctions related to the equipment in that building. Furthermore, when the user is traveling, the reception unit can also preferentially receive information about malfunctions related to the area to which the user has moved. In this way, it is possible to preferentially receive information that is highly relevant in consideration of the user's geographical location information.

[0040] When receiving malfunction information, the reception unit can analyze the user's social media activity and receive related information. For example, the reception unit automatically receives malfunction information posted by the user on social media. The reception unit can also analyze the user's social media activity and receive related malfunction information. Furthermore, the reception unit can also receive related malfunction information by referring to posts by the user's friends on social media. In this way, the user's social media activity can be analyzed and related information can be received.

[0041] The reception unit can customize the reception method by reflecting the user's past feedback when receiving malfunction information. The reception unit can, for example, suggest the optimal reception method based on the user's past feedback. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and customize the input interface. This makes it possible to customize the reception method by reflecting the user's past feedback.

[0042] During the analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the failure. For example, in the case of a failure with a high level of importance, the analysis unit performs a detailed analysis to identify the cause. In addition, in the case of a failure with a low level of importance, the analysis unit can perform a concise analysis to respond quickly. Furthermore, the analysis unit can adjust the priority of the analysis according to the importance of the failure. This makes it possible to adjust the level of detail of the analysis based on the importance of the failure.

[0043] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the failure. For example, in the case of a failure in a home appliance, the analysis unit applies an analysis algorithm specifically for the home appliance. In addition, in the case of a failure in a residential facility, the analysis unit can also apply an analysis algorithm specifically for the residential facility. Furthermore, the analysis unit can select the optimal analysis algorithm depending on the category of the failure. This makes it possible to apply different analysis algorithms depending on the category of the failure.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, identifies the cause of a failure based on the user's past analysis results. The analysis unit can also select the optimal analysis method from the user's past analysis results. Furthermore, the analysis unit can analyze the user's past analysis results and improve the accuracy of the analysis. This makes it possible to improve the accuracy of the analysis by referring to the user's past analysis results.

[0045] During analysis, the analysis unit can determine the priority of the analysis based on the time when the failure occurred. For example, the analysis unit prioritizes the analysis of a failure that occurred recently. The analysis unit can also analyze a current failure by referring to failures that occurred in the past. Furthermore, the analysis unit can adjust the priority of the analysis based on the time when the failure occurred. This makes it possible to determine the priority of the analysis based on the time when the failure occurred.

[0046] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the failures. For example, the analysis unit prioritizes the analysis of highly relevant failures. The analysis unit can also postpone the analysis of less relevant failures. Furthermore, the analysis unit can also adjust the order of analysis based on the relevance of the failures. This makes it possible to adjust the order of analysis based on the relevance of the failures.

[0047] During analysis, the analysis unit can adjust the use of technical terms in the analysis results according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses detailed technical terms. Also, if the user does not have technical expertise, the analysis unit can explain the analysis results in simple terms. Furthermore, the analysis unit can adjust the use of technical terms in the analysis results according to the user's level of expertise. This makes it possible to adjust the use of technical terms in the analysis results according to the user's level of expertise.

[0048] During the interview, the interview unit can adjust the level of detail of the interview based on the cause of the failure. For example, in the case of a failure of high importance, the interview unit conducts a detailed interview to identify the cause. In addition, in the case of a failure of low importance, the interview unit can conduct a brief interview to respond quickly. Furthermore, the interview unit can adjust the level of detail of the interview depending on the cause of the failure. This makes it possible to adjust the level of detail of the interview based on the cause of the failure.

[0049] During the interview, the interview unit can apply different interview algorithms depending on the category of the failure. For example, in the case of a failure in a home appliance, the interview unit applies an interview algorithm dedicated to the home appliance. In addition, in the case of a failure in a home appliance, the interview unit can also apply an interview algorithm dedicated to the home appliance. Furthermore, the interview unit can also select the optimal interview algorithm depending on the category of the failure. This makes it possible to apply different interview algorithms depending on the category of the failure.

[0050] The medical interview unit can improve the accuracy of the medical interview by referring to the user's past medical interview results during the medical interview. The medical interview unit, for example, identifies the cause of the malfunction based on the user's past medical interview results. The medical interview unit can also select the optimal medical interview method based on the user's past medical interview results. Furthermore, the medical interview unit can analyze the user's past medical interview results to improve the accuracy of the medical interview. This makes it possible to improve the accuracy of the medical interview by referring to the user's past medical interview results.

[0051] The inquiry unit can determine the priority of the inquiry based on the time when the failure occurred during the inquiry. For example, the inquiry unit gives priority to the inquiry about the most recently occurred failure. The inquiry unit can also inquire about the current failure by referring to failures that occurred in the past. Furthermore, the inquiry unit can also adjust the priority of the inquiry based on the time when the failure occurred. This makes it possible to determine the priority of the inquiry based on the time when the failure occurred.

[0052] The interview unit can adjust the order of interviews based on the relevance of the failures during the interview. For example, the interview unit gives priority to interviewing highly relevant failures. The interview unit can also put off interviewing less relevant failures. Furthermore, the interview unit can also adjust the order of interviews based on the relevance of the failures. This makes it possible to adjust the order of interviews based on the relevance of the failures.

[0053] The medical interview unit can adjust the content of the medical interview depending on the user's level of expertise during the medical interview. For example, if the user has specialized knowledge, the medical interview unit uses detailed technical terms. Also, if the user does not have specialized knowledge, the medical interview unit can conduct the medical interview using simple language. Furthermore, the medical interview unit can adjust the content of the medical interview depending on the user's level of expertise. This makes it possible to adjust the content of the medical interview depending on the user's level of expertise.

[0054] The guide unit can adjust the level of detail of the guide during guidance based on the importance of the repair. For example, in the case of a highly important repair, the guide unit provides detailed guidance and provides an accurate repair method. In addition, in the case of a less important repair, the guide unit can provide simple guidance and respond quickly. Furthermore, the guide unit can adjust the level of detail of the guide depending on the importance of the repair. In this way, the level of detail of the guide can be adjusted based on the importance of the repair.

[0055] The guide unit can apply different guide algorithms depending on the repair category when providing guidance. For example, when repairing a home appliance, the guide unit applies a guide algorithm dedicated to the home appliance. Furthermore, when repairing a residential facility, the guide unit can also apply a guide algorithm dedicated to the residential facility. Furthermore, the guide unit can select the optimal guide algorithm depending on the repair category. This makes it possible to apply different guide algorithms depending on the repair category.

[0056] When providing guidance, the guide unit can improve the accuracy of the guidance by referring to the user's past guidance results. The guide unit, for example, identifies a repair method based on the user's past guidance results. The guide unit can also select the optimal guidance method from the user's past guidance results. Furthermore, the guide unit can analyze the user's past guidance results and improve the accuracy of the guidance. This makes it possible to improve the accuracy of the guidance by referring to the user's past guidance results.

[0057] When providing guidance, the guide unit can determine the priority of the guidance based on the time when the repair occurred. For example, the guide unit gives priority to guiding the most recent repair. The guide unit can also provide guidance on the current repair by referring to repairs that occurred in the past. Furthermore, the guide unit can adjust the priority of the guidance based on the time when the repair occurred. This makes it possible to determine the priority of the guidance based on the time when the repair occurred.

[0058] The guide unit can adjust the order of the guides based on the relevance of the repairs when providing guidance. For example, the guide unit gives priority to guiding highly relevant repairs. The guide unit can also guide less relevant repairs later. Furthermore, the guide unit can also adjust the order of the guides based on the relevance of the repairs. This makes it possible to adjust the order of the guides based on the relevance of the repairs.

[0059] The guide unit can adjust the content of the guide during guidance depending on the user's level of expertise. For example, if the user has specialized knowledge, the guide unit uses detailed technical terms. Also, if the user does not have specialized knowledge, the guide unit can provide guidance in simple terms. Furthermore, the guide unit can adjust the content of the guide depending on the user's level of expertise. In this way, the content of the guide can be adjusted depending on the user's level of expertise.

[0060] During identification, the identification unit can adjust the accuracy of identification based on the cause of the failure. For example, in the case of a failure of high importance, the identification unit performs detailed identification and identifies the exact part. In addition, in the case of a failure of low importance, the identification unit can perform simple identification and respond quickly. Furthermore, the identification unit can adjust the accuracy of identification depending on the cause of the failure. This makes it possible to adjust the accuracy of identification based on the cause of the failure.

[0061] The identification unit can apply different identification algorithms depending on the category of the fault during identification. For example, in the case of a fault in a home appliance, the identification unit applies an identification algorithm dedicated to the home appliance. In addition, in the case of a fault in a home appliance, the identification unit can also apply an identification algorithm dedicated to the home appliance. Furthermore, the identification unit can select the optimal identification algorithm depending on the category of the fault. This makes it possible to apply different identification algorithms depending on the category of the fault.

[0062] During identification, the identification unit can improve the accuracy of identification by referring to the user's past identification results. The identification unit, for example, identifies the cause of the failure based on the user's past identification results. The identification unit can also select the optimal identification method from the user's past identification results. Furthermore, the identification unit can analyze the user's past identification results and improve the accuracy of identification. This makes it possible to improve the accuracy of identification by referring to the user's past identification results.

[0063] During identification, the identification unit can determine the identification priority based on the time when the failure occurred. For example, the identification unit prioritizes identifying a failure that occurred recently. The identification unit can also identify a current failure by referring to failures that occurred in the past. Furthermore, the identification unit can adjust the identification priority based on the time when the failure occurred. This makes it possible to determine the identification priority based on the time when the failure occurred.

[0064] The identification unit can adjust the identification order based on the relevance of the failures during identification. For example, the identification unit prioritizes identifying highly related failures. The identification unit can also identify less related failures later. Furthermore, the identification unit can adjust the identification order based on the relevance of the failures. In this way, the identification order can be adjusted based on the relevance of the failures.

[0065] The identification unit can adjust the identification content according to the user's level of expertise during identification. For example, if the user has specialized knowledge, the identification unit uses detailed technical terms. Also, if the user does not have specialized knowledge, the identification unit can perform identification in simple terms. Furthermore, the identification unit can adjust the identification content according to the user's level of expertise. In this way, the identification content can be adjusted according to the user's level of expertise.

[0066] The providing unit can adjust the level of detail to be provided based on the importance of the component when providing the information. For example, the providing unit provides detailed information for a component with high importance. The providing unit can also provide concise information for a component with low importance. Furthermore, the providing unit can adjust the level of detail to be provided according to the importance of the component. This makes it possible to adjust the level of detail to be provided based on the importance of the component.

[0067] The providing unit can apply different providing algorithms depending on the category of the part when providing the part. For example, in the case of a home appliance part, the providing unit applies a providing algorithm dedicated to the home appliance. In addition, in the case of a home equipment part, the providing unit can also apply a providing algorithm dedicated to the home equipment. Furthermore, the providing unit can select the optimal providing algorithm depending on the category of the part. This makes it possible to apply different providing algorithms depending on the category of the part.

[0068] The providing unit can improve the accuracy of the provision by referring to the user's past provision results when providing the components. For example, the providing unit identifies the optimal part based on the user's past provision results. The providing unit can also select the optimal provision method from the user's past provision results. Furthermore, the providing unit can analyze the user's past provision results and improve the accuracy of the provision. This makes it possible to improve the accuracy of the provision by referring to the user's past provision results.

[0069] The providing unit can determine the priority of provision based on the time of occurrence of the parts at the time of provision. For example, the providing unit can provide parts that have recently occurred preferentially. The providing unit can also provide current parts by referring to parts that have occurred in the past. Furthermore, the providing unit can adjust the priority of provision based on the time of occurrence of the parts. In this way, the priority of provision can be determined based on the time of occurrence of the parts.

[0070] The providing unit can adjust the order of provision based on the relevance of the parts when providing the parts. For example, the providing unit provides parts with high relevance preferentially. The providing unit can also provide parts with low relevance later. Furthermore, the providing unit can also adjust the order of provision based on the relevance of the parts. In this way, the order of provision can be adjusted based on the relevance of the parts.

[0071] The providing unit can adjust the content of the provided information according to the user's level of expertise when providing the information. For example, if the user has specialized knowledge, the providing unit uses detailed technical terms. Also, if the user does not have specialized knowledge, the providing unit can provide the information in simple language. Furthermore, the providing unit can adjust the content of the provided information according to the user's level of expertise. This makes it possible to adjust the content of the provided information according to the user's level of expertise.

[0072] When giving advice, the advice unit can adjust the level of detail of the advice based on the importance of the repair. For example, in the case of a highly important repair, the advice unit provides detailed advice and provides an accurate repair method. In addition, in the case of a less important repair, the advice unit can provide concise advice and respond quickly. Furthermore, the advice unit can adjust the level of detail of the advice based on the importance of the repair. In this way, the level of detail of the advice can be adjusted based on the importance of the repair.

[0073] When giving advice, the advice unit can apply different advice algorithms depending on the repair category. For example, when repairing a home appliance, the advice unit applies an advice algorithm dedicated to the home appliance. Also, when repairing a home appliance, the advice unit can also apply an advice algorithm dedicated to the home appliance. Furthermore, the advice unit can select the optimal advice algorithm depending on the repair category. This makes it possible to apply different advice algorithms depending on the repair category.

[0074] When providing advice, the advice unit can improve the accuracy of the advice by referring to the user's past advice results. The advice unit, for example, identifies a repair method based on the user's past advice results. The advice unit can also select an optimal advice method from the user's past advice results. Furthermore, the advice unit can analyze the user's past advice results and improve the accuracy of the advice. In this way, the accuracy of the advice can be improved by referring to the user's past advice results.

[0075] When giving advice, the advice unit can determine the priority of the advice based on the time when the repair occurred. For example, the advice unit gives priority to advice on repairs that occurred recently. The advice unit can also give advice on current repairs by referring to repairs that occurred in the past. Furthermore, the advice unit can adjust the priority of the advice based on the time when the repair occurred. This makes it possible to determine the priority of advice based on the time when the repair occurred.

[0076] The advice unit can adjust the order of advice based on the relevance of the repairs when giving advice. For example, the advice unit gives priority to advice on highly relevant repairs. The advice unit can also give advice on less relevant repairs later. Furthermore, the advice unit can also adjust the order of advice based on the relevance of the repairs. This makes it possible to adjust the order of advice based on the relevance of the repairs.

[0077] When giving advice, the advice unit can adjust the content of the advice according to the user's level of expertise. For example, if the user has specialized knowledge, the advice unit uses detailed technical terms. Also, if the user does not have specialized knowledge, the advice unit can give advice in simple language. Furthermore, the advice unit can adjust the content of the advice according to the user's level of expertise. In this way, the content of the advice can be adjusted according to the user's level of expertise.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The reception unit can analyze the user's past failure history and suggest the optimal input format. For example, it can automatically display as candidates the details of failures that the user has frequently entered in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict failures that will occur during specific time periods based on the user's past failure history and suggest an input format. This makes it possible to analyze the user's past failure history and suggest the optimal input format.

[0080] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the failure. For example, in the case of a failure with high importance, a detailed analysis is performed to identify the cause. In addition, in the case of a failure with low importance, a simple analysis is performed to allow for a quick response. Furthermore, the analysis unit can also adjust the priority of the analysis according to the importance of the failure. This makes it possible to adjust the level of detail of the analysis based on the importance of the failure.

[0081] During the interview, the diagnostic unit can adjust the level of detail of the interview based on the cause of the failure. For example, in the case of a failure of high importance, a detailed interview is conducted to identify the cause. In addition, in the case of a failure of low importance, a brief interview is conducted to quickly address the problem. Furthermore, the diagnostic unit can also adjust the level of detail of the interview depending on the cause of the failure. This makes it possible to adjust the level of detail of the interview based on the cause of the failure.

[0082] The guide unit can adjust the level of detail of the guide based on the importance of the repair when providing guidance. For example, for a highly important repair, detailed guidance is provided to provide an accurate repair method. For a less important repair, brief guidance is provided to enable a quick response. Furthermore, the guide unit can adjust the level of detail of the guide based on the importance of the repair. This makes it possible to adjust the level of detail of the guide based on the importance of the repair.

[0083] During identification, the identification unit can adjust the accuracy of identification based on the cause of the failure. For example, in the case of a failure of high importance, detailed identification is performed to identify the exact part. In addition, in the case of a failure of low importance, simple identification can be performed to respond quickly. Furthermore, the identification unit can also adjust the accuracy of identification depending on the cause of the failure. This makes it possible to adjust the accuracy of identification based on the cause of the failure.

[0084] The processing flow of the first embodiment will be briefly explained below.

[0085] Step 1: The reception unit receives information from the user. The information from the user includes, for example, photos of the faulty home appliance or household equipment, text information about the fault symptoms, etc. The reception unit receives photos taken with a smartphone and provides an interface for inputting the fault symptoms. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit uses an image analysis algorithm to analyze the photo and identify the location of the failure. It also uses text analysis technology to analyze the failure symptoms and identify the cause of the failure. Step 3: The questioning section asks the user about points to check based on the information analyzed by the analysis section. The questioning section instructs the user on how to identify the faulty part and the items to check. Step 4: The guide unit guides the user to the repair points based on the check points identified by the questioning unit. The guide unit guides the user through the repair procedure and the necessary tools. Step 5: The identification unit identifies the replacement part identified by the guide unit. The identification unit identifies the part model number and compatible parts and provides the appropriate link. Step 6: The providing unit provides a link to purchase the replacement part identified by the identifying unit. The providing unit provides a link to the product on an e-commerce site (e.g., a link to the product page on Yahoo! Shopping (URL of the product page)) or a link to a technical support page. Step 7: The advice unit uses the guide unit to provide advice when the product is unrepairable or when an expert is required. The advice unit advises the user on how to handle the unrepairable product and how to contact an expert.

[0086] (Example 2) The AGI system according to an embodiment of the present invention provides support for troubleshooting household problems. This system allows users to take photos of broken home appliances or equipment with their smartphones and input the symptoms of the malfunction. The AGI then analyzes the photos and the input symptoms, asks about checkpoints, and provides guidance on repairs. For example, if an air conditioner is not cooling, the AGI instructs users to check for dirty filters or insufficient refrigerant. It also identifies the necessary replacement parts and guides users to purchase them through e-commerce sites (e.g., product links on Yahoo! Shopping). For example, if the filter is dirty, the system provides a link to an appropriate filter. It also provides advice on repairs that are beyond repair or require a specialist. For example, if the refrigerant needs to be refilled, the system provides contact information for a specialist. This allows users with a little knowledge to resolve the malfunction themselves at low cost. This allows the AGI system to quickly and efficiently resolve problems with broken home appliances and equipment. For example, users can perform simple repairs themselves, such as cleaning or replacing air conditioner filters. Even if a specialist is needed, they can receive appropriate advice, providing peace of mind.

[0087] The AGI system according to the embodiment includes a reception unit, an analysis unit, an interview unit, a guide unit, an identification unit, a provision unit, and an advice unit. The reception unit receives information from a user. The information from the user includes, for example, photos of the broken home appliance or home equipment, text information about the malfunction symptoms, and the like, but is not limited to these examples. The reception unit receives, for example, photos taken with a smartphone and provides an interface for inputting the malfunction symptoms. The analysis unit analyzes the information received by the reception unit. The analysis unit, for example, analyzes the photos using an image analysis algorithm to identify the malfunction location. The analysis unit can also analyze the malfunction symptoms using text analysis technology to identify the cause of the malfunction. The interview unit asks about points to check based on the information analyzed by the analysis unit. The interview unit instructs the user, for example, on how to identify the malfunction location and items to check. The guide unit guides the user to repair points based on the check points identified by the interview unit. The guide unit, for example, guides the user on repair procedures and necessary tools. The identification unit identifies the replacement part identified by the guide unit. The identification unit, for example, identifies the part model number or compatible parts and provides an appropriate link. The provision unit provides a link for purchasing the replacement part identified by the identification unit. The provision unit, for example, provides a link to a product on an e-commerce site or a link to a technical support page. The advice unit uses the guide unit to give advice when the product is unrepairable or when an expert is needed. The advice unit advises the user, for example, how to handle the unrepairable product or how to contact an expert. As a result, the AGI system according to the embodiment can accept and analyze user information, ask about points to check, guide users to repair points, identify replacement parts, provide links, and give advice when the product is unrepairable or when an expert is needed.

[0088] The reception unit allows the user to take a photo of the faulty home appliance or household equipment with their smartphone and input the fault symptoms. The reception unit, for example, accepts photos taken by the user with their smartphone. The reception unit can also provide an interface for inputting the fault symptoms. For example, the user can take a photo of an air conditioner and input "not cooling." This allows the user to take a photo of the faulty home appliance or household equipment with their smartphone and input the fault symptoms.

[0089] The analysis unit can analyze the input information and identify the cause of the failure. For example, the analysis unit can analyze a photo using an image analysis algorithm and identify the location of the failure. The analysis unit can also analyze the symptoms of the failure using text analysis technology and identify the cause of the failure. For example, the analysis unit can analyze a photo of an air conditioner and identify a dirty filter or a lack of refrigerant. This makes it possible to analyze the input information and identify the cause of the failure.

[0090] The questioning unit can identify points to check based on the cause of the malfunction and instruct the user. The questioning unit, for example, instructs the user on how to identify the malfunction and items to check. For example, if the air conditioner is not cooling, the questioning unit can instruct the user to check for dirty filters, a lack of refrigerant, etc. This allows the questioning unit to identify points to check based on the cause of the malfunction and instruct the user.

[0091] The guide unit can guide the user to the repair points based on the check points. The guide unit guides the user, for example, to the repair procedure and the necessary tools. For example, if the filter is dirty, the guide unit can guide the user on how to clean the filter. This makes it possible to guide the user to the repair points based on the check points.

[0092] The identification unit can identify the required replacement part and provide a link. For example, the identification unit can identify the part model number or compatible parts and provide an appropriate link. For example, if the filter is dirty, a link to the appropriate filter can be provided. This allows the required replacement part to be identified and the appropriate link to be provided.

[0093] The advice unit can provide advice when the product is beyond repair or when an expert is needed. For example, the advice unit advises the user on how to handle the product that is beyond repair or how to contact an expert. For example, if refrigerant needs to be refilled, the advice unit can provide the contact information of a specialist. This allows advice to be given when the product is beyond repair or when an expert is needed.

[0094] The reception unit can estimate the user's emotions and adjust the input method for malfunction information based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable the user to input malfunction information quickly. This makes it possible to adjust the input method for malfunction information based on the user's emotions. Emotion estimation is performed using, for example, facial expression recognition technology or voice analysis technology.

[0095] The reception unit can analyze the user's past failure history and suggest the optimal input format. For example, the reception unit automatically displays failure details that the user has frequently entered in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict failures that will occur in specific time periods based on the user's past failure history and suggest an input format. This makes it possible to analyze the user's past failure history and suggest the optimal input format.

[0096] The reception unit can customize the input method based on the user's current situation when receiving malfunction information. For example, when entering malfunction information at night, the reception unit can provide a simple input method to enable a quick response. Furthermore, when the user is at home, the reception unit can provide detailed input options to collect accurate information. Furthermore, when the user is away from home, the reception unit can provide voice input or simple options to enable quick input. This makes it possible to customize the input method based on the user's current situation.

[0097] When receiving the malfunction information, the reception unit can select the optimum input means according to the user's input method. For example, when the user inputs the malfunction information by voice, the reception unit automatically converts it into text using voice recognition technology. Also, when the user inputs the malfunction information as text, the reception unit can analyze the input content and convert it into an appropriate format. Furthermore, when the user inputs the malfunction information as an image, the reception unit can identify the malfunction location using image analysis technology and complete the input content. This makes it possible to select the optimum input means according to the user's input method.

[0098] The reception unit can estimate the user's emotion and adjust the design of the input interface based on the estimated user's emotion. For example, if the user is nervous, the reception unit can provide an interface with subdued colors to reduce visual stress. Furthermore, if the user is having fun, the reception unit can provide an interface with bright colors to make input work more enjoyable. Furthermore, if the user is tired, the reception unit can provide a simple, highly visible interface to make input work easier. This makes it possible to adjust the design of the input interface based on the user's emotion. Emotion estimation is performed using, for example, facial expression recognition technology or voice analysis technology.

[0099] When receiving malfunction information, the reception unit can preferentially receive highly relevant information in consideration of the user's geographical location information. For example, when the user is in a specific area, the reception unit preferentially receives information about malfunctions that frequently occur in that area. Furthermore, when the user is in a specific building, the reception unit can also preferentially receive information about malfunctions related to the equipment in that building. Furthermore, when the user is traveling, the reception unit can also preferentially receive information about malfunctions related to the area to which the user has moved. In this way, it is possible to preferentially receive information that is highly relevant in consideration of the user's geographical location information.

[0100] When receiving malfunction information, the reception unit can analyze the user's social media activity and receive related information. For example, the reception unit automatically receives malfunction information posted by the user on social media. The reception unit can also analyze the user's social media activity and receive related malfunction information. Furthermore, the reception unit can also receive related malfunction information by referring to posts by the user's friends on social media. In this way, the user's social media activity can be analyzed and related information can be received.

[0101] The reception unit can customize the reception method by reflecting the user's past feedback when receiving malfunction information. The reception unit can, for example, suggest the optimal reception method based on the user's past feedback. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and customize the input interface. This makes it possible to customize the reception method by reflecting the user's past feedback.

[0102] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can increase the accuracy of the analysis and provide detailed information. Furthermore, if the user is relaxed, the analysis unit can adjust the accuracy of the analysis and provide concise information. Furthermore, if the user is in a hurry, the analysis unit can perform a quick analysis and provide information that focuses on the main points. This makes it possible to adjust the accuracy of the analysis based on the user's emotions. Emotions are estimated using, for example, facial expression recognition technology or voice analysis technology.

[0103] During the analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the failure. For example, in the case of a failure with a high level of importance, the analysis unit performs a detailed analysis to identify the cause. In addition, in the case of a failure with a low level of importance, the analysis unit can perform a concise analysis to respond quickly. Furthermore, the analysis unit can adjust the priority of the analysis according to the importance of the failure. This makes it possible to adjust the level of detail of the analysis based on the importance of the failure.

[0104] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the failure. For example, in the case of a failure in a home appliance, the analysis unit applies an analysis algorithm specifically for the home appliance. In addition, in the case of a failure in a residential facility, the analysis unit can also apply an analysis algorithm specifically for the residential facility. Furthermore, the analysis unit can select the optimal analysis algorithm depending on the category of the failure. This makes it possible to apply different analysis algorithms depending on the category of the failure.

[0105] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, identifies the cause of a failure based on the user's past analysis results. The analysis unit can also select the optimal analysis method from the user's past analysis results. Furthermore, the analysis unit can analyze the user's past analysis results and improve the accuracy of the analysis. This makes it possible to improve the accuracy of the analysis by referring to the user's past analysis results.

[0106] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. This makes it possible to adjust the display method of the analysis results based on the user's emotions. Emotions are estimated using, for example, facial expression recognition technology or voice analysis technology.

[0107] During analysis, the analysis unit can determine the priority of the analysis based on the time when the failure occurred. For example, the analysis unit prioritizes the analysis of a failure that occurred recently. The analysis unit can also analyze a current failure by referring to failures that occurred in the past. Furthermore, the analysis unit can adjust the priority of the analysis based on the time when the failure occurred. This makes it possible to determine the priority of the analysis based on the time when the failure occurred.

[0108] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the failures. For example, the analysis unit prioritizes the analysis of highly relevant failures. The analysis unit can also postpone the analysis of less relevant failures. Furthermore, the analysis unit can also adjust the order of analysis based on the relevance of the failures. This makes it possible to adjust the order of analysis based on the relevance of the failures.

[0109] During analysis, the analysis unit can adjust the use of technical terms in the analysis results according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses detailed technical terms. Also, if the user does not have technical expertise, the analysis unit can explain the analysis results in simple terms. Furthermore, the analysis unit can adjust the use of technical terms in the analysis results according to the user's level of expertise. This makes it possible to adjust the use of technical terms in the analysis results according to the user's level of expertise.

[0110] The medical interview unit can estimate the user's emotions and adjust the method of interviewing based on the estimated user's emotions. For example, if the user is nervous, the medical interview unit can provide a simple and highly visible medical interview method. Furthermore, if the user is relaxed, the medical interview unit can also provide a medical interview method that includes detailed information. Furthermore, if the user is in a hurry, the medical interview unit can also provide a medical interview method that focuses on the main points. This makes it possible to adjust the method of interviewing based on the user's emotions. Emotions are estimated using, for example, facial expression recognition technology or voice analysis technology.

[0111] During the interview, the interview unit can adjust the level of detail of the interview based on the cause of the failure. For example, in the case of a failure of high importance, the interview unit conducts a detailed interview to identify the cause. In addition, in the case of a failure of low importance, the interview unit can conduct a brief interview to respond quickly. Furthermore, the interview unit can adjust the level of detail of the interview depending on the cause of the failure. This makes it possible to adjust the level of detail of the interview based on the cause of the failure.

[0112] During the interview, the interview unit can apply different interview algorithms depending on the category of the failure. For example, in the case of a failure in a home appliance, the interview unit applies an interview algorithm dedicated to the home appliance. In addition, in the case of a failure in a home appliance, the interview unit can also apply an interview algorithm dedicated to the home appliance. Furthermore, the interview unit can also select the optimal interview algorithm depending on the category of the failure. This makes it possible to apply different interview algorithms depending on the category of the failure.

[0113] The medical interview unit can improve the accuracy of the medical interview by referring to the user's past medical interview results during the medical interview. The medical interview unit, for example, identifies the cause of the malfunction based on the user's past medical interview results. The medical interview unit can also select the optimal medical interview method based on the user's past medical interview results. Furthermore, the medical interview unit can analyze the user's past medical interview results to improve the accuracy of the medical interview. This makes it possible to improve the accuracy of the medical interview by referring to the user's past medical interview results.

[0114] The medical interview unit can estimate the user's emotions and adjust the order of medical interviews based on the estimated user's emotions. For example, if the user is nervous, the medical interview unit starts with simple questions and gradually moves on to more detailed questions. Also, if the user is relaxed, the medical interview unit can ask detailed questions from the beginning. Furthermore, if the user is in a hurry, the medical interview unit can prioritize important questions. This makes it possible to adjust the order of medical interviews based on the user's emotions. Emotions are estimated using, for example, facial expression recognition technology or voice analysis technology.

[0115] The inquiry unit can determine the priority of the inquiry based on the time when the failure occurred during the inquiry. For example, the inquiry unit gives priority to the inquiry about the most recently occurred failure. The inquiry unit can also inquire about the current failure by referring to failures that occurred in the past. Furthermore, the inquiry unit can also adjust the priority of the inquiry based on the time when the failure occurred. This makes it possible to determine the priority of the inquiry based on the time when the failure occurred.

[0116] The interview unit can adjust the order of interviews based on the relevance of the failures during the interview. For example, the interview unit gives priority to interviewing highly relevant failures. The interview unit can also put off interviewing less relevant failures. Furthermore, the interview unit can also adjust the order of interviews based on the relevance of the failures. This makes it possible to adjust the order of interviews based on the relevance of the failures.

[0117] The medical interview unit can adjust the content of the medical interview depending on the user's level of expertise during the medical interview. For example, if the user has specialized knowledge, the medical interview unit uses detailed technical terms. Also, if the user does not have specialized knowledge, the medical interview unit can conduct the medical interview using simple language. Furthermore, the medical interview unit can adjust the content of the medical interview depending on the user's level of expertise. This makes it possible to adjust the content of the medical interview depending on the user's level of expertise.

[0118] The guide unit can estimate the user's emotions and adjust the guidance method based on the estimated user's emotions. For example, if the user is nervous, the guide unit can provide a simple and highly visible guidance method. Furthermore, if the user is relaxed, the guide unit can also provide a guidance method that includes detailed information. Furthermore, if the user is in a hurry, the guide unit can also provide a guidance method that focuses on the main points. This makes it possible to adjust the guidance method based on the user's emotions. Emotions are estimated using, for example, facial expression recognition technology or voice analysis technology.

[0119] The guide unit can adjust the level of detail of the guide during guidance based on the importance of the repair. For example, in the case of a highly important repair, the guide unit provides detailed guidance and provides an accurate repair method. In addition, in the case of a less important repair, the guide unit can provide simple guidance and respond quickly. Furthermore, the guide unit can adjust the level of detail of the guide depending on the importance of the repair. In this way, the level of detail of the guide can be adjusted based on the importance of the repair.

[0120] The guide unit can apply different guide algorithms depending on the repair category when providing guidance. For example, when repairing a home appliance, the guide unit applies a guide algorithm dedicated to the home appliance. Furthermore, when repairing a residential facility, the guide unit can also apply a guide algorithm dedicated to the residential facility. Furthermore, the guide unit can select the optimal guide algorithm depending on the repair category. This makes it possible to apply different guide algorithms depending on the repair category.

[0121] When providing guidance, the guide unit can improve the accuracy of the guidance by referring to the user's past guidance results. The guide unit, for example, identifies a repair method based on the user's past guidance results. The guide unit can also select the optimal guidance method from the user's past guidance results. Furthermore, the guide unit can analyze the user's past guidance results and improve the accuracy of the guidance. This makes it possible to improve the accuracy of the guidance by referring to the user's past guidance results.

[0122] The guide unit can estimate the user's emotions and adjust the order of the guidance based on the estimated user's emotions. For example, if the user is nervous, the guide unit starts with simple steps and gradually moves on to more detailed steps. Also, if the user is relaxed, the guide unit can perform detailed steps from the beginning. Furthermore, if the user is in a hurry, the guide unit can prioritize important steps. This makes it possible to adjust the order of the guidance based on the user's emotions. Emotions are estimated using, for example, facial expression recognition technology or voice analysis technology.

[0123] When providing guidance, the guide unit can determine the priority of the guidance based on the time when the repair occurred. For example, the guide unit gives priority to guiding the most recent repair. The guide unit can also provide guidance on the current repair by referring to repairs that occurred in the past. Furthermore, the guide unit can adjust the priority of the guidance based on the time when the repair occurred. This makes it possible to determine the priority of the guidance based on the time when the repair occurred.

[0124] The guide unit can adjust the order of the guides based on the relevance of the repairs when providing guidance. For example, the guide unit gives priority to guiding highly relevant repairs. The guide unit can also guide less relevant repairs later. Furthermore, the guide unit can also adjust the order of the guides based on the relevance of the repairs. This makes it possible to adjust the order of the guides based on the relevance of the repairs.

[0125] The guide unit can adjust the content of the guide during guidance depending on the user's level of expertise. For example, if the user has specialized knowledge, the guide unit uses detailed technical terms. Also, if the user does not have specialized knowledge, the guide unit can provide guidance in simple terms. Furthermore, the guide unit can adjust the content of the guide depending on the user's level of expertise. In this way, the content of the guide can be adjusted depending on the user's level of expertise.

[0126] The identification unit can estimate the user's emotion and determine the priority of parts to be identified based on the estimated user's emotion. For example, when the user is nervous, the identification unit prioritizes identifying important parts. Furthermore, when the user is relaxed, the identification unit can also identify detailed parts. Furthermore, when the user is in a hurry, the identification unit can quickly determine parts to be identified. In this way, the priority of parts to be identified can be determined based on the user's emotion. Emotion estimation is performed using, for example, facial expression recognition technology or voice analysis technology.

[0127] During identification, the identification unit can adjust the accuracy of identification based on the cause of the failure. For example, in the case of a failure of high importance, the identification unit performs detailed identification and identifies the exact part. In addition, in the case of a failure of low importance, the identification unit can perform simple identification and respond quickly. Furthermore, the identification unit can adjust the accuracy of identification depending on the cause of the failure. This makes it possible to adjust the accuracy of identification based on the cause of the failure.

[0128] The identification unit can apply different identification algorithms depending on the category of the fault during identification. For example, in the case of a fault in a home appliance, the identification unit applies an identification algorithm dedicated to the home appliance. In addition, in the case of a fault in a home appliance, the identification unit can also apply an identification algorithm dedicated to the home appliance. Furthermore, the identification unit can select the optimal identification algorithm depending on the category of the fault. This makes it possible to apply different identification algorithms depending on the category of the fault.

[0129] During identification, the identification unit can improve the accuracy of identification by referring to the user's past identification results. The identification unit, for example, identifies the cause of the failure based on the user's past identification results. The identification unit can also select the optimal identification method from the user's past identification results. Furthermore, the identification unit can analyze the user's past identification results and improve the accuracy of identification. This makes it possible to improve the accuracy of identification by referring to the user's past identification results.

[0130] The identification unit can estimate the user's emotion and adjust the display method of the identified part based on the estimated user's emotion. For example, if the user is nervous, the identification unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the identification unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the identification unit can provide a display method that focuses on the main points. This makes it possible to adjust the display method of the identified part based on the user's emotion. Emotion estimation is performed using, for example, facial expression recognition technology or voice analysis technology.

[0131] During identification, the identification unit can determine the identification priority based on the time when the failure occurred. For example, the identification unit prioritizes identifying a failure that occurred recently. The identification unit can also identify a current failure by referring to failures that occurred in the past. Furthermore, the identification unit can adjust the identification priority based on the time when the failure occurred. This makes it possible to determine the identification priority based on the time when the failure occurred.

[0132] The identification unit can adjust the identification order based on the relevance of the failures during identification. For example, the identification unit prioritizes identifying highly related failures. The identification unit can also identify less related failures later. Furthermore, the identification unit can adjust the identification order based on the relevance of the failures. In this way, the identification order can be adjusted based on the relevance of the failures.

[0133] The identification unit can adjust the identification content according to the user's level of expertise during identification. For example, if the user has specialized knowledge, the identification unit uses detailed technical terms. Also, if the user does not have specialized knowledge, the identification unit can perform identification in simple terms. Furthermore, the identification unit can adjust the identification content according to the user's level of expertise. In this way, the identification content can be adjusted according to the user's level of expertise.

[0134] The providing unit can estimate the user's emotion and determine the priority of links to be provided based on the estimated user's emotion. For example, when the user is nervous, the providing unit can provide important links with priority. Furthermore, when the user is relaxed, the providing unit can provide detailed links. Furthermore, when the user is in a hurry, the providing unit can determine links to be provided quickly. In this way, the priority of links to be provided can be determined based on the user's emotion. Emotion estimation is performed using, for example, facial expression recognition technology or voice analysis technology.

[0135] The providing unit can adjust the level of detail to be provided based on the importance of the component when providing the information. For example, the providing unit provides detailed information for a component with high importance. The providing unit can also provide concise information for a component with low importance. Furthermore, the providing unit can adjust the level of detail to be provided according to the importance of the component. This makes it possible to adjust the level of detail to be provided based on the importance of the component.

[0136] The providing unit can apply different providing algorithms depending on the category of the part when providing the part. For example, in the case of a home appliance part, the providing unit applies a providing algorithm dedicated to the home appliance. In addition, in the case of a home equipment part, the providing unit can also apply a providing algorithm dedicated to the home equipment. Furthermore, the providing unit can select the optimal providing algorithm depending on the category of the part. This makes it possible to apply different providing algorithms depending on the category of the part.

[0137] The providing unit can improve the accuracy of the provision by referring to the user's past provision results when providing the components. For example, the providing unit identifies the optimal part based on the user's past provision results. The providing unit can also select the optimal provision method from the user's past provision results. Furthermore, the providing unit can analyze the user's past provision results and improve the accuracy of the provision. This makes it possible to improve the accuracy of the provision by referring to the user's past provision results.

[0138] The providing unit can estimate the user's emotion and adjust the display method of the provided link based on the estimated user's emotion. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the providing unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. This makes it possible to adjust the display method of the provided link based on the user's emotion. Emotion estimation is performed using, for example, facial expression recognition technology or voice analysis technology.

[0139] The providing unit can determine the priority of provision based on the time of occurrence of the parts at the time of provision. For example, the providing unit can provide parts that have recently occurred preferentially. The providing unit can also provide current parts by referring to parts that have occurred in the past. Furthermore, the providing unit can adjust the priority of provision based on the time of occurrence of the parts. In this way, the priority of provision can be determined based on the time of occurrence of the parts.

[0140] The providing unit can adjust the order of provision based on the relevance of the parts when providing the parts. For example, the providing unit provides parts with high relevance preferentially. The providing unit can also provide parts with low relevance later. Furthermore, the providing unit can also adjust the order of provision based on the relevance of the parts. In this way, the order of provision can be adjusted based on the relevance of the parts.

[0141] The providing unit can adjust the content of the provided information according to the user's level of expertise when providing the information. For example, if the user has specialized knowledge, the providing unit uses detailed technical terms. Also, if the user does not have specialized knowledge, the providing unit can provide the information in simple language. Furthermore, the providing unit can adjust the content of the provided information according to the user's level of expertise. This makes it possible to adjust the content of the provided information according to the user's level of expertise.

[0142] The advice unit can estimate the user's emotions and adjust the method of advice based on the estimated user's emotions. For example, if the user is nervous, the advice unit can provide a simple and highly visible advice method. Furthermore, if the user is relaxed, the advice unit can also provide an advice method that includes detailed information. Furthermore, if the user is in a hurry, the advice unit can also provide an advice method that focuses on the main points. This makes it possible to adjust the method of advice based on the user's emotions. Emotions are estimated using, for example, facial expression recognition technology or voice analysis technology.

[0143] When giving advice, the advice unit can adjust the level of detail of the advice based on the importance of the repair. For example, in the case of a highly important repair, the advice unit provides detailed advice and provides an accurate repair method. In addition, in the case of a less important repair, the advice unit can provide concise advice and respond quickly. Furthermore, the advice unit can adjust the level of detail of the advice based on the importance of the repair. In this way, the level of detail of the advice can be adjusted based on the importance of the repair.

[0144] When giving advice, the advice unit can apply different advice algorithms depending on the repair category. For example, when repairing a home appliance, the advice unit applies an advice algorithm dedicated to the home appliance. Also, when repairing a home appliance, the advice unit can also apply an advice algorithm dedicated to the home appliance. Furthermore, the advice unit can select the optimal advice algorithm depending on the repair category. This makes it possible to apply different advice algorithms depending on the repair category.

[0145] When providing advice, the advice unit can improve the accuracy of the advice by referring to the user's past advice results. The advice unit, for example, identifies a repair method based on the user's past advice results. The advice unit can also select an optimal advice method from the user's past advice results. Furthermore, the advice unit can analyze the user's past advice results and improve the accuracy of the advice. In this way, the accuracy of the advice can be improved by referring to the user's past advice results.

[0146] The advice unit can estimate the user's emotions and adjust the order of advice based on the estimated user's emotions. For example, if the user is nervous, the advice unit starts with simple steps and gradually moves on to more detailed steps. Also, if the user is relaxed, the advice unit can perform detailed steps from the beginning. Furthermore, if the user is in a hurry, the advice unit can prioritize important steps. In this way, the order of advice can be adjusted based on the user's emotions. Emotions are estimated using, for example, facial expression recognition technology or voice analysis technology.

[0147] When giving advice, the advice unit can determine the priority of the advice based on the time when the repair occurred. For example, the advice unit gives priority to advice on repairs that occurred recently. The advice unit can also give advice on current repairs by referring to repairs that occurred in the past. Furthermore, the advice unit can adjust the priority of the advice based on the time when the repair occurred. This makes it possible to determine the priority of advice based on the time when the repair occurred.

[0148] The advice unit can adjust the order of advice based on the relevance of the repairs when giving advice. For example, the advice unit gives priority to advice on highly relevant repairs. The advice unit can also give advice on less relevant repairs later. Furthermore, the advice unit can also adjust the order of advice based on the relevance of the repairs. This makes it possible to adjust the order of advice based on the relevance of the repairs.

[0149] When giving advice, the advice unit can adjust the content of the advice according to the user's level of expertise. For example, if the user has specialized knowledge, the advice unit uses detailed technical terms. Also, if the user does not have specialized knowledge, the advice unit can give advice in simple language. Furthermore, the advice unit can adjust the content of the advice according to the user's level of expertise. In this way, the content of the advice can be adjusted according to the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, questioning unit, guide unit, identification unit, provision unit, and advice unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives information from the user. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the received information. The questioning unit is realized by the identification processing unit 290 of the data processing device 12 and asks about check points. The guide unit is realized by the control unit 46A of the smart device 14 and guides the user to repair points. The identification unit is realized by the identification processing unit 290 of the data processing device 12 and identifies replacement parts. The provision unit is realized by the control unit 46A of the smart device 14 and provides links to replacement parts. The advice unit is realized by the identification processing unit 290 of the data processing device 12 and provides advice when the product is beyond repair or when a specialist is needed. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, questioning unit, guide unit, identification unit, provision unit, and advice unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives information from the user. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the received information. The questioning unit is realized by the identification processing unit 290 of the data processing device 12 and asks about check points. The guide unit is realized by the control unit 46A of the smart glasses 214 and guides the user to repair points. The identification unit is realized by the identification processing unit 290 of the data processing device 12 and identifies replacement parts. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides links to replacement parts. The advice unit is realized by the identification processing unit 290 of the data processing device 12 and provides advice when the product is beyond repair or when a specialist is needed. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, questioning unit, guide unit, identification unit, provision unit, and advice unit, is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives information from the user. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the received information. The questioning unit is realized by the identification processing unit 290 of the data processing device 12 and asks about check points. The guide unit is realized by the control unit 46A of the headset type terminal 314 and guides the user to repair points. The identification unit is realized by the identification processing unit 290 of the data processing device 12 and identifies replacement parts. The provision unit is realized by the control unit 46A of the headset type terminal 314 and provides links to replacement parts. The advice unit is realized by the identification processing unit 290 of the data processing device 12 and provides advice when the product is beyond repair or when a specialist is needed. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, questioning unit, guide unit, identification unit, provision unit, and advice unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives information from a user. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the received information. The questioning unit is realized by the identification processing unit 290 of the data processing device 12 and asks about check points. The guide unit is realized by the control unit 46A of the robot 414 and guides the user to repair points. The identification unit is realized by the identification processing unit 290 of the data processing device 12 and identifies replacement parts. The provision unit is realized by the control unit 46A of the robot 414 and provides links to replacement parts. The advice unit is realized by the identification processing unit 290 of the data processing device 12 and provides advice when the product is beyond repair or when an expert is needed.

[0150] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0151] The reception unit can estimate the user's emotions and adjust the input method for malfunction information based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize the input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to enable the user to input malfunction information quickly. In this way, the input method for malfunction information can be adjusted based on the user's emotions. Emotions are estimated using, for example, facial expression recognition technology or voice analysis technology.

[0152] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis accuracy can be increased to provide more detailed information. Also, if the user is relaxed, the analysis accuracy can be adjusted to provide more concise information. Furthermore, if the user is in a hurry, the analysis can be performed quickly to provide information that focuses on the main points. This makes it possible to adjust the accuracy of the analysis based on the user's emotions. Emotions are estimated using, for example, facial expression recognition technology or voice analysis technology.

[0153] The medical interview unit can estimate the user's emotions and adjust the method of interviewing based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible medical interview method can be provided. If the user is relaxed, a medical interview method including detailed information can be provided. Furthermore, if the user is in a hurry, a medical interview method that focuses on the main points can be provided. This makes it possible to adjust the method of interviewing based on the user's emotions. Emotions are estimated using, for example, facial expression recognition technology or voice analysis technology.

[0154] The guide unit can estimate the user's emotions and adjust the guidance method based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible guidance method can be provided. If the user is relaxed, a guidance method including detailed information can be provided. Furthermore, if the user is in a hurry, a guidance method that focuses on the main points can be provided. This makes it possible to adjust the guidance method based on the user's emotions. Emotions are estimated using, for example, facial expression recognition technology or voice analysis technology.

[0155] The identification unit can estimate the user's emotion and determine the priority of parts to be identified based on the estimated user's emotion. For example, if the user is nervous, important parts can be identified with priority. Also, if the user is relaxed, detailed parts can be identified. Furthermore, if the user is in a hurry, parts to be identified quickly can be determined. In this way, the priority of parts to be identified can be determined based on the user's emotion. Emotion estimation is performed using, for example, facial expression recognition technology or voice analysis technology.

[0156] The reception unit can analyze the user's past failure history and suggest the optimal input format. For example, it can automatically display as candidates the details of failures that the user has frequently entered in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict failures that will occur during specific time periods based on the user's past failure history and suggest an input format. This makes it possible to analyze the user's past failure history and suggest the optimal input format.

[0157] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the failure. For example, in the case of a failure with high importance, a detailed analysis is performed to identify the cause. In addition, in the case of a failure with low importance, a simple analysis is performed to allow for a quick response. Furthermore, the analysis unit can also adjust the priority of the analysis according to the importance of the failure. This makes it possible to adjust the level of detail of the analysis based on the importance of the failure.

[0158] During the interview, the diagnostic unit can adjust the level of detail of the interview based on the cause of the failure. For example, in the case of a failure of high importance, a detailed interview is conducted to identify the cause. In addition, in the case of a failure of low importance, a brief interview is conducted to quickly address the problem. Furthermore, the diagnostic unit can also adjust the level of detail of the interview depending on the cause of the failure. This makes it possible to adjust the level of detail of the interview based on the cause of the failure.

[0159] The guide unit can adjust the level of detail of the guide based on the importance of the repair when providing guidance. For example, for a highly important repair, detailed guidance is provided to provide an accurate repair method. For a less important repair, brief guidance is provided to enable a quick response. Furthermore, the guide unit can adjust the level of detail of the guide based on the importance of the repair. This makes it possible to adjust the level of detail of the guide based on the importance of the repair.

[0160] During identification, the identification unit can adjust the accuracy of identification based on the cause of the failure. For example, in the case of a failure of high importance, detailed identification is performed to identify the exact part. In addition, in the case of a failure of low importance, simple identification can be performed to respond quickly. Furthermore, the identification unit can also adjust the accuracy of identification depending on the cause of the failure. This makes it possible to adjust the accuracy of identification based on the cause of the failure.

[0161] The processing flow of the second embodiment will be briefly explained below.

[0162] Step 1: The reception unit receives information from the user. The information from the user includes, for example, photos of the faulty home appliance or household equipment, text information about the fault symptoms, etc. The reception unit receives photos taken with a smartphone and provides an interface for inputting the fault symptoms. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit uses an image analysis algorithm to analyze the photo and identify the location of the failure. It also uses text analysis technology to analyze the failure symptoms and identify the cause of the failure. Step 3: The questioning section asks the user about points to check based on the information analyzed by the analysis section. The questioning section instructs the user on how to identify the faulty part and the items to check. Step 4: The guide unit guides the user to the repair points based on the check points identified by the questioning unit. The guide unit guides the user through the repair procedure and the necessary tools. Step 5: The identification unit identifies the replacement part identified by the guide unit. The identification unit identifies the part model number and compatible parts and provides the appropriate link. Step 6: The providing unit provides a link to purchase the replacement part identified by the identifying unit. The providing unit provides a link to the product on an e-commerce site (e.g., a link to the product page on Yahoo! Shopping (URL of the product page)) or a link to a technical support page. Step 7: The advice unit uses the guide unit to provide advice when the product is unrepairable or when an expert is required. The advice unit advises the user on how to handle the unrepairable product and how to contact an expert.

[0163] 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.

[0164] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0165] 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.

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

[0168] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0181] 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.

[0182] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0183] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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).

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0197] 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.

[0198] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0199] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0200] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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).

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0214] 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.

[0215] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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."

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] [Explanation of symbols]

[0235] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives information from a user; an analysis unit that analyzes the information received by the reception unit; an inquiry unit that asks questions about check points based on the information analyzed by the analysis unit; a guide unit that guides the user to repair points based on the check points identified by the inquiry unit; an identification unit that identifies a replacement part identified by the guide unit; a providing unit that provides a link for purchasing the replacement part identified by the identifying unit; an advice unit that provides advice when the product is beyond repair or when an expert is needed, according to the guide unit; A system characterized by:

2. The reception unit The user takes a photo of the faulty home appliance or household equipment with their smartphone and enters the symptoms of the malfunction.

2. The system of claim 1.

3. The analysis unit Analyze the input information and identify the cause of the failure 2. The system of claim 1.

4. The interview unit Identify check points based on the cause of the failure and provide instructions to the user 2. The system of claim 1.

5. The guide portion is Guidance on repair points based on check points 2. The system of claim 1.

6. The identification unit Identify the replacement parts you need and provide links 2. The system of claim 1.

7. The advice unit Advising if the item is beyond repair or requires specialist help 2. The system of claim 1.

8. The reception unit Estimate the user's emotions and adjust the method of inputting failure information based on the estimated user emotions.

2. The system of claim 1.

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